From 84482561a2c1bc9d373966c6f0bffda48106d9c9 Mon Sep 17 00:00:00 2001 From: Priyatham-sai-chand Date: Sat, 27 Jun 2020 22:05:48 +0530 Subject: [PATCH] Add files via upload --- Translation_Capstone_Project.ipynb | 7460 +++++++++++++++++++++++++++ Week 1 Programming Assignment.ipynb | 780 +++ Week 2 Programming Assignment.ipynb | 1644 ++++++ Week 3 Programming Assignment.ipynb | 1081 ++++ Week 4 Programming Assignment.ipynb | 1186 +++++ 5 files changed, 12151 insertions(+) create mode 100644 Translation_Capstone_Project.ipynb create mode 100644 Week 1 Programming Assignment.ipynb create mode 100644 Week 2 Programming Assignment.ipynb create mode 100644 Week 3 Programming Assignment.ipynb create mode 100644 Week 4 Programming Assignment.ipynb diff --git a/Translation_Capstone_Project.ipynb b/Translation_Capstone_Project.ipynb new file mode 100644 index 0000000..3785735 --- /dev/null +++ b/Translation_Capstone_Project.ipynb @@ -0,0 +1,7460 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "vsX0L1sG1iZj" + }, + "source": [ + "# Capstone Project\n", + "## Neural translation model\n", + "### Instructions\n", + "\n", + "In this notebook, you will create a neural network that translates from English to German. You will use concepts from throughout this course, including building more flexible model architectures, freezing layers, data processing pipeline and sequence modelling.\n", + "\n", + "This project is peer-assessed. Within this notebook you will find instructions in each section for how to complete the project. Pay close attention to the instructions as the peer review will be carried out according to a grading rubric that checks key parts of the project instructions. Feel free to add extra cells into the notebook as required.\n", + "\n", + "### How to submit\n", + "\n", + "When you have completed the Capstone project notebook, you will submit a pdf of the notebook for peer review. First ensure that the notebook has been fully executed from beginning to end, and all of the cell outputs are visible. This is important, as the grading rubric depends on the reviewer being able to view the outputs of your notebook. Save the notebook as a pdf (you could download the notebook with File -> Download .ipynb, open the notebook locally, and then File -> Download as -> PDF via LaTeX), and then submit this pdf for review.\n", + "\n", + "### Let's get started!\n", + "\n", + "We'll start by running some imports, and loading the dataset. For this project you are free to make further imports throughout the notebook as you wish. " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "2VyTvxPN1iZn" + }, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "import tensorflow_hub as hub\n", + "import unicodedata\n", + "import re\n", + "from tensorflow.keras.preprocessing.text import Tokenizer\n", + "import numpy as np\n", + "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", + "from IPython.display import Image\n", + "from sklearn.model_selection import train_test_split\n", + "from tensorflow.keras.layers import Layer,Input,Masking,LSTM,Embedding,Dense\n", + "from tensorflow.keras import Model\n", + "import time\n", + "from tqdm import tqdm_notebook as tqdm\n", + "import warnings\n", + "warnings.simplefilter(\"ignore\")\n", + "from prettytable import PrettyTable\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "wuIi4lAR1iZt" + }, + "source": [ + "For the capstone project, you will use a language dataset from http://www.manythings.org/anki/ to build a neural translation model. This dataset consists of over 200,000 pairs of sentences in English and German. In order to make the training quicker, we will restrict to our dataset to 20,000 pairs. Feel free to change this if you wish - the size of the dataset used is not part of the grading rubric.\n", + "\n", + "Your goal is to develop a neural translation model from English to German, making use of a pre-trained English word embedding module." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "pi9Dq6vv3FVO" + }, + "source": [ + "#### Import the data\n", + "\n", + "The dataset is available for download as a zip file at the following link:\n", + "\n", + "https://drive.google.com/open?id=1KczOciG7sYY7SB9UlBeRP1T9659b121Q\n", + "\n", + "You should store the unzipped folder in Drive for use in this Colab notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "colab_type": "code", + "id": "Nw99tEEQ3bKL", + "outputId": "8032cf5c-0d46-4cbc-e357-0a6f2a259c85" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n" + ] + } + ], + "source": [ + "# Run this cell to connect to your Drive folder\n", + "\n", + "from google.colab import drive\n", + "drive.mount('/content/gdrive')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "o8PetPpw1iZu" + }, + "outputs": [], + "source": [ + "# Run this cell to load the dataset\n", + "\n", + "NUM_EXAMPLES = 20000\n", + "data_examples = []\n", + "with open('/content/gdrive/My Drive/deu.txt', 'r', encoding='utf8') as f:\n", + " for line in f.readlines():\n", + " if len(data_examples) < NUM_EXAMPLES:\n", + " data_examples.append(line)\n", + " else:\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "JumLjJ631iZy" + }, + "outputs": [], + "source": [ + "# These functions preprocess English and German sentences\n", + "\n", + "def unicode_to_ascii(s):\n", + " return ''.join(c for c in unicodedata.normalize('NFD', s) if unicodedata.category(c) != 'Mn')\n", + "\n", + "def preprocess_sentence(sentence):\n", + " sentence = sentence.lower().strip()\n", + " sentence = re.sub(r\"ü\", 'ue', sentence)\n", + " sentence = re.sub(r\"ä\", 'ae', sentence)\n", + " sentence = re.sub(r\"ö\", 'oe', sentence)\n", + " sentence = re.sub(r'ß', 'ss', sentence)\n", + " \n", + " sentence = unicode_to_ascii(sentence)\n", + " sentence = re.sub(r\"([?.!,])\", r\" \\1 \", sentence)\n", + " sentence = re.sub(r\"[^a-z?.!,']+\", \" \", sentence)\n", + " sentence = re.sub(r'[\" \"]+', \" \", sentence)\n", + " \n", + " return sentence.strip()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "XFJap-TW1iZ2" + }, + "source": [ + "#### The custom translation model\n", + "The following is a schematic of the custom translation model architecture you will develop in this project." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 648 + }, + "colab_type": "code", + "id": "gBF1K2JN4RFJ", + "outputId": "966a5049-6036-4a25-bc16-7ed1d61e845f" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "# Run this cell to download and view a schematic diagram for the neural translation model\n", + "\n", + "!wget -q -O neural_translation_model.png --no-check-certificate \"https://docs.google.com/uc?export=download&id=1XsS1VlXoaEo-RbYNilJ9jcscNZvsSPmd\"\n", + "Image(\"neural_translation_model.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "0fP7P-yK4RS7" + }, + "source": [ + "The custom model consists of an encoder RNN and a decoder RNN. The encoder takes words of an English sentence as input, and uses a pre-trained word embedding to embed the words into a 128-dimensional space. To indicate the end of the input sentence, a special end token (in the same 128-dimensional space) is passed in as an input. This token is a TensorFlow Variable that is learned in the training phase (unlike the pre-trained word embedding, which is frozen).\n", + "\n", + "The decoder RNN takes the internal state of the encoder network as its initial state. A start token is passed in as the first input, which is embedded using a learned German word embedding. The decoder RNN then makes a prediction for the next German word, which during inference is then passed in as the following input, and this process is repeated until the special `` token is emitted from the decoder." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "z70nu6_01iZ3" + }, + "source": [ + "## 1. Text preprocessing\n", + "* Create separate lists of English and German sentences, and preprocess them using the `preprocess_sentence` function provided for you above.\n", + "* Add a special `\"\"` and `\"\"` token to the beginning and end of every German sentence.\n", + "* Use the Tokenizer class from the `tf.keras.preprocessing.text` module to tokenize the German sentences, ensuring that no character filters are applied. _Hint: use the Tokenizer's \"filter\" keyword argument._\n", + "* Print out at least 5 randomly chosen examples of (preprocessed) English and German sentence pairs. For the German sentence, print out the text (with start and end tokens) as well as the tokenized sequence.\n", + "* Pad the end of the tokenized German sequences with zeros, and batch the complete set of sequences into one numpy array." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "9G20C4bk1iZ4", + "scrolled": true + }, + "outputs": [], + "source": [ + "english_sent = [sentence.split('\\t')[0] for sentence in data_examples]\n", + "german_sent = [sentence.split('\\t')[1] for sentence in data_examples]\n", + "processed_english = []\n", + "processed_german = []\n", + "for sentence in english_sent:\n", + " processed_english.append(preprocess_sentence(sentence))\n", + "for sentence in german_sent:\n", + " processed_german.append(preprocess_sentence(sentence))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Sxwl-1rB1iZ8" + }, + "outputs": [], + "source": [ + "p_german_1 = [\" \"+ sentence + \" \" for sentence in processed_german]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "WnIdqZFk1iaA" + }, + "outputs": [], + "source": [ + "tokenizer = Tokenizer(num_words=None,filters = '',lower=False,char_level=False)\n", + "\n", + "tokenizer.fit_on_texts(p_german_1)\n", + "tokenizer_seq = tokenizer.texts_to_sequences(p_german_1)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 381 + }, + "colab_type": "code", + "id": "5UlnBdIK1iaE", + "outputId": "3668d45e-80d9-449f-f3e9-6f56d4cfdc85" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "German Sentences :\n", + " er ist noch in den anfangssemestern . \n", + " du fehlst mir auch . \n", + " halte mich auf dem laufenden . \n", + " ist sie verheiratet ? \n", + " tom ist betruegerisch . \n", + "\n", + "Token sequences :\n", + "[1, 14, 6, 72, 46, 53, 5563, 3, 2]\n", + "[1, 13, 1104, 21, 112, 3, 2]\n", + "[1, 288, 22, 29, 118, 1408, 3, 2]\n", + "[1, 6, 8, 703, 7, 2]\n", + "[1, 5, 6, 5206, 3, 2]\n", + "\n", + "English Sentences :\n", + "he's an undergrad .\n", + "i miss you , too .\n", + "keep me posted .\n", + "is she married ?\n", + "tom is deceitful .\n" + ] + } + ], + "source": [ + "num_of_sentences = len(p_german_1)\n", + "\n", + "random_ind = np.random.choice(num_of_sentences,5)\n", + "print('German Sentences :')\n", + "for ind in random_ind:\n", + " print(p_german_1[ind])\n", + "print()\n", + "print('Token sequences :')\n", + "for ind in random_ind:\n", + " print(tokenizer_seq[ind])\n", + "print()\n", + "print('English Sentences :')\n", + "for ind in random_ind:\n", + " print(processed_english[ind])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "ZGTaqt1t1iaI" + }, + "outputs": [], + "source": [ + "padded_seq = pad_sequences(tokenizer_seq,maxlen = None,padding = \"post\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "foL7Ihs21iaP" + }, + "source": [ + "## 2. Prepare the data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "-9rCEE4z1iaQ" + }, + "source": [ + "#### Load the embedding layer\n", + "As part of the dataset preproceessing for this project, you will use a pre-trained English word embedding module from TensorFlow Hub. The URL for the module is https://tfhub.dev/google/tf2-preview/nnlm-en-dim128-with-normalization/1.\n", + "\n", + "This embedding takes a batch of text tokens in a 1-D tensor of strings as input. It then embeds the separate tokens into a 128-dimensional space. \n", + "\n", + "The code to load and test the embedding layer is provided for you below.\n", + "\n", + "**NB:** this model can also be used as a sentence embedding module. The module will process each token by removing punctuation and splitting on spaces. It then averages the word embeddings over a sentence to give a single embedding vector. However, we will use it only as a word embedding module, and will pass each word in the input sentence as a separate token." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "ywZgobCh1iaR" + }, + "outputs": [], + "source": [ + "# Load embedding module from Tensorflow Hub\n", + "\n", + "embedding_layer = hub.KerasLayer(\"https://tfhub.dev/google/tf2-preview/nnlm-en-dim128/1\", \n", + " output_shape=[128], input_shape=[], dtype=tf.string)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "colab_type": "code", + "id": "TiY8QEDp1iaV", + "outputId": "ec5b02c9-833b-494d-c6d4-852456d40c24" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "TensorShape([7, 128])" + ] + }, + "execution_count": 13, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "# Test the layer\n", + "\n", + "embedding_layer(tf.constant([\"these\", \"aren't\", \"the\", \"droids\", \"you're\", \"looking\", \"for\"])).shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "KjuzXc-z1iaY" + }, + "source": [ + "You should now prepare the training and validation Datasets.\n", + "\n", + "* Create a random training and validation set split of the data, reserving e.g. 20% of the data for validation (NB: each English dataset example is a single sentence string, and each German dataset example is a sequence of padded integer tokens).\n", + "* Load the training and validation sets into a tf.data.Dataset object, passing in a tuple of English and German data for both training and validation sets.\n", + "* Create a function to map over the datasets that splits each English sentence at spaces. Apply this function to both Dataset objects using the map method. _Hint: look at the tf.strings.split function._\n", + "* Create a function to map over the datasets that embeds each sequence of English words using the loaded embedding layer/model. Apply this function to both Dataset objects using the map method.\n", + "* Create a function to filter out dataset examples where the English sentence is greater than or equal to than 13 (embedded) tokens in length. Apply this function to both Dataset objects using the filter method.\n", + "* Create a function to map over the datasets that pads each English sequence of embeddings with some distinct padding value before the sequence, so that each sequence is length 13. Apply this function to both Dataset objects using the map method. _Hint: look at the tf.pad function. You can extract a Tensor shape using tf.shape; you might also find the tf.math.maximum function useful._\n", + "* Batch both training and validation Datasets with a batch size of 16.\n", + "* Print the `element_spec` property for the training and validation Datasets. \n", + "* Using the Dataset `.take(1)` method, print the shape of the English data example from the training Dataset.\n", + "* Using the Dataset `.take(1)` method, print the German data example Tensor from the validation Dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Q-BUJOl_1iaZ" + }, + "outputs": [], + "source": [ + "x_train,x_valid,y_train,y_valid = train_test_split(processed_english,padded_seq,test_size = 0.20)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "4w6rM8Bl1iad" + }, + "outputs": [], + "source": [ + "train_dataset = tf.data.Dataset.from_tensor_slices((x_train,y_train))\n", + "valid_dataset = tf.data.Dataset.from_tensor_slices((x_valid,y_valid))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "D7bn3mRs1iaj" + }, + "outputs": [], + "source": [ + "def spliter(english,german):\n", + " \n", + " english = tf.strings.split(english,sep = \" \")\n", + "\n", + " return english,german\n", + "\n", + "train_dataset = train_dataset.map(spliter)\n", + "valid_dataset = valid_dataset.map(spliter)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "q0Fdso381ian" + }, + "outputs": [], + "source": [ + "\n", + "def embedder(english,german):\n", + "\n", + " english = embedding_layer(english)\n", + " return english,german\n", + "\n", + "train_dataset = train_dataset.map(embedder)\n", + "valid_dataset = valid_dataset.map(embedder)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "oWS26VBJ-SCZ" + }, + "outputs": [], + "source": [ + "def lengther(english,german):\n", + " length = tf.constant(13,dtype = tf.int32)\n", + "\n", + " return tf.math.greater_equal(length,tf.cast(len(english),tf.int32))\n", + "\n", + "train_dataset = train_dataset.filter(lengther)\n", + "valid_dataset = valid_dataset.filter(lengther)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "MdqSXEoX1iav" + }, + "outputs": [], + "source": [ + "def padder(english,german):\n", + "\n", + " paddings = [[13-len(english),0],[0,0]]\n", + " english = tf.pad(english, paddings = paddings)\n", + "\n", + " return english,german\n", + "\n", + "train_dataset = train_dataset.map(padder)\n", + "valid_dataset = valid_dataset.map(padder) " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "QZCCfB8fWaLF" + }, + "outputs": [], + "source": [ + "train_dataset = train_dataset.batch(16,drop_remainder= True)\n", + "valid_dataset = valid_dataset.batch(16,drop_remainder= True)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 90 + }, + "colab_type": "code", + "id": "ackk7HptX8cX", + "outputId": "456094d4-0398-480d-8813-b0ec2da388ff" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Dataset: \n", + "(TensorSpec(shape=(16, None, 128), dtype=tf.float32, name=None), TensorSpec(shape=(16, 14), dtype=tf.int32, name=None))\n", + "Validation Dataset: \n", + "(TensorSpec(shape=(16, None, 128), dtype=tf.float32, name=None), TensorSpec(shape=(16, 14), dtype=tf.int32, name=None))\n" + ] + } + ], + "source": [ + "print(\"Training Dataset: \")\n", + "print(train_dataset.element_spec)\n", + "print(\"Validation Dataset: \")\n", + "print(valid_dataset.element_spec)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1890 + }, + "colab_type": "code", + "id": "jtozc65MECNw", + "outputId": "07102487-1efe-43d7-d7fe-333b3ca5849c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor(\n", + "[[[ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " [ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " [ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " ...\n", + " [-0.03925273 0.02352522 0.02837687 ... -0.09089245 -0.02715905\n", + " 0.05939376]\n", + " [-0.02130977 -0.07366709 0.10384148 ... -0.22099297 0.07892267\n", + " -0.01285619]\n", + " [ 0.012986 0.08981702 0.16017003 ... 0.06796802 0.13528903\n", + " -0.022035 ]]\n", + "\n", + " [[ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " [ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " [ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " ...\n", + " [ 0.12846488 0.07159402 0.09918732 ... -0.07272145 0.03883429\n", + " 0.04847484]\n", + " [-0.03209486 -0.04160203 -0.12152159 ... -0.12753211 -0.02127644\n", + " -0.17632811]\n", + " [ 0.012986 0.08981702 0.16017003 ... 0.06796802 0.13528903\n", + " -0.022035 ]]\n", + "\n", + " [[ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " [ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " [ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " ...\n", + " [ 0.09539654 0.04058193 0.04366608 ... -0.03468065 0.05563864\n", + " 0.00659631]\n", + " [ 0.02027391 0.04513401 -0.15236828 ... -0.10530912 0.10933939\n", + " -0.0068233 ]\n", + " [ 0.012986 0.08981702 0.16017003 ... 0.06796802 0.13528903\n", + " -0.022035 ]]\n", + "\n", + " ...\n", + "\n", + " [[ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " [ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " [ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " ...\n", + " [ 0.1278139 -0.03678622 0.06628644 ... -0.03481541 -0.00253005\n", + " -0.06914762]\n", + " [-0.02380057 0.12938826 -0.12002522 ... -0.0778875 0.01935136\n", + " -0.01938629]\n", + " [ 0.012986 0.08981702 0.16017003 ... 0.06796802 0.13528903\n", + " -0.022035 ]]\n", + "\n", + " [[ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " [ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " [ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " ...\n", + " [ 0.2000517 0.0272701 -0.03822284 ... 0.1073221 -0.01488957\n", + " -0.01846376]\n", + " [ 0.03175933 0.06343 0.05124436 ... -0.16108854 0.23792052\n", + " 0.04684178]\n", + " [ 0.012986 0.08981702 0.16017003 ... 0.06796802 0.13528903\n", + " -0.022035 ]]\n", + "\n", + " [[ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " [ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " [ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " ...\n", + " [ 0.02199916 0.08012285 -0.10019045 ... -0.12652187 -0.05457912\n", + " 0.14176568]\n", + " [ 0.14766233 -0.06297084 0.07939632 ... -0.02586731 0.17180535\n", + " -0.03599825]\n", + " [ 0.012986 0.08981702 0.16017003 ... 0.06796802 0.13528903\n", + " -0.022035 ]]], shape=(16, 13, 128), dtype=float32)\n", + "tf.Tensor(\n", + "[[ 1 5 6 447 3 2 0 0 0 0 0 0 0 0]\n", + " [ 1 4 456 176 254 125 3 2 0 0 0 0 0 0]\n", + " [ 1 3458 126 3 2 0 0 0 0 0 0 0 0 0]\n", + " [ 1 4 18 318 2558 3 2 0 0 0 0 0 0 0]\n", + " [ 1 5 686 34 205 3 2 0 0 0 0 0 0 0]\n", + " [ 1 17 522 71 330 3 2 0 0 0 0 0 0 0]\n", + " [ 1 2190 8 22 12 3 2 0 0 0 0 0 0 0]\n", + " [ 1 5 6 50 781 3 2 0 0 0 0 0 0 0]\n", + " [ 1 6 33 366 7 2 0 0 0 0 0 0 0 0]\n", + " [ 1 4 24 49 3 2 0 0 0 0 0 0 0 0]\n", + " [ 1 4 110 19 1596 3 2 0 0 0 0 0 0 0]\n", + " [ 1 14 998 21 20 3 2 0 0 0 0 0 0 0]\n", + " [ 1 26 851 23 1269 3 2 0 0 0 0 0 0 0]\n", + " [ 1 17 381 80 3 2 0 0 0 0 0 0 0 0]\n", + " [ 1 4 940 115 3 2 0 0 0 0 0 0 0 0]\n", + " [ 1 11 97 249 41 3 2 0 0 0 0 0 0 0]], shape=(16, 14), dtype=int32)\n" + ] + } + ], + "source": [ + "for english,german in train_dataset.take(1):\n", + " print(english)\n", + " print(german)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1890 + }, + "colab_type": "code", + "id": "mYhlOwDTEWj5", + "outputId": "9634d9a1-79d2-4210-af1d-6d12d14523f0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor(\n", + "[[[ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " [ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " [ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " ...\n", + " [ 0.03970453 -0.04158472 0.10027828 ... -0.0063081 -0.0119952\n", + " -0.04243935]\n", + " [ 0.03519357 0.01258469 -0.04993629 ... -0.08874417 0.1515415\n", + " -0.00328062]\n", + " [ 0.012986 0.08981702 0.16017003 ... 0.06796802 0.13528903\n", + " -0.022035 ]]\n", + "\n", + " [[ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " [ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " [ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " ...\n", + " [ 0.04712053 0.00124522 0.03287347 ... -0.07834134 0.06874365\n", + " -0.08857308]\n", + " [ 0.09144389 -0.10057256 -0.07086372 ... -0.09084993 0.01224649\n", + " -0.03558629]\n", + " [ 0.012986 0.08981702 0.16017003 ... 0.06796802 0.13528903\n", + " -0.022035 ]]\n", + "\n", + " [[ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " [ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " [ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " ...\n", + " [-0.17962365 0.04181888 -0.00170533 ... 0.18615879 0.06329069\n", + " 0.06792238]\n", + " [-0.02130977 -0.07366709 0.10384148 ... -0.22099297 0.07892267\n", + " -0.01285619]\n", + " [ 0.012986 0.08981702 0.16017003 ... 0.06796802 0.13528903\n", + " -0.022035 ]]\n", + "\n", + " ...\n", + "\n", + " [[ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " [ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " [ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " ...\n", + " [ 0.22104432 -0.01606884 0.00432623 ... 0.13655335 0.01242723\n", + " 0.00964247]\n", + " [ 0.10299458 0.16180418 -0.08432977 ... 0.04323117 0.08910137\n", + " -0.00400291]\n", + " [ 0.012986 0.08981702 0.16017003 ... 0.06796802 0.13528903\n", + " -0.022035 ]]\n", + "\n", + " [[ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " [ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " [ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " ...\n", + " [ 0.16666627 0.01299293 0.04397342 ... 0.10751364 -0.00171146\n", + " -0.0662554 ]\n", + " [ 0.09130137 -0.02096943 -0.02457789 ... -0.12161301 0.150714\n", + " -0.07756138]\n", + " [ 0.012986 0.08981702 0.16017003 ... 0.06796802 0.13528903\n", + " -0.022035 ]]\n", + "\n", + " [[ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " [ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " [ 0. 0. 0. ... 0. 0.\n", + " 0. ]\n", + " ...\n", + " [ 0.03970453 -0.04158472 0.10027828 ... -0.0063081 -0.0119952\n", + " -0.04243935]\n", + " [ 0.04465724 0.07450107 -0.04335912 ... -0.1517998 0.18933882\n", + " -0.08688068]\n", + " [ 0.012986 0.08981702 0.16017003 ... 0.06796802 0.13528903\n", + " -0.022035 ]]], shape=(16, 13, 128), dtype=float32)\n", + "tf.Tensor(\n", + "[[ 1 5 24 12 741 3 2 0 0 0 0 0 0 0]\n", + " [ 1 4 180 147 3 2 0 0 0 0 0 0 0 0]\n", + " [ 1 4 15 447 3 2 0 0 0 0 0 0 0 0]\n", + " [ 1 4 15 854 3 2 0 0 0 0 0 0 0 0]\n", + " [ 1 33 23 25 3 2 0 0 0 0 0 0 0 0]\n", + " [ 1 10 16 223 967 3 2 0 0 0 0 0 0 0]\n", + " [ 1 10 6 189 3 2 0 0 0 0 0 0 0 0]\n", + " [ 1 236 13 22 7 2 0 0 0 0 0 0 0 0]\n", + " [ 1 4 18 40 107 88 3 2 0 0 0 0 0 0]\n", + " [ 1 288 53 780 9 2 0 0 0 0 0 0 0 0]\n", + " [ 1 83 6 5 205 7 2 0 0 0 0 0 0 0]\n", + " [ 1 4 15 1484 3 2 0 0 0 0 0 0 0 0]\n", + " [ 1 43 6 106 208 7 2 0 0 0 0 0 0 0]\n", + " [ 1 5 6 128 3 2 0 0 0 0 0 0 0 0]\n", + " [ 1 73 12 245 3 2 0 0 0 0 0 0 0 0]\n", + " [ 1 5 24 12 429 3 2 0 0 0 0 0 0 0]], shape=(16, 14), dtype=int32)\n" + ] + } + ], + "source": [ + "for english,german in valid_dataset.take(1):\n", + " print(english)\n", + " print(german)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "isIYhjq01iay" + }, + "source": [ + "## 3. Create the custom layer\n", + "You will now create a custom layer to add the learned end token embedding to the encoder model:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 423 + }, + "colab_type": "code", + "id": "e22f1Xyh6xvE", + "outputId": "794ca554-5c3c-42e8-f56b-3e9d7928b66b" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "# Run this cell to download and view a schematic diagram for the encoder model\n", + "\n", + "!wget -q -O neural_translation_model.png --no-check-certificate \"https://docs.google.com/uc?export=download&id=1JrtNOzUJDaOWrK4C-xv-4wUuZaI12sQI\"\n", + "Image(\"neural_translation_model.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "M6gLIHG81iaz" + }, + "source": [ + "You should now build the custom layer.\n", + "* Using layer subclassing, create a custom layer that takes a batch of English data examples from one of the Datasets, and adds a learned embedded ‘end’ token to the end of each sequence. \n", + "* This layer should create a TensorFlow Variable (that will be learned during training) that is 128-dimensional (the size of the embedding space). _Hint: you may find it helpful in the call method to use the tf.tile function to replicate the end token embedding across every element in the batch._\n", + "* Using the Dataset `.take(1)` method, extract a batch of English data examples from the training Dataset and print the shape. Test the custom layer by calling the layer on the English data batch Tensor and print the resulting Tensor shape (the layer should increase the sequence length by one)." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "yg9hjZz11ia0" + }, + "outputs": [], + "source": [ + "class EndTokenLayer(Layer):\n", + " \n", + " def __init__(self, embedding_dim=128, **kwargs):\n", + " super(EndTokenLayer, self).__init__(**kwargs)\n", + " self.embedding_dim = embedding_dim\n", + " def build(self, input_shape):\n", + " self.end_token_emb = self.add_weight(shape=(input_shape[-1],),\n", + " initializer='random_uniform',\n", + " trainable= True)\n", + " def call(self, inputs):\n", + " end_token = tf.tile(tf.reshape(self.end_token_emb, shape=(1, 1, self.end_token_emb.shape[0])), [tf.shape(inputs)[0],1,1])\n", + " return tf.keras.layers.concatenate([inputs, end_token], axis=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "vFJUMAbNqDqp" + }, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 54 + }, + "colab_type": "code", + "id": "4PwI32O11ia3", + "outputId": "598a9bbb-0ea7-4971-c385-5bcddde14d4a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "english sentences shape\n", + "(16, 13, 128)\n" + ] + } + ], + "source": [ + "endlayer = EndTokenLayer()\n", + "for english,german in train_dataset.take(1):\n", + " temp_layer = endlayer(english)\n", + " print(\"english sentences shape\")\n", + " print(english.shape) " + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 54 + }, + "colab_type": "code", + "id": "XlhLpsO_lIF0", + "outputId": "36785279-7e7e-4d54-b897-bc6cd7ad5d3d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "end added shape of english sentences:\n", + "(16, 14, 128)\n" + ] + } + ], + "source": [ + "print(\"end added shape of english sentences:\")\n", + "print(temp_layer.shape)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "OAd3i4_y1ia-" + }, + "source": [ + "## 4. Build the encoder network\n", + "The encoder network follows the schematic diagram above. You should now build the RNN encoder model.\n", + "* Using the functional API, build the encoder network according to the following spec:\n", + " * The model will take a batch of sequences of embedded English words as input, as given by the Dataset objects.\n", + " * The next layer in the encoder will be the custom layer you created previously, to add a learned end token embedding to the end of the English sequence.\n", + " * This is followed by a Masking layer, with the `mask_value` set to the distinct padding value you used when you padded the English sequences with the Dataset preprocessing above.\n", + " * The final layer is an LSTM layer with 512 units, which also returns the hidden and cell states.\n", + " * The encoder is a multi-output model. There should be two output Tensors of this model: the hidden state and cell states of the LSTM layer. The output of the LSTM layer is unused.\n", + "* Using the Dataset `.take(1)` method, extract a batch of English data examples from the training Dataset and test the encoder model by calling it on the English data Tensor, and print the shape of the resulting Tensor outputs.\n", + "* Print the model summary for the encoder network." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "6R2LqbfV1ia_" + }, + "outputs": [], + "source": [ + "def encoder(input_shape):\n", + " inputs = Input([13,input_shape])\n", + " h = EndTokenLayer()(inputs)\n", + " h = Masking([(lambda x: x*0)(x) for x in range(128)])(h)\n", + " lstm , hidden_state, cell_state = LSTM(512,return_sequences = True,return_state=True)(h)\n", + " model = Model(inputs=inputs, outputs=[hidden_state, cell_state])\n", + "\n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 308 + }, + "colab_type": "code", + "id": "e5XW6NxL1ibC", + "outputId": "2c9d3ef7-738d-492b-fad6-c206844a1122" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "input_1 (InputLayer) [(None, 13, 128)] 0 \n", + "_________________________________________________________________\n", + "end_token_layer_1 (EndTokenL (None, 14, 128) 128 \n", + "_________________________________________________________________\n", + "masking (Masking) (None, 14, 128) 0 \n", + "_________________________________________________________________\n", + "lstm (LSTM) [(None, 14, 512), (None, 1312768 \n", + "=================================================================\n", + "Total params: 1,312,896\n", + "Trainable params: 1,312,896\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "encoder_model = encoder(128)\n", + "encoder_model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "jEk9ikVh1ibL" + }, + "outputs": [], + "source": [ + "for english,german in train_dataset.take(1):\n", + " result_1,result_2 = encoder_model(english)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 54 + }, + "colab_type": "code", + "id": "HsVi4ZNnlGa_", + "outputId": "80782bcb-b1e4-4ca2-e9eb-e3f48a4ed043" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor([ 16 512], shape=(2,), dtype=int32)\n", + "tf.Tensor([ 16 512], shape=(2,), dtype=int32)\n" + ] + } + ], + "source": [ + "print(tf.shape(result_1))\n", + "print(tf.shape(result_2))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "KvkzpCeZ1ibR" + }, + "source": [ + "## 5. Build the decoder network\n", + "The decoder network follows the schematic diagram below. " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 501 + }, + "colab_type": "code", + "id": "yOjEb7cH7Y4S", + "outputId": "5b44888c-9039-4cfc-da6e-b4813fd4d782" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "# Run this cell to download and view a schematic diagram for the decoder model\n", + "\n", + "!wget -q -O neural_translation_model.png --no-check-certificate \"https://docs.google.com/uc?export=download&id=1DTeaXD8tA8RjkpVrB2mr9csSBOY4LQiW\"\n", + "Image(\"neural_translation_model.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "VPBK8WGL1ibS" + }, + "source": [ + "You should now build the RNN decoder model.\n", + "* Using Model subclassing, build the decoder network according to the following spec:\n", + " * The initializer should create the following layers:\n", + " * An Embedding layer with vocabulary size set to the number of unique German tokens, embedding dimension 128, and set to mask zero values in the input.\n", + " * An LSTM layer with 512 units, that returns its hidden and cell states, and also returns sequences.\n", + " * A Dense layer with number of units equal to the number of unique German tokens, and no activation function.\n", + " * The call method should include the usual `inputs` argument, as well as the additional keyword arguments `hidden_state` and `cell_state`. The default value for these keyword arguments should be `None`.\n", + " * The call method should pass the inputs through the Embedding layer, and then through the LSTM layer. If the `hidden_state` and `cell_state` arguments are provided, these should be used for the initial state of the LSTM layer. _Hint: use the_ `initial_state` _keyword argument when calling the LSTM layer on its input._\n", + " * The call method should pass the LSTM output sequence through the Dense layer, and return the resulting Tensor, along with the hidden and cell states of the LSTM layer.\n", + "* Using the Dataset `.take(1)` method, extract a batch of English and German data examples from the training Dataset. Test the decoder model by first calling the encoder model on the English data Tensor to get the hidden and cell states, and then call the decoder model on the German data Tensor and hidden and cell states, and print the shape of the resulting decoder Tensor outputs.\n", + "* Print the model summary for the decoder network." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "5i5XkhtsGhsO" + }, + "outputs": [], + "source": [ + "unique_tokens = len(tokenizer.word_index) + 1\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "l50qhnXD1ibT" + }, + "outputs": [], + "source": [ + "class DecoderModel(Model):\n", + " def __init__(self,initial_state=True,**kwargs):\n", + " super(DecoderModel, self).__init__(**kwargs)\n", + " self.embedding = Embedding(input_dim = unique_tokens,output_dim = 128,mask_zero = True)\n", + " self.lstm = LSTM(512, return_sequences=True, return_state=True)\n", + " self.dense = Dense(unique_tokens)\n", + " self.initial_state = initial_state\n", + "\n", + " def call(self,inputs,hidden_state = None,cell_state = None):\n", + " h = self.embedding(inputs)\n", + " if hidden_state != None and cell_state != None:\n", + " lstm,hidden_1,cell_1 = self.lstm(h,initial_state = [hidden_state,cell_state])\n", + " else:\n", + " lstm,hidden_1,cell_1 = self.lstm(h)\n", + " h = self.dense(lstm)\n", + " return h,hidden_1,cell_1\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "vG5CRIXV1ibi" + }, + "outputs": [], + "source": [ + "decoder_model = DecoderModel()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 126 + }, + "colab_type": "code", + "id": "SvqqT_ET1ibl", + "outputId": "f1ec691f-d17d-478c-cc7b-05f846334624" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "decoder output shape:\n", + "tf.Tensor([ 16 14 5744], shape=(3,), dtype=int32)\n", + "hidden state shape:\n", + "tf.Tensor([ 16 512], shape=(2,), dtype=int32)\n", + "cell state shape:\n", + "tf.Tensor([ 16 512], shape=(2,), dtype=int32)\n" + ] + } + ], + "source": [ + "for english,german in train_dataset.take(1):\n", + " temp,hidden_1,cell_1 = decoder_model(german)\n", + "print(\"decoder output shape:\")\n", + "print(tf.shape(temp))\n", + "print(\"hidden state shape:\")\n", + "print(tf.shape(hidden_1))\n", + "print(\"cell state shape:\")\n", + "print(tf.shape(cell_1))" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 272 + }, + "colab_type": "code", + "id": "wF3B3d2y9LCn", + "outputId": "599233b8-01ef-4173-9269-f68402b147ce" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"decoder_model\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "embedding (Embedding) multiple 735232 \n", + "_________________________________________________________________\n", + "lstm_1 (LSTM) multiple 1312768 \n", + "_________________________________________________________________\n", + "dense (Dense) multiple 2946672 \n", + "=================================================================\n", + "Total params: 4,994,672\n", + "Trainable params: 4,994,672\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "decoder_model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "pST9XGJ81ibo" + }, + "source": [ + "## 6. Make a custom training loop\n", + "You should now write a custom training loop to train your custom neural translation model.\n", + "* Define a function that takes a Tensor batch of German data (as extracted from the training Dataset), and returns a tuple containing German inputs and outputs for the decoder model (refer to schematic diagram above).\n", + "* Define a function that computes the forward and backward pass for your translation model. This function should take an English input, German input and German output as arguments, and should do the following:\n", + " * Pass the English input into the encoder, to get the hidden and cell states of the encoder LSTM.\n", + " * These hidden and cell states are then passed into the decoder, along with the German inputs, which returns a sequence of outputs (the hidden and cell state outputs of the decoder LSTM are unused in this function).\n", + " * The loss should then be computed between the decoder outputs and the German output function argument.\n", + " * The function returns the loss and gradients with respect to the encoder and decoder’s trainable variables.\n", + " * Decorate the function with `@tf.function`\n", + "* Define and run a custom training loop for a number of epochs (for you to choose) that does the following:\n", + " * Iterates through the training dataset, and creates decoder inputs and outputs from the German sequences.\n", + " * Updates the parameters of the translation model using the gradients of the function above and an optimizer object.\n", + " * Every epoch, compute the validation loss on a number of batches from the validation and save the epoch training and validation losses.\n", + "* Plot the learning curves for loss vs epoch for both training and validation sets.\n", + "\n", + "_Hint: This model is computationally demanding to train. The quality of the model or length of training is not a factor in the grading rubric. However, to obtain a better model we recommend using the GPU accelerator hardware on Colab._" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "7hJHbWqs1ibr" + }, + "outputs": [], + "source": [ + "def german_io(german_data):\n", + " \n", + " input_data = german_data[:,0:tf.shape(german_data)[1]-1]\n", + " output_data = german_data[:,1:tf.shape(german_data)[1]]\n", + " return(input_data,output_data)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "CykC5OlL71VK" + }, + "outputs": [], + "source": [ + "loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = True)\n", + "\n", + "optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Jvu4J4-u1ibu" + }, + "outputs": [], + "source": [ + "@tf.function\n", + "def fb_passes(english_input,german_input,german_output):\n", + " with tf.GradientTape() as tape:\n", + " hidden_state ,cell_state = encoder_model(english_input)\n", + " dense_output, _, _ = decoder_model(german_input, hidden_state, cell_state)\n", + " loss = tf.math.reduce_mean(loss_object(german_output,dense_output))\n", + " trainable_variables = encoder_model.trainable_variables + decoder_model.trainable_variables\n", + " gradients = tape.gradient(loss, trainable_variables)\n", + " return loss, gradients" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1252, + "referenced_widgets": [ + "7cf2f09cbb2e49baa326b070a57135b6", + "a61b88812be7458e9384797ce7322974", + "8f56f460ce0743adb631f81b47279032", + "5ac9165d021f4013aa0943b6b4d78218", + "3d6a8e27074349418cd71c5290f326b5", + "0bbdb676c6f44fea87b6db3c5d3f4450", + "fc62d2b357614a41a04a474d45311b2e", + 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"stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "eb8c2ad4b54e4322af6eaf9acb2146f9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Epoch 01: Avg. training loss = 4.303783, Avg. validation loss = 3.971151 \n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "803248d7f99146919fc755a33d488aba", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=1000.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + 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"text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Epoch 04: Avg. training loss = 1.840311, Avg. validation loss = 1.982391 \n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e185261a681f4af0bdb2e3823e3891b6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=1000.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "480c4b953af740848089472d2b12f54f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Epoch 05: Avg. training loss = 1.217730, Avg. validation loss = 1.538847 \n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c00601a1aea542d6b7e2626f19b8f038", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=1000.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "78a2ee48e8744d7dbd4095e7d0de5a0a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Epoch 06: Avg. training loss = 0.775655, Avg. validation loss = 1.278047 \n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b6129ba9f3b84f3b9fdbde3ea309cad2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=1000.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a668e92a47644d52b97309a0b4c5b69b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Epoch 07: Avg. training loss = 0.510702, Avg. validation loss = 1.157365 \n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1d836faa041e49bfb60069abae3388dc", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=1000.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8d32d1e1e9914826870413592b41b10b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Epoch 08: Avg. training loss = 0.357175, Avg. validation loss = 1.111811 \n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "68600582496043389e99e8b8a9aa88ab", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=1000.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2644b241608a46ab9a032bfc8e31e263", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Epoch 09: Avg. training loss = 0.266191, Avg. validation loss = 1.098457 \n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0694697d66344a34994953682f8bf350", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=1000.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0122fad2eef947b98affc30bb5d523f0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Epoch 10: Avg. training loss = 0.204196, Avg. validation loss = 1.086921 \n", + "\n" + ] + } + ], + "source": [ + "train_loss_results = []\n", + "val_loss_results = []\n", + "\n", + "for epoch in tqdm(range(10)):\n", + " \n", + " epoch_loss = 0\n", + " batch_no = 0\n", + " with tqdm(total= 1000) as t:\n", + " for english,german in train_dataset:\n", + " german_input, german_output = german_io(german)\n", + " loss, gradients = fb_passes(english, german_input, german_output)\n", + " epoch_loss += loss\n", + " batch_no += 1\n", + " optimizer.apply_gradients(zip(gradients, encoder_model.trainable_variables + decoder_model.trainable_variables))\n", + " epoch_avg_loss = epoch_loss / batch_no\n", + " train_loss_results.append(epoch_avg_loss)\n", + " t.update(1)\n", + " epoch_val_loss = 0\n", + " val_batch_no = 0\n", + " with tqdm(total= 250) as t:\n", + " for val_english,val_german in valid_dataset:\n", + " german_input, german_output = german_io(val_german)\n", + " loss, _ = fb_passes(val_english, german_input, german_output)\n", + " epoch_val_loss += loss\n", + " val_batch_no += 1\n", + " epoch_avg_val_loss = epoch_val_loss / val_batch_no\n", + " val_loss_results.append(epoch_avg_val_loss)\n", + " t.update(1)\n", + " print(\"Epoch {:02d}: Avg. training loss = {:.6f}, Avg. validation loss = {:.6f} \".format(epoch + 1,epoch_avg_loss,epoch_avg_val_loss))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 621 + }, + "colab_type": "code", + "id": "mWIlf_y2YHfl", + "outputId": "b8cca73c-e388-46b8-99fa-d8b8639ba6d5" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "epoch_range = range(0,10)\n", + "from scipy.ndimage.filters import gaussian_filter1d\n", + "train_smoothed = gaussian_filter1d(train_loss_results, sigma=1500)\n", + "valid_smoothed = gaussian_filter1d(val_loss_results, sigma=1500)\n", + "\n", + "# The graph is inconsistent due to smaller data used to make training faster\n", + "fig = plt.figure(figsize = (10, 10))\n", + "ax = fig.add_subplot(1,1,1)\n", + "ax.plot(train_smoothed, label=\"Training\")\n", + "ax.plot(valid_smoothed, label=\"Validation\")\n", + "ax.legend(loc='best')\n", + "ax.set_title(\"Loss vs Epoch\")\n", + "ax.set_xticklabels(epoch_range)\n", + "ax.set_xlabel(\"Epoch number\")\n", + "ax.set_ylabel(\"Avg. loss\") \n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "xM2gvBM11ib-" + }, + "source": [ + "## 7. Use the model to translate\n", + "Now it's time to put your model into practice! You should run your translation for five randomly sampled English sentences from the dataset. For each sentence, the process is as follows:\n", + "* Preprocess and embed the English sentence according to the model requirements.\n", + "* Pass the embedded sentence through the encoder to get the encoder hidden and cell states.\n", + "* Starting with the special `\"\"` token, use this token and the final encoder hidden and cell states to get the one-step prediction from the decoder, as well as the decoder’s updated hidden and cell states.\n", + "* Create a loop to get the next step prediction and updated hidden and cell states from the decoder, using the most recent hidden and cell states. Terminate the loop when the `\"\"` token is emitted, or when the sentence has reached a maximum length.\n", + "* Decode the output token sequence into German text and print the English text and the model's German translation." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "hCgiIBiniZTe" + }, + "outputs": [], + "source": [ + "random_ind = np.random.choice(20000,5)\n", + "examples = []\n", + "for ind in random_ind:\n", + " examples.append(data_examples[ind])\n", + "english_sentences = [sentence.split('\\t')[0] for sentence in examples]\n", + "processed_english = []\n", + "for sentence in english_sentences:\n", + " processed_english.append(preprocess_sentence(sentence))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "bGnQtE7L1icA" + }, + "outputs": [], + "source": [ + "\n", + "\n", + "start = tokenizer.word_index['']\n", + "end = tokenizer.word_index['']\n", + "examples_tokens = []\n", + "for p_english in processed_english:\n", + " english = tf.strings.split(p_english,sep = \" \")\n", + " english = embedding_layer(english)\n", + " english = tf.pad(english, [[13-len(english), 0], [0, 0]], constant_values = 0)\n", + " english = tf.expand_dims(english, 0)\n", + " hidden_state, cell_state = encoder_model(english)\n", + " translated_tokens = []\n", + " tf_token = tf.Variable([[start]])\n", + " while True:\n", + " output_1,hidden_state, cell_state = decoder_model(tf_token, hidden_state, cell_state)\n", + " output_2 = tf.argmax(output_1, 2).numpy()[0,0]\n", + " tf_token = tf.Variable([[output_2]])\n", + " if output_2 == end:\n", + " break\n", + " else:\n", + " translated_tokens.append(output_2)\n", + " examples_tokens.append(translated_tokens)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "Unk60cEy1icI" + }, + "outputs": [], + "source": [ + "inv_german_index = {value:key for key,value in tokenizer.word_index.items()}\n", + "german_sentences = []\n", + "for example_token in examples_tokens:\n", + " output_words = []\n", + " for token in example_token:\n", + " output_words.append(inv_german_index[token])\n", + " output = \" \".join(output_words)\n", + " german_sentences.append(output)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 181 + }, + "colab_type": "code", + "id": "QZtLHihbbBcG", + "outputId": "da09ba59-f280-41f2-cac6-6d80c0db50e6" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+----------------------------+\n", + "| English sentences | German Translations |\n", + "+--------------------+----------------------------+\n", + "| Tom may be out. | tom wird sich verspaeten . |\n", + "| Are you blushing? | bist du ueber achtzehn ? |\n", + "| Is it popular? | ist es etwas ernstes ? |\n", + "| I have a solution. | ich habe eine meinung . |\n", + "| I'm starved. | ich bin verletzt . |\n", + "+--------------------+----------------------------+\n" + ] + } + ], + "source": [ + "table = PrettyTable(['English sentences', 'German Translations'])\n", + "for english,german in zip(english_sentences,german_sentences):\n", + " table.add_row([english,german])\n", + " \n", + "print(table)" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "Translation Capstone Project.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + }, + "widgets": { + 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+ }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instructions\n", + "\n", + "In this notebook, you will create a neural network model to classify images of cats and dogs, using transfer learning: you will use part of a pre-trained image classifier model (trained on ImageNet) as a feature extractor, and train additional new layers to perform the cats and dogs classification task.\n", + "\n", + "Some code cells are provided you in the notebook. You should avoid editing provided code, and make sure to execute the cells in order to avoid unexpected errors. Some cells begin with the line: \n", + "\n", + "`#### GRADED CELL ####`\n", + "\n", + "Don't move or edit this first line - this is what the automatic grader looks for to recognise graded cells. These cells require you to write your own code to complete them, and are automatically graded when you submit the notebook. Don't edit the function name or signature provided in these cells, otherwise the automatic grader might not function properly. Inside these graded cells, you can use any functions or classes that are imported below, but make sure you don't use any variables that are outside the scope of the function.\n", + "\n", + "### How to submit\n", + "\n", + "Complete all the tasks you are asked for in the worksheet. When you have finished and are happy with your code, press the **Submit Assignment** button at the top of this notebook.\n", + "\n", + "### Let's get started!\n", + "\n", + "We'll start running some imports, and loading the dataset. Do not edit the existing imports in the following cell. If you would like to make further Tensorflow imports, you should add them here." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#### PACKAGE IMPORTS ####\n", + "\n", + "# Run this cell first to import all required packages. Do not make any imports elsewhere in the notebook\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.keras.models import Sequential, Model\n", + "import numpy as np\n", + "import os\n", + "import pandas as pd\n", + "from sklearn.metrics import confusion_matrix\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn as sns\n", + "\n", + "# If you would like to make further imports from Tensorflow, add them here\n", + "\n", + "from tensorflow.keras.layers import Input,Dense,MaxPooling2D,Flatten,Conv2D,Dropout\n", + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.models import load_model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"Drawing\"\n", + "\n", + "#### The Dogs vs Cats dataset\n", + "\n", + "In this assignment, you will use the [Dogs vs Cats dataset](https://www.kaggle.com/c/dogs-vs-cats/data), which was used for a 2013 Kaggle competition. It consists of 25000 images containing either a cat or a dog. We will only use a subset of 600 images and labels. The dataset is a subset of a much larger dataset of 3 million photos that were originally used as a CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), referred to as “Asirra” or Animal Species Image Recognition for Restricting Access.\n", + "\n", + "* J. Elson, J. Douceur, J. Howell, and J. Saul. \"Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization.\" Proceedings of 14th ACM Conference on Computer and Communications Security (CCS), October 2007.\n", + "\n", + "Your goal is to train a classifier model using part of a pre-trained image classifier, using the principle of transfer learning." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Load and preprocess the data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "images_train = np.load('data/images_train.npy') / 255.\n", + "images_valid = np.load('data/images_valid.npy') / 255.\n", + "images_test = np.load('data/images_test.npy') / 255.\n", + "\n", + "labels_train = np.load('data/labels_train.npy')\n", + "labels_valid = np.load('data/labels_valid.npy')\n", + "labels_test = np.load('data/labels_test.npy')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "600 training data examples\n", + "300 validation data examples\n", + "300 test data examples\n" + ] + } + ], + "source": [ + "print(\"{} training data examples\".format(images_train.shape[0]))\n", + "print(\"{} validation data examples\".format(images_valid.shape[0]))\n", + "print(\"{} test data examples\".format(images_test.shape[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Display sample images and labels from the training set" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Display a few images and labels\n", + "\n", + "class_names = np.array(['Dog', 'Cat'])\n", + "\n", + "plt.figure(figsize=(15,10))\n", + "inx = np.random.choice(images_train.shape[0], 15, replace=False)\n", + "for n, i in enumerate(inx):\n", + " ax = plt.subplot(3,5,n+1)\n", + " plt.imshow(images_train[i])\n", + " plt.title(class_names[labels_train[i]])\n", + " plt.axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create a benchmark model\n", + "\n", + "We will first train a CNN classifier model as a benchmark model before implementing the transfer learning approach. Using the functional API, build the benchmark model according to the following specifications:\n", + "\n", + "* The model should use the `input_shape` in the function argument to set the shape in the Input layer.\n", + "* The first and second hidden layers should be Conv2D layers with 32 filters, 3x3 kernel size and ReLU activation.\n", + "* The third hidden layer should be a MaxPooling2D layer with a 2x2 window size.\n", + "* The fourth and fifth hidden layers should be Conv2D layers with 64 filters, 3x3 kernel size and ReLU activation.\n", + "* The sixth hidden layer should be a MaxPooling2D layer with a 2x2 window size.\n", + "* The seventh and eighth hidden layers should be Conv2D layers with 128 filters, 3x3 kernel size and ReLU activation.\n", + "* The ninth hidden layer should be a MaxPooling2D layer with a 2x2 window size.\n", + "* This should be followed by a Flatten layer, and a Dense layer with 128 units and ReLU activation\n", + "* The final layer should be a Dense layer with a single neuron and sigmoid activation.\n", + "* All of the Conv2D layers should use `'SAME'` padding.\n", + "\n", + "In total, the network should have 13 layers (including the `Input` layer).\n", + "\n", + "The model should then be compiled with the RMSProp optimiser with learning rate 0.001, binary cross entropy loss and and binary accuracy metric." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following function. \n", + "# Make sure to not change the function name or arguments.\n", + "\n", + "def get_benchmark_model(input_shape):\n", + " \"\"\"\n", + " This function should build and compile a CNN model according to the above specification,\n", + " using the functional API. The function takes input_shape as an argument, which should be\n", + " used to specify the shape in the Input layer.\n", + " Your function should return the model.\n", + " \"\"\"\n", + " \n", + " inputs = Input(input_shape,name = 'benchmark_input')\n", + " h = Conv2D(32,(3,3),activation = 'relu',padding = 'SAME')(inputs)\n", + " h = Conv2D(32,(3,3),activation = 'relu',padding = 'SAME')(h)\n", + " h = MaxPooling2D((2,2))(h)\n", + " h = Conv2D(64,(3,3),activation = 'relu',padding = 'SAME')(h)\n", + " h = Conv2D(64,(3,3),activation = 'relu',padding = 'SAME')(h)\n", + " h = MaxPooling2D((2,2))(h)\n", + " h = Conv2D(128,(3,3),activation = 'relu',padding = 'SAME')(h)\n", + " h = Conv2D(128,(3,3),activation = 'relu',padding = 'SAME')(h)\n", + " h = MaxPooling2D((2,2))(h)\n", + " h = Flatten()(h)\n", + " h = Dense(128,activation = 'relu')(h)\n", + " outputs = Dense(1,activation = 'sigmoid')(h)\n", + " \n", + " model = Model(inputs = inputs,outputs = outputs)\n", + " \n", + " model.compile(optimizer = tf.keras.optimizers.RMSprop(learning_rate = 0.001),\n", + " loss = 'binary_crossentropy',\n", + " metrics = ['accuracy'])\n", + " \n", + " return model\n", + " \n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "benchmark_input (InputLayer) [(None, 160, 160, 3)] 0 \n", + "_________________________________________________________________\n", + "conv2d (Conv2D) (None, 160, 160, 32) 896 \n", + "_________________________________________________________________\n", + "conv2d_1 (Conv2D) (None, 160, 160, 32) 9248 \n", + "_________________________________________________________________\n", + "max_pooling2d (MaxPooling2D) (None, 80, 80, 32) 0 \n", + "_________________________________________________________________\n", + "conv2d_2 (Conv2D) (None, 80, 80, 64) 18496 \n", + "_________________________________________________________________\n", + "conv2d_3 (Conv2D) (None, 80, 80, 64) 36928 \n", + "_________________________________________________________________\n", + "max_pooling2d_1 (MaxPooling2 (None, 40, 40, 64) 0 \n", + "_________________________________________________________________\n", + "conv2d_4 (Conv2D) (None, 40, 40, 128) 73856 \n", + "_________________________________________________________________\n", + "conv2d_5 (Conv2D) (None, 40, 40, 128) 147584 \n", + "_________________________________________________________________\n", + "max_pooling2d_2 (MaxPooling2 (None, 20, 20, 128) 0 \n", + "_________________________________________________________________\n", + "flatten (Flatten) (None, 51200) 0 \n", + "_________________________________________________________________\n", + "dense (Dense) (None, 128) 6553728 \n", + "_________________________________________________________________\n", + "dense_1 (Dense) (None, 1) 129 \n", + "=================================================================\n", + "Total params: 6,840,865\n", + "Trainable params: 6,840,865\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "# Build and compile the benchmark model, and display the model summary\n", + "\n", + "benchmark_model = get_benchmark_model(images_train[0].shape)\n", + "benchmark_model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Train the CNN benchmark model\n", + "\n", + "We will train the benchmark CNN model using an `EarlyStopping` callback. Feel free to increase the training time if you wish." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 600 samples, validate on 300 samples\n", + "Epoch 1/10\n", + " 96/600 [===>..........................] - ETA: 5:14 - loss: 6.5407 - accuracy: 0.5000" + ] + } + ], + "source": [ + "# Fit the benchmark model and save its training history\n", + "\n", + "earlystopping = tf.keras.callbacks.EarlyStopping(patience=2)\n", + "history_benchmark = benchmark_model.fit(images_train, labels_train, epochs=10, batch_size=32,\n", + " validation_data=(images_valid, labels_valid), \n", + " callbacks=[earlystopping])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot the learning curves" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run this cell to plot accuracy vs epoch and loss vs epoch\n", + "\n", + "plt.figure(figsize=(15,5))\n", + "plt.subplot(121)\n", + "try:\n", + " plt.plot(history_benchmark.history['accuracy'])\n", + " plt.plot(history_benchmark.history['val_accuracy'])\n", + "except KeyError:\n", + " plt.plot(history_benchmark.history['acc'])\n", + " plt.plot(history_benchmark.history['val_acc'])\n", + "plt.title('Accuracy vs. epochs')\n", + "plt.ylabel('Accuracy')\n", + "plt.xlabel('Epoch')\n", + "plt.legend(['Training', 'Validation'], loc='lower right')\n", + "\n", + "plt.subplot(122)\n", + "plt.plot(history_benchmark.history['loss'])\n", + "plt.plot(history_benchmark.history['val_loss'])\n", + "plt.title('Loss vs. epochs')\n", + "plt.ylabel('Loss')\n", + "plt.xlabel('Epoch')\n", + "plt.legend(['Training', 'Validation'], loc='upper right')\n", + "plt.show() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Evaluate the benchmark model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Evaluate the benchmark model on the test set\n", + "\n", + "benchmark_test_loss, benchmark_test_acc = benchmark_model.evaluate(images_test, labels_test, verbose=0)\n", + "print(\"Test loss: {}\".format(benchmark_test_loss))\n", + "print(\"Test accuracy: {}\".format(benchmark_test_acc))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Load the pretrained image classifier model\n", + "\n", + "You will now begin to build our image classifier using transfer learning.\n", + "You will use the pre-trained MobileNet V2 model, available to download from [Keras Applications](https://keras.io/applications/#mobilenetv2). However, we have already downloaded the pretrained model for you, and it is available at the location `./models/MobileNetV2.h5`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following function. \n", + "# Make sure to not change the function name or arguments.\n", + "\n", + "def load_pretrained_MobileNetV2(path):\n", + " \"\"\"\n", + " This function takes a path as an argument, and uses it to \n", + " load the full MobileNetV2 pretrained model from the path.\n", + " Your function should return the loaded model.\n", + " \"\"\"\n", + " \n", + " mobilenetv2_model = load_model(path)\n", + " \n", + " \n", + " return mobilenetv2_model\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Call the function loading the pretrained model and display its summary\n", + "\n", + "base_model = load_pretrained_MobileNetV2('models/MobileNetV2.h5')\n", + "base_model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Use the pre-trained model as a feature extractor\n", + "\n", + "You will remove the final layer of the network and replace it with new, untrained classifier layers for our task. You will first create a new model that has the same input tensor as the MobileNetV2 model, and uses the output tensor from the layer with name `global_average_pooling2d_6` as the model output." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following function. \n", + "# Make sure to not change the function name or arguments.\n", + "\n", + "def remove_head(pretrained_model):\n", + " \"\"\"\n", + " This function should create and return a new model, using the input and output \n", + " tensors as specified above. \n", + " Use the 'get_layer' method to access the correct layer of the pre-trained model.\n", + " \"\"\"\n", + " inputs = pretrained_model.get_layer(name = 'benchmark_input')\n", + " outputs = get_layer('global_average_pooling2d_6').output\n", + " \n", + " model = Model(inputs = inputs,outputs = outputs)\n", + " \n", + " return model\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Call the function removing the classification head and display the summary\n", + "\n", + "feature_extractor = remove_head(base_model)\n", + "feature_extractor.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can now construct new final classifier layers for your model. Using the Sequential API, create a new model according to the following specifications:\n", + "\n", + "* The new model should begin with the feature extractor model.\n", + "* This should then be followed with a new dense layer with 32 units and ReLU activation function.\n", + "* This should be followed by a dropout layer with a rate of 0.5.\n", + "* Finally, this should be followed by a Dense layer with a single neuron and a sigmoid activation function.\n", + "\n", + "In total, the network should be composed of the pretrained base model plus 3 layers." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following function. \n", + "# Make sure to not change the function name or arguments.\n", + "\n", + "def add_new_classifier_head(feature_extractor_model):\n", + " \"\"\"\n", + " This function takes the feature extractor model as an argument, and should create\n", + " and return a new model according to the above specification.\n", + " \"\"\"\n", + " \n", + " inputs = feature_extractor_model.get_layer(index = 0)\n", + " h = Dense(32,activation = 'relu')(inputs)\n", + " h = Droput(0.5)(h)\n", + " outputs = Dense(1,activation = 'sigmoid')(h)\n", + " \n", + " model = Model(inputs = inputs,outputs = outputs)\n", + " \n", + " return model\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Call the function adding a new classification head and display the summary\n", + "\n", + "new_model = add_new_classifier_head(feature_extractor)\n", + "new_model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Freeze the weights of the pretrained model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You will now need to freeze the weights of the pre-trained feature extractor, so that only the weights of the new layers you have added will change during the training. \n", + "\n", + "You should then compile your model as before: use the RMSProp optimiser with learning rate 0.001, binary cross entropy loss and and binary accuracy metric." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following function. \n", + "# Make sure to not change the function name or arguments.\n", + "\n", + "def freeze_pretrained_weights(model):\n", + " \"\"\"\n", + " This function should freeze the weights of the pretrained base model.\n", + " Your function should return the model with frozen weights.\n", + " \"\"\"\n", + " \n", + " model.layers[0].trainable = False\n", + " \n", + " reutrn model\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Call the function freezing the pretrained weights and display the summary\n", + "\n", + "frozen_new_model = freeze_pretrained_weights(new_model)\n", + "frozen_new_model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Train the model\n", + "\n", + "You are now ready to train the new model on the dogs vs cats data subset. We will use an `EarlyStopping` callback with patience set to 2 epochs, as before. Feel free to increase the training time if you wish." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Train the model and save its training history\n", + "\n", + "earlystopping = tf.keras.callbacks.EarlyStopping(patience=2)\n", + "history_frozen_new_model = frozen_new_model.fit(images_train, labels_train, epochs=10, batch_size=32,\n", + " validation_data=(images_valid, labels_valid), \n", + " callbacks=[earlystopping])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot the learning curves" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run this cell to plot accuracy vs epoch and loss vs epoch\n", + "\n", + "plt.figure(figsize=(15,5))\n", + "plt.subplot(121)\n", + "try:\n", + " plt.plot(history_frozen_new_model.history['accuracy'])\n", + " plt.plot(history_frozen_new_model.history['val_accuracy'])\n", + "except KeyError:\n", + " plt.plot(history_frozen_new_model.history['acc'])\n", + " plt.plot(history_frozen_new_model.history['val_acc'])\n", + "plt.title('Accuracy vs. epochs')\n", + "plt.ylabel('Accuracy')\n", + "plt.xlabel('Epoch')\n", + "plt.legend(['Training', 'Validation'], loc='lower right')\n", + "\n", + "plt.subplot(122)\n", + "plt.plot(history_frozen_new_model.history['loss'])\n", + "plt.plot(history_frozen_new_model.history['val_loss'])\n", + "plt.title('Loss vs. epochs')\n", + "plt.ylabel('Loss')\n", + "plt.xlabel('Epoch')\n", + "plt.legend(['Training', 'Validation'], loc='upper right')\n", + "plt.show() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Evaluate the new model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Evaluate the benchmark model on the test set\n", + "\n", + "new_model_test_loss, new_model_test_acc = frozen_new_model.evaluate(images_test, labels_test, verbose=0)\n", + "print(\"Test loss: {}\".format(new_model_test_loss))\n", + "print(\"Test accuracy: {}\".format(new_model_test_acc))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Compare both models\n", + "\n", + "Finally, we will look at the comparison of training, validation and test metrics between the benchmark and transfer learning model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Gather the benchmark and new model metrics\n", + "\n", + "benchmark_train_loss = history_benchmark.history['loss'][-1]\n", + "benchmark_valid_loss = history_benchmark.history['val_loss'][-1]\n", + "\n", + "try:\n", + " benchmark_train_acc = history_benchmark.history['acc'][-1]\n", + " benchmark_valid_acc = history_benchmark.history['val_acc'][-1]\n", + "except KeyError:\n", + " benchmark_train_acc = history_benchmark.history['accuracy'][-1]\n", + " benchmark_valid_acc = history_benchmark.history['val_accuracy'][-1]\n", + "\n", + "new_model_train_loss = history_frozen_new_model.history['loss'][-1]\n", + "new_model_valid_loss = history_frozen_new_model.history['val_loss'][-1]\n", + "\n", + "try:\n", + " new_model_train_acc = history_frozen_new_model.history['acc'][-1]\n", + " new_model_valid_acc = history_frozen_new_model.history['val_acc'][-1]\n", + "except KeyError:\n", + " new_model_train_acc = history_frozen_new_model.history['accuracy'][-1]\n", + " new_model_valid_acc = history_frozen_new_model.history['val_accuracy'][-1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compile the metrics into a pandas DataFrame and display the table\n", + "\n", + "comparison_table = pd.DataFrame([['Training loss', benchmark_train_loss, new_model_train_loss],\n", + " ['Training accuracy', benchmark_train_acc, new_model_train_acc],\n", + " ['Validation loss', benchmark_valid_loss, new_model_valid_loss],\n", + " ['Validation accuracy', benchmark_valid_acc, new_model_valid_acc],\n", + " ['Test loss', benchmark_test_loss, new_model_test_loss],\n", + " ['Test accuracy', benchmark_test_acc, new_model_test_acc]],\n", + " columns=['Metric', 'Benchmark CNN', 'Transfer learning CNN'])\n", + "comparison_table.index=['']*6\n", + "comparison_table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot confusion matrices for benchmark and transfer learning models\n", + "\n", + "plt.figure(figsize=(15, 5))\n", + "\n", + "preds = benchmark_model.predict(images_test)\n", + "preds = (preds >= 0.5).astype(np.int32)\n", + "cm = confusion_matrix(labels_test, preds)\n", + "df_cm = pd.DataFrame(cm, index=['Dog', 'Cat'], columns=['Dog', 'Cat'])\n", + "plt.subplot(121)\n", + "plt.title(\"Confusion matrix for benchmark model\\n\")\n", + "sns.heatmap(df_cm, annot=True, fmt=\"d\", cmap=\"YlGnBu\")\n", + "plt.ylabel(\"Predicted\")\n", + "plt.xlabel(\"Actual\")\n", + "\n", + "preds = frozen_new_model.predict(images_test)\n", + "preds = (preds >= 0.5).astype(np.int32)\n", + "cm = confusion_matrix(labels_test, preds)\n", + "df_cm = pd.DataFrame(cm, index=['Dog', 'Cat'], columns=['Dog', 'Cat'])\n", + "plt.subplot(122)\n", + "plt.title(\"Confusion matrix for transfer learning model\\n\")\n", + "sns.heatmap(df_cm, annot=True, fmt=\"d\", cmap=\"YlGnBu\")\n", + "plt.ylabel(\"Predicted\")\n", + "plt.xlabel(\"Actual\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congratulations for completing this programming assignment! In the next week of the course we will learn how to develop an effective data pipeline." + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "tensor-flow-2-2", + "graded_item_id": "KDxTq", + "launcher_item_id": "aYhdg" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Week 2 Programming Assignment.ipynb b/Week 2 Programming Assignment.ipynb new file mode 100644 index 0000000..edfe8fa --- /dev/null +++ b/Week 2 Programming Assignment.ipynb @@ -0,0 +1,1644 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Programming Assignment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data pipeline with Keras and tf.data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instructions\n", + "\n", + "In this notebook, you will implement a data processing pipeline using tools from both Keras and the tf.data module. You will use the `ImageDataGenerator` class in the tf.keras module to feed a network with training and test images from a local directory containing a subset of the LSUN dataset, and train the model both with and without data augmentation. You will then use the `map` and `filter` functions of the `Dataset` class with the CIFAR-100 dataset to train a network to classify a processed subset of the images.\n", + "\n", + "Some code cells are provided you in the notebook. You should avoid editing provided code, and make sure to execute the cells in order to avoid unexpected errors. Some cells begin with the line:\n", + "\n", + "`#### GRADED CELL ####`\n", + "\n", + "Don't move or edit this first line - this is what the automatic grader looks for to recognise graded cells. These cells require you to write your own code to complete them, and are automatically graded when you submit the notebook. Don't edit the function name or signature provided in these cells, otherwise the automatic grader might not function properly. Inside these graded cells, you can use any functions or classes that are imported below, but make sure you don't use any variables that are outside the scope of the function.\n", + "\n", + "### How to submit\n", + "\n", + "Complete all the tasks you are asked for in the worksheet. When you have finished and are happy with your code, press the **Submit Assignment** button at the top of this notebook.\n", + "\n", + "### Let's get started!\n", + "\n", + "We'll start running some imports, and loading the dataset. Do not edit the existing imports in the following cell. If you would like to make further Tensorflow imports, you should add them here." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "#### PACKAGE IMPORTS ####\n", + "\n", + "# Run this cell first to import all required packages. Do not make any imports elsewhere in the notebook\n", + "\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.keras.datasets import cifar100\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import json\n", + "%matplotlib inline\n", + "\n", + "# If you would like to make further imports from tensorflow, add them here\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "\n", + "from tensorflow.keras.layers import Conv2D,MaxPooling2D,Dense,Flatten,Input\n", + "from tensorflow.keras.models import Model\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Part 1: tf.keras\n", + "\n", + "\n", + "\n", + " \n", + "
\"Church\" \"Classroom\" \"Conference
\n", + " \n", + "#### The LSUN Dataset\n", + "\n", + "In the first part of this assignment, you will use a subset of the [LSUN dataset](https://www.yf.io/p/lsun). This is a large-scale image dataset with 10 scene and 20 object categories. A subset of the LSUN dataset has been provided, and has already been split into training and test sets. The three classes included in the subset are `church_outdoor`, `classroom` and `conference_room`.\n", + "\n", + "* F. Yu, A. Seff, Y. Zhang, S. Song, T. Funkhouser and J. Xia. \"LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop\". arXiv:1506.03365, 10 Jun 2015 \n", + "\n", + "Your goal is to use the Keras preprocessing tools to construct a data ingestion and augmentation pipeline to train a neural network to classify the images into the three classes." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Save the directory locations for the training, validation and test sets\n", + "\n", + "train_dir = 'data/lsun/train'\n", + "valid_dir = 'data/lsun/valid'\n", + "test_dir = 'data/lsun/test'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create a data generator using the ImageDataGenerator class" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should first write a function that creates an `ImageDataGenerator` object, which rescales the image pixel values by a factor of 1/255." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following function. \n", + "# Make sure to not change the function name or arguments.\n", + "\n", + "def get_ImageDataGenerator():\n", + " \"\"\"\n", + " This function should return an instance of the ImageDataGenerator class.\n", + " This instance should be set up to rescale the data with the above scaling factor.\n", + " \"\"\"\n", + " imagedatagenerator = ImageDataGenerator(rescale =(1/255.))\n", + " \n", + " return imagedatagenerator\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# Call the function to get an ImageDataGenerator as specified\n", + "\n", + "image_gen = get_ImageDataGenerator()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should now write a function that returns a generator object that will yield batches of images and labels from the training and test set directories. The generators should:\n", + "\n", + "* Generate batches of size 20.\n", + "* Resize the images to 64 x 64 x 3.\n", + "* Return one-hot vectors for labels. These should be encoded as follows:\n", + " * `classroom` $\\rightarrow$ `[1., 0., 0.]`\n", + " * `conference_room` $\\rightarrow$ `[0., 1., 0.]`\n", + " * `church_outdoor` $\\rightarrow$ `[0., 0., 1.]`\n", + "* Pass in an optional random `seed` for shuffling (this should be passed into the `flow_from_directory` method).\n", + "\n", + "**Hint:** you may need to refer to the [documentation](https://keras.io/preprocessing/image/#imagedatagenerator-class) for the `ImageDataGenerator`." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following function.\n", + "# Make sure not to change the function name or arguments.\n", + "\n", + "def get_generator(image_data_generator, directory, seed=None):\n", + " \"\"\"\n", + " This function takes an ImageDataGenerator object in the first argument and a \n", + " directory path in the second argument.\n", + " It should use the ImageDataGenerator to return a generator object according \n", + " to the above specifications. \n", + " The seed argument should be passed to the flow_from_directory method.\n", + " \n", + " \"\"\"\n", + " # I couldn't get this one right. If you get it let me know So, I can recitfy.\n", + " image_data_gen = image_data_generator.flow_from_directory(\n", + " directory = directory,\n", + " batch_size = 20,\n", + " target_size = (64,64),\n", + " \n", + " class_mode = \"categorical\"\n", + " \n", + " )\n", + " return image_data_gen\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 300 images belonging to 3 classes.\n", + "Found 120 images belonging to 3 classes.\n" + ] + } + ], + "source": [ + "# Run this cell to define training and validation generators\n", + "\n", + "train_generator = get_generator(image_gen, train_dir)\n", + "valid_generator = get_generator(image_gen, valid_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are using a small subset of the dataset for demonstrative purposes in this assignment." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Display sample images and labels from the training set\n", + "\n", + "The following cell depends on your function `get_generator` to be implemented correctly. If it raises an error, go back and check the function specifications carefully." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Display a few images and labels from the training set\n", + "\n", + "batch = next(train_generator)\n", + "batch_images = np.array(batch[0])\n", + "batch_labels = np.array(batch[1])\n", + "lsun_classes = ['classroom', 'conference_room', 'church_outdoor']\n", + "\n", + "plt.figure(figsize=(16,10))\n", + "for i in range(20):\n", + " ax = plt.subplot(4, 5, i+1)\n", + " plt.imshow(batch_images[i])\n", + " plt.title(lsun_classes[np.where(batch_labels[i] == 1.)[0][0]])\n", + " plt.axis('off')" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 300 images belonging to 3 classes.\n" + ] + } + ], + "source": [ + "# Reset the training generator\n", + "\n", + "train_generator = get_generator(image_gen, train_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Build the neural network model\n", + "\n", + "You will now build and compile a convolutional neural network classifier. Using the functional API, build your model according to the following specifications:\n", + "\n", + "* The model should use the `input_shape` in the function argument to define the Input layer.\n", + "* The first hidden layer should be a Conv2D layer with 8 filters, a 8x8 kernel size.\n", + "* The second hidden layer should be a MaxPooling2D layer with a 2x2 pooling window size.\n", + "* The third hidden layer should be a Conv2D layer with 4 filters, a 4x4 kernel size.\n", + "* The fourth hidden layer should be a MaxPooling2D layer with a 2x2 pooling window size.\n", + "* This should be followed by a Flatten layer, and then a Dense layer with 16 units and ReLU activation.\n", + "* The final layer should be a Dense layer with 3 units and softmax activation.\n", + "* All Conv2D layers should use `\"SAME\"` padding and a ReLU activation function.\n", + "\n", + "In total, the network should have 8 layers. The model should then be compiled with the Adam optimizer with learning rate 0.0005, categorical cross entropy loss, and categorical accuracy metric." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following function.\n", + "# Make sure not to change the function name or arguments.\n", + "\n", + "def get_model(input_shape):\n", + " \"\"\"\n", + " This function should build and compile a CNN model according to the above specification,\n", + " using the functional API. Your function should return the model.\n", + " \"\"\"\n", + " inputs = Input(input_shape)\n", + " h = Conv2D(8,(8,8),padding = \"SAME\")(inputs)\n", + " h = MaxPooling2D((2,2))(h)\n", + " h = Conv2D(4,(4,4),padding = \"SAME\")(h)\n", + " h = MaxPooling2D((2,2))(h)\n", + " h = Flatten()(h)\n", + " h = Dense(16, activation = \"relu\")(h)\n", + " outputs = Dense(3, activation = \"softmax\")(h)\n", + " \n", + " model = Model(inputs = inputs,outputs = outputs)\n", + " \n", + " model.compile(\n", + " optimizer = tf.keras.optimizers.Adam(learning_rate = 0.0005),\n", + " loss = \"categorical_crossentropy\",\n", + " metrics = ['categorical_accuracy']\n", + " )\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"model_1\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "input_2 (InputLayer) [(None, 64, 64, 3)] 0 \n", + "_________________________________________________________________\n", + "conv2d_2 (Conv2D) (None, 64, 64, 8) 1544 \n", + "_________________________________________________________________\n", + "max_pooling2d_2 (MaxPooling2 (None, 32, 32, 8) 0 \n", + "_________________________________________________________________\n", + "conv2d_3 (Conv2D) (None, 32, 32, 4) 516 \n", + "_________________________________________________________________\n", + "max_pooling2d_3 (MaxPooling2 (None, 16, 16, 4) 0 \n", + "_________________________________________________________________\n", + "flatten_1 (Flatten) (None, 1024) 0 \n", + "_________________________________________________________________\n", + "dense_2 (Dense) (None, 16) 16400 \n", + "_________________________________________________________________\n", + "dense_3 (Dense) (None, 3) 51 \n", + "=================================================================\n", + "Total params: 18,511\n", + "Trainable params: 18,511\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "# Build and compile the model, print the model summary\n", + "\n", + "lsun_model = get_model((64, 64, 3))\n", + "lsun_model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Train the neural network model\n", + "\n", + "You should now write a function to train the model for a specified number of epochs (specified in the `epochs` argument). The function takes a `model` argument, as well as `train_gen` and `valid_gen` arguments for the training and validation generators respectively, which you should use for training and validation data in the training run. You should also use the following callbacks:\n", + "\n", + "* An `EarlyStopping` callback that monitors the validation accuracy and has patience set to 10. \n", + "* A `ReduceLROnPlateau` callback that monitors the validation loss and has the factor set to 0.5 and minimum learning set to 0.0001\n", + "\n", + "Your function should return the training history." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following function.\n", + "# Make sure not to change the function name or arguments.\n", + "\n", + "def train_model(model, train_gen, valid_gen, epochs):\n", + " \"\"\"\n", + " This function should define the callback objects specified above, and then use the\n", + " train_gen and valid_gen generator object arguments to train the model for the (maximum) \n", + " number of epochs specified in the function argument, using the defined callbacks.\n", + " The function should return the training history.\n", + " \"\"\"\n", + " history = model.fit(train_gen,\n", + " validation_data = valid_gen,\n", + " epochs = epochs,\n", + " callbacks = [tf.keras.callbacks.EarlyStopping(patience = 10),tf.keras.callbacks.ReduceLROnPlateau(factor = 0.5,min_delta = 0.0001)]\n", + " )\n", + " \n", + " return history" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train for 15 steps, validate for 6 steps\n", + "Epoch 1/50\n", + "15/15 [==============================] - 11s 743ms/step - loss: 1.0774 - categorical_accuracy: 0.3967 - val_loss: 0.9810 - val_categorical_accuracy: 0.5667\n", + "Epoch 2/50\n", + "15/15 [==============================] - 10s 647ms/step - loss: 0.9670 - categorical_accuracy: 0.5367 - val_loss: 0.8666 - val_categorical_accuracy: 0.6000\n", + "Epoch 3/50\n", + "15/15 [==============================] - 9s 633ms/step - loss: 0.8358 - categorical_accuracy: 0.6100 - val_loss: 0.8097 - val_categorical_accuracy: 0.6667\n", + "Epoch 4/50\n", + "15/15 [==============================] - 9s 633ms/step - loss: 0.7275 - categorical_accuracy: 0.6867 - val_loss: 0.7484 - val_categorical_accuracy: 0.6583\n", + "Epoch 5/50\n", + "15/15 [==============================] - 10s 653ms/step - loss: 0.6560 - categorical_accuracy: 0.7033 - val_loss: 0.7666 - val_categorical_accuracy: 0.6417\n", + "Epoch 6/50\n", + "15/15 [==============================] - 10s 673ms/step - loss: 0.6627 - categorical_accuracy: 0.7133 - val_loss: 0.7740 - val_categorical_accuracy: 0.6750\n", + "Epoch 7/50\n", + "15/15 [==============================] - 10s 640ms/step - loss: 0.6027 - categorical_accuracy: 0.7333 - val_loss: 0.8330 - val_categorical_accuracy: 0.6000\n", + "Epoch 8/50\n", + "15/15 [==============================] - 9s 633ms/step - loss: 0.6221 - categorical_accuracy: 0.7500 - val_loss: 0.7647 - val_categorical_accuracy: 0.6917\n", + "Epoch 9/50\n", + "15/15 [==============================] - 9s 627ms/step - loss: 0.6372 - categorical_accuracy: 0.7133 - val_loss: 0.8383 - val_categorical_accuracy: 0.6417\n", + "Epoch 10/50\n", + "15/15 [==============================] - 9s 625ms/step - loss: 0.6037 - categorical_accuracy: 0.7367 - val_loss: 0.7184 - val_categorical_accuracy: 0.7083\n", + "Epoch 11/50\n", + "15/15 [==============================] - 10s 634ms/step - loss: 0.5290 - categorical_accuracy: 0.8000 - val_loss: 0.7152 - val_categorical_accuracy: 0.7000\n", + "Epoch 12/50\n", + "15/15 [==============================] - 9s 631ms/step - loss: 0.4615 - categorical_accuracy: 0.8400 - val_loss: 0.7218 - val_categorical_accuracy: 0.7083\n", + "Epoch 13/50\n", + "15/15 [==============================] - 9s 627ms/step - loss: 0.4641 - categorical_accuracy: 0.8233 - val_loss: 0.8215 - val_categorical_accuracy: 0.6833\n", + "Epoch 14/50\n", + "15/15 [==============================] - 10s 638ms/step - loss: 0.4746 - categorical_accuracy: 0.7767 - val_loss: 0.7595 - val_categorical_accuracy: 0.6750\n", + "Epoch 15/50\n", + "15/15 [==============================] - 10s 640ms/step - loss: 0.4272 - categorical_accuracy: 0.8367 - val_loss: 0.7713 - val_categorical_accuracy: 0.6583\n", + "Epoch 16/50\n", + "15/15 [==============================] - 10s 640ms/step - loss: 0.3775 - categorical_accuracy: 0.8600 - val_loss: 0.7660 - val_categorical_accuracy: 0.6583\n", + "Epoch 17/50\n", + "15/15 [==============================] - 9s 627ms/step - loss: 0.3475 - categorical_accuracy: 0.8733 - val_loss: 0.8566 - val_categorical_accuracy: 0.6167\n", + "Epoch 18/50\n", + "15/15 [==============================] - 9s 627ms/step - loss: 0.3619 - categorical_accuracy: 0.8633 - val_loss: 0.7964 - val_categorical_accuracy: 0.6333\n", + "Epoch 19/50\n", + "15/15 [==============================] - 9s 627ms/step - loss: 0.3050 - categorical_accuracy: 0.9033 - val_loss: 0.8057 - val_categorical_accuracy: 0.6583\n", + "Epoch 20/50\n", + "15/15 [==============================] - 10s 653ms/step - loss: 0.2810 - categorical_accuracy: 0.9033 - val_loss: 0.8329 - val_categorical_accuracy: 0.6750\n", + "Epoch 21/50\n", + "15/15 [==============================] - 11s 733ms/step - loss: 0.2612 - categorical_accuracy: 0.9333 - val_loss: 0.8622 - val_categorical_accuracy: 0.6667\n" + ] + } + ], + "source": [ + "# Train the model for (maximum) 50 epochs\n", + "\n", + "history = train_model(lsun_model, train_generator, valid_generator, epochs=50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot the learning curves" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Run this cell to plot accuracy vs epoch and loss vs epoch\n", + "\n", + "plt.figure(figsize=(15,5))\n", + "plt.subplot(121)\n", + "try:\n", + " plt.plot(history.history['accuracy'])\n", + " plt.plot(history.history['val_accuracy'])\n", + "except KeyError:\n", + " try:\n", + " plt.plot(history.history['acc'])\n", + " plt.plot(history.history['val_acc'])\n", + " except KeyError:\n", + " plt.plot(history.history['categorical_accuracy'])\n", + " plt.plot(history.history['val_categorical_accuracy'])\n", + "plt.title('Accuracy vs. epochs')\n", + "plt.ylabel('Accuracy')\n", + "plt.xlabel('Epoch')\n", + "plt.legend(['Training', 'Validation'], loc='lower right')\n", + "\n", + "plt.subplot(122)\n", + "plt.plot(history.history['loss'])\n", + "plt.plot(history.history['val_loss'])\n", + "plt.title('Loss vs. epochs')\n", + "plt.ylabel('Loss')\n", + "plt.xlabel('Epoch')\n", + "plt.legend(['Training', 'Validation'], loc='upper right')\n", + "plt.show() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You may notice overfitting in the above plots, through a growing discrepancy between the training and validation loss and accuracy. We will aim to mitigate this using data augmentation. Given our limited dataset, we may be able to improve the performance by applying random modifications to the images in the training data, effectively increasing the size of the dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create a new data generator with data augmentation\n", + "\n", + "You should now write a function to create a new `ImageDataGenerator` object, which performs the following data preprocessing and augmentation:\n", + "\n", + "* Scales the image pixel values by a factor of 1/255.\n", + "* Randomly rotates images by up to 30 degrees\n", + "* Randomly alters the brightness (picks a brightness shift value) from the range (0.5, 1.5)\n", + "* Randomly flips images horizontally\n", + "\n", + "Hint: you may need to refer to the [documentation](https://keras.io/preprocessing/image/#imagedatagenerator-class) for the `ImageDataGenerator`." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following function. \n", + "# Make sure to not change the function name or arguments.\n", + "\n", + "def get_ImageDataGenerator_augmented():\n", + " \"\"\"\n", + " This function should return an instance of the ImageDataGenerator class \n", + " with the above specifications.\n", + " \"\"\"\n", + " \n", + " image_data_gen = ImageDataGenerator(\n", + " rescale = (1/255.),\n", + " rotation_range = 30,\n", + " brightness_range = (0.5,1.5),\n", + " horizontal_flip = True)\n", + " \n", + " return image_data_gen\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "# Call the function to get an ImageDataGenerator as specified\n", + "\n", + "image_gen_aug = get_ImageDataGenerator_augmented()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 120 images belonging to 3 classes.\n", + "Found 300 images belonging to 3 classes.\n" + ] + } + ], + "source": [ + "# Run this cell to define training and validation generators \n", + "\n", + "valid_generator_aug = get_generator(image_gen_aug, valid_dir)\n", + "train_generator_aug = get_generator(image_gen_aug, train_dir, seed=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 300 images belonging to 3 classes.\n" + ] + } + ], + "source": [ + "# Reset the original train_generator with the same random seed\n", + "\n", + "train_generator = get_generator(image_gen, train_dir, seed=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Display sample augmented images and labels from the training set\n", + "\n", + "The following cell depends on your function `get_generator` to be implemented correctly. If it raises an error, go back and check the function specifications carefully. \n", + "\n", + "The cell will display augmented and non-augmented images (and labels) from the training dataset, using the `train_generator_aug` and `train_generator` objects defined above (if the images do not correspond to each other, check you have implemented the `seed` argument correctly)." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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eS38Y2SDxewB/WfX7CPDDOIXKx8OUqu2PA3iP/j/elv8T+mFYtW4FgHP0/2sAfKhq23wA62LlPwXgRv3/dQDuqdp2FICB0a5Ht/0WwJVVvxOgO/T0vdyjAMB5Vb/fD+DxWJlH9Lr3Zli5zvqY/r4YQC/2NKaMOdBt41D+G6XdXwFgVdXven0eE6ra6t4mWLNi7fbnI9ThJgD/XfX7IgDL91LvEdvey6hf9QTwDwCuqfp9oZXZh7a8NyNhHkDuQD/rQ/1vlLbsY3il3EE7hteUCyiA9QCmySh+ukI91CfBj+nWIAjGgB/KAgBBEKwMguC9oGXqqwBuFZGGIAgKQRBcHwTBUQDOAPAWAB+oOnRQ9f9NAMaKSFPVumkANlZtnx7btullX62j1uDt2/FawlQAa4M9g0JMAt2EDWvBD4fxVeu2VP2/H7QyD4fpAK5V16HdIrJbzzupqsz6WPlJsfKf3su5c9qnRroeO+43q465E+xTk0eodzWq6xe/N9Dfk3XbziAIeobZZtha9f8BADuCIChV/QZGvpeOVwejtZOwbQVB0K//fTnPo7qtTAUnSiNhX/uQYbS290owCdH6ro1tG60tDzdGVPfp7UEQDL7Cejn2HT6GH8JjeK1NAB8HsBnAP4pIgzCwxetiZZpA94LtAFIi8nkAzbZRRC4TkY6AUQ536+qSiJwrIscIdVjdoDWkhGEQBMF60KXhK1qHYwFcCbppAGRgPisiHSLSDlLdNw93LIejCt6+Ha8ljGSQGM4AUET0xfdyzvEPQRCMqfqrD4LgR1Vlglj51bHyTUEQXPRHXI9tuzp23LogCB7eh+PGjSfTY9vNeLI3w4rjtYG9GuJGQB/IChomDFMm3pb3qut7GRit7QGj16+6XobN4Ad39bGqz/XHGAmHO5/j1YeP4YfwGF5TE0CdRV8MYA6AdWCQinfHit0JUsEvgDPzQURn928EsEREesGAGe9RS9QEALeCH8fLQD/f0T5q3wtS5ZsA/BzAF4IguFu33QDgSQDPA1gE4Gld53CMCG/fjtcYRjJI/AjA/xGRmSLSCODLAH4yglV2b/gOgGtEZL4QDSLy5tgLNl6nbg0qUCcMVHG0iJzyR1wPAPwXgE+JyDwAEJEWEbn0FVzPbwAcLiLvEwY7eDfowvTrfTCsOF4b2BdD3HB4FsB7RCQtDFrxzr2U/yGAC0TkXdpW2kTk+D+i3iO2vX2o33ZQSlAdnOunAP5aRKaISCuAv7cNbiQ8aOBj+MvHwTOGvxp+pP7nf/7nf/7nf/E/0Lr5CwCdoK70X0HD4+dBw8N28MOuVcvPwJ5aooUAPqz/vwKxgEag0eIJkLHeDOAWqMYCMd2SrpsEfsBsAcX5j1oZVOmahqvPcNdTVfb9oEGjW6/tf/bh/gQA5sTWnQkGOejS5ZlV26aAH+Q7Qfe/ao1VvO4XQHVT+tv0tFP2Vi//+5O0++Habvj8wcnTY6Dm5w7dZ0SNna4/S/exNne5rr8JwA1V5RagSls9Sr1Ha3sj1k+3fxHsz7vBKM8pMNhGJxjI4qOxvjRaW87p8Tfr379CNX/7ei3+9ydtyz6GV/Y5aMdwiz7lcDgcDofD4XA4HI5DHDXlAupwOBwOh8PhcDgctYyDKmu9w+FwOBwHC4RRd3873LYgCDwap2O/wtujw/HycCj3GXcBdTgcDofD4XA4HI4agbuAOhwOh8PhcDgcDkeNYL+6gI5paxyVbiwWGUG2XCoDAEplphkLygGaxjL9TK6eVU7p3LVUGGIZS8OR5PZkmsdI6BWWS9w+2M9j5uqyAICtW3oBAC0tLVqQ+5WTeQBAtjmBbMCy9U1cnnU2I+svfZ7pQfrznTxmVgAA557/PgDAW9/KPNlrNm4GANz03X8AAHS0MzXOxX/21wCAw488mnXW6XgiqUsBRNeJRJdaTegt2yMpjpVL6/Wn9Zh2Dpv6B2ndruWTQWV/iZkHLFmcNZq8LjMYETLypkMT1/zdPwcA42EDQIVg562oS9FjYPIktufiANtUT18vmlraAABjm/hQpozjQ2uo4x0eP34sACCb5PqBvgKPkSzoyUSPxbNv2s6+gQTP0dXNtt43xEaTy7EOpWQKxZL2PW1YKe1HRX2C1hSSwnLZTEavL9ryCtYudXU5j2Fh+xVRRrLMfL5BfTsA4K6ffR8AcNH5ZwMAFpw0BwDwpS9+GgDw3g9+nHVsY6qdnp3rAAC/u/2nAIDzzmM6oKcfuYXlErzuHZuYm7UuybrX19Vj5aqVAIBpM2cCAFauXgUA+Ld//Q4AYEzTOADAZz77OQDAli1M1/Nf//FtAMCs415XU2383W+fHwBAQhtEUgeJRJK3IWljVyL2W4BkrC0ldGNSN9gxbZmyMS+2PqmDWEIHd8uRWyqzbZaKmp5SF9lMBslUMqwHUBkfbbAta98ph++caJ8S3SGl7d5+JxLRQTLQfh4EtgyAFMukrP46cm7YuBMA8MBDSwEADY1jAAAnnnAYAKC+kf22qalR65jQpV6evieZsrPy/rT1+UIReXuXFq0M9y3of4r6XizaCwV2vfx1883311T7BoCPfeSd0UEtdgdGvCGJRDguFvPM1xzow5pQX7IiAIBkKq3bed+tPYk20CB8gej3iI6Xop3Bvo3K4XOrqoaW7eplG+4rsZ+U03pOPYYN3WWtY9gHdFnQNpPW/XJ1dXoDJLp/UA6/2fIFnjM/FB34S3ZHdSf7XgtQrl5dhar+g8r3TX9fn55H23O5BCnxXIN5lpo+mWP5ogfvZf0z7D9JHVDqdNxI6H14YPHSmmvjU173JoavjLUBu882xsQeG6NH2kHsk1uX9q1gbdfeAYGO+OEzt2c6wmxAXxkohWNypeDeHRZZoFTUbyMd57LZumgp608jHM+223dzsTRc4sk9+171MRPWT8I2ri8kvUFWTsrVNa/6ptLvosZcC6665FQAwJKnHgUA3LmyHwBQn84OW4dSifum9MN++5LHhm3jzgA6HA6Hw+FwOBwOR41gvzKAZhVKqAU3qRbg0LqaSg1bqyAIkFPLWq+yGE31nNFnc0ndNwcAKIIz/sE8Z8jG/GVTLC/1nDFnsrRqNbUUtBxn59k6ru+Y2MHtYxqw7qU1AIBZs+cCAE447kQAwC9v/V8AQEMzzzlt+gwAwKRJRwAApkyaDQBY+dIWAMD6Ncy3/blPf4P7tRyh1z/MzdoLguGNDyHijOGeBSKL4cwbIcwi49aCfUCKrFrlXpmliXc6l2Y7nNRGNi8lvLsvrA+QzbFtijIEE1pbAQAtbcyH2tTIPpDQZ5XW/jNUsiXbcL2eq6OFHalnkI2loZ0sd08fmcHBZL3uV0Qmq51OG0RGrb6DebVc6/qUnjyljEqpwLafzrLu/Xn2pyDP9amMWv/KUetfaKkrJ1Cf5nX12Ra9jroc+3Q6zWN0d3XxOhp4P4bMSqzMz+AgrV65Ou43NMgxINPA41ofl1TF2l60+md4vQW1YI8dSza2MKTW9K7uyP0ZO6YVjj0RxC3Ho5fWZZRR2BM2SsULGCui74/wvaLr1eAqEqCg7dIIE9vHWIFAmcBSSRmWkh1T2QJlKYt6HFufzaX1nPouM/cN3Y6gcmH1Wjah1tmXVj4DADh2Hj1A+gfYL19cRUY7o8bdc85mnuJ0lu26rIOLtWdjLQsFYwDV26VQwKDWt6D9uFTgtnzIAPKGFEoxJuqVvJQOGQT7+DP6Ek2IINBnYUgnbYzS+x9nb7WcMckJbT8hQxG+x6PPI2QOjU2orqSO/4MFnrOYUNba2N1YRws9j/R3ybbrOTNZZc8TxhLF+mtQxRyVo8cOhvnf8L9HR7kUZQqDyn/CPp3RDjOoLGFYv0S0TUvtOSbtgXTSvAmU/TW2LsbO2W97rOWgqqUVbTCNHdy8y2Ljdshi761ylUc78kY7SthWo5tLOjYmU8NPcUauQ7Tfxd9n+4aoF0U5vKexPh37hq/cL5uT8EYeNq0DPbvpLbK1z77HkhgOYb9QelESo3+1+ze9w+FwOBwOh8PhcNQIDkgaCPPLNWu7IanWrzhDmEol0dRI5qSv16z8WvWk+rrqTL+omsD6Blq92tppqe/cQeawMMBzS5Iz6ZZm+of3dHO/bJb7Deb5u29zH5qbqH6bdxSZv7Vr1gAAentpaWppJ1swfuIsAMC2LVsBAL+5k5Fj0yrEm3vkKQCAAC2j3p+gysgRN8RW/O5HPURFT2O/48eJ6XHiFpFq/V+8kZjhYngbRG0jnTalZFTrYAjUZ7uHEhFMaGW7HttcrDzsFNvg9l5tq2m2xUKebb1jLNtPo2qE6srKoOVVd6GW2pxawRrHknloUNY7m+M5t3Wx/KbNvcjkjAHkvgXVUTUpK2lWrIQKmYx9SCjTUVfPumez3L6rV4+nVqxMxphBHk8JUHT1ltDRzP7z4pbdAIArr/wgAKC+zH40pJTHn3/4Wp47xWPsXL8EALBi6UMAgPknUhO4deMiXrey/X29O7hfTG8TlIMqq1y0BzRpn9/eT9axp6cHAKBkJca0joWjArt7pmFKhkuuT0oV05AwTZyyZqEGUNnl+L7h+8CWeq7QyhnVTyVEWTt93sUSUFB9UH8/23z/QF63sczO3WR4h5Qxs744cyb12u3j+LzTGdUt6qho76w4G1S0ugZAWjVHdXVk3Jc9T014Y9N4AMBRR87SfXnuVS9tAwB0dW3nsdTS3jZWvV4aSGkXVOcS6l1K0ffq4GAeA3o9xuQXlJnPKztU0Htkeq+RGKfaQvzaR3rZGj1QoQuMeTCPh0RCf8e0bAbTCdnYVCrFtH1m0Le2nTD9XvRIieoXdqgJZRsX0wmFMs8oQ2GMWMgGaVsowzSA6kJhpwjdj7gcGhpAUUXfIVMX6r3s3mB4jKT9i60NtbkxDxIgCL+JsvreHOhlX5bQw8yqv8dXzgiVOvSRMh2oPScdS8uxtmCsVKgRDCrPoG/APGzqo8eMxamoMH9xjw89p8UdCPXYiGzfJ8SKWntJp4bXyI3UHu2U9r08/Hf26PUK2cM9/GCi+5kGcE9nPhsDeD/nTO9Az26+M7b2cltqBGbPdIamv5SYR0IczgA6HA6Hw+FwOBwOR41gvzKAKuNDoDPbUhgpzdbzP3GGMJ0W9PRwnjxGGZOGRuohens4w+0bIsOXq+exB/p4jKBJNQ+DZOsa1LKfyZJNGeoXPQctBUnVG41RlmXH5u0YO65Fy1B7dN/v7wNQYR2POOJILo88AQAwbepRAIDJk6kBXL+JkQbPf/3bAQBtbRNYp5i1IY5yMHKUotBCob9jbvuV5UhT/BhDOBqskVjZYuy3o4J0nVpL447sZuVK8feQEg09Q/oEUwkk9GE3qL51QK2qvarhq1dLW1nvfE6Z5ZyGqkrmtU8M9Oipud+UCWTYcsqwGLPSkON/2uskjKhoG3tVR5HQiJklPZZFrxtSvVFfN5f1jcqI9CqzUsjrOVi35rG8L8Uu9teZ09SK2JXGti4eo0Gt5fWqkzxq2rEAgMH+FwAAE8ewUf/05q8AAI48bAYA4OkHfwkAOO+C0wEAq5aRAZw75ywAwM03L+S1JMjmHTGL2ttNG3agTy1q06Zy3ZKlqwEAzz7DY0yZzHM0NrLvmy7rpdUvAQBmHce+XCuw6MmJKOEVRvI0bahJL8TanFSYPbNOprTNmZzBmL1kuN1YFDuWnYMrLCJiGCkZ0fJJbbPpMpDOaFRoC1mnJ0srpRto/dOmI9J3T9MYPvc69SixgTVR1vtgLIPeB7OYZ2xFIhlqVie2UVfeuYHMdlFZuaUrXwQAjJ84kdenLGNjE71XFi9eDgCYdfHrAQANzWQji1rHQkmZQF0WlPmpryuGmtxB7Y9DgxV2kPtq9EZl/F0DiGGIob2wWMailEoo2f2zZhYyK1Y0poEOjznC2zRWl5J+G4Wsd8jkVBiTokXB1Y5lUT/jZ4jrm0qhpCvqF2S6RGOYra309fbq/kHIHFW03tGosuFlxq535Fsa1XYZSxTEXqsIyiFLk9W+293JqOymkwq7ommzwpPUbhu3b1d7TsbwJWLt01isUrLCDObzpjXmGJLL8fu4mDdvGj7bfCKkAKuOuOczr2gztThiBUOCPUD1SDscytpGjSnKvXNMAAAgAElEQVQXifXdPVjF2EnCOhk7OVwbGf7cssehoo1VYuX2PEr0ftVrXIVpE1qw9El6O23T75VMYvjY+8YA2jtU9hIsxL/hHQ6Hw+FwOBwOh6NGsF8ZQNMyGAORjZ29FFjEwChDmMlWrE859fMeHOzVMlw/po1WiGzWLGMDWo5MX51qBiWlv+vJliRUd5HLUgs4qNYts2S3jWnDoGqvLLXNNs0FZtq+xmZaatdqtM9ckhaoecfMBwA8v4i5O8485008p07eE7EcafGcf4kqDeAeBllj8GIWsVBGptb0VGg913LhySKLis2vShtotoORNIAHRED6GseEAq37xtKV1ApZLLM9Njfx4Tcr05wocvvk9gbUq97OdCBdO8lYlVQ7lFSrlgUYND2SsXfGsI/vIMs9aRzLp9PGjOhvZfEatJG0tzZURUXktny+QesStd5ZpEWLLtinnSKtxxrTw37V0sVWsn0LmbIH7yFrfvyRxwEAli9iVKtU/Sxs2rwLAJDTnDXPPfIHAEC2eDIA4LEH7gAAnHfuuQCA8hA1ffkhXrDlSXvwfkZVnDgpp9fAOt58800AgKeefJblHnyA2wudKArZxh2dZDyPOZr5du66614AwEc/+lcAgGOPOQYA8Mij3PfjH78GAPCrhc+jlpA2tjnU20VZuVC/rW3UWO1UUpAyljDMoWcRNrWsHVN/p2Mav0r+Pxvjk5HtYQQ3yyOog2KpHCChllHLSVVUtjmnethCqDHivlLWPqX9IZW0QVsTp2oC1USMhQxZE10m0ym0tTKXZI9qbo+ffz4AYFY3tUpbN/O98cRjTwMAsi1ZratqAl9kHxo3fgoAoKyZVwsZHi9VsJxXGqFULfPpZAGpnL7Phjj+ZNIsk8kpA6j51IzRdwYQw1AUuojr6GP/CYCwAVXyiFn+SWUUgqgmp0JIRBmzSnhQKxfTkof/MWasUjljdQM9d6VslNWJ673C3/ZtoRds31BDluOvYH2l4sEVJ0qCCnUX3WCaxzB6afRDpKwRnUMtlr1HdX2lrrZbEK4zBrC/lx4wYl4tcQ1gLbdtRSKWEDpk/sKckNqGjBmsyuHX08dv74x+/5rGGerdZAxxBcN3qLjeWKJVGIEyHF1/Zzn3kmEU5uHLB5FOW/nPvqp/h0f0ukIWPHaQEaN/BpaLmb/nzKTHSM/undjWa/0ieo7KQewk2q9MX19yDaDD4XA4HA6Hw+FwOLC/8wDmo7Nss5pWIsapTiPGEKYzWQSqPGvUXGjTdHb80krm1ksmbV9unz5jEgBgQCMV9amOKhtjAlvbyXT0d6seKkNrbVq1LoP929DaSA3VQDctSxYpdNacOQCAmdMPBwCsWklm4vwLLgQAzDlsDADgxu9Qw3T1hy8DAHRpwrNwEm8WKrN821MpV/QxcaoudO1VQZ4ZdMwSlxxBAxgaCo0ptO0xq0QZlSifbi/bd0ydrjoMi+amy1JRtXRqnd2xcQUAoK6e7a+16TCMb2F72d1J5q+pjUzWEbOp+TG9UdoaRWDWZS5Nb9fYpNYfE2KZZS1mDUporj82sqhFMJc1H3PdJ2YhzChbmR1ihFKzHucL7FfLFy8DADyibN76DdTUpfrJ9qW1k0+eIZg9nVrZW37MvJr9fWTQP/7XfwEAmD6BDHv/IKMjnnvuRQCAJcvIvrWNow4vk2SdurrYT9etZ+Ss66//KgDgmqvJ2l166XQAwKJFS3DSiYzs29tLhmTbNp7DNH7PPEPWcNJURoOcsnEmAKC1RvMAGgttOjWL/Gb59MxzwjSBSdMAppIhg5cybYi2tZDZNo1fmFMvqilJpqKMYMi+jcBaJbStShBUaZLY5ksJshl1qtkdHIpGbzQ9YTKpef7SOT2HRZWLRhhErC4pZQpz6RS6d7I9juuYwT2zquFTr4BMlgz/rJnUkv/qHjLes2aT8WtQ/enmLewXk6ayDUoy6kqSSGruTWUAS4k8kgGv04z0BdWVZCw/oLI5WWUAK4RM7Y76e8qElP2Iec0MyxdEDfHIaH+IM9DhESL6pj3rYPkmLV9pSAzGCIxEQkLWpmjbEtHPu3hkxzACZOycYXRGWDRRXZoOz6IoS2W/UNcUi/oZmFdXmA/XNkdzyIXdMybyK+l9Kxs7osuSMvYJAAVjqUyfrvlfk5rnNvx0ij2uWm7jppULmSKJfjgmTaNZpbUEqBXs383vk3GTJ+ku5nIR/bi0vMQhu2uHirV107WGxUfI3Rdh0uL5/2LMckrSwx9ib5FFQ6Y+enieft/4wDDaqRGA2idKCfXsEL33e+jzotE/D5uuniO7dmJbn3lPDs/ZlWFR2aPav721cWcAHQ6Hw+FwOBwOh6NGsF8ZwHIYFUhnpTrjN5fhMLKcGRR0Ra4xhWTa2AvOrl9YwchppuEwfVNPj+b703yApYDLlAqkLF9NY46WX2P6Sr0sd95ZrwMArN1And+m9asxYyKZvtUvLOY5esliHH74EVpP6g9nziRTeOyR7dyXac2w4KzzeE69goQGirQsiBZ9y1i8HCrb7QHZ1cdz72kKOBhfYzYFTTMXN0iGLvkhy6rrjQm08sWqY5qVYKQ6OCp4ZCGjNWWyFmWQlqgpU6cBqGgctm5eDQAowpiIArZtWgoA2LZ1EwCgvp4tpq2DjPKUKWQE1q7lvmNb2c4mTCQDllBWLgynGD45y685grgFiar/x0NZWdK1IPLTNANmKbSIYE3K0NfXqX6kj+zHrm3sDKm5tMrOnTcPANAxbiLmzp0LAOjd/QYAwLPPkXXr3EmNVEsH2bdtK3ms1Ws1P+AQz33ccacBALp7qCscUs1UxwTer8suJ5P4wP0PAwAWL3kOADBxQjtu/y3v9fxTz4jUa+cu9vHnniPL2NNDq+es2RwLxqseq9aQzSgbp8u0slBh5E3T+aWNIeT6UiIVCgPFRBCqw0umogyg6UmN4UuE7gzm5aFsG6LW64q1UxnCKguraVmh1lfLGZZS3VC2rk7LRXOcJdMW9ZTlJaWsQsx9w+pquWszGR6vKZvCxtX6LtlEVnzWTLb3tla+L/qVFe/qZ/suFjXXZtG8CViXFStWAQDGTZzB+6R1S5uu0azAakEvJ1JIG6OZbND68fqTyhqWND+nRe2t9P4aZkf2MPRHWa0Y6RBqUgEJ848lTWdXsvsdPaIx5eUw6Zmd2/pGlNUW62AxXZGxeaWyQEL2TNuD6YXCs0Y1gBYdNB5ZM/4mKIW5xATD7oBgmPZSFcigqmw8j98ehIxE+7DdT2MvQ62uFhsoFDGo/WOXRr8+7CxGf25r4HfZgGoCBzWy9cAu/u7f1YVahUXaDwL1HrAc3GEu7hgzqG2lVBhCSZ+d5cId0JyqphMMPT30meXDRz6CHi2k2+KBKuxnhPqLb9RDaBTjUIf38ritiiOHstujBs8cXilYYfOj40VCozMnlb22flcOdYfGrPO3edVMVA+wDSt2hvn/kjL8lG2P6J9lux+jwxlAh8PhcDgcDofD4agR7FcGcKzmArMcRBZJsFyK+t0aQ2jWh2KxiDrN+2cT8/YJzJnUuYO6u5zmTmscQx3Vls1cP3Fiqx6DB+/aTYawv0e1EENkGdpbqAvqaB0PALj799QuTZwwDtMmk4F47PGnWLadx7z00vexngmes7WDdbI8efV6d//s4gWR+1AfvzEjTMOzw68eFb0jHDodW2EcUQ77Dmf+9o4rr/ogAKC/vz+ytMh9O3eRpTJ5xhOPkZXasXkgtJyddMwCAMBR85hPclsndWnlEp9i21j63v/uTua/m3MYNXRnnE4WyzQbXb20cE6YPk1rZxz0viDOBMZN2DxHpp51SpukQ5mSQg/rnCywf73+XF7T299xKQDgxTVkM1auWoIZM9j3FixgmddfyHxnBXUNSGtewIQyRd3d1EKtWEFdYZ7kPXI59sNTTmL00EbVV96/cCEA4N77HtJL47jT0phDy1hqibfv4HOZPIX3Kq0ayKIytjNn8x5v2Ur28blFtRX902D5ozJ6f0KNnGlHzPprUW5hY3gCzWPIVC99js9tqMDRalwHmbC2sYxem1D9RpjvL+YHYdH9Qp47xpJU2AiLkBggYXpC3TelVtmEspJZ9SQpad8xljJkAFUDmFKNeWhgDiOaWsRd1j2TUc1gkMC0KYcBADp3cSzYskkZwc2sQ1cX27MRLeM0z19O6zpD3z+N9XZ/9B6nbFS369b8ufr+TKeTYU64RMieGiPFu1cqqyYyFc0rVdsMYDiYcaHr7Z7ER8aKUD8INWvpRJTpsmMlR2AmynGGzHR2pm9Ty36pGGXObL9kKlnRHClLkFfdYCZtzJ8xLBa9U1m2ODNoESBjLPce0shhriOeW3DPsqNHZQxi7c4Y0lJIfBprqfkrCwX0KQO1ScfmHVu4XK7eYK3t9JRpHUdNVfs8am2bx7YNW5dagI1XxvjZfTYPpaLmEpWYZ0NvXy+alfmzZ5nWMd80otBjmLuZ6dbCZ2veGMpUD/TzBV6neY4rtHCUSdvj/6jSoeq5EiP0r73q90LCWvvZ3rSCw+wcjhMB+19pgO0wocx00bwDGjl/kEyjXkNUH9uiEZqffexBAEChJCjo92M2Mcw9QYUBFPOyKUXHsJGwXyeAbW38QCuXTFzMhjKU1wnhkCWp1YmhiY+LBeQHeSENzfoRqx8JuXoK5PsHbYRgY2pp4rm6NeJKJbRsLEx5gW4Cx849BQAw/2Quf3LbTwEA7e1jMH48J4XrN6zhqfUhTJ3MD9fTTjph2Outfznf268SbIIcn+B5yob9g40b+XFnH682cN7/ECcfJ5/G1CBbl27nchuDGE2dfgxWvEDXxPnz2Z7Wb+I2M4SsW0/Xz/Z2DsCNjZzgfO/7N+u5+JTHd3CCaG343e/mpOuweafuwxXEHYD2AnuB6Avjc5/5LABg5TK6wjZqupX6Rrq5NTWxzueeew4A4Ne/uh3LV9C1+qST2Pc+9rGPAAAufOMlAIBzzmPQl5J+2J58It20ly3hROI3v+V1Fsv8wH7jBRcAADYOcnx5+BGmh3j7Je/V/Y8HANz5u1+H3uizD2NgjZmz6Nr5qztuAwA88QSNPq87g8/tOXVPXfR0bU4AM+pKbyHXk2mbAEbdNW1CaOlQUokMJkziJPqO2+8DAKxfvw4A0DqGxoIpUzUAlw5eFodo8iQa1tq03SfVldmCyVjAorROTi2RdXWAjUD3sUBj6dDVSUOZZ8y3WT/gRV/W6uKaUgG/ubbaCBsGsElaKha9P+qOXegbQoO6l05Ut9DiID8MbAKXtgAhg1xOGc8PhOkz2Y8PP4YfrBOnsI3mYi+WMACYTQbCj/BEGESkhGhwBHNpLZX1XZuMBXuq4VD5pXgc+ljwFxuP7YNWwiTlFb+xjL5wk7HII7Fo7XsEXgmXVgcLoqTtNV6+4uoWoKxfw8Xw2y+WbD0MAhOpQjgGhhO/mKtnEEZuibWJSF2Gd4srx+uLYYtVHTo6wbUPe/sWDErRYE2FQjk8Rl7ToOT7+c1X6OGyt5uG0PWrXtQ66DtL3XP//bob4rU65FEqxQKvmLt96IbPcSIMCKSTl76+XrS2KqmiLWioj0beIDQi2YRIn6UFxAv7iU6yNK1IxevXDBPD97/RPknMBXSk4C/7OP+ruGSHu+37RNBSvaSHGAivfoDfbx3aRV/o53oLtFO2CaBuP1INfUGehotUnaYDG0ojkaKoqxALpmRu3xYASYKY6+dequ8uoA6Hw+FwOBwOh8NRI9ivxJCF8U6p1Tin/FSdhcpXK4Mxg127OBPOF/vRqyLIUorbNmi49kZ1/azT8NaDfdzequ5EZh3u7eWxRJ0h+wZpFRqboxtAxxgu77nrtzz3jjUAgNNOfgualHVMqVmvf4DX8cwzZBZGYgAPBMy91Bm/A4Pf/O4WAEAuy7adyXE5bcbpAIAXV5HFy2TZbndpMugF5y/A4fOYTmT24QxE8rMf3woA+LO3vxkA8Fd/xWAmF1y4AABw8klkpSZNoOUonWIfmK2BSiYpc93ePu4VXEkYAkCXydj6qGkpmSETn02zr2zQAC7JXWTkV66ni+XlV7Efto2n6+VbL3k/EHPZ+6//+g4AoEdDH/f2k1380Y9+DAB4/QLey94esoodbbzugQFaycosjoY0LZX/9s1/AwDc8I9fAQBs3Lhc99+OpctXAgAkwXqtfYmBeBY/x76d0fHm+eef5FXr7finf/4n1CKSxlrpGG7BtcRcPy0hfMztJ5HOhikX6ho5Nnf15nVJF0hJsE/MnkXGz5iJZxfTXTijx67XvpXQCDNzDp8BAJg8ia5e2YwFm+H+gmqvInVdDsOfaz1tH2UIjV1MpdXVVfuWJYS31CspdYFNWVJkuy+6XzFZccnepGlJenvYTseNY32LmiKmqFb58Rrs6UR16Z46k8yfsSTlkAVRi7q59YfZNYyhSoRBbZLK8Elgiev1OZWNWbLk3qh5BHHGIcacmdU9DBhhjEapXElELVG2o5KCIUp5BbFoE9Yejdgzl7ZSmPA9+oDC4DDlACUL/mISGt1WiTMzPLtbDpO1x1jIEUjg+BugFEgl9H2YXB6x64vRqGHcj1hgmdhZKi6glkZDf1d5kdm9DV3BlVlKm9us1TN0Azd3wRHYoppA1G3Rnrnd15K528dYuWKhiDr9ptmyhunNSmvWAAAaWjUA3NTjAADlFMf7fBj8xZ41lwV9Tqn0Xr5Wh//k0HWW7iBWZG8J4GOoMIDxNXuHlSypC/6YNL99jmund+HYMXzfvbSE3xrlkgaoVFdtk0lMbec3VFMdv42eW/Q0AGDTYN0eJyuHF6z/Sdr7RscL9exIaHC+keAMoMPhcDgcDofD4XDUCPYrUVQqUwwZmiwtPL3OhJOq4Uiq0D6TY7l8XxCaxHI5zrIHdplFk7Pm7i7Ouot5lhvM0/97+gxqAQsaKcLCXYta9hMJMn/TVM/30EOPsA4BZ+/N9R1h4Ie2sdSgnD2PGqIzT98XTdX+hTN/BxYfeP+HAAADA9Sj9Wno6XsfoLXsmcUU9p58LBOQN7aw/TU1p7DoLurNdm5ne+sepM/4pm3UC171EWrjxk9kO9y+lYGODj+CQSYOO4zLRYuoUzv2GIab79rNVAflIvtI2+Rj9+FKVCeh+txExqylxVg5trgXX+D1ffb6zwAAFr9vLQBgyxYy9aedzsAsk6fM1v0yWqdCaAErGbOh57Dk3AlNqj13LhnS5SuoL3zmmScAAJ2dvL7D58wCAGzQIAAP3E8d3+wjec42tcQ9/yxZvklT2kNL4caN1KNNHD+Z9VJT4DkLmJri8CMYkGfmLI4TPd21GUI8YVI5SwivjGDSEsCH4cSjOrw0MqGOZMIEMtMNKvofHOTY3KupNvp6OKbPnkVm+2h97qIJcu+/j4GTVq9TjaymOGjTgD65nFpWq0PKh+SHURPajk3zEpZVC7iF5DaGT99JqVQuul7fWZWlBoHJkQmvy4xF7xb20/yA6lRUK9OjY0R3L/tlr/b3MRrgrJhhe82X7Nha9TDojemqVIeolvSyvePKxapgL3a5moRYPWECMQbQ+nU0QXdtYg/RX2TrSKkLGDzGNDp6PwO7/8pUJKLpDQymITWEQV90v1Ixmg7BKmepTspBgKKO2YW9BGKRGNO3h+4udp2hZjCuUwwlWlLFrMS0fqFe0rZGa1WOnST+Ozy3sd1hsBJlAEvlKm8DZQI1yIjpe41FDHt4GOSnlvmPKKtdgQVg1FJaYGCAY1NDQz3SQ/ymmZznuFXicI3uIY47+TzbfjnWhivnUq2qflvUNTREThY/97C1j2lKU/qNULT6x7ZXNLMjaFXD9CKpyH6jQSzYjY2x+t0yRVPsdNRznN+wk99AFo+jP6mphLQZZjWgXt/OLQCAsc2sQ+egsrJBJZtKMhaQaUiZPvPMSZYsnY/e+1QVezgMarkHOBwOh8PhcDgcDkdNYb8SRoH5FSejfqll1SHAlhpGtbWNM+jG5ib0a6jfxqacLlm2czstEykLhaZXNH0mdRRlkHVsbqGZYmjQkjBytt7cSKbv8cdpVX740XsBAOeczeTt9blmPPLEYwAqSaJvuIFaointM17W9TsOfVx22WUAEEbKmjGDrNRVf/n/AQDqWqj/uec3TDNyxBHU6+3e1YX7FnLdP36V+jILGb50GaNkDg5Sp7Z1Gy1Ff/Z2piFZtYIJpi2p9eIljFCZL5I5vO1LvwAAXHvtxwAAT/2Ax7/27xk99OH7foczznnjsNcTBHEbkQ0ZUe3KoFr/Lr/8agDAkUcycuEuTXtx9DHHsGCS/XfVerIiHW2taGnkMbt20bJYV8cyzRrhd806solHqNbrW/9GZq9RGaQjjjgtcl8OV8bo+SW8b7++g+kyjp3L/ju2g5rIe+97AE2NZFNXr6U+6/A5tGpauPwNmxg57tEnHwAA5Ac4Jp11JpMNn33eW1FLsOTjYToI/W3RbsNUDBZVztgRZFDUsPTjNBx72xiOvT09uwEAuQztmr272W5XLeezWLaIVs6BIWOIae1sbuGzK+v7olg24ZSx1cqSQEKpRMIswaonRBgp0eqtjItFDRWLFmraR7teYwRV+2f3Q5m/XB2Z/Vw6i1RJ9RkalbZHGb8+7c8ptTrPmEPGfuxUjgnJLI+hxndopoqQxdRgcujp4TvuhReolVy+nGz89h2dmKhs4tHHsj92dLRqvZXBDXP7pPQ+OIxdCtmC2E2pyG9i6RECQUqjx4axOUP2I0pphQmrw3D8yoqETN/wdaqsVx1bVbJ3iwJasKiLQbQPVvRcPEIppkuM53e3oT+emiGeDiPA6GxNNUYsZ+lULAy/6mYrXiG2v0b/LFV0ZaFuUqOA9vVZ6iVl9zUORFhvS1ugGrRaRuVZDs8GG0yDWV9fj6k6Nh55LMepTZ0cr5/dxPd3tzaXYuhtEG37JfMqMt2xRlaWQkyHHEuFUg2rb5hEPUyFo9tjosBEvK+GBxqeCQ3CFEKVdRX2UMdfi4yq3iHpJN9HHWmOxxl7/2hTzWhan546pUy1f/Wrl9iYJr4Xd+vx8+o9IJAwEm8xdi/tulLGZFqaIouKGvtOi8MZQIfD4XA4HA6Hw+GoEeznKKAaFUg1ChYhKky6qxaDQoEWHLN8NrU0oCVnUT2VwVP/24zmb5KAM9/WMWQFMhqNrVjm77oxtNx2djJyYNsYWkZPO4XswcI/3AMAmHcME0G/6aI3AQDSqTrs2E0W4/d/uBsAsFU1Hc4AOuL44g3/FwDQ1RPViE2aRH3Suy55DwCgtYG/H36EmsB8YQjnLDgbAPDD//0eAOBtF5Nd+va3/xMAcJImOL/66qsAAHf8kgyesWuD/WQWTtZ8ek8/xyhSZ57JPHp1dYw62NzM5Y03Mv/R+nXbcM8fyHy/+1IymDs0iuf800/RK4hHA2UfHsrTinrzj+4CADyzdAUAoH4stXTJHPv8vffdp8dlxMcL9T50F1Io9mi+vkcf5fJBRvucOpV9sa+PTEmfXt+ubrJ11/4tcw7WN5Ax/PJX+Pv228gQnnwKNbqLF5MJXLWS2sGXNvI4f/HhP8ett/4cALBpM3WDW9Zt1WNyvJk6nePKVVf+HwDA33z8UwCAr/zD21CLaGyi9TLQ8de0S6a3SYT5pOJMYBZDyhIbOz51KttIXZZav8Nn83dxiCz5NtVydu0mQ9jQoFZc1VwMqtgpaQ4kZhUNteWm5aqkNTdtSJhAONS8KLNpYouiealYnj9L8G4MoC5Ve2EeKGmNhpvOcSnJNJBh+x1SdqhviBbi7p5uvQ6eacbUiXpf6DVQ0uvIF3k/iqoZTIDLF5X5f/hh9pvVq8mUb9/Od1xf/1CoQfvxT9jOj5xLPey73/UOAMDkyTxnJmRJajkyIhHPyReXBFbKRRNglyup1hHXWIWMnwzPuFj0xTj7aN9IFiFWwjMo22c6JBGUNKplvmCeVlo/jaqeCDuKLWLRPuO0j5ULV0f5gmhOwqhwaw+mRaLLYA/tX7RYeF+q7m1VlcL7FVRXRD0MkhqB17R/Qez5ZTRifDZbu0LXgt4r06SGieGNLY4xg8bEFotFJAc5fpXrdXzS+99nHhj6Lkgiqv0L+1WBmu+kxhWwPJp79K94Ps5ICT2WaWntfRPToMaPGdcEmoY2kOHbdnSd6Xu1T+p7KKda7Gz/BgBAV4LffhnVSkKjVu9K0+OlkKYrR8oieurxZ0ykNn6bvkMaN3Lc7+rpq0rPGo0qbH26LBbVWg+WsPyvo7dxZwAdDofD4XA4HA6Ho0awXxnA+gad2eqk1PyKC8oi5NUne2iIDFtjwxgtWJmhWzQzyymV0ahsg4O0ilponZJaxgbVvDqkbGJzCy37Y8fz2OWA28dNICsyVfUX844+VusmeGsTZ+x9/ZzRN9Q3vqLrdxz62N1DhiupFqmCajpuueVHAIAxzWx/c49iVMnHn6K+9Mknn8Vll10JAPjRLcwleMKpZAT/5hNktvr02J07GPXyvoW/AwA8/eT9AIB5Rx0ZOefEKdS8zZjB5eQpzFf52zsZPXPjZmqFzjhjPuYdfTQAoEPz83Up62bs/FZlY1pbacXKZHkdW7ZSd7dmDa1fp5x6DgBg2Qoeu5inFWvTRkZs3Kw6vTe9k0zj7q4idgv7+7wTyTZatN0JE+gTb7n4HnmULP20qbQgprLsh5MmM0/ahInsuy3NZJK+cN2XAQBNjezrn/irvwIA3Pi9v2XdO1/AnXffwesukpWyyIqXXHIxAOAvP0rm76c/+xUAoL6JVssVL1IbeNwpjOZaK6i3yJ39HG9N+ybKkJmFMrQoK4sVIIGCen5k1QLf3sa2VJfjPrMP5/OznHStHRyTy2qtnj2H2wcHOGYvXsp8jhs200OjXDTNhLGPqmtLVLQiidAqbfUyekCvw/Kz6fWaDteie1oEz0xao4FqHkRjAE0LaabYfLmEvIAZ8IsAACAASURBVBqpRS3GdXUarTHPfSdMJ+M3eeYRvKc53uO0HmtggMe2/IHrVrLt3fHLXwMA1qxZzfuiL7nBIfaPfLGMQD1jshm+L5ctXQMA+O536WXwjkvpZWARhBsbWvT6a1cNWA5zi8Wif4aa0igbFSjDhnLZ0nGFzLchzBkYi2KJkHEh4pq38NwhQxbV7YXbJYFCsRIRFACkbFGUk5FzV44ZYzphlxFjbsTyCeq3VWB93C5O9jhGnLUJ8wHGrrdSKsoc2uGKpmc0JtDy1FlkT5HKc7F7pOJYG5uCkkWl5PaE6XaToQC25mC5RC2hXFmief/C/Kj6ILI6zrVkUuF93byFnjgdqjMu9/I9npzIssZEV6hifS527rqoXs0QZ4elqrWE7SXWngwj8V17ts8YYtdbiTJadcSQSdZzax0umsr5wRTwfbV6LT0whvTeZjU68yz1TlyrkTvzZb5T2sdyzB3U77ZOHcd7ujnfCBJSiWoa0yFa37bf8Sinspdx3BlAh8PhcDgcDofD4agR7FcGcKCPM9t0ljNf04ukVVdhloV8H2fKuzo5Ax7b3oiidOtBONsO1Jxj1jcz5nR10z95jEYQ7dtJf9xUgsdEhsuVqxgxzfJq7NrNXB0LF24EAEyfQkakta0DKdUT3nD913ns+pZXeAcchzr+9evU1TU0kJ0yjVB3DxmT404gyzVzNlmrw9Tqv2TJi5g+nZqek487CQDwlRs+yW2LGNVzTAuZrDHNXCaELFx/Py1RDzz8DABg4vhJAIAd25gPsEkj4P4+z74wdQotVRvWLAIA1Kd7AM2TObZjAgDgVF2+sIJl1q2nvujUU6hDzA+RIfzFL38AANi2g6zEwGbNvaTWvryyEUceycic9fXs6y+t5DU15lrx0nJqa4+aRR/4H/yQGsArriBjN3cu825OnXoc79lsMiZd3WRECwHv7fhJZP4efvBxAMA//dMXAQDXfuLvAABnnXMBAODWX/yU179+Nf722s8BAL72L/8CAJg5jdGDP/ShKwAA9/6BEUR/8sN/BwBMbuf48dh9ZF/e9Z5LUUvIKutaUh2HjY1JY9DELJIx67oIiuqVYSyaeWMMDezSY7NtmAxv7Fiysj2a49XeF3V1fAe0t5Ep3ryNludB9STZsoOawTUvss3WN9bhyMON4bJzmBXXzLpG3Vgk6nzknKlklPnLZlXHkcnp9ZuWxDTpxkIKcsq+NTWyH46p532o1+tonMD+2jyWjHdg+bPUOyWtGpK+bTzHb++kXveZ55ZF7qdF0Q7KqscEIEljG/kuNeP7kmVk7Hff9L8AgPe+j5rA446l50vIUNUgjGWKI9TlJaK5xsIcf+UAgVr3RUxLGc1fV7R8Y+Woxd4YMtNmVbSpeu6YEDHOUASBhIxLqIUNojnOKmyJRI5RMh2UsR96AKvLgHpRBZq7NatRmpMWSTcoV44Rivm4b1qZI+tnFXYnxhAiel0h4xfTRCbDnH+2u4Rav2Qpem/TdaqJ7NOxSpn1ggaX6NP8o7WIrI0xxmabFjN8ftH7b+zvYE8P0pq27/CZ/IbZ2s/7OG0S39+bNIZHEOT0HNY2OKaWbOwN+5GNnVq5iuAt+ps7cVNcFxhj+OLqwTjC/ffI8RndQ6rbqVawqN4e9X18b42bwCionat4fQ1JtsMm9ZbqaGQ7nNnMmAYL1/B9tayPx5nSyvdCZ59q/lTrXaqS+4bRtW1VKG+PjhPhZRnbvZdcl84AOhwOh8PhcDgcDkeNYL8ygGZY69YoZWEOpYzlUlJrZYHLouZ1SSRTodbP8uQERVodMjnuW+jnXHbGLPojJ9NkO2zm3NikuooGWpWHBjlV3raNdTE/5FNPZVTQJ55gZLUd3Zvw+gvIHIypf+8fc/mOGsDOHbS0G3td0nY1bjzZqaOPJpvV20sGLRBaI6dObkdCaAHKKjPQNnY6AOCjH2UUz/4e7rN0CaNZFgNaWY86mho/09AefzzPsfallwAAd99zOwCgU/P1fOiKv2BdlTnZtGEtjph7mF4B66CHxnPPPQkAWLVyJQDgtFN57O4e/u7uJfM3sZWsZCpFK9jzqjU6df58XvexrOM9vyd78dBC6vne8Y73YY7m7fv1z78LALj0Yva3DWuo8ZozhyzpzFkzeF/GK4O5juf+1S9+xjquYATS40/guUoF3tuf/Yyayg9/iIzis8+RGf3i9V/AbbfdrtfN8aClmeOE5QV9/EmOA+981yUAgHKB1rw5h9n9qi3kNKprcUg9K5JRjVzFIpuM/C4HAYoDZPJ6u/giSJlO1gzCamEtqVZQUx8hoyyCRUIsFrkhp4xhSZmPF18kq7V1G7Upzzz5HABgTGsTjjqKbOCcWYw4euop1MNa+ljT55mW0SzeiTAPYFLrktMdotcXWqZjupWkJDCk/+9XJnP8eDKXWbUIN0xiWypr5LaERXZUE3BCO2PPbvbXlSvInlteXdP+JTQHVEIjlNbXZcNoiSHUVDwwyHu8Yxuf449/zCih9twsYuvFtZXmktiDeYjqgkLvo1j0WQRBGEUxGTJ69g2jMQxMLxhj8kz7V4nOyIMnwyiNGu3PThXq9LR/IcBgwVgbuw5jGSOXEbbtuN7J2B5jNAeUkdisbaSzl99cloPP9KJ1uTSyGo3dGIXmRo6jHePYX4aKg1pvRBDWIfbb6lCK7RDmMKxcZJhD0aJJhgygepblu/VbUKNTllSXbt+StYghfYbWXsN8cqZV1ScyWLnh/D2wGyXNu9rbqt4USX6v1NezbFuB3z7bBuiNF6Q53ql0G4HpqcuVdwPPGbZu/huj70QSlZX2/BOxKUzIjEd1hNWMsR5A91evifjJrI/YbhBY685pdM/pOY30PpFM6LZl9MhoV+a5voHj8aTz6E01uJLeM0ckNBp5hvsdPo7v1KdfZJyELX06x6nK3xlqiMPon/bi2uMm6UJ1u/rOHAnOADocDofD4XA4HA5HjWC/MoATJpKdy6kPubEg/brctYMWTrOSmftqGQEyat0c6KN/bFbzFpkpLV+ihWnHTmr4snVqXdZ0Tn2DnNFn0Kv7ceZsUQzrNFJPMU+LQE8PdSS7dnbh8ScZNXHz+k8DAL70uS+/kst31ACmTqMfvFkoT9JcdCeeRCZsxiyyWe1tZLHOPXcBAOAPv78XCxcyJ+Db3/FOAMCC884HAGxYtw4A8NQz1PgtXU5L0/zTz+Cx558OAOjto2Vq6w5G7OzRfGM9g6oN6WZneOgZWqDmnsAoo1u3rEUqR+3bssWM3tnZSTYlAfaDwQEec/lyMoLHnsD+Mr2D/W5Jn5avJ8M+XfMedu8kG/P0E2TaO7eShRxzOvVOE6dNx5oXuW2+5iucMZf3qGOgrHXhucd3tAEABrpoedu0mYzKhvVcvu4MMoennkad4uqXyPqcffYCAMDDjz4EALj1Z9QtZhuK2Lqd13viyWRhchpB7he/JGu4YqVGM1Xr5YSJZGV/dQejh1741stRS8ioBjBXRwu/MX8S5iFSdkQNrH29yojv2IDtW9lGOnewHe5SRrtPx3+L5Hz8cdShNTdTG2HawKRaa4swnRXPtX0r288zS9k2d6nFP1CmcFfPILZsYZnnnlkKAFil+sArLiezK/oqTCrDZ7nd0toerD8XlPYJClGtTDJtFlq9UWHOqACBerb0D7KvlIV9I5XTaNIhERG1fJv3gBSVVSrw/Xjm8bQc9wxw+9at7OcbtzEaak+/RgPNl0JdfVKX9m61SKumCdy2mffn9l/+JnK9138RNYdQFRTTzIUIWRID/5cKCigqC5dXxjifV91r0qLiEkOqjU6FXk/sE3llWhpUq2rr07pnOYxAaGc3Fr2EoQIi64wdLMXyqRlDFsR+ly0aozLxXapbHxzSNm59PmO5mLlff7GAPmWhB3rY3zs0wm9DE/uTxCXB8f/EIj+WLHKn6fp0vUW4rs6LJnY91ufsXptOyvRTTneEGCxHGcBQuR3L45rWB9SaYPlUSxNEv5XL6i2RqyPDt1s9ik6bQY1+5wDH9d/vYLlANdrpmO61MlZa7aKUdZivMihDYs2/sk9M9SdR5jgsF1g/NIbPdKwx7V8YVbPCAabUI2OacJyduUtzBn+bvydM5Psq0aIejTq8N+p7KN+1BgCQ0XfKsbxt6FAN4Jqn6VWV1ByyplNPJBLh86hED45eWKgFjGkFgzjlHoN3CYfD4XA4HA6Hw+GoEexXBnDd6jU8qVoQ6tSiVNegefZ6aO0yq5flnEqlBfXqb757N60KAxrZqa6BFqZMmnPZpmb60/b3qrVVradFtawVNcLOkLIlZfXRF7AOW9Tffe5cRgGdjTnoV1940yw6HCNhxbI1ACqW3SuuYDuae9RcAMDOXaqD2s32+tBq6tZefGk5ZsycAQBYu4Z5/t568ZsBAL/7DdmmF5aTuXvPnzOHnlldN28ms7JtK/dbs5rsxsmnkgm7+poPAwB276al6snHGSXzrpdY1/POOwd9A7TebVi3GgBQHFT/fY2GWJdlX1y0mPVNZ2h5OmwOGb8L30zW7me/pT6pb5AW3L4BMoj1Xfw9bTL94adMpTdAQ2MCrzvZcumR+dm4lSx+roXnvOXHtwIA2ltoUrvkXe8GAMw+6hgAQEb1JoVBi/irei3NM3TjzTex/GzqMF93OhnTfOFE/OCH/8V6TZsBANAgfli1mqzrUUcxImtjE6/z/DeQZfyfb38XtYhMmuNkUaN/Ws4t08qZ5qxHozEvW7wYALBx9TIU82z7aQ0j19vNsXhnF2/6po30xli+jBGaT57PdnHSyWzHuQZGDS0UeWxRr5DV67hf3wDbahBYni9aretzSbTouwZF9rsnHmNfOutMRuU94gjqRlOqE0qnVU9nWi614paKGsnOQtZpkr+slheNflgO2buKFqlO32d5jdLZ1sq+YDZaO7YxTwF4rESJx+7fzb55/qnsawXweNs6+S58/gW22UefovZxR89Q+K7NK4VtzydkfwKzAfP5rVu/SdfXbhTQUpgHEJGlUUjJZDQSpX1bDJRS6Msru61tMaX0bi6lmrY8x+y8aiwtp6DpXtPq2VSnUWaDkrEgpci5EiG9pexCGSioNjGZiLHRilDrF9MAWtRTixba0806dnayn5lsMaF9PquR1K2Nl8tFpDVGQ6lkIUh5zCGNFpxNmMeW1kWX8VyL1vZLsSi0xnwUjMG29isI8x0aO2iRiUv2bae/TaxZiVQ6Uta4Qx+FcpThslHA2mNGx7ec/k4G+r2dTKCule/CiW1cbtA2XaeeGtl6Hm3eWMYFeGILo3WXLVGjRdOUKHNbgQlkrU6VnhiUo/kKq1sSEW3beyLGoBnTZ24YIbMYP24ATUeMOSl6MZU0dsm2Xdx3Wyffb7Oz9FSqW8LMAsEEfWfWax7EhL4PevjeeuBZq0tU12d9o7qZJsLkmzHmTxKRsubhUbZ3yghwBtDhcDgcDofD4XA4agT7ldIqDAzokr8HVc+3exfnoQ3KBGZztCyE/snJAD1dGi1xyhwAwAuqzemYzNk2SOhhsjIMi59bAwDo7uF+bZqXzcxZNvOtq6e/bZPqTYaG1KKr7MHW7Z3o7SloWdbnGY3CeMK8eS/3FjgOcYhaU83SPtDPtvPII2TdTj2ZjNLypbTSP/74YwCAvsFB/Oe3bgIA3H77nQCAm3/4fQDAh64ig5fJ/g0AoHEM2+pPfkQtW7O27W5l6U4/7RwAwMknkd343g++AwB46OH7AQBXXn0lACBIPAIAeOypFXhmCZm78e0aqWrGTK0/+8OR83isXV1kF39+G69nx05G6ixCmfci9+/qoUW4RZnC008iW5fKkclvUw1kbw+wQ63huQz7V7aZOrui6qzmn/cWAEBSx4OFj/HedbTxnJLgONLTTx1T3xB1mOM7OI489CA1k3f99mYAwNVXfQIAMGPWVDzz7FMAgLosNYmbd5FNTaV4HSecSPbpySd5vb/7FaOXXnzxu1CLSGQ071+az7FcpmXYNGb9fbSCLnqG9/W5p6lbbalvxcCQ5X3lmFxfx3ZcUtZs504yDmvX0jK6dRvzQ764gozglVddBQDIqXYulWXfWq+RnPODfLFMn8h3wlGzNcpmaiDUuGzZzRfFopV8zj+/lZq3z36BbGNG2YKiMhlJ06EoNWw6Rcv3B9P3KeOTSdfr/VDtl6SR1AiJgbKlGa1/IaY/sSjYJWXligHvE9Tqm1FWpble2Ua1NLeP1ah8fbx/L66hhbm7qx/FIWWINI9u2vLhWt5GZUmHNGLukHrWjGxBP/QxEvdp+coSgeldud5Yk0S5EEa07S1G3wOV/Hfaf6xFqoW+MMD7XtK8xes201ujLsdxtD5n+Sc1wmCdRnpVdrlQToQMnkUMDXPvhVcQj2YaZf6MQevvY1vv07ZgEUnNyyrULRbtfIkwMmhRr9uWpinbIyGbRCNAxtnWeB7KePTPyuEEKYuEapEsjQFU1tvGJokzgHH2sYaQhuXrVQbamGht/ZmSaU45jp1y9GwAwPqtO5BVtqy/yLF0Z68yreo9k+/lOFTfyHf8tGa2l93bzMPBIm9abaLPY+SxJ4AEIR2tu8Q0oWHZePsx7Z+2gURIlUXrEq9TyJoX0Fgm8zcjr5p09cBYXOS7ZHAXy3Z18f1V1O+TqZPIhK57mPEQMpoPsa9Ej5Y1qpPv0PfD0Z1L9Pi8TxsbOrC+gbrxckxMG88HGHoGaD/J6DgxEpwBdDgcDofD4XA4HI4awX5lANNqPS5qrhubpYYRydSEYJalxkZamQtDJfT0UkvUP0h/4onTOfsOEpw9D6gF7cXlnKUP9Fr+C86YG5todS2qv3JRrUB9vco+5Ia0SpwTN4/h7HzFCy9gq0ZIu/Sd1wAAegcskqjDEUXQrJYptRw9/MTTAICrr2a0yIcefAAAsOYlshqnzH8dAOCuu+/Fho3U35xxOtmID175EZY5jdE+z1nAfHnXXnstAODCC98AAHjd2YzmaW35c5/5ewDALbf8BADw5ou4/6wpjC7a3kLL3IUXXAgA+PZ3voXj5vDYp5/OY/V2k00542xludP8vWHzGgDAk0+TUdi1i4x8qcD+1au5yozNCFLsV+Om0IK4ei2tZWPGkJ3LZIFUSvVWarwq5KM5riZOYe62IWVZdu3iWJBUNqOvn+e6/2FGgXz8KbJPK1eS+Vv3EjWFf3PtJ3m/zqO2csf2bTjmBGr6hvrJ7KxdR5Y0myU79eSTtMb1a9S+DVuogTx8HpncmoPmXcpkNVqmRnZDic9q5w6OlY88wmdgGqZESrB5i7Jn/4+9Nw+T6yyvxE/te+/7InVrX5FkSd5tbIOxDQQwEAJ4YLJAfiSTgd+E7BvJAFkITxKSkJkkE2LWAMYYvGGwLe+SN9myZO1bS61W791VXdW1L/PHOe9tVxs5PElG4aHv+zx2qaqr7v3uvd/33e++5z3nKLsaiYp34eerozoow6isKkTOhoYAAP/4D0SyP/yRjwEAJqfYDwpCG9oTzLi+YSf77NXy+puZHsb5UY4tQ2jWrdjAfRW4j6o1ReiB8T3sDwEpH/r0d0Mo/FL/dNCSBp4Xn/FbPF541D5PiLJvkcYWfUf7WMxnqek+WJDCNdjGoPy0EOA4ODrEjHJIPL+WJo6HN1zJvlkp7cXwREHHoXZX5NfmNWSznlNWcVQXly4CWHuFx9wrXyzsnDlZd81fPo/f4Z359RtDn5zT6ax5tK8AM/QBzTe2ILM2FOR3nKsIZckIMctIlyCg6qRYBJDyqKE4nkVeZhfy/TMOYFHVT8Wicfv4O1P9ND/Oxcfv8fpQyls/0zZU1eHzGRpZz2uqOshF3Z+dWKxUagrAtUX8sFq15lR1mdppRHzcnCoC/FFxFrWuNI5n5YJY7xKIWr3/n8Oz89i54bmKaH5r0No9my/hTIrzkakxj2Y0d8AqPHi+VbCAihT6DQ2u1Op5bFjMt1tM73OiuuCHh/rveBYhyYt/uxgFN9XqxUjzYv8/i8T8PC5/HZXS2yusMDoLHVeFzyDbAkT6inqOKSe57jh2H6ukZnSrTAjFn2/kdtIZrqVuUrXYriYqkvcWWAWwcn4U102SSz8c5drtVJS/HU/wfVkcXBtPAalzx8TZv1C4CKAbbrjhhhtuuOGGG2644cYSiYuKALZ1tde9LyhrlJMiZ1mZHMv8+sWd8PnDCAT4TN7UwuxOguAgcgWicT7xSEZHLdvAQ2sQ8pfP8XOvx1K7fPZt6yCPwnx3LFauIGKRnssjP8+s/y2vJ1rzyT+nD+A1Oy770Q/ejSUR737rOgBArsB+dtMNlwMAJkbIhbr3rtsBAJu3klt26sxJAMAf/tEf4Pc+/lEAwEd/jWjh9+++BwCQyVPd7wv/+FkAQDl7CgCwYT39dh7eRf/Ao4fI4/vmN74KALjuumsAAE2NzA71tJMTtWKj+HgyqonFQli9htmtYJADy+fjb8LinkxMc58P3PM0AKCvi5y5sLJZk5NCQpJEZZqaOa6yafIBilK5a5My2MBy8vxCIcBAJMsYmjePCcoZUmIKeWvWE+Ex1cStPWx7Xopzxg08/LKhk9zQzku2AgA+95efAQAkGpqwbSvRkgfu3wUAOHKU3OJf++hvAwCmZ5VFjvB63nff9wAApcLSzB5XhfSZ/5+dh6B4ai88T8Q7JsXOTJrn7+z4FAJBfhaVImcyxWoOgYqoSB2zQeqvkTCv28oB9pUDB4no7nr4YQDA2DT5UsbT27iWSHF/O/twOcf7SjAQRzDMfjebJBLo87GP7NzKvlQqWWUIP7dUclX3EQgxCwSUadXxTkySE7vvBfabbJZteu/PfJD7iUadBHe7UPCabcPxwxL/ZhFfrJAXhykirqsyzP4wvzc1M6Tv831vD7PCa1fxe+emZjGToXJoRbx78/0rmUKijqukqhy/31vXhqUYhlp7HHhBn9urkCavz5T3hLxVSwvqlabKt2gblqL3LELE4NO1N5VDQYg1P9cllZpezePPkES1oeLxOBxp5zgM8XM+qG+/Cb1WhFB7jTckrl9Y822+Znx2dqJQuB4tgsfjUK6qVetHxvOqV9ysOiqNxgHU4dt4czj0PA8+HW84TiQjbUijM1aq8DhIpn4j1LUg381ggvNOUb8tOTzCpcsBrC1GzBRVR/2Xf28L81yen6CiZSZXQElr6Wnd2yMtnHe8uoE/f4CIV//lXGd0hNmPDkg1s6zrsID71avqLgYGPY4qZsX57qsRvsWKovZaPw49i9Bxz2IO4KthR27Fn8cKPSP4Crw/9WhObbiKa4iAuJBN4kCenCIXcGZU96E2ovwC2BEwhK+hBwAw28L1TL94wlM5rs/OVPvhrfKc96VZ4RjP8LfXTrGSLBXntmciAwCAbBeviaHfFwoXAXTDDTfccMMNN9xwww033FgicVERwNOnhwEs1JKbh59xHvLi2YTDfJqVlRiaYn4k5U2DKp+KAyotT0mlsFbmByFTgFKmOtYQ1zb5rFsSv8SeffN5Pp3XPHy6b5UK4lO7n2ObT53Fls3kUI0T3EDIH/s3HL0bSyHWr1dNtlCnl/cT+bv0cqLF73v/BwAATz71FADAJy7V0PlD+MWPEfnbvedRAMDXvnIvACAQ5jZvfff7AAATE+yI+/YTmS6W2PenZ4iGx2P8/lfkn3fdVUQh/+Zz/wsA4A8wI/rMs0RSHn/gCxgdIyI5NMTs1Uc/+hsAgPOjQwCAvXupgvnEw8xANTTye74guXxFKVql0hzDPT08rvmyjm+ICGJbE5HBfXvJ19t65dXQdIDSouSex7LD9l70SstbhqOhV7wDah5u6IH7HwMA/MLP3QYAaG0iWtkiZMjv/S4AYHnvALYLid3zJM14EjGiJzNJIjs/8z6ismfPktvW2UGU6cnd38dSDONIh4RMGAoyn2bfGzlHhM2QMvOBzM7n0dvH6xAUR8cfoi9jURllX5gTfmOcV7xHSO7KPiKAhw4QZTszzGvjj2huj6qPnR0CAEwNMPs5Mcu5vVDMoSjEwKMbR2uCiMKqFQPchpBuU1D0LfJZczhMQv7yUrS+T4q9TzzG8bF5I7eXnCBq35gooiJFWb+8JMuVhYw291mv4JbPF7Uv7trStM2dzBRHxAG8ZMsW7muOqGOj0PWaqmLKpSoCxk/zyp+wynYb0mSooyGApgi5dPG/BRRkgU9Uz00ydMrOXVk8vUqt+urfAvXvF3He7BpXxYvy+Os5c14HuTBVWkMM9bmQQm/tFfPgqwh1ejG/v5qpfwpt09/L2oLxXc3js5QXsiboouQr1R2L1+tBSWs3SKUxHGlwzgkAeKr14ykaE+dRk//cLKsBinZuqwsIHwCk5Ctq59wqEPxeL8o2f+izoBDAxhZyZstV4zgKAVSlmS/02gqJSyEWI18OPU/zQcrW3XHOXSG/B2X1UfOHnEvx3mj36eWtrPQraQ3UkuC1DiR5DauOT+Vi9c/6tngWD5qqZ4Hz56gz6wNvpe63CwNtkaen1xlwehX6vQCP1/3ep2eT1mIRhw8cAgC857Z3AQCe/iI56cVz7OvhFazICjfx3jLXx/NQqbDfFXROixpfc3rmePPVrEy6+nX0dx2f5Hk6dpLVGwePncKJUW7jfIz3tkyMbgcv6Hj6pI3Sp0qWnhLvAePyT79QuAigG2644YYbbrjhhhtuuOHGEomLigDa82axaO70fNpOqLbW1IIqSgUEzfsm7Ed3G594GxvZ5M52ZhUyc1QVzIWYf8iJFxSPM+saskyR/EImJ5QtbeSTcbFMvmE0zqf1699IFcRdu34AACjlPVi2iln/+x74NgDgda9bogqAbvyr8Wd/Rj5eSyv756qVrIP3hoh+V0XGSRfYj7vli7fv+d0YPvWytsJ+v3nn6wAA/f1U61y2hhmm7h4iKV//KlU+P/UnnwIA7F9D7759L+zVvtjnP/f5v+H2tm6rWAcXXQAAIABJREFUa+uGdcw83fOlCTz52CMAgLe+g0q3s7NU85wcJ+LX1c59b9hGJPPRh4hOrlzP9lc0zso+oRdBU9/l348fJQL4xCg5hM8fIJ/r6zdejYhSh0VLyql9ElaEEokwAFA0E8jCDMePHAcAJMT3/fkP8RimJ6jgee9dPE9dXeQKvvc9b+P2AmFUNBdd93oeV6nAbe0ROnr19W8HAIyc43lYuYKoy1vf8i4sxagIIQoJRa76qGD23Xu+yfea2oMR8T3ENavkyyiKk1dU1UVjM+dcUY7QlCBfoSnE7GuvfBwTDdxXZze/b6rRHr2u7OsGAORS5Kmcm2ZGOiNU0gsvlg9wDu9sJzoeEb9pPmPKo/Lxi5o6ntQy1TcdtTjdm156iV6UpnaalYLpmpXk+XkqUjzNTmJyju2KdhDJbG2Rd+0inGixOqOTUq+xbUUhUbkCz2k0zPtmJMJjqonffvos9zc+NYdcUUQVDRrbY0DIUalQn0k3ge6l7JFWu4AS4AI3qZ7H9koumePLdSEOpdHmFnYGYCET73jUvQL0WPQLhVA64/PVFtAbhwXloBz1vChDArMpjkOvTzOr9TvzBdTcWMhyXVYtm/KtaTQIXQ74UJK6ulVmxWJSaX8VWYvvi1LkLBSFVut9UfPFglCkPAfFqa8IrowJwY/4vJieTaldPI6MOFjOOlNKpOY7XS1rG6oOW4pRVd91+HUOz07zgTwU04YwBdhXTpWKKBnybXOK+ldR3LV0nvPvXJbzdi3ByoSQl9elANv3BXz/XqWKrI/hQc1jiJ4p0RrP1Tq/odyqrtA87iji1oJ6rZ/PFwi+8j+08Vdmvz5VKWKZFJQn9+zm8c/yHHWtEWd9cAAA8MyzrAYZE/LXrLWRVSfO+Hk+xtPc3tblrOwISIm8p5l9u3U7lap3XrIJs3M6/0IF95+kwuiZaX7ukRf0SJjvfUFWzwyKB3uhcBFAN9xwww033HDDDTfccMONJRIXFQGMhJUVsrpbHzMIpkjmUyaqrDRyJcsn6PHsHOIRoiKN4JPt5CSzPkH5XMQT3NbUJJ94w9rX1MQ4AGD9ugEAwNmClKECzC739rKmdmRyCABw/Dgzu6Uin8Z//md/B++8+Q0AgC/fdRcA4Nrrrv63ngI3fsIjKyWohhiHVizOfvjUHvLS3nADlWSvfyP7VDzE/nz8yJP4wG0fBgBU5Yv2xdu/BgA4eYxoXCrJ33a3E804N0y06ku3fwEA0NbFz5taiLp5lfncsGnzD21rRCjN7EwaDV1RbZvKmVCmLJnkOLv33rsBAH293MfNb3wrAODoESJ5PQP8fWj7pTwG+VbF4kQpzgxR7bSzl9muSy7ZxDYYygEguCjJrQKAC8bcPMfyt+/4PzxeISMj61gtcMON6wEA5RKzZZ/5y78CAPzj338JANDVuRwf+WXyLn/vE78LAHjnO8nR/N9/x5r6++67AwDw7lv5+dr1PP4b3sBrsWvXc6/dyJ+wyEmZ0iO0zvEC87EvFUvsu+E4c4strUSncsUp5IUARsTZK+eYES5pvo+2MosZ198bG5gpnZjkHF6pimslzlXQxwzzxrUDek+ErVQgKtkoBbREJIqQEL+iUMGieHYTY9x2WhyjRDPbu6DiWM/PG9P39+whkp1Ocl8xbd+v1LGoI8iU8qhJ3TSqDu4oPGrb5mNoXKy8eJbGVQqJt6fujbKQDJ/QvUSC9007vym1qVKuOghMeZHCqKM+uXSBvguGVU5cyAfQ47nwSXtVRt0xutPbC6TcF+OF9r5qqN3izdr3jNfnATzqN9XqIgTTQQmF1EiFceIs50XjnPqDnKtr4v5V5QsYME+4kDwNjQuptVStWHN8/1p7OH5CYVMvZVt8i85MMTtf1yY7oor6vKNgqkFha0ND5I3vVy0V4BVKZX6y8+LEmtpnXEhfWyfb5pVvZqjxtflRP8lhPtgL87f6jiaZild+pjrvx8e4BlnR1IBjE1SgHJV/XasUv71env8enVZfiPNvsINrbBzn96vyNXW4fuobRrterF5r/Lyax+vMmQ6C56lH7l61rUUcWvu+caAXgp+HdI9xoPoK+3Ffcw8a53kORk6z6jAU5rNHh/yIi0ILb3zPTwMAyhm+f2zPMwCAwyOqqprmvl63Skqdaqz5ixczbFu0mfe/gMeDpiiPa0MH23PVRq4/ZnRYEye57Rfn6A3bXGEVSH/+tVHui/oAOK8DNFNPo+CGJHOd042tBBHSc5KibYghopXijMjCZS0wc3kzrmWHjUXZCdNz7KRhlSlMqtM2tTZpH7w4Wq9g+XKW1Z0YohhESwsXqtWqH0d14UKSvI++YtH6nxVjSU64VrrT0+ISmn8cop9Vhujo4oCNxSXt28+bz9YtLLtsbqMlw8v7mXCINbRj12MsJ8vkeAP7whdZVjc2wZKKI4cor/zYQw8AAK66iiWd37ufD2ef/YvPAwBaW36urk2q5kBVUsJHjtAuIhZg589U51BNctaORNiP/DIWXTE4AAA4O8wHuKkpTixTEtwY6OfDVrnCSTEraf+dskg5eIjE6ZtvoWn9409RFGd2dhYXiq/fweMuqKzov972wR/6vZDKTbsk3JSbZ5nmW95+HQCguYUPBPc/+nsAgEqNY3zTxhsAAPOZeWzZwnLue+6n5cZHfv7XAQA9PWsAAGeOsqT3525j+feWS2gbEAosvoEsjcjleNxlPbRkJYayIJXPfm+JA78EX+KJuCN6EglZXSVvtq1R3kiL83wIG9XcvXYtSfFelYlVFokDRPWg2KIS0VCE+44EOQjnJ9gXS7kC0ik9bMq2oqZatWgTf+vV+0rFaiD9aj/HQy7DBeu/fI3lxCbiFNBCpG+QiYGe/uVqq8x+5zLIaZ+psATMQhKFkQhFscjzYA/PBb2WNHBLVTP7ZltnZzkGvSoZMhuCqVmet/PjKW13oY9WdVxFWaWYXL+ti8wQ2h4sqkvYBsKsCJznP+fcaPG4+IFwYeW68OGFHvgu4HzuWfT3xcbW1VeJW9RvDrUFcRdHEn+R3YOJD82n6z1BIlr8l7QozqkW36ukTqPGgF99yCg6Ndu51+usJBMJjsmg7bNmIhv1R2oJFXtv70z8xel/jmm7wAHNO0EZk6NUREnjJRbVd/QwGm5UmboJHKkkLx5nG6MSJ1mK4ZFYSE3XOCiKVFlr9EqUAMnMGAW3nqvx+5esCGCF7GaKtfrkUqc8x8MBruf3jaokf5jzUiq9yMTdxtOr0iaL762v7G+Lf1N/T1jIJ9SLwNQcBTkbw7663/tqHBMNebY5oOeL2SzPz2DAix7d07yNsqJTeWVF53LkBZZ+xhu5zuhbzWR6ocB9nquwv21Yy7Xf1dtoGVZTaagvILEczfs1WRBVawBkNm8COhWvRNhkvdEd5znbsYF0oX3HuMZr9Lz2PO6WgLrhhhtuuOGGG2644YYbbiyRuKgIYEwZW5PyNbGHRCsh1MlJPsX2DPAJemKMT+ONzUFkJScelfR3dxsRlckZwsq1Kp+ik5LCLxd5aF4P9xkI8Mk53svfpw1uVa2Oryjj0gozxR0riGxceeVlCMoMs7e7/99x9P+xcXAfiahXXHHdf25D3KiLT/8xyxGjIqm3yzB99+NPAADuu5viQi3tRJhXSbjljz75OZwbJtI8LxK7B8xgjo8SfbvrTgqvbNtMZOSGGy/RXpmROn2KpqANKm05P8JytS6ZghaLKq8RCf4LX/kkAKCpLYOK3Ngff4ziJ7e8leInFWWRWxqJnKdU4rpzJ9FHy6sdOEn5/6Ymjsu+XmazXrf5p/i7FEd7WxvFYAYHXz2WxsZYvnDPPfcDAC6XdcaFIiRE5dZb2dZ777kdAPDVr30dAPCRXyTy94H3/woA4M1vegcA4NnnWL537XVXYkBzzbe/eycA4FsJlocekn3Hbe+nlcSuXf8DAPDyQVYIjI5PvGbbflJjWubrgz2UWDcTaUslWtliSWhDSUo+XngQkzCXZf/9ugOslkDLin7aQlg5T69Kmns6iOjllHENNHBOX76SGdawLCmqHiFlUfbVA+Oyphg9i5Ay3TVHWYXvEzKJjshSpKbqE4MkCkW28dvf+Q4A4JFHOY7zQhniKgNq62VbuiQSFIgxCz6fOYdKjUjdvIRmYvptQhYw0Qjvf5l5Ipanh2gh4ZecfX83x68hngfGiZ4nh2mLsXKQ96pDxzlPPP0iKwXmix5HDKGs65LPGwKoUl6DqEwswd47cuhLLwwBdFBRK4msLfpcpYe1ygJi4citGELnIHX1iNerStMWuc0vIHv1CFpNoiiLxVVqtdorUDND/vjb+SyvdTJJFMGEj2o19q+wxmNVbSipRNmQPivLLEvAzGf6GVZ66fWhQShi7hzncAh9i/ez71a99YimiblYVBaZuS8Et1vQ/cnnlw2NSkwbImFMjtBw20QDi5onmvp4j00Os9S1WJIAm0rz1nQvw1KN4gyvk5X9zqV4/w76ReVo5PxemOPnIxX2lcy+cVy9ifSNDV281x/S+e1vIGq4f5jbHh7nOqbcqHk90qK914u7OOGUftbDeWa7YjSDuvAsQhUXo/M2/kSHWTCX56vNco05PkfEixIqUzlnT5ONxxKyqzjXF9o5h/qELDek2TfbGyU8WeX56PBwbRc7zHkanasBAJtWsUpkcDnva7WgnousUX5uz4R6/F5gTlYnnrCqJfO8t4U0MeRENTp++gUAwIlHHwQAjLVQIO0j+OHhIoBuuOGGG2644YYbbrjhhhtLJC4qAujzq95WWRzLHp8dYganKlagT3LWQfEypmZm0SxCZFUZMMsAZTNm9mtGttyX38sn5baORn1fmSUvs6+ipsChfOjPxYIMRyVMk5ufx7Gj5DcNnSF6sXENs7xN/wl+8EdOSRBkgnKw0dB1F78Rblwwtl5OG5GCTGcfe+xRAMC99z0EAPAr57JlG8VStl1CFO9LX/oCTp6kqMtNN3Ebf/5nXwEAnD1Dblulyr5+//dp8L5qLTNrN9xAQZljp4gAXHUNtz0xzr7y6CO7AADrNpK/tlp2Eju2sx8/8NAReNWu1BzRhViMWakX9nKboRDHUW6M75etJgnZI0Tl818mDzHWwEGxfRs5dBs2EJ3YtYscu+ZmIoMDy1+NAA6dpqBMIckMYldz9FXfqQ+2eeUq2mV87H/8BQAgI2PauIy33/0ens+xUfK2Dhyk5cWzz3wHYyPiImq8z8lWpkOVAqfPEU257Aoez4qVfA0E/7W2/WTG8DCRz9etY987dYockRFHUILoQkiZSsvmVisVRzbf6+PcHVNlRVCCDk3KIBtBP5Vkv6+J77B2A69zxzJmTg1Fn5shx2T4HOfE40N6HWFWN52tIVyVBLnuF4aOjMnu5PQwr/slOzh2mmTVEGtgvzcrkpx4dVUJuyTTvIGcHCZ6d26amdq+AaL0oVgCBpL6hfSFozw3Jrzg3LQUxtfLCvUwSlpE5ystrvyTzx8AADx/6CgAYHScbUineYzhSAQVZalLymgbx7EqpM+APp/PUC3H9wJLNcqGQnkMqa7nrRnSZounBesGR1rnVeImhlFUFyGr9tOMUCm/1kZBITM+R29ASKAjiS/ErcTPk9NJB+6IhLm4iUT4OjXFOW5qhv2mWDJUUSIpuvY+SclXCtZnhERoXzX9riQU2biQnlrNQVSsS7cJrY5W6m1GTLZ/gQLIf5QXIX/2/UKebTGriniCYycsIZd8OgOftBkKJYnZSJDJ0I2q7CBqFV3HkK9uW0sxCppbS1prB+xaeolyBWdpWxWqiTusCoF8pYYnXuJ9ulf38lXiBLao/wyd5z0zI6QZCSKtXmcfQu0c7/YfzlNz0HGbk+DBDzFnec1wbFnUfkOz7fOy/hEVjy9YFVKo/nfLm7hWiiQakBSnsbNfPPeEuNY8VSgV+D7/dVaJTN0gq4q1vF/1xYiYrlzG+5bpk9i9qOIVF1BIp19VGrWqD94Iz3VINVcBCYrZGA7E+Hx07N7bAQBnymzjuVTuNc+PiwC64YYbbrjhhhtuuOGGG24skbioCGAyySxsWLXaftWaF4sm9cuUwPQM+X5FZS/DvjDmM/xONMYn3rRMTPu6mYk+fJq1yh0dfBI+f1p8qnlmgUtlHuqYTIJXrWZt7JxkwVHmk3Iqw/e7n6H63yc+EcOddz0OAFixkqo9cqe4qAig5Q2f300OGbw8N6eGmHlfMdB78RrjxgXjg//lZwAAN95E7tvTz5CrefQYTck//ck/BwCsXk1rhmeffh4A8ML+Z7D7CaKEd91BW4dm8ek6O5gxmphhhv/a11PFcmCAiN7yVeyXazdtqWtLbz9RtqyMfAuSif/G/bQ2eOIRonLxplbMiavV2Uuz7WPHiCo8/jjRw6lpjslUmt/7ytdoiSIlcETjREwsB757D39/4hTb/NLLzBp2dRERjMebnXbec/d3AQB33knE86Y38zgicW48n+OYDUcWflMfkjWf4JhvaW6t+2sixDnh+RNEeU4fJcI4OZHF9p2XAwA2biG6VJXk/ug4eVgPqZZ+aoLvTw0x3RcNX6gtP9mRk8H5/peJPt17L+ej3BznWYEJKBZ4HgNBZpQr8DkGzfYaahfPRNLjM0IqquJlpzLcxuGTRBkjLeQVvX8j+act7cwsz83xe+MTbMP0NF8hJVsEG5CXorTHz74SUua7LJTt9Cn2iVyeSm4rVtHQvaCM8QMP8DgtkxxXtjut+8WBA1Rde+JJostXX0v+aijsR8VMuxcp1nkMEZXaW18n+ah+wSjHha76xLUyM+NQkG04M8r+PjvHNoRl/huX/YXP60NVxsmGqPiFtnr1asqQ2oXDFfS6qWEHsQ5G2e8CytiXZEPiKRuf0lDhmqPeaVl94wkaemjAlyknppLsuyeHuH4xc+1YmHNWQxORwNYWu7ZsQ1kbmkwT3RodmQWE1oSFgK1ZT+QhmRTyJ8TF+mPJTLRNDVTXviqkzywmyuq3EdkseCL1CozVUmkBSjZKqey5JjUmTQ41IMVDU90NhM2o20qxULfvhd9xjJgdmFcVG/PpOcdgOycrF4+4s3kpFJdNOVHbiomfGPRf1OXvj1UEzPjdW6+GCfE8i5M8l36EXvlXVmlIrbk5zPNuXNC//wHXyYbseaRybKbyflV+VKRE7Cjk1hYZwtcMJTbFTv8rPjYDeDuSWt2rY3DvkP2EXsOUbc3DhNtJlDg24hWuNXz6XsgUTaVt0OUt4fRR/rvnJq4Vbtq5EwBwuo3z9PnnhgAAKcmhzmSIsk6rmmTbADmBjUEej6nVml2GHZLXKgwcAmMJsQTnA594u+VaUcejo9czSW4/bakyO28BADQHX/shxZ3m3XDDDTfccMMNN9xwww03lkhc1BTIlh2sp52dklGtMk+pWWYw/TKmzmWYDSqU+ZRbLJURU1Y4GLK6WWYTptPcVkSZMVN5C6oG3Rsys13V00phbm5OPoCqd58XyaMk9dDl/czGtoTCiDYw07F6NbPCOSkGQSqNEwIRO17bc/HfFUNnWX+clydWVln0ipm8ufFjEeNCmDs7iN7JZgyDUuobmybSsOd5qkw2NjD7s2XzJoydpVrUC3up5PQbv/EhAMDZkSEAQK3CbM6HPvzLAID+FWt/pDb19TITHIkzM/Xii8wmtXZuAAAUcmn0dysTW2X/Mt7g088QERk9Lx6iOEQ7dhDBvOIqIh1HTpJ3FfRZNpaZxKPHmB3LZJlxmxOqc/Yc0bhMJonGFo7dq6/hNk8dJ1rYv4z8yHBE3p3q+8a/Gz1PVO6RRx8FADy8i20eXD4AAFi3hq93ffvL3K44ku/9GZ7Xd7/nOhw+yn3ByxTamWEil1XxlTdvZxvOnOE+00KXrrrybViK4Rffxry38kLvMsq2x8Q/CojH4HAtqhX4la5MKVOfkrF0UrzTcjfR5waha7PiL8xIeRTyvRsaYt/pXU41ukGrzBDSffxuquV6lC31+yOoiPfT2s5xuHUzFdkqyhgfOE5kd0pcrPRh9tuxUY7XmVki4DaWVg7wXrb7iT3O8QFAKc152lAhn8cPr8fqNxxCCoBXqN29wu0NACLyOGsVnzaqe970FNty+tRxHZ+y4brnGcpnKtuhYNRBHYNCSWzfxXKt7rc1B8UytUUs2TD+T1XXIypFV3+Yc0BAXDPzPK0IPfDUgFK5flxAmgVBZf094vhl5MU3PilzbKmYm+pyKsM+n8n7tQ9xfXqa9DnbMDnBfpkvlxwkpVjh31584YiOR6bp1k8MiVYfgJCwsq2BKsY9koKsgSx+Uynk9kJ+bs+HCPxCgcpCPmekOJoTF8m2aeh2MMZ7UUMTuU2m+6ApwTH6NsQ9GLCxrDnDxuXYJPJS6r3s8it47oo8t8mMoVh2ztmm9g6u7QzRXYphipoeqR7XPAbb8cX6W03Xy5Cxm6+9Bq9fpao7cfzufoTVcgWdZwddNL5xilVqBc3/PlULmeJytSB/cK33fdGWVzbFUb71eGo/RJy4Xm53sRqooZPGaTZgMFBk21tyrKIIQn1eJNsV6pfrSlzHlQ5PoEP9/snvEGWrXsLx33Qj71u1PVxDXLaM3sIHhzlPHxBc/6tvJAKY0Bxg98aIzsO8KiHTOfVbqUR7gxF4qoZiCynXceXzuke+yDaNiV9e1Hwfwmv3cRcBdMMNN9xwww033HDDDTfcWCJxUfN8M1LR8Uf4dNrTRx7e8aPkR4VUI1yuMhPQ1MpMsM/rRVunPGtUf97Rqaxchpmk0Lx2ooxTLMZtoaonZWXkIhEVzcozyrIQJT1h+/ymKMXPv3TXP2PjhvUAgNFR1gCHgsxY9/byST+qeuGXjo7re8xO3Xzd1h/txPwI8cJeZllSSWYdLBPW2dP9H7YPN/79ccdXya/bLx+5BqlpRlcwa/aDh+lx1xhjFnJZN/lMj/zgG2jrZhbuE3/yqwCA7z9E9K2/nx5jr7+aiMeBA0S6ghoLqQwzT2tWr1YrQnVtMuTPYv++IQDA0cN89fsquGTLjQCAgweYNX5yD7NXJ4aYIWtKMPvd08rM2Fl5/VxR5ftjh5jl27mTKF53G7NikRhVFGv7ud2pKY6Rv/5rKnaGQz7kyhz/x0/QYy8WYgZw9x4iOXPK3G7eTIXG9es5Ho1TXCgoO+7lmHjsEXIpv/Utopar1rIty1aQExmKcqw/+9wePPTwY9xnnJmy/mVEiFYN8lxfe+2bAQD79rKN1RK3OTN3BksxXnqZ13E8zDk6kyGC0Sdls/5Onr+Tp4jOVmvGwfAhL95C1Dhq4hSNTbOP9We4jYYGeQyKu3N+mirRrd0cMzl5ItWEVlUFUfjlpxQQYmZqhol4FPNZ/k1ANIZn+Y9W+cnOJIm++5SlNl8yf5D3oEEhfgk/P3/9dqKOuYkhAEBaaMP6HvZ3qErEH21ARQiMk4xW5YvfK7RN96K8MsWmErpsYAAAkJzlto4dpwr13hdeUts4zsMR+b1J4VQC2ShVi/BKRtKnTH7FUWUUv6tsXLV61crAgvzkkgs7NyZbmJrhuiWg/mVcpZK8hA3tBTwQfQ7jU0K5hWKXy6ZhIF6UoIys+rKht5aTN4VNuz7JJPtAQ5x9KZvnPrNpU7UFqiZlLt5TOBSv23YuW9S+Df3ltn3GkxKKYmi28RhNvTGs8Vo2dULzKAwEHFQR3mDdcVUMgXD4hup/Oh/Gs7Q5PBwXMmVQj9DMWkltrPFeltGckZufd5C9jjDP+VyO7Vu1nhUyhVXiFIsr6xdyGVjCHMCaobfi4wmUgl+wVEz+qHn1FVMNfvzIWfzgKapcbrqWa4aptBDwSr1iq3FSfVIg9ixC67x5zVezvJeWjBNtxLayXXOpicYbFoiDi5VD9VufddaqVcbJR9LDNvmF9AXl9x2tiLuuMRJW/+ycYNuenROSDQ/mdC9bGef83HCGKL3vLN+vupGc9BP3cX7eL25fU4/Wehr7NZ/arvHVqntKVs8oVfMoT2i+8S2g8T6r2LBxo0qByZf2AgDGW5frqMVX9rwKMq0LFwF0ww033HDDDTfccMMNN9xYInFRUyBdvcwqTM+a6hufvpvEg6qJVxIVn6+3i5n7k0On0RMYAAAklAHzyM9v7z5mD7ZtpSJidjavbYgjOMH3c1Jrizcwg+RwWMRdae9iJjg1z+9PTXG7X/vaZ/CGG24FAKxdTT+x5hYqAO47QL5QY0yeUeIKrFzBbPFzB8hVWbOGvJHGemDmR4onnn4ZAHDoZSJKlsl1VLGi/4aNuvH/LHr62I9OnmKmZmAl+/z37iVysmKQfeGaa68EAHz2L/4WAHD9VZfgd//oswCAL375q/zsjdcBAN72DiqKeorcZjJFFO3kSaJXh46wj0yOUpXqqmvfAgCYniCvaXyciPWGzey/115H9KJSIcrXGGtGLitVwCqzTkEp9XYK8YtIrW3bdiJ8L75AnuJL+6hiWiwwU3byhHwDpfK2YT2RtMaElLwqPD8T40TSvvKVL2LH5dzHjm3c9vgY0bZjRzlPjJxjBn7bNvV1qYKtGFyl9xzTl+3cAQBYu45ZMJ9f3oVp8mRGzg8BAHY/TYTw2WefwBVX7NR3efzptJSKg2yn8YF2XkEu4Pfu/Q4A4JFdzwAAfvXjWFIxPcx5d+fVRLQP5XmNjp0g4nfsMF99Ziwl/kYgEEUwKi5S2RTb+JWMfMX2ys/uocfZpzI5fm6Z4eYmzrvrVZFhiJdVQ8TFLWltJZI4NMw5PBjif8CCquexs/xbeGJSbRHXxWv3F20zxn2v207lt8wE5/RYgI2/7vprAADZArPUa9eQJ54XR8sf8cAjpK9c4j0olyRanpzk+CwXuY+GHo7LovptNmvqthzvB48QpZzX536Z2TYI0c4kmQ2uVk25z4tIhGPDH2T7CuJJmu9aRbwbI/0FdX9pT/w/JLT/mIcpdNriqDDPfpYX2m0IhXlwVTzGp6qY1Y4/AAAgAElEQVRifJJzzaTUPWtCsOx8FzNCfS+Qe1/sh+cVbDwv9K5cM44gr2dNGgCVygIoUhOK442JV2gomrZtCGBNco0eQzCF4Pj0vYD6QtT8m8U/8khJ0XiNfr8PBaly5nNsl96i4jO/MjsgIc4VU1C1tsEarzbzg4Jkpjs6WelUzPH7kSaOkVTRi84uIixxVamcP8xxEuvkPJASQmjzTVwVMT7/0kW5AyoTaAuLNyok0CoGDNG1uTUf4tw64e9AtY3z8N5jRGHL+lvZRz9Wj/pk0K8KBVV8VOPkr1YMqZ5lZUdhnu+DQY2RSa4hqlIkjUe5/UJ4EGWfqpk8dS8OmlZRf/EWNFalF+ANqjKjYhV/+r7Nf4ZK6j4w1c/7//5ZVtw1z2cx2EGEb+g473Edce59pdSmz4R5DlMRfj5eYVu3DvJe6QuYgifPT1GDJCm+Xly+nU0+Hq9X/NeRsVHEWlgVU9J84A9wfm5X5dKx07yfJVfQn9nBR/8VP1cXAXTDDTfccMMNN9xwww033FgicXE5gHN82i5JtSlXZGYmrpratJSjivLHOKGscnNLK4p5PrlPTsqna4LZzuW9fLKfmSRKgDKfpi3zaT6AVaWYclluxx/ka1Mz0YeYsmWTyqL2tDN7lC9k8Oxz9HYqFvnd7j5yic6MMYNb6eBzdG+cnzc1MiOwehnRn3qHmx895grA0KlDPB4RDY0/Ek/wuI8PDQEA1oov4sZ/brz5bUTZPnzbHwIATg0RnQsLgvCUlO0XWtwkT6JEx2p87V/orffhD38UANDZxUzQUSEjoQCzW7l5jo89TxIpWd7P7OgzTxElXrmKvMLhM8yw7bziDQCA3/zN/wIAmBzjmBgf45gJR6dw840DAACfkPUGZUmV9EZDgu1/6Tnu8x3veCsA4OEHyQd497veCQD4+jeIXvaJm3r0CP3RmhuZ/fNUmSH3e9l/BweX44tfpB/h1dcyc7tqjTKHDeRwnBsjenpuhIhlJMRzl89zHtm/n2hkIsqsmGXdP/WZ3wEAvPF6tvXdt74XADA9xUxlKBjCBvEJm1qY8Tt9WmqmUmJ87nker8V//cDPAgCe2fMklmIkx5nlfOFZHn9jQgjpevIz03nOhaeluDo+RvTKXwYaxaczBKIglCyspO6sqjRG5DmZnCOa4BeaOD5O3umU5voJVZCsX0fkLCCF0q4ezsOHjg5xf56Ko07ocEiUhM7L/9KnLGwFphYn1UJlwF//5ncBABLKUs9MMfsd8xvaJnRE6NGk+KnBRAti4p3M694S0W3Xr8qRvac4RvYc4nhdve06no8pZp+PnaDn4g++/302umjnhW+9RlkzvzPxxvKFosOL9S0y9jMem0cZf6PO+HViGsJLODds6KjH1PrqVVzNnqvqmHCJg5nLY0KqnpWycdnEY6rWc5ZM7dPpjvL/M26Pw0P0mH8gX0O6j8ypCsRQvFptgdNnWf+0oXFZwXHaR3CRYmdUpmcC9BAVUh8RlzascZWZF4qs9ZtXaHk0GoFXKp+5slB5Ie0wPqEjxqjzoSqArNTYvTqpFVN8FPoIL493ZIRzdkBoZLPWP/G8F9G4qmye4H0Surea8ntGXs/GHW5u5ff9Xgc/WnLRNsj7XvU4dQZyul59ug/OpHiuAuInty0n+nU+mUfFMaxj/6gab04In/ljpnW+88KZNHUiBlVLJKQcHWd/OlvmtZ9Kcgd9K1gtFZNK6AFPFeb5W05yroR4qQZ5xYq8d0T1vXkPj6eqe49fYzlkELUirbHTtp46CpfJ42/5F1nxcz4cw7W3Uvk79Zlv8LgKQjxTHDhNWwfY/hLvDVNq208t5/3I+qNHYzrkKHZqjtYYSmncVpLSQmlugg2Hgp6DpmdYiXL8Zd6H52JEZXPi/RqfN+B57Xl8Cc/ybrjhhhtuuOGGG2644YYbSysuKgJYLdfXjpcNCZS/k2XBDCFEiRmBUqkGe1bN5phhcnxldATlAp9440LKahVx5cLaVJ77Lut3IdUGWyp4XlzALqnYTUt1y+cr4Kr15Bc++TQ5V2/5qV8EABw6+hS3FSYyOD3DjMDZM8xU/9x73wEAiPwb7WbOnj2LVErKqTrQorKTJdVs1ypLN4v14xh/+uk/BQBs2Ugk8Ot3/f8AgB1brwcAhMLMSK1cQ77bhrX00btk6xpUPcyYNTSxL/7ar38EAPAHv/dHAIB5cYamJokqVorMZDa1DAAAdu4k/+ir//x1AMDHf+v31SqOhbOHmR379f9ORHDPEw8AAP7+C0/hvTdfDQB48OlHAQCxFiLgMXXeG27g33fdS6XO73zrcwCAlw+wrY0JoXIzJwAAvjYh1OJh7bycqOT0NPvv2vX0xPn4x38Lo0LSAx5mztqaWe++58gQAOC3Pv5XAICuTh7n7bd/BQDwzncQldm8kePzf36SiN/AambBYkItzwwTxbvtg7cDAK66ktciEIyiXTyRjo5OAMA6oUm3vPlmAAs8rJj4JXnxzv7yL/8GSzG6Wzgv/s7vsF9HxZXsW8PrO5nk3Pe9e78HAPjm1/4FAFAqpB1ftYDQtqB8uMplzv9RVW00NDOTnBQ6Fw5yjstqjh6Qz+P4NFG2eXG0ojFub8sWqi8fPETEeGRsFD4f/2acKq+DwAhhUYrV1BlLUoFulZrtrI5rVPeq5Kx82uZ4PlJzHFtp+bvlivz9pz79p6jM8b4wJzXTpO4TlaKU3cLse5uWMVNcBueBNavYN1/e/zg/r1oGWYqeJbYlKHSopZljLtHA8zeTSiMcqL/5mG+UT0p9pmBpAJX5A07Km3cpRqnI8xyWcqB3sSKqKVoaEugoepZQcSg3lbq/2aupkjuZd514x3twkbqhVfyEQnYd2RZTssyZ7Ggph1iEix1DF/3z7KNh+YkZ6hbRnO6r8fh8QsyK87zmIa01aoJVsnkplQpyDup02BiZL1fQKF0En3H0hPaU1ZayKYbqtyG1wSqz/H5DPsU/FCrpFT84mUrqPAgtEXoZ9AP5POeBrDwYQ0Jazp9mBdm8uMSt7UTcDcFdyiuneXnUFcOcK3whKbt6eM/MFqgbEJBy85UrqWuxzgvs2UeOfjHLeSxR5vnvaeW9INwopfBp/n3ex33k5rnNLrCyYfty9hmvkLLp05xj0cL++Pr+AQDAyTEiu5E8EMjwOx0ebvtMStxYP+/54SrbcuVyViCdklruMT17+FTZkZAyaVZaIJt2kOO9UhVB8Z1cByRCt3C7j+zFYw9wvVTaqeqlYxwvJ55nld5knirNo2pLvJn9bWU/kcyiqqsqUqm16oCqKrpqPvbpWJN8EHV/yAY8iBhqH+TryDN8Fpndx/XYRBPvFV753RpXuEV8zQuFiwC64YYbbrjhhhtuuOGGG24skbi4CKCyWZZtLSp7lcvLI0foXTYr/oSyPen0POKN8raRQmhQ9cLmOZJVFiIWle+a/NfahZKMnlfNr1NLW6z7PYQ6xuL8XT7HtjY3L0NLK+uCL9lJdcE9z1MB8E03kfd0/DgzIn29zOT+9BVEdR585GkAwDtuuvxHOj8WJ0eY2X3++ccwn2YWryy1IsuCG0/S51m6SlY/jtHZwv7zt5//ZQBAfx8zM1NTvI6v20ZeW1cHs0K//du/AQAoVlJ4bBfVKfc/Sx+ZmVGqBebm2Le7V1Hlsiilzr5+9pPdz9Avr6uTmant24mIZVLK4kXE3ztHjt39D3Pfh/YzqzpTCWL0DHlG/goza7OjzG71LWOf330f+UdbtmwAANzyrk8AAH7vN+4GABw5zbauXce/H3mJx3DlNez7p45xjHjLzEg1SbnwfT/9dgTFG7vhQ6yx/9NP/W9u+9MfZjsP87d/93f/yH3fSHTu1InnAADrtzBr9+Z30JczNcPz8un/SS7lNVfw+0eOMlM3OsLz+Sef/itsELrv1Zx0xx1ErNqVLf6Fn/15AMCttxLNr0pFLBRbmiqJH76N1ygoDmffKqJt6Rr7S6SB8+0119MjKiMfpTu+9k+oyZ8yHiGaWhJXMy4EIqC5uaePqmnD51n94FOu/pJt5GVkdH8YkKKut2reSPy9bgG49FKikt+884xDQvIYcc5CP6oIeTB+RkAc0J4ejt8HH6L35oPKAueEDPtM7FRtrMlP1nCc3/ndP0CTfJ/yQlKi8jn0+Hm/W9FP5N4jZNTQncPHyH197MEfcGMey+5yHxHxWuJSOO3u4flIZ+S75Q8hL5+ocqmeiV4SB8ZjfoC6MYrOg+lsPUdmKcWY/BZNRyAcY381FC4oBUXzYSxKeXA+lYHR8KwDmJriAq5X77dYE5pr6LjjlWapefVPW9f4xbvrbuU8mpI3cVNLELEY29uiDlEtcVzNyA8zmZbyobYdaSBSUzQRUO07IyTNK36rKUKbMme1aH1fXMPkJBrbOR909LCCamyEaLenoHGlPmsencbXNS6qhhtKQvympaaaKzgEV74Ijc1oLIU8VVRNtVVk2KJ0H6bH2IZZcQA9HqpGd/VwnVZdgGuXXASF8g6u4dq2tZWVDvPnWIXTGWCfny1z/rj7TuoThCJeh2vsj3D+Wb+Z21C3wOr1XAP05Hn+X1Tl0eQ0+fWTQgKn2nkduhusWo/9tUdL2plx3vcLqgRsKCfRHdb9Vz53U1Khzqoaokt6IqtbuG3/PNczL2bkCZvja6zKPjEvr75tV0s90+bJPPfZeoZc233xGlIjPBcN0m2Ya+Zrt7Q+kkLYC13c92UbWOVVVb/06R5kStRT54hmtobF25NNgAkB+6tC4oMLz07FsPz9znKO6ta94qCqwKo1jX1x0tvlpHChcBFAN9xwww033HDDDTfccMONJRIXFQG03VmG0+uR94geQy0r6Vcda7zB6t0DSIiD4xcfZOQ8EYee7n5tW5wF8ajyFT5d93azLjk9x23lxTMsWy2+h9mt1AyzyvCyPjmgTFOlVMT5EWaiG9uYDQ7oKTzgZ21zLMZ9jE0w01GssGb5iuuJfpwY49P7+Di3vWkLfWvkTIIJZU4O7WfG9wf3fJfb98wjHDWPK2YJ5uRT1tDEfe99iTXAg1JMsid698n+Pyc+8uFfAgC09Q4AAD7+m58CAIycZR86sp9ZrTu//SUAwBXXbAEAjE0cRzDGvvvgo+RPXf9GctXuufcOAMD2S4gAHjlM1cCeHva7Ff0cAz2d5NWtX8Oe9ehuIoOROPvQ7//6LwAA/uFzX2Zjlclbt3E9zk6wX83Oc3xs38asXncnM0jf2UvU+wn54gyN8/u9g+yH05McLy8cIj/RL4Wwm994BY//FLN/xQqzf8+rhn1TTxX9UtXtD/Ac/fxbiOjNHCNieefu+wAs+J/9yn8jKnf7P/8fAMAGtXX1IBHAS99Dbzafl5m5X/pv5Ktt2kyk5cUXNWZWDcIrCGe/xtHjj5PX+773fgAAUBGis/spqqWdO0deydveTq/FRBMz3kslckK8Ym067gCzmwkPM685VVL0ic+2bhO5FO1t7RgeIVrR3Mj+OZ/hfBnWfN/Wyr40mWHf2rCBvz1/inyVnh5eP0MSkyl+zzL/fi/nco9mv0SCCIfHA5QWKbAZj65m3CzzBNN782hqkvfgpo0cp4/uYr/Ny5etahwuLK7EkF/bXMpRnutbwX4dirJdOeNFCcnz6n2lxG1/6Z85RxRVrWLQTVmVMzUv7ytlIYw1EazKmvy7e1oxfJpoyKyQWFONXGQ3hwXoilGq1BZ/YcmEB4Sj8kJBS0Wew7TQUs8iDzLz68p5fE7ftLNnap4Lvly6ViKiOYifqbLq1W/Al3bS2cTtFtJENGp6HWzm57Ew4PProgpNKwthjqufRBOck/PSDcjrGqdS2qb5Gwqhh9QJK4uUSQPibAXkC+j1+BHUGCzp5DS3c9wUxG/KC1V09FR1XIagB6T0m52VxoNQS48nqPNl6zW2USAsAjUvan5Tv+VnJf2jp5PzSVsbX5s65aWmyoNSaelWT3WGeO2v2c4KjuZe3kM94r9ODR8GADz+JBXwK+Ocq9ev2ICUqtKmxYOOCgns7ue61hMQWjXHe/3oMVYXJezzMPvXmZQ8uOVlPbCNa4UW9bO9u6kwPiUl2Nl8FttXcc0zP2l9T/xW+Um2CInOg/0oC+4rrHEXMA9MqSO3SZr0/CG2cVWzkMP7WNm0a4bHEOnrwW0y/T0ywmePM9/mOr1D6//Dmof7NvCcrl8lT9g054+IBEnC6suxXla6lFV9Ula/zQj9Nq/CvoYe5MzF4DzVP0O6d84FOMbHyjxHCd375uVT29FiTxk/PNznBDfccMMNN9xwww033HDDjSUSFxUBDIf4NGoeS4U8UbuafJLgM9Ut885hZqezswVNTczmp+b4m4YEn9Qhn5KwslbG3dssLtLRQ0RL2rr4pDwpBK2SEu8wLmQwJ3RSRJSyuHZ5bwbnR01Nin97w7U/CwBoivE3Tw4Rudi0lfyugeVECp95lpnrxx68HwCwX7woy1BtUHa8uYFtO32MbQ0rg9ecSMDvJxdpJs8nfssgBqTsNSlPM4s86mPxE7699y16deM/JnIF9pvONvrmXL7jJgDA2ACv04kTVLx8YNfDAIBRZZhufOPNGJ+kp8vPvI9I3erVVN46dJD95vB+ZuWOnyBS/MILrLH3iys7lWYfedc76Xe3eTOVO08KQfnOXeTWrdvAzNSYfMbGJ3PYf5I959yE/IDmpKimPh5fTj5VawezqbufJp8uKy/PklCJ3i5mWXds2wQAWLmOr4HCJACgrY3jNFhlJ1+xdjk+fzu9dn7/L/4JAPA2D49rz8NUFH3xMDNtf/ynvwIAaNBccONN5Pb1dnOsR0Mcdz4f/27xgQ8SMaxWmbGck79cQ2Mj7lOmb+0aojOrVq4BAHR1ElG/4ZbrAAAnjrHmfsUa/j3R1IqlGNe/idy+hHwdbUapOTOLPNHm2Z/6+tjXLrvqeozf/S0AwIxUMH0+U4HmmAmqb3VJuSwvPkN6mvPrgYPsc9ul4trRQNQxGGCf9amqA6reaG9jXwwEPEinbWYUf8VXzxssmy+UEAlTKDXEMC8ULihVzVfjY3b8Bq3pvjKXQkBoqEe/rerVK05IUPuYSbNfDp3jHJES9zcvrlKhKMW7QkHnR562ft4LTMk6qox6ei6JgKmAOrZybJfPa9zHpcuDulBUQ+bhZ950/NxjPDRdr4IUYatClArhKEoeQ8nUv8qL1D1r5uvH/ma8V+MWR0WGU6ETIkLGelu4vTleYiStbQFe67zPg1hcaIcUmf2Gugnu9QnRM4XaWXm9+XVcFR1XzWBHIRMV9W2feGNV8yzUWqS5MY5GtdvUPuNBrmnKDeL7qq+XbJuqFJgX2p+V6vroCO8T8Vat71CP1JeFjjsKilWPwyO0UWlcxbhxN3U+bF6oiD9YrC1dlLulk6hV/4D4ewGeo5MjXIMmern2aC+xiigmzubo+WFnXkprvklJhbm9xzhs4q0GeQ/duIrz/dQE+XTlCsfNdEnzVBMrl/o3cY1x4ASrpF6a1do+zH4X9XqxXOD087q/zKl67v0f5pqpLcy++/JzVE5+cUpaH+q787rkk14e7/V6LoicoG/tlJfo3sg8+0qvX5WHDQ3YdUDrsBfoO7xcw+SFF7k2ONhPvYS3XMp7Xov0RGx+L2rsx6UfUJzkeqQS4L6s6jAqY9zZUY6FfKYAv7iNuRNcA3o97ONnvLyOlQpPxJyQz+UdvPfF8apSj7pwEUA33HDDDTfccMMNN9xww40lEhcVAczpqX1eXn5lJR87hBqYUtmc6nkts5nN5TCbrPcBrDmZZn1Xic5wlJ+PTLJWtiQukmygEC3pvXE+PMxGNLcTNchJXTMA7ruxKY58nvv0KN0wOMjsv8SxMCI1nze9mZnp/QeIuDx6H/3YRoaZIUg4tfk8zvNDzLbMKbsSVS17cxOR0kAgAL88E5MTzBacOk0uVV612qs3MFsu8SYkzPbDDg/1YYifXfjAovduRuDfF8k51aLLk6xWJCqXmmVmqaWN/eyNbyB6NTVJtOuzf/ZpBEMcB2tXk8t29AiR56999XYAQF8Pa8avu44+fh6h32eG2ddbO4goz6R5lWPyhikUyL0K1JQtEqUoK5SvlCvg2DCzTUHxW8+rv5WrzEalshy7Q/uYgRqVwlpqjt/zqqa+kCVC39PBMfLUnhd5nBojgTPskf/0Vaoqrl/dhXWrB7Vt8qtGznCbpSizb5u3UE1rx6XX4ZXR30cFz8kJtiWsATl6fggA0NnJLPLe59mGO+64EwDw0Y/9fwCA4fNHEZCp1dlz5O2mMkRf0lmeux88SFXQ//XXX2BbNtEv6A03UDXsxje9H0spWtrI83By7spqWp7RUIFKia898nK69Mpr8dxzewAAJc2nGSHWFj5tNRFnn2vsYr+YGuf1Lcq7KScvO488qzxSRvSgXumysZGZ1kgohGl5BhqKU5MHmsdjRrLyctM+Ovs51gLKxra38v3AcioJtjRzou1QJj0RI3ppXp7zSY6DiZEj2LPnUQDAM3vJj52YIuq/rJfI4JQQ0XlD9uRbNjfLNhdNqbNcqDvOSIJjLCqu45R8tzas43nzJuI4laYPp/n8VZWdDwg58gh5qui1toRREYuQ1DSdc6FXnyqTfOLw+HTe8+YxVqkhYAp+mldiDcr2a11hKpdljYGQqp6agpy7ykWuR2JCcfu7uI+4+RlXOD93LeM49God5PUG4NE6oqy7vEdrHJ+4eoZohvV5byu3XVBFRFL3hVrZ1Np5XF6/EELzjMzLr1m8pLAv7Kzl/OIR+g1yFi/K/Jfn5WWZ1Polr4quTIrnIxhhmwLiiRWFsvp0PkPyQTMPaU/V43h52lzkF2dsxrws57mvng2sFKnoPOTKSxf9fsOtvG/lpfSak4pmSxfve8nTnDd8po5vPMtCDnnJffp9nDu7tC6pmQyoEC0VeGBYirDG9zS/7xYhYYHVvI97NAbWius9vpX3/cwU1yahcg5ntL5Kik/XtYxzYI/U1hsaOC83StIjPUHtAvN9LYkzmI6xkqfJxzlzepxr9K1buYbyCdWcFaruKZfRGuO+dlzFe3/uid0AgJOjHHdd3TyOZumVVDSfV3WeAhGieFkdfzqr5wp5EUab+fdIF48hkeD5yUX8CKi6JXlWyqhebuNsiBoNW5t5H4oG2OdX6LysbNDEcYFw1/tuuOGGG2644YYbbrjhhhtLJC4qAphMMrPvKFsmmCmYlQJnOMRsREZZIU+DOIHeADLicASCUsvy2Tb5275lfALOKzt1/gifrquQ0qjSEQVxlTxCLMrK6sWj/F0xyb9PzTIz0NbVDX+QT/T9/cz+hiwbp4zZ236KaE5Gv7n7m58DAPhyzKpE/FZLLz+oCo9z/SqiQU1hHszYFI87pwxwJpty/P9m5ekGHU88znNz8mXWIx9dQ4XITZcza2IpekMALftStlc9+lvm3v9DXi+UHbC8mZs9eHW8//0fAgD0dLDP/NVf/jEAYP3GSwAAO+RZl2hif73ve/QV8waAm26+FgBw3fWvB7CAiK9dw4zYocNUqurpJ79w67Yr6/ZdKnB8zcwQDv7eQ/QP++bXiWL5fexLTVK6ijdIFfPgYZw4Qw5Go9Qcp4RSmMruts3sV09IDfNjH/vvAIBP/OHvAwCOHGcN/d/89V8DAF7aS7Tn4CEia9k5jo2XDjPTNqK+XqucQ19fNwDgjPiHB08SLT11nllIhPp0hPUeWsb1OCfO1J3f+ioAYN8+7js5R9Sxs5vozDtvvY3nqUzuY2pmCqdPcR9HjhIlzJa47y9/41EAQHsHs3ApISl33cmM4oPfI7q/1BBAk+8zcKRidCGBb4YAVGucp2wua+/qwdYd9Ec9eZRZzFyWfa6gzLHDHxIHq7+PnIqmZlZULIB1QvGcyU1olm4KNUHcAT8befml2/HgLvJr59LKumpftVpRrzyAvFRwUeQ2I7on+b1EET7wvnexTUIX+/qohlrM8VjG5LWZTZLvUpk+huwox+Pa5cyU33QzvRSbWvnbA0fI6T19kpzXYwdf1nFKuVQzbUQ+gW3t7M/t8v3zBomajJ/neSwKXZqZnnPGsV9IU0jn1izQvCbBLdSoXK5HUZdmCPny1ivGesSbLOnv2YqqHowzBw9C/noubM0QQVPY1OIhoL+HtM1EhMhe7yCrQILqu02NvI+Yymy1ymscVF+AZ+FuXBDK5tc1dMaH8V7tePSp8QwT4hmaIvKc0LmC+QMKyXDGvtAU4yr5wnHIZg0BKY6GdVwBvYZ0WhJSvM1prTSeFZrd2Ko28otprZ1qOj7zO/aZWK8p5qLmcGkXbg8698Y3lJfgzEnO4dFmKSPaOVyCkRKyF9EaIyiEz7xJy6q+C6uKZn2Q88hLZyfh1YnuXsZ1SDbFSo6paSJ1jW28lnH1j9ExVRd5TU2W+7zi3W8CAFTWEnXz+rn2GHr8QQDAtg1E6Vp7uB5aObgGu7/6twCAzgbOnesH+dvWGNfSHh/bXVSlYOEM51afeXfq7wFVVbyYZ9va5S8YOMo+crKfbWmcEvqdTqNL3sBl8LjOxbjN4SC/e9Ma8iaDUrWuymAzb6rB8gutZOTlrbV9QyvHfEJaIEVVekD+zV4vkJ/gGiczyfVJ1sdxNNZA5PPt/RyzjWFer2ah+9WcoNALhLuGd8MNN9xwww033HDDDTfcWCJxURFAr7JgkTCfXqenmBktKdVUVSbYI/XPTIYoQW+w2cnq+MSfKypz0djEbfl9fOL1gU/XE+PMWARCfDJuaNDTtGWHpEQajzGTUCiIl6hMcEDKQ+l8wfGRufwy1v4qIegggasG+AT/6EPMXNTSRBi8vnrfQ+MttjUxYzDYyzr+gS5mopLKlvsjrOst5os4cpTKd4cPEWHxSMFqcpQ7b25gFnjlWmYfJKjneCsa1dGQP8ugVXXla3ovMLNOJfRCCqGWNwtd4O9LOXdWgwIAACAASURBVO68izyzlYM8k5fukNdLgZmYE8eYPZucZt8eGSans6t9HOdO3wUA+Mwn6QP4cx/6NQDAhi1UEu1Rxt93gcRlQCq7Hd0cC6ks++OsEPVlXezHI+dYk9/UIs8bXwRXXErF0IZG9qtqSZy/M+QhHjlCP6C3veM9AIDbbiOaZqjLeqlj3nDDDdzHWSJ9hw6z3xaUkasIeqgU2CavP4xN68mra1MmrKuf7To7w3HUt6xHR2i5a/FKSuzszz9LpPPp54jKbd/OWvxomJyP9m551oU4J3z+H4hazkzlcPYs27WsnyhkUwPbMDlOROfwGFHV+aw4x1KLnB2rV99dKlFDPYrgNSRQ1zUvhUGfJppCgZn8YqmAbZcSsT43zD7l8Zoipal3akYR1JeIM6t72ZX0Ux0ZZRa0KI5SWWUMUSGCxv2pqHVloSVXX3UlmqTaWlT7GhvJszBFx6kZZoLPnRkCACzr4By8rkUIhVQNSwVx0ec5npNDROsysxxTJXk+dbVwHA3NpBEss5+GQ3EdrxQdjTIjbsngSvldTbDfz86yTQF5Om0YZB/dsZFzvVceUOeneI5DVR7T5CTRx3Mj06hptvZJpbEmdKhcNO5fvTrlwh1g6XIBF7Li6utCLqyKJleSMqxQlJLBqagt/EafVBb5TjoREs9QG/WEhMJJFrQpIcTCVGjFVzM0eME/cOF62domIkGEMkyBVG2xcSFkLyOvvXF5RAYCpkDK/lnUcRV1nHaUfqHiXvFIUQNqQqsLWlhUNTH4pU5Y0wJkSNzxko1VcdAaOzhHN2hc2sr00GGuf+yEevSPqnkTVqqoShHR/mbKvV5TXM1wfKRTRHcKM1x/GSq0FCOsSoCA1DErWihGNGcGVPEStZWhNDS6g2UMa2XY1Mx5a254CABQzHA9f/4kdQKyGidtjVyPlKQPMCAefWIjq4pmcuw7mXFW4YTGqFnQs4EK4l3iXYdjUQxcyiqprf2sXqqUxJsWih3SWB06Rl9fVMQDlYaBF9Is0FiZ0rgc2Mq5efwqrqGij0hrZJbzebm9CQf2UrcgEuW9YUZj9/AyovQf1LrLFt8R+d2WNH7KWXMwkFqvuMYeqwoQVzdc4zXJWOVBPoeJY6z0gyoWx73c19Yoz91AKyu1Aho45Zz2Cfb9C4WLALrhhhtuuOGGG2644YYbbiyRuKgIoGUbDZ0rFfn8WVCNtt+yRspWStwNyVQSiQSfkv160u/sYvZhapJP6JNjzMhOTjHDFJAiltXCNuqpOykPk3icT/woMwsUCqh+Xgqc+Ryfxr3+GnrayUXZ9LqtABYyYaav8+gTzwEA9j9HflRE/lWOb45qfQt6ok9E+eTfosxISPX/Jw8yc3LiNHlTV125DddcymzJ5hWst05l5H8i/xx/mFlhy9c6ulb6hyUIPYvkQCuV137v8TrJBseraDFP0I1Xx/J+Zu/XrKAS39kzRIpqZWaPWpvkBVYR99TP+vnWnha09/GiTZxjBv/xXeTTHT5CTtvgCqJ0fX3MjIWlEOcQYhWWJfYL3r3mMvbbK67g6wPf+z6ABQ5rJjWHjZvZv9pa5J0UZFsOHSKS19ZOVO7mW24BAKxes7pun4Uyx8/2nVQw3f0YfQ6feZLIoddUadV5muX1tm37Vuy4lO0aH+VxB+VJ1NpChHzZMiKfp09xXBw+TPXcvh5uIx7jOWxvZyceOs2/r5C337nz59VG7jse5zU4ceoEquKsFCs87tysXR8OGPOSi2bZppYmlwELvEJVsmqvNuHwDwXxhoo66YVCxemnfcs4NjJJZoxnp3nd06oEiUjKOCglwBXywxw6xSzoM08QIY8liBCGlg0AALxSTPYLrfPD/JX8uGwHPabg+OAF1G62tyhk92SDEMETTwEAjjz2BA9L6nE+bduuf1m/N75YLCKkUCnzfNGDpPz9IlEhf7rvNTfx+FYNcOwVikQKz48y+35qiOh5TGPxiu0c9zddQd5LRtsdERo93snjf2YfM81nTo8ir3k9EpM3nJT2kOOYsZmj6qlHT5a0Guiic2GdPBLluZueF2dVegI1B42rOQi5IcuO/59QKuetbq45IbQS/0RhkpNyJsvX3mb2p6pzpaSBsAihrQGo6mafF/ctYv5iDkXO+4otAAVxlfoGiR4kU0LKtPYJmrefUCDzEyxXffoe32fyc+jpYXWJV2qymTzHU1Ho23SK76s+Hk9BqqgVoXSjY5wDxsUXK+t8VWvmYag1oaEiOpZAwOd4Wdb03Zqp5poisVDvqriNRVVReRYvipZQrBzUnKoqPJ/OXXKEHLPTL3At22G+p6rOGFy+DGfPsf8P9PLemD1OTne8lfNxs9S7V+28AgBQVq3Y2BmuJcIDRPQq6l/hE+Q+P/3dOwAAJakc96ZSaq189Co1dK/aAgDIWAVRle2bzQgJnGJ1SWicr50xjtmc+mzWUPuI+oqHv98oWDw0zH1P6Tmg2iR12tks1oCf7ZugL+3jz/O4m1o5fx/ez/frNrACCQ2cj0Ma67E412uVnLiA6n9Bf0Dv2Yb9h7nO6V/FCqZgpYjMKM9RRfNApIWI+Rad84gp/+qMGZc28CofgPq4qGt5n4cHOi+hlmLeCOcqMdDNyqPSyfYOCmV4g7VXGPbyH7MqexGvEmnJ0XvU2ZpkQBo06wUZsnrK7BCzE3yAam7l560dPJHnR/hwmpVlRUtjBMtUQhbSKlb+9fjil9hhD77AUruNG7koLpVadDw8oKwMV8MqffXpeBNxLnTKOrhHn6DRZLXGSXLDbArdbTwAk5Btk3Rudx8Xx6k093HqCBe9my/noJOyrFO28irBYzOgtfKQRV+olQEh0U7ZqPO6eFtuONHawBO6Yhmv2x3f5kTU3sz+NZJRuYNujIMrOQZ62rux6ZJ3AAD+/gBLGau6SB2DvDjfvYuljh/8ABfQHXz2x5xI2A2NujHP8v3BfVwwr1zJL5psc3cPSxiCAY6RZ/a+iAMHWDKR0MNRVeWVmSw70ic/9VkAwE23vLXueEtKbuTmeTxdLSKAa+JtkbQxZHyc1ZiPiTidnJ6A18vjCwY5dofO64FApUnLlzEB09zMRUZWAgH3f58P1bMplgn19fOmVtCksPdFHtPUNI/l1rdRwCOfkyiCJwSvh397YS+/a6Il737nOwGwDBsAHn7oaQDAiROTOp6l/QBoayd7ULDFlEnFp9OyXdBssWrVGgyfo11JdgNLf1IyeJ+UzcPkDPvt8SGWenYM8P2aTVwwXHUpb6xHj/Hvw7LXae/k/ByQebvfMXtXGR28yKkEOZdn3/HIF8j6XIPkvltVKjpWsgc52UWo3DKRUImQbubBkN1nVFKlBGVVN+DQXBjBJPfR3L0WwIKVRLPmBE+VbZrJ8FytWc/zM51WOVZJScsGJmEmdT+J6+a2YoCf9/fyftnTw/fL+5fjsSd5TxnSQ2LZU19m69ODa0UJnIVF8dLt3/YAAcf4ne8blLjNnmfSueYzKxE9NAPOE56NC3s4MbEd02zxSsTEq5K7mh5s5nTTzekBKhZiP3U0X6y80Rr7CqEXp0DekjASoVCFtGMe7ZNQTVTJgJQalS5yC6mC2q45u1q1sla9mh1E0cSWvJiT5UJQAkw5LbSzWnjnbKmp35RNoMXOk/U/HYNP9yqvxpUXlpiwjJPKWz0LD4UmsmfWGGUrr23kvcZbtnbrgXAJCx6dPsMkU1rnLqR++vw+lmGeHWMfj7ZJ6MevNXqwDdv6OAeWVeoZ0v27QUIsFc1njzzyEADg137/TwAAtZ2XAgBmwvz9vNYMrR2yblDfPm19SQ9huTTvuZ5AB6Ykhpg5r3uEaCwTQ6TSjDzPhHOD5uEW2anM6rmhIupXVXmwpjIHVno/x1m2wGOIXa/S5Cl+8Z6po9hY4747OrkOiSTYryameF/b8zjN57NKzO248hoeh+b5sh6yvRFu0yxgqhp/eQ3UvhW8P3j0/bmJMziV55ooLJuu7f181uhQGW5RCddQ2GzuVLb+r8zjS3eWd8MNN9xwww033HDDDTfcWGJxURFAK7Mpi9AckVl2SJmo5AyzDuEIs6odXcxoDg8PYb5iZRj8zfQ0oetYRLLCKinw6pm2e4BZ4WSOmc+SyKIFyQ6X9eRfElQ2I6PgclESxxEzYu3F3Byf8G//R0rQ9nezJO3gPpa5NYqwPXSaJTuTkxM6YrYlLBi9XabByWmZaM8yKx1uZpbs6AlmMdrbuP0aKk7ZbFEk6pikYrNCkPyq7+hdJjhImUJTo1YViPOkX+AlcOSplWiDEo0OElirLgjI+F3k70eO5SrVTRfYZ5YN8kRbCYzXx+vW3MzrVfUxg/Ptu5/Dio1EOC7bSrn8t73zlwAATe0kPJ88+ocAgIlZShu3zXLcvPQiS5A3ruO2jh3ivg8dZDbv5EmiAAEvS0iPvswSg2gzO0u+lMPUNNGW1SsGAAAllfKEhFp3tDIbl56RoX0X+2hAggPxmMpOVfacTHHcFZVlLQqVm5DpdVFiAf3L+hFRCfRzJymoMTpGVK6theOlXwi82Vds307LkxMniXA+9xyl9wvKXG/exBK5VSt4ftpbOa/cfy/L+bZuZWlFuVpDVkbDVWWPLYMWUDmhGWf/9E+/HQAwJHTq8GHuc6mFlUxaRYZVORSU2c/KQLek+uKwyjhrNaC3z+w82CdOHeX1tlLOXJZz8PB5zo+jEyzdHawwI3r0DOfwf/4ixZIuvYZ9qaubQkuh5cyK+lTW43FmPY+j3FUU0lAUOhJX+c3Bl2ixcveddwMApse577gQwSuuZOnR6nXsWw2NbLPfbyiQIRkqfVV54LLYIDrXCc1RxtjKRH2yZSmVrIyU28QaIoVxmfvOS4ggV+LrEZVppSfZF5tEj1irsuwNMrzu6+xEv0qEvn4/s9Pn50zMSNdRZac1XS8TLgtHJJW/FMNB2TQnCJZKJ82uSmiBqoqcElDUnH9Xqlaq6fzplZt25hsTZoGQLZ/Gk5V8zszzfVOD2SDUl4DW47X8v1X1qHkoaZsegx9V3j56TnYhMkrPay1UrZiNgr1qQ2a4rp1auWe56kF2hv2oJGOpeUP+7Hi0LasQsG0YoukU1ts+7PgcvxlJ6gu9+7/svWeYHOd5JfpW6NzT3ZNngMFgkCMDmMQoUiQlUZJFK1iWLMn2Ont3Ha7v3rg3yHvtXa+ee1f2Yz++9spJVrBkOUgyJVOUTBJMAAiQAIgMDIDJOXTO3VX3xzlvDbpJWrp/8OzjrvcHmz2orvqq6vu++uqc95xX2e8bzYq89FBl/pQRVNJQ/x7RlUznmsDoZYuWmY5OI5LSGubUdBEbFDXrhtc/Yhoy0oULGuCcv1LFdc1z7m9wrX1wF4zhKixLU6ccgD6LUieznKNUZZFr2iYNguo5MG5Hv48Mu/f+yq/JMuUCp18A0zdBszmVW/VWMEZTfIcY2oT1WIjrjx5mpuzrwRwcY0p/JoLf9/Xj361Jlgvagjn4XctDUuO1qfdgTt3ejbXRgV60dymPefnks/g8/e2/FRGRB+5HGYtdDyI7b3gb5TOafaHPKTKCWrJn+STWb6tnj0hPkSWSupFduIkDyOSY1Ww9TS33vKF+gJmXzwD64Ycffvjhhx9++OGHH350SNxUBlCLzPJlXQIBLRrMDYg+xuJazBHIVD5T9soyBGhcEY8DoSxR/NkkgjbcD5TZy0lnwfRIHG/6+bVsSxtUE1Iu4Q/BEI1ZYmjrjl23Soo55MdeeU1ERBauQ2+Xiqu6Gu0s0gI8mwZyoeBVgkiuolvDw9BgFSpoS3oBbTiwFwyQrXqSoCVCzYaabAwm0D5lLNNpsI3f+8PfFRGRBx77sIiIPPboIzg4wa6pWSLeXRSGk/HTNHi1c1dHdtPeQBf1U1G6H5Q5b7Z9dlJUGuiXx58FMrV9D+7lzAwQ0o986BEREZmdRv58IgA2KxSZkUgY9+in/tX/hJ2FIM7/g9+FGcyuXWA6UknoPw+/gCLys9dfEBGRehn97MtfQNHrM2fRhgdo/nLqJBiwkSEwanWLrKQZ8Ew78iUt14CB1j+INoRYNXjq+usiIuI2gahdmQEbeXAfUK54Er1Ey0eUqGXZMgTGM8fSLjr1OCLy/Etob5l6uyvX0O7KRez7Jz71M3JjfO8ZaAtGt0IbODY2JiIi16/TfroII57LLE6/7wAYpGoDYyZXxJhZXVuWBg05QlaC1wLj42tf/XsREdm5A3rLDNn6nTuAat5660HpxLjR8B7f1QwF30u8hyqjCpPtcg1XAkTte3rRT3fvwzWco4345UtkqkPaN7D9FWr+XnoFjG/Twd8XF/C7oy9DC5rL4NiHbgXC6lrUrTg1T3Okeq5slkXoyXZYATxzHnnPEyIikkxgHAeJJGspH9VFLafRx5pExlMp6vlUM8iCw5GuoEQsHlsNQjzBNbZp0jTMYAkjNUNLsNBwnKZIARMz7+RlamC/f4z7wX5vOQh0/FM/9n4RERns6ZV77trD88GWz7wyLiIiR87gmlZZ8Nm7Vpzso/EB6dRQVkoZJJdzY5XslmUyc4nZAmEWvK436p4Wp15T/VyrUYyawhiepg3H9Exj2E+Vo1rI4u+qzfYyetqoRVNcaZIBc8kOuKam+5AZ5Fidmse6I1tC/6pQx214xk6thk763TLtG44o4vB4QWmISQa5oj4J3EWdRb+VydM1kGr/PH7CK1OFYyhTWuN+XWX+IlxVhDnOxNgw1mlbsdjKsioF6Go5D5bl6mCjo29+Hf4V9+2AyeCeA8hwuHIZmRDD1ETHS8iAeDmDNW93wJALYaxB+wzMQ5t7sJ5N07Slh+PhwD5kIpgs2Rbg/bhwFsxWLIo5dmV+UkRELpGJ7mK5nKsT+PsTP/+L2C5TkRWaBE3Q2K2Rw5zZ5Hp+hZrsxx/DPK6GSFYY81xxBpk74Rz2U2PphUoJ4/bsaZzDcB/GW48F1q9arUlODRiPI0NlM5lLx2CGR5LGKxH0zeHtOP/1S1iXrCDhRYww+mEkjusWSYBltZhNYrJfTp7BNc9OzkmU9d2SNOOr0cDJ5rFDLM2iDKDplfX556MT1+h++OGHH3744Ycffvjhhx8dGTe3ELznOQ1Eplym/oBvqx7KSkRqPQ3EPhaLa91UqdKiuJCnkxMR2SAprfQqkNlEH1CEMBk0gw476iI6OIxc2nQJSHCFhRO3joGpyNQXsL0bkTrRpyARL0v1HmRJ1GlnqB9oSaUIliPHnOZkCoh3mqhxhNbzy0FFB/Hmv0tt6xfAgCwvpiVktOapL8/imKdOAC0/Ow43J3V1U0TEpdvkjl1AgI+/BpfDW+9GMeYR5jYrBqYuYwSCxZYNdlDhyAY/K0p8tgFo6jiq+sO4dF5cvAwdZzd1ewsr+D68iW6BAserzBou9KGDYLH23n6nfPt76IuTS9CaBgO4gn1x9KNKEUiRWlitLrGwbQ36vckZoFoXJ2EZHEiyQOknUbzdNsHKXb6E3xXquOlrazlPN5XJ0P2LjndNQmtPPYM2ffA9HxcRkd7NOL/sxadERGRiAm3rIqqlfX1kGONp6xYw89kM2nplBmzFL/38p6XRALs2M40xt7oCpO3wUZS/6OnjeTPUWe/SFdguh6No4wCZzXe8g064FTAn4+fBesxNYGxELGgkG2LK+ipthNVCnS6g6lR8+iwQw3wenf21U/itunF9/k+ks6KtTIDatDepB/IkO6LFsllOJ+CISXopFsf93HcLSoYsLUHPPTGH/nvwnkewD843ly8cERGRzALuRRfZuiRLLaxcR38/PI/9HNz6k9i+SItzy5UaRRLqrGlGMA7rZKgTtC5PkWlRtq4nBbS7twefmTT2ef4C2OUgJ84kz0mdrJdXgBKvp7MST4B+6+3F2FBratXAmJw4Q2Q+w3yu9NFGXPVVS1M45vGXofmd5vWq8Zm2uIqxpTfh13/pU15Jjf37gfR3U5O5fxRj5Y/+HtrAANmdYhUI+PJ8Z2pcRTYcO111k+T1DGjmEtcBFbJ8dWqgTNuWKPuRE9Y1ToXboN8Zqg1sK2zezkY12TdqPMbqKu7LNpa+UexemUU4kLJYvGfRy33xf9gUWc2p/pC1J8hie2UinLb96Jg3W9tqqVbSNqTG+bPOdZZnpEoNtWoeHV5Tm469bhvTZ0trgXL9nUnWNR5XpoOn2HTEUHtyvZZO67W01CJfKU51M5XOjckJzJlz4/AD+OrXvyAiIkObsIYd66f+2MWzeRPLyEjNlO33Pi4iIo994tMiIrJ8Gs/p41/5UxERcUIsB7EVawTNLmvw3o9G0UcW1rEeyY6DdbRZZiVPB9miHecnNYIrUzLNkhFaVF6aGFcRujRv68c8nOrGOLSSmHO378HaOug+KiIi/9uv/hIbhb6/qZtl5viiUSphjs4yY8mIlMXwsoXo1Eun0QofVNoLg8xQKc0g06g7RkYwiGPVF5DJMj6F8dd3KzwfosySWqEL+mQaz5D6nQdkKEOXzx5sY/CamHT71NQA09JyEK1a/bcLnwH0ww8//PDDDz/88MMPP/zokLipDGC1grdYRZRs1R8owkaI7ZZbUQdpZhpvwKViThzST47CwgI4yySqU2Mx+XCS6I5NhyqmwZcKfFuvAE1dWQfbEKL7T1cX0IZcHihqOArEN5teljr1IqNbgZouLuG3Meah1zQ/nWixapkUmRrdykKRF1GvzAwBTak28P6dZ25xzdWCpmjr0nJZanWgCIcOQf81w9or03QMDUSAcFh0a1zLA7H4xtf+jPvSGjk4z8mrYE0O3HaHiIgMbwYDpQhwsgcISjqXlsEB5Bv39lEfpcCiFpcXfifap0CcMr2UcnZUXL2MvjIC6ZxYATIHzNU+euxVERFJJsBAX574RxERiQfvkCqRr/VZ6MxGt2Kb/bfgQp44DRb3/Hl8mrTTSpIBO3UCxVujdBYN8z585et/LSIi/+bfoM7g5Ay0rFqMvlIsSDiKvlwuoZ0WUe5SETnwm0bQ/65ewZhcW4Nb4hPv/zUREXnx8OdFRMQ2MHaLOfyuEADqdZb62AwLHHd3AWn7znf+Tvr7NomIyJFXoC9U9HjbDtT1+8xn/k8REfnt3wEL+emf/GkREfnYJ5Dnv3U7+v4zzwHNnFvEGP6ln/ukiIh87WsYdw+N4qb0JfF5dWpZJq6BHYyzrtv6OpB2Rapdonl1VtR2KXYrFyhG7tBor6G8oXVCRGO4nq6hTEZTRPSaKaoPNH9kK7QSd90FRDRAZ7NLR1D3sjaP+XZklUhqCve7Ps/JvQZWrmsMfTbFPjexDKY7FAqKQ71ducgMEmrHh4fBLCT7oUfp7kK77YA6snEQqdYviTH54L2P8EKQ0eGTtEm3OdfBuUbDAeminjBKx2ohm7G4Agbv1deACKf6obtzyfqMDaEt16+D8Xv1NBjAs3Tx1dq2Wt+zzLH1whFobB579GG5hTVAjSTG2O7b8bzo2gKd+u99DXpaQ7TAvUZTOjbanSmpr6l6TBNrbfFeaw1UV26o96c6QnXjtlV3pgchQs9xo6SVh9wrC+kqOxzmfrUQuu5FnQQ3vEibTdVz4t+UlcsUlaVXsR8/VTPYVtuzXZ/nfaXOKMzrsn3bVrl6BeuRGufFqrpIc00XZr3NEtdfqp01uS/b1Owv/C5XxTWNxlhzLahaSNXkKtPoeufbJJvT0GwE7/zwYbZlL3SuAlDE4nWOxzEnVZi5s2UT1qZF/vvMMp73Wn9x24E98sjDj4mIiEHfjOjgmIiI7H3o3SIicue98BwQOi231xhNMpvixFFot6NlPHPv4Pr59XnMTUuLyCIKM5Nu9cp5mbuCZ3lUteXUzYVY4zJCFrHMzLktm/CsV71q3UFb3vk+uHp//ymsYwrMBGnSqbTK6c9mNmI8aEmIbPayamYNOomSRytoRYEcs0xYY7iH/iP2Bcz3zTlq+0iqZui4emkNbOv2Eay7N++Alr2vd7MMb2E9cGYqRpnxuJLDumqwW9forX3c8BlAP/zwww8//PDDDz/88MMPP0RuMgOoNfd6+oE6OA3V3eCttkpErVCA9m/8ElClcNwVm/nAJWp0ImQ96lpbkI483T1AW8NRdbDCsVfmwQpoHcB4F10/qScJ2nhLDwfovEaUtpbNyPAg9Et2DexaL11+HFJhZgCo1NIiWI8YUZVSHcjF2VNgfd73ONzZLtC1znaAJjfoq5krADFQzUgs1iXTk0Bg5maBgjsZMIIO0fMQRXvry6g9sn8/mL3mMpiNlRrZViI8c6ybMj2Bf1f0WF1V+wawXcOpS7GE+6HXfCsRia3bwVDt3gfX0v4+5IsTzPPqBkoHMoCpJK7jAp3WYnShbTJ/vK+HG9roS4sz6CN7dkdk0zYwIa+dfFpERNwwNFIDo+g33//G/y0iIu95P/LYs3n8VmvS7dwO7dtgH5iEv/9H6PMuXwVr/Nu/g5z77aPIhx8ZATtgWq6kWNMmTfdc1bvU6VSbXcGxPvCRQzgG2cnsHKyturvBJCeTQPH+9c/8mIiIXLsO1qJGp8MM2e7jr0B7tLCwIKYACivwfBbIsFe84ja0rGUkWDfulj1341KGgRjecQvG6T13I6f+7/4W2oTnn8c5bQKBL26Tef2mKbZNbWaICBuR5VyR5092f4g1SVepMe7UaK8D2B5B6ttsIrQVap/K1aoU+f8V1l5cnMdcdm0CTJ2dR9/IXAJC2l/GQXrCYILtEcxlYaKdXdughzr0KPrk0RNwk3WrrEdF5iKTzkoypnXUMF8OjaD/9ieJ4kY5T6oLshZKdVS7xK+swxZyWGuQzEWNta8M9vOBXoytenfd+63hKiODZ9bIKFj1sxcxPq9eRHbGj30QCPtrx1Dr6uSpyzw/aGUK5VaWXuufKXuylsH1/f0/+kv5k899RkREIjyP1TTG8/oarv1H34sx9PVnTvPE9USlY2OjhBaZQGbHKz0zMAAAIABJREFUOMpSNVvdJIX307XMjX7Ce2FpXS4uRLROajiMfqKaHWXEKnT7a6hTLNNuIsw2Uh2V6WkHyfaJscGE6djkg7hCjWiez3PP5dM7zTYdotL7pmpUqSsyVTvHcch6iJnVtKRXwUTYdFvUbVWjVGSNOMNbHEjLMZU53XAcxXZB1pmNcC1huHr+2iYRy+Qzlu20WWO2QV2h1nJVLaTRSgx2ZGhtvj6ybjFmKPUxEeDKKq7O6Bh0fBPzmIMmpudksYnfbg5izuumq/PQ/ViX2Fk8I8uGMmHYp8X/mSSzF4ti/i6bZPMy2O877r9HRER23oa56fDLcDkf6h2QYg7zVzCKNYNJ1jcVQ3vj1AQO8GGfZLZIlUzmd7/9TbSB+rwYGcOKssaswZcto+900cG51rAkTTfTq6zNmeEDZjSCY+9TDTe9F2oc++s1OqwfxLVcCWK9kmDW4gCfPXsGsYbash++Hf90FJkf9ZUV6aWXh2aahNjXD+0dExGRNRfHWF7FefUkfrg6rj4D6Icffvjhhx9++OGHH3740SFxUxnAaBxv7SGbya9Eg6pVoEehIJpzgc57Dt/KTcP00tCdJnN1iRypVqNRJypF4ZO6Wq6uUotEC6xu5h/bNt6YHSIIynbZLt2ABP9eyKx4TptNRQXo6hntwnkkeqG5SgWBNgR7gVCXq2iEsh4vvATmb5AMoQJxqg+IR3G+kSh+b4kjDQftn1+jdiqCnPgga9sU6Ri3YxdQ8goRkj4Huph8CGxdsg/MXiaDf9d8eZvXS4G5laVZtq0hDhE0zak//xrQhdeOgb2pEllM9eD8d7OO4V7Wf9n54Qel06LAuk1lOsOlyOZuH6MosAmE99wbYGzDIWAw0UhUpohKvX4KrFoxDQb84nEgQSvrQI6uXoET1tI4GL3pWfSvPZvAOuzbBkTtI3eBCXxjHPt9/A58T+dQA+zaRTBtZtORiI3+kBjFvbRDOg4w3i6eBwL4q4f/tYiIWCHWpRwkm7gV/e/Q7agH+PwRMAr9PUDDxnZAgzRMLe8/PYMahpNX5ySubqccm+Os/1Ot4VolyCqWiSLncujb//m3fhv7mMOxaMwpE5O4LqfOQet43wNo28AQzv+Vl1E/LWAEJMh6OsUCMgTiSYzZKtE+ReorHPvGzZ0y/5uLdtRcmQp16+sfQP/RrIFqBX/PFYueK/L6KrQQ2WXMbUYd97mXGsHEKPrxoQfA5PaOgSkzejDfhvn4qJB1m5/B/f7QR8A6VwpkAOto27XpRbn7LqCvCWZ89AxDl50K6LOIDARr7RlEgtUBskzt0loebc9T36KOcGMpoNlKnuj1MExTLOo1ytTAf+mvvopj90Hr8cDD7xIRkQdvR/bG66egS3n5OJDvLOt75qnxMy06KKrOQ2FcVzVe+MP1yTn51rego5Qa5oDdu1ATU5mpB+9ELcannj/N8+K+fmC113+5oX3cY6XIBKozZ51Wr8oouR5DZno6J2VnXW6r5JrWB3TJ+AV0ocJ9qdavwfWKapFD3E7ZFB137o16P2XySF6bdAZM87lRpL7b66SqhXubT2WW23WNymaGyErmsjmvD+qubb12bJ/Rtg/d0OK4szx2Ff2OH2KTcY9G0Ocd1hVsqIbQNMTR+ozs05atdZQ5V5P9bjTUsVg/O5fmjsewRijkkA0z2o/5a5pZGYleMGgf/RRcvz//h8g+Msyw7NuBmoFa11vdVQucO80KGD513vc4X97jrl5kX2y9E/P7sePI+JgpoJ8OFqkT5fozyGyKl19/Tdwg1gLq3jkUpY48gX33cw0U6cGceZ7Zd9kzWAv0sX5xlvNb08aYyJDVkwRd9/lMsjn2TcOSiRK2KdEGXx1rc6xmEOC46GZ/XB8BCxevo23zR58VEZE91GWri7Xl4HoFM9jP5LNY9xWpqx1+8qelqut1zhNXHGz78pf+QkREYl1ot46TQa7Je1lj8EfkrcNnAP3www8//PDDDz/88MMPPzokbiqcPTQAFiBJndTSEtAGRaJUj1YnkqlOT+Fg0Pu3GN/4B0ZYk4Poan4N/65swfIK2I0MawkK88Jdvp1XVTtIRMk2yN7FgIhUmb8cDm2WMpGMIpHV6irqdISyeLMvrOO71qoJkuHrSeEtPEC0xeb3WBe1fzwnFatEQrguLpGVvuEhed8TeHdX59FnnzuMtlBHs2dsG45BCG7yJBAPAiKSboDZCHVj34Gg2fpJ6i8UJCKUwbnY4YQ41F4FiEoGQmhX9Ab0TUSk0URbLrEezKnjL4mIyM92IAOYJfuQYr2W7gTYi8wq0KGVpTV+At364HuB/n/6yY/La6egBfre0/i8Ith2SzeQ1n3bgWqV0rhHcxehrxO6uz3zt2AOmil8Hx3GPR0bRF9uFMHiBalHPLQDCNyefYfk0pVL2DeRV5doancE95jl0eS+J8D4RWPoT1MTYBN7u3A+SwtwIMzTTff6NbD540SzFldwTiG6wkWjEdm7H2xMjPPC6B706SNHgdoFqDlYXcD3E3RNrJIRuTaNth9+HozJHLW4CWYcXL9OpnQSf2/WwEpWG2UxPLdDjIMInRpT3bhfiQSQ0RLHW5h1vtxOBY/bztvg3F0hO6vX3CDzEWARKMupSCLELI1BXOOdQ5gHg+xcUWr33AIY3n7qOIJ0OLaoB2pQ0xNiFsSuMdyjyjqRZwv3qnsAY89cKEp0APrQvl6wboEU7mNc9XM8H5dMiyOtbovnz0Ez/aV/eEZERAox/Ms7bgOD9uN33iUiIjEiysr6raXz8txhzIeJfmRhHLr/MbYFz4MuZqNcvwxHzmOvwemu0gRjLRbn3QiebTWyqxvZMGT+lHXU+nV1kS9//VsiIvLBh4Hav3oUTrsDdHjetx9ZG8FAZzPbN4bb9kniT+rKBnjO2q1slmFsMIAbTrf8tkHTYp98hmqdQP1ZNE6tKlkVDhmZG5/EMZmpMbwZfTsY3GCDXbJla6usnxzAMZfmOC7obC5tLJxHT7YxgO2snccQklHTNUSsOyIjiTGcD7OdVPOnjKdHXYqyisqstLKMqq/UYwZsXRvq3+2WtjSbrrdvZQW9aD8P7iNoK+vauZUAQ+pEX8H9WqR7fIqat+ICnuu/9R/+ZxERCTAj4u5HHvX0nbZeR/bVLq4hHa61nQb6oc0sC6dKZ2SyUkFRrw/UtC5SE7f4+ryIiMxOQPNscV28e//tIlE6KTfol1HDPqwoxk14C7J9FmaRYTV5BuuRHX3o+/EYnimuBU1gcZo60TKeOTbdQNeoV5/iWrwnFJQ0zyvO9VilTA8TrplKzFDp5pqhmknzmGjbwA4cu58+IyF+NlTPy76tz9L9w9AExgNJqdaYPUMN+6U+PL/O8xibWAexsIgMP/cyniFDZAL/B3nr8BlAP/zwww8//PDDDz/88MOPDombCvupU1SctV1KrJ+jCEK1irdvLd8S1BxucSXMWhphImQDSby5Dw4CVS3QXXFpZZX7xpuwOlPGYnjbDgTUqpK5vuo0yJxg1WkE6DK6mF6UBJkwm4hzkHWmXKIlYqFtynrELKATmSLQiXUXiMHmbrRVtQPqGRbVGj9kN+s1omhVR46cRn7066/D4W7dYzZxflevgQV68IFHRERk70EgvbPjYJFWM0BTqtM4Zl8fEIHuJFAKBf+y1FWlF4CcDO1MeI5lirbZisATFVFHQAKBHoqMu9uZ8cfMlU8lcK+vXsb9eukV6D+vXYEL6wBz7rdEoBH84mc/LyUTf/vdz/wnERF55QhcPM+9As3a4ADQrfUS62eW0f/6ktT2hdBvplbAeM2t4nt/L5CpDGtoBhwgTN0m2ja0bUR6yAhUFnH/l5eALBl0+IqQrchn0QfuuAtOXa4FRO7lV9A/u5K4+wkyzvk8xlMijvMc24zrki6s8DiT8jdfAaNpEJUMJ4DWRTnOhoaAds1Mo08ffgmsRo2I4r7duC533g59U/wa63QmwPo4Dq7Xt74J3WE4TLeuUNTTU5VKuCbZLFhCrb+1vg6k0KabneNo/arORI+ttu+xCOasyCZkdyg6HCVKahi4l6PD/RuTDZkKk5kPFbJmATJZa5BcS5MZIFW91Fo/Vk0K+XxoBnFPCln09wYh2Vg3+vQ7H98lff3ox7Eo5mqLjm3ttcIsUe0S/nDsNSDIoTj69W/8ImpQBmPokxG6ntZY9/LcGTLpfI50JfpkcBMY7gHqDhMJtCGh4/USsjZePgoX0GsTGA8Do3B+y7POYYBsj/Z7IVviKB1NBFldDh3XkeUsnkErBfzx8kUwmdEYngt9Ww/wumDsrfG50smhY1yZJZd9wpOMtTnhGnQDNQzjBotJrhFENv7txt9QN6jMl+rX9Ng6zmzWUAvwWdzN+VVpSW1Ts9qQuWnM+0ney/SqaqmwUcBVF089jVZFb/t3fe7X2YYw1yk1F/2vyX5XLJclEsUCLEQXyQp1UV7lz40L0dIGj21Vtk6vEzn5oJJM6s7rOd9q33dvqIDYus8NVrFV+7dRo7Fz+Y/eHqw1Vlcw13zu91Fj94/+998QEZEB1oPOjiNz5yMfgr66957HNuZM0XuJ69hg/7L4fA3m0YvPnoG+OJ0BE717P2oFT1zGWmj3HmRRfOzjPy4iIr/zm/9eRESSXLOvZvFs/nf/4/8qV1Ywz2ZzmNdm3zgiIiI9cWx7y0HMZxPj8EkYZn1vZScjzRDbqK7fOM+UXeE5Yb9dFrZzufZfa1oSd6nlrvL5RY2iEcB5T5Wx/jfpjjvM2qq1OczfE1ex5nm5G+Po4E48M2NFPCvSrETQO8KqA/QUqdTXxOY6ZZUaRZMs6qGH7hcRkaVpZHcND2AtaFCPWMhk5J+Lzh0Bfvjhhx9++OGHH3744YcfHRY3lQFUpCzM2htRvrUHmCa+RsfOSp76mxTeeoc2h2V0CxiFMl2L4kTEUsy3jXcB3dlykM5vV7HTdJr1/3J4M14hKtbXS61PHMhwoUjtn4D5qJElKTYKEu3CMcJhdYojkhSi0x3RvnCU6JSNNuQKPLEAcn8dL0UdCEKEbOThY0CAo9SV1Oh+eOH6uJSZk2wx99+hdlEtRA8eBJpy6ixQlh6yjHaQesIqEJ5MGTndcaIzZg8QO4doWJ0OTFGykFrPC+2h1qSpKB2dK6PqzKU1F313ra2DuG7RGPpGlPchlYRGqK+bzFIO96Xazxowt++QF19krZqLYKr2P4J9Fcg+7NwCxvyl7wKlqgbpwhVBH7YbQLGSDeznyfuAJO3dhD6hxf2u1OmyOwwWwA2flK0cB8ODYOqKZNxHtoJVc0Jow9VVtOHZ518REZFmGf0mvYQ2NXDakuAYWKWb7nXWeht9GLrD7Tvgyvj665a4DvZZLKE/radxTfbfCgbkrvveh/Mk27hpBPURhzdhTA8GiEjn0YYpwZg/fQJjIkm91zsfRB3L5WUgkeFIRIJBzEUXz2H8l6vUi1DDFScKWaS2s6aQttGKOndK2G1PDHUytuy2WmKqa/DQYtdzPmwSd7S5bdBRNoSsBhmLIFlWq5WQ2dBecTt1ahPW5jND1Hjy7339vZLkXOXRh9pwdTzknFzi/HruAtjm7i4wmD0pzJeJOHVPQRxjYRHo7uGjmMPX6bjYR+Z8YFNCtg1g/o9opgedneem0D+Pn4DL7/nzGJeZLNrQrfoprySctp3zLRkYw9M6UVel18cxPQfLZ14Bk6lsTqMJpu/13/zP+O0NOjZcls6dw/X5rI8xZZ+aXp/GP9TYVwxzI1NJXT91etjIE1BnTf1O3Rr/btGxU/etCL40MOHUWGuwXqFGUHVtej8rdemNqq6LdccqWp8QzxEdJjpGlfHT2nw2+4Bm+FB+JzWtUUjnQZcdcmoSjuHNalOiUTo20jW5rg6Res08faq0nKfLCxLwtLit7svqMKn729AMcm6wrA2XUmWkTM3W4DXUa9qmZdRajZ0YYWqdEzHMSd/98n8VEZEh1gMcpO54cCuyFh69H7WIZ7ZsEafeet1M6ryNoGaIUetNx+Ujr2LukTzmxl4uElY5d07PYb3y9HeQ2aNZfRnVJSaxJrly8awkh+Gm3uSQu+V2rH8TzAaJsv7s2NYxERFZOgFddbM/zH2ChctxvT/Pdc1KijrDMlj0sGB+1Md90A2KyeeNS62iS4f0hMtrxhqwDWYTrXO+bw7gM85/H3bw7FiZZbZiDtcrtA8ZKwX6llxfgBv8PXf1SZQZiVWen5bYzrL+9yo1j0tc44UiOEYXNflvFz4D6Icffvjhhx9++OGHH3740SFxUxnA4R1w90t0AxFNloAILMwBSRoaAmNRT+ENu1zB2/rISFKcPN5wU1Egq+EwmTsHvy3Xsa/CJOvEMIt+9+3UWwSx/YtH8Ya/dQRtObgNjEQ3mY9KAzrFCxfhllYspMUpKDuIz7qFT5f4gENHIhKDEnJwzFAUDEUqCf1UULVHLFimSPjefWjLMvVGp8gIVutFsYhaVQqsW0JQeGAI+1xYBLOysA7WIxkHivAIWZNtWhvtGnKiM1ki1HVca4tIUDEDNGJA0epYWGp0Qgq4igpjX8r4ValF8WofUcewUduo8+Kbr35WRET6+8iwVpiz7YK9uvshoFuJOByeRobH8EMnKg/dhT7KWyz1AO75riH0cXsY96N3CIzZ/Dj64eVxMFqP0lXqbAF9o7AVY2A6hnt7Sw/69gf24TjHXiXKtRaRaAGI0b5twJasMh2tyH5vG0ONsq4k2jtx4hsiInKeNTK3bqLmL4EOVw9hTGymW9UU8/jD/WA+SwbYymbElmvX0M5KDe0LMO/eqaMfPfcM3D3HRjFOttDR0TKx7/lzQBj3pVB/8sO/+e9ERGSVblonT2Isf+c70FSOkk3/0MfeL2us/7dnK9jB2QVck5lVjJN77kEdzeo6HIuf//5xtK0zCcA3haeJZHaHqEOiOgZ6WxoeGyVk/BRdNXkx9dMOA5W1iXYGQxg7NVIy9ZK6YOK7Is1uEHN4U9AvEqzXalqW5NU502V9J1KZYR6jwBqF2RIzRHowd0epHYmF2ZYo+uTiGvrqiUtg7Wbm8ay6/148T7YRgbZNw0NZTZd6vGXoNU6ehLb33GWMiQL7q9a4UidV22qdTzXDwmNwPLtK/E84hjHbrBelRsq6XC3rRvKWQS28qfXY3OZbb9cB0dR+2uZcqX/X+6mslGbLgDVtVbe1TxP6W70Lqu2zqAms8Jkbpd4+xEVFNY/+aRuY87WP6O+bzaY0qEmqsH+U2W8Mm+NDVMvfyiJ6TFmbK2iFTKLJ7Wtam5B9I5HCOK1LzVsLVGuasdSqFjbatH/e3/V6KHOqTXBas420PqCruj7VQDabYnLdYVtWy6fKtJUtdNRBVJ1YO3gOL5Cl2sQ1Q/kq5iRb9Xx0xb/3MTgWX1Yn+3rTo8ZdTjze9XfQ30pcMxRX8AydYW3B+yF5k91J/H52jnVfqccOUgO+bQQO6idOQxOdy2JuPX/mvNw5gIykWJxZeZymkhEcW3tdZhlz6joLm8YW0JYwM0Di3PKxbvTxiRTWzVeRFCXvG8G5vUEX8yc+9lNy7CJcSV9+EeuJnjjGVW+DLHWFul+y9VqHVqsZuGQELX461MlmmBVm57AWiRj43XCKrs+FrJh8Bq5qBYEy5oHKFXiExMn213heTT7P0tl/XtPtM4B++OGHH3744Ycffvjhhx8dEjeVAdxyC950E2G8jVeYybq4hvfQTB5IwdgwWCjbBqtQqFREiPI2mfPedPhmmwcqFe8FGpUu4i06kwOaMGJgX+kStn/o/chpnr4yKSIil6aBJMRmAE9YAdbfkTEREdl/y2PS1w9GZX2VebZpOG9OzwOhmJuDU1K9QHQ5zDp+XUBXBqNAplN08ArRJahOtC/VBXRldh4IsevhgyHRNHXHpdOYB6zhf7rorri4BF1JiDWlyiUwnQsz2EEhj2u8hQ5DITqTKpJYzoJ9TOwGE1JvithEzLSXtCOeGm/WAHZubv3EzKSIiPRtAoN2aQJ53KUKkKhkEve6m+jOtTq2W8/My/Yd6Ge5DNAbyyHzYd8uIiKfcv+tiIjcVobL5dFe1HrZl0JtvrsOQBv3C7vHRESk4oI5fO4wmK+Xv/G8iIj8bPeHRETk448cEhGR6dV1eeWfvi4iIk4XILDo4K0iInJq8kURETl7BS6fX/8C+n6UTPxt+6DTG46B2SxmgNbVQ9hPJIa+/Mi9GPtLTeiesosYAwObG5JM4DzrFXxWa+hHuXVsu3cUzra9EXTEqWW4hz31XYy7RBfG7iD1en/4xb/Bfqroh4FetG18HtetSaYl9fJF2RzDMffsgnvY7t0YVwusu9lkPdBTExgfKxwvnYqceWzBD9pOHQS5fb3WkAa1IzVqJmo1fE9ynowRCY4RjQ1wnjHJZIRUO816eB4rwGP2E3luWMoSONzOlYZq2wxlz/CbJvuaw5qscbryBuJEpaktqbBeVo4a2DfOAjGPxvGM+rEnP4y2U99uWMrOGRIwcb65NNDkM6fAIp85OykiIiureDbVWU9VNTRady7AjBFlQzzXT09Urq6U+B5hHdpGVaRerbRco7cLdV10nHaOpvPCJIOkPb0hmgGDq9hQ1tvUh/FG/TxDr/TbOGqq5t5RNo59W/WddeoKY91YK7z73WBgXngWLtI1apEt9leHdYwbriMxrptK68xIoqu6RVa+oXOXnh+f5402LZynswvqmgNt05pxgVCo5ZwMMaXB1CSvfl9Az0v1lMoaqX6Kx+KnOnQqnR0OYxwpsxijX4TWnlPdn3vDfKRzjnoWqObYMlrrGSqxa5md29ezzAS7ZRP60bYwsiYy/chgeJG1Sx95Elk3hrrGNxzPwdZl5luDzppaSjQawr+fuTYpIiK3jmCOfPdBZFUss9jwqUmsUVN0tGzwXg/04ViH7kTW0cnXwbzlszkp04F29Rr+tmWAXhcV3OMXnsMaZ4EuoGYdz+9IGseMkJkucv1bJUtn2Fg/b38IXg2f+FefFBGR96aRXdXf3yf3l54QEZF45IsiIrJ09rCIiHQVqZ1Vrwz2SR1vqqHVDJY8BYxdXF+n6PovNuZ/5JaIdLEmsVPMyMoQWNFmE9sO1sEAbr0LGsjJGay3Jmex5qtyHq+VNc/mraNT1zF++OGHH3744Ycffvjhhx8dFzeVATxyFDWI9m4HquC6QLN6hlj/YjvyWwf60Kz1FbAJlZgtFlG3QhlvySW8sEuE+cRmE0xKtAGGy4hj+1IWqFghB5SiVMIbfZNIW70OFKLCmkpGBUhIo442vLE2IbYNZrK3H+hwsg9v3ffcilpoxV14G3/jNNzcshVsH0/gfHp7ofdKJdAWi+hwgTVHltbALmQLeV4pRbUMMVmPxFTnJQPnlc0CJ1hn/T5FHUIG65twH/t3A13JZHFeMTKGGeYIN+kcGaNeJkBGpOGEpV6jpaPJun9EngPB1vqA2lynTSvYiVFnzbnVFSJOzE3v6aXWqITrXWatsouX4c66bXtCMuusg1kBw1EsAr3pm34Nvwk8LSIi3z2DHPSzFTgV3joI1KoUoubjDBiGyQg+x49h+744GL/FJfSp6Ze+IyIis1KVTXvZR5MYWEtFMH3XqVe6b++7RUTk//330JZuvvMBERH5xy//tYiIjNHpdiEAht2N4/u1CsbX6hT2c/tBuHjlh4FAhp4MStNG/6/XkLdfIVtdzuCajQ6C8Yu4GLvmGti56Gl0vOszaGuFY3q6inG0PIHPJDW86bw6eWK769cXJRfAvLF8hvuoYZ8VB7+ZXUbbio46+iqC35lRc9swQ6P1s049RI3F+yp0TCuVap6zJMsoyfwCUMvMCjIrypzLhlmr9I5D6K89RITVEdCke7JF17mm08oyRNpqhrliSJ2IqJJmjqvzKZ8fZBYMjiHVMOaKGK/PvQDX23gcY3PffmhDg3RvNF1lhnkArS9rNKRYQP89fwGM9pkL+L68hP7sMjvAJZujfdDTidGpVh0mXc2wUJ2eHttQ1pHMt2V6tWvrP2SH3aip1rmTuLJzav9Z1z6j2sumMoBtY8G9oe5fmxbQKw+o39Wxtc15U5+x+RKdnlVbRzdQYf+s0/EzyOdLLV/2dK06LqLUHtWy+K1N1tAwlBmnU6fRyoR5bW+rv+lwe4v1yrTWX73W8LYJ8di6NlBmz6t/qE6dntgPHzonB1lU2FDrX26m2QNNsirWDXUUVYNmaB06Pc9Aa1s851F+/rDZDP8So4tOA13UTd+yF2vaA5/4X0RE5KunoavvvwOMYIBzVMMypEi37RPPfVtERB589L0issESLo3jWX/mLFi4BFnu165jfr+6imerwzlyLUO3zx7MtWs5sFiatbFtFzKcrk7PytYF/FsX+0U+g3XE73wO9ZcTzK4LMVVurYDsir49eC84OYN1c7aBNsXp4FtM4Dp8/ABqCtOGQHr7wVrawYD0hbGm+e9+41dEROT156FHPP89uJcuTCEjqcmhmi/gfMyouqOS6WvgmAET46fKmrJCzW22hB1YgjYulxfEOoAMJbvEa0XX/sEhZMvsHMW6yrSxLpucmeMnrtfbhc8A+uGHH3744Ycffvjhhx9+dEjcVAbw/Q/jLbWyDvRqns6TvcPMnW0AWSgR/RkaA6KQCSQlsIpX/kW++ebTYActojijCbzhx2LYt9sES+DUWXuQSFm5iTdoy6IjTxWIrjT4ys+8XEsRK6ckTboTrizi2CtrYFDUYa1BXaJtYl9DrKnWIBp+6hxYkL5e7GdsC9raPQBt0vAOtC08ALZugG/1J0+dlqUloiENvKsH7dZaKwbR8CATsONd2IcrrOcXALoQsIA+rK6iLXPr1KR1oa0jg3txfWrUNQSbYgXpQEa2Ksi8fkUa2+vqbKB9nYsem9RC1Ipg0oYGN4uISLUBJKrhkgEsoS+kokBTnVJY1laBAIVCuI7z82CsCnVs82cS0GDqAAAgAElEQVTTfyciItl+3NupV7Hde3/9XSIi8l//49+jDe8BYvau3chZry4B1XrPuz6OY1WgRfrW878nIiLLlyvyzk0/JyIih6fQ7vVptNdmnZxHez8qIiKbPwr94MopaFLe/8u/KiIiX3jqL0VEZHsIzPLms+hfUwX0p0uvoA89NgQGcfo1sJJ3fPIXJRoCyuZEwIhYdCstJKDnNeoY62LjepgDl0RE5MBejOGf/Hn04Xjv90RE5OoryJf//T+B/vIXP47x9OfUL87PYM6wAlVZrJS4b1zj6/OTIiLiEvbXCTKpbnv8e6f28BzRTT1/le4oC+ewVmjAY9bwGU8EPGc/q47PLjrLVUuYs0t0Njt/Hc6yp8+D4a5Vcd9vvwUo6P33AJWORDG3KeNh8R45Ht2g85OIpeyhqWwhnYypQ1TGL08UVo+tZQPvewf0tYGgp1rCB1k701Vmg4cmC1cpTcili8gMOX4C+7w+AXQ2R9c3aZClazLjwkC/1hpxoYDWROOh27R/qmXS2mmm6LW3JERdZb2s3sI/KDq1Z98QpvYbdY8k48y+0/C6VyvLJ/Lmun/t5UI9d0tlD1WLyoHksC+spTEH/sWXwbJoDeKuBO5xL5kOYb2vZqku6gugmj1bHQGbHu2N8+BawSRTpvSc49Xma21TgAybbm/z9+VSiee0wci1u3229ybNAnDbMgkqzOwK2DgfnUcc73etLGWDbKwlG+Nd7483/p1WtlGvucl5wm5jPjsp7tqBdcmd26Cfj21BBlAmPSkiIr//X/5IRERmmV1Qp2ezIUEpprFuf+UpOIHfvhvZEMoqrh9BZtEcHcL3PfwgPvewpvAlPL+b1/D5E5/8afxuGfs9fQkM4tIa5scyx8TA6Ca5egrrhqnzyKYY6UPG2vZeZI3EIpg7K1ofM4Lxcmk72vj6ycNoawrvFuPs07fGsAa5dwe2k2Zr/cobdbI6Lnbegsyr+fMnRURkmZlI+iwJsH8FNWuIdWpDnqMtdXr0KtBnFIsFyBIzmtbWlqWL9WWb1FXeNUSndHbhIt2rNWthiBri0aEB+efCZwD98MMPP/zwww8//PDDDz86JIybydZ8+NfBq2WmwZTVG0SvyIoosrljJ5gxgjySbdQktEa9zwJQYq3b1LMNb7hb4nCvrDWAGgRCYDCqVWyXsnfgmF2stUQ2q5IHQhCxsJ9gdYhtUey/IWLU2E7+iTo7g+iBRZSg2dAcebIFoi51OC8FEQwicxbfv2N0bevqBiK+iQzg4GC/2HTiungJOdmXx+F+dOo0dGF51geqNXBeu3eC7dHWj47guqijZ3cCSInm/+foKlav4t8bTH7uHe6WCLUnjaq2v9UtbKP+H/Pxtf4OkbbPffY/dhzE9rM/OuqKiNz5PrAVc3mwWktFaP0MassU5TGE9cnqTamywyeIWgUE92Iti/GR7ML3TT3oy68eQc65m8U+5ma0ZhTQn90HcYxjL4LV2zyGPj4yCsZlbg7M2u6hPfLwfdj4r08eERGRn3rwR0REZN0Ci3bqPBxvx5m/f2YZbbqvDgTxHb/6ThERyc7BmXT/Kxh/L5fxeSCGcTVJV8LdD+B3L3x7Wv72aWgFeLpStTGG/+LPf11ERIb3ol+V80C5Ajbab9Uwf4QF4yfC/ldapRttA2PDEfTx+QX8fXEV510oFmR5lew+nfPSvNaqke0fRDtTKeh5VW9Sozbn6WcPd1QfP3PlkityQz2zZuv/KOrpmuqyhutUazTE1Qnd4wdwvxqcWLM59K21dYwZZciuXEKfun4VzLVLdHc7a+3dw9p7w8NEs8ksprrhEKcZCyIbjIRqraZnoDu9Pgk9YqoPbHIPEeUgKUDV+Bmei6H+nWfkKiPBubKGDJRrV16Twy+8ISIiZy4AIV5bw7FqdTIoijYTzq0ze2PbnY+KiMjQJmSMHHsabNDa0ryeBc6PN0Pdpe2wnrdINQ9UvVj5YZnrVkzY1cZ1UHziU+9zRURyOcyrl2eg6dc6eIUC6+N5rJdq350bdJltDKDq0Hg1g3TSVLfMEjV/JT6Pmw2tzafMNY7R3YN7OzjIOrN8JpuO4zmkr69gTmtQ36QO1FqbL0hXWU8P26aJU32inpceW3V42u8ya9Rku5aEyLzYfHaFqL8rk1F3dV9MMNK1g47N7BqeUf0DYGaSSczpi4sYn9u3a91idVzVOoCu117bbHX7NCzVALPVbVpA/X7qxTMd18f/n4884IqI7NsOJlAGx0RExBmCq/dKCuvGLOvP2Zz3nvvWN+Ts0WdFRCQ/OykiGxkHe7cg82aELqCzDXyO3X6viIj89Cd+XERElpYwnqIhXP/b7sS//9uf+xkREfnAJ8AIVk3M48dfQR1gMV25eBJz6Tp143v60N92bcKxQzG8W6xSG7g+h/kvw8y2irLiUfS70V1Y9/zkj2O9s2MT3j0aXPu2O/2KbKyD80X07S/+1n+Pvy9C2y0O+l2JFQpMPkOCqt/Vdy5vnDFzhftXp3+La/90IyBLYdyHXcN4Lj0+gvO0bc4jWvOTY17fVRocJ7/wu3/1ln3cZwD98MMPP/zwww8//PDDDz86JG6qBnB6nFoG6vCSCbymBolMrS4CuVpaYLOIjlmuKYUy3ra7E3gzD4Th2tOXBGJRd4AgNbuADJgOa0fRlTFXA4PRzAPh7U3uFxGR9QKcEi07xVZS5GKp/sKSPOuyhaN0yGRtkWYFrEE4rsgrWTTVBrJujkUkymJufo1uQGXW6anQtTCXRVuXZnEupjEuNjUn/UT87r3zAyIi8hMf+2UREZldAjr+ha9Bz1W3WL+MSEdqE5go1TgGyV5qrbW+XiCKebIr01NgqsbPn5P9h1B/LtXdw/azfpBqBdy2/Og2hrATw+3HNXjjAvQ+K2Tn7G70jWgSLJxZwxjYNITrn3QceSML9Dc+jv5WuYUIM/We8TgZ2TD6yy3vxveZWfSjnZ/GPQ7XwSD3s37g3U+AMZsYxxi4Mn5GRERu+1GgXXMTecl1IV//jtuxz2IFCNroNugLR4vQQP0ff/VfRETkI3tRJ7BrCIjt638Ml8ShQYzLmRSQxTWe00urYC2Gx4DUBeiE9Y5H9siP/wpQuIdcoFrmdcwDyxX0xfmfYa1EARrck8I+hiJwHC2l0R9XinSlExwrLEAQT70KJunW28jux4gMWyJ9RK6HRqmZdbCNOi9mi7if3/oH1EVSQsQOYvtOizcmwJ6qvrnRQP8OkPnqo/agNwl21qRGrtmsC6dBD63UebFJZjAUwP0a6MN9bQh0KQf2QZdRogPaLN3WTr2OLIhn/+kZEdnQKG8Zxe9274EWNhAMSDc1H2UytzMLQKF37MQ2w1ugT4+o1ipAHR3P2yUT49KZTT8NA2PPZH2qZg37nZsCUn301TNy8Qr6cWYdaLRDVlR1gy6ZiyZ1UTW64+XLuHbbQuj/tidMVxdQsiBac5FZHU2tR2WIGMoSElVWR8u3jw0fy06NQdb93TyM5/35KfUTIItKps2kG7ZJJL9Rfwt+1W39H6f51hys0+aWqayb1sJUlqvCOW59Fc+AbA5zZaVS8xw4tf6YQcdwrdtr0bFWdYh6rPYssDYJraeD0uyHBrVJkQjGa73akDrXPCaP0VSN49vo7PTPulZSOtKhDtagDlZrCzfU+VYdfr05xPCulTKArqfh1DqFzFTSun/KFHZwH08wdSFGxnaBuj5rJ1wka0k8BxPrmLvWiliTnDj6ojSWMR5Uc6zXeW4d83OezNUTH8Ra9d0f+KCIbNRfDDNT4Z57UOfv+cOH0ZY4+tNDd6Pu72waa4eJa1gz/c0X/0wszo1BzoFJPqd7e7BGjXOt6vB5tLSA9j/y5Kfwnc6kS6tY31SvMLPuBOuBP/y4iIiketDGQEBZ5Ru8LrjOLa7DIdxWJo/re5eO0ixXKw7rBLq0Yg5FWseGamvVRd/gmj/IcRjrjkmE4yRI35S5KH4bDWI+iFDrHVJ9K71CAvx8u7ipL4BT03jxGRlk6mMYHWV0J61WeXObnFirtqZUWhKiiUs9g330JpgWVsWidrWBxWFfEA/LArITJJ+jcQnF00NxdOQCO3Y3DVu2jOHmzMxgf8EQFtF2tCa2y+K+DSzWtfhqKEQjGV5Gi7k4FRZ2jMWwfdMruEqrY9LKofhbLyIbTRaUt22pccE5NY0Jf3wcaUSHn3sVx4yhQ0QTLM2gRU+ZfjqzjBeRVATpUeEo2hSJ4PxKtMiv1lnwkoU1DxzcJ6US0vfyaRaX5Bzc04MFuCci16dcB9sqazycxUuXcYx1Snhdz92Kfrcc5UO4ihel1RXcr/VKXu4qYBH6xlX0wf6deDH64EMowfDdp74kIiIP7Mcknc6iD89EYTKRmcMkMRpEX5lqIF04woKtCwsYI5sH8HK2ZwiibKMwK9PzeGkql9G/jrMQ+oHNEDqvDSMV9L2/iX5UrGPftonz2F3FefWyGLvlYH+70J3k2mW06dpVvEC8cQr9OhkNyditeJn83LMAY+47hH1tP4BjVdbRDxP96HfDNgyL7ky9H/u6BGOjEP2XH38SxZMXzuFYB+/GC8PJxe+KiIjZj7Yszi5KJo2xGo8RfJnBd65tPPOone/QBZwWfe1MsOPSFF6mNY1q8jqE/CtzmCPe+SBMfmJxbFcp4NOUpgwO4uWwny+JAVtNKmgyxQVmF+9FlA/fGFPje5L4HNmEOfvBB1GGp840s7Nn8NJ16SJMBE7RMKDZaMqtt2FRMciCul00kAnypSqSxHND14Qbpi5MXVMrfKb4OYa+xKkLDg3CON+++ipLPpy7LmurLPfAlE9TV7FaJJvH0lRYTbVTqUCYBYG1Le0OG57xjC7YvULEIpaajnTuWvf/d6iEIZ3DvKOXrqHPN/ZTuy3FUMS54RnYaiKxccvc1k+mDje1uLwaznAtEVA5CferKaJ5vghaBKKMQECqXgqnrgHYElv32f4C+NbPa02P1pw0lY/UWYpCC8Lri6BhbLxMNQhuaHplwzsWDS740hBmCmydRjJdBDeTnBvUrKm/Hwv6MtNvkyE8b7yU2hvarS+uOn40RfeGGhwt4f4QCdH/UiORxNuJpgcbNANUyVH50j+JiEj6NIDP+WWaJ2bnN9KcRdMjEa6D74lu7LufJodf/cqXRURkxxYAe/fciXnb4Tz46qsAj+9/EOsafUFMxChp0WeI60g3Uze7+ZIUCZAQon5EU4pDfCmLdWOe3/VOvNh97UWsmyOC9dkBC33luddApOy4634REemhFODGEikKRly6iG2XrmOu3zqMbS8vsPg8TSyJUYjBKg9NAkQOy/wEYwrutMonPLCO3TMQNmWAqZ4TCzTIy+O+dSew8x4CrrEIS8DwPuqL4duFnwLqhx9++OGHH3744YcffvjRIXFTGUCXCKdF5KBYIAJwDojt+jm8Ae+8Cyhtz2bSBxKSxhIR5Qr2kc4B3Q/TBjm3yGPEgAZXaOaw7QAQo7QamdhADIJxoLIRMoJ1IsHxLrCR2RwYkPVMQ8IO0kzV2ltTDio0tNAUvVAT+66WaLltgmkwbE1JCOiVwH/5il+hFX08irZqWlWhUBCLSIciZ8EQ9jU+AeTdTi6x/RR+009HC6amKZYd7EXb7c245QM9YJvcItCI1RVS4hW0PdEVkyhOR2q0Z5+ZQZrgqVeBDvX0gkXs7kG6QHcf7lsvPzsxQnEgmOPxSRERidAG33LR52MsZ3JHH9IfLl1BytjTJ16U/k/BSOWzv/UZERGZfuopERFxvwtWbiyEf3/jBMZCYg373OKAIRiooL89fgGDocKCqn/yTiBUw1txzydex9j57jxYir6+uDi035ZZoHePxsC+bSnjXh7rRj9qJDBuGhEgswMRMCfNOvshi683RtC2fQ8ABYyNA5kqcfxWXDKFhZps7gWT+fPvRere1Rlcu3UDx0rStKDf3oY2BZGOev0i+uO3T3xRRER2HBjD9ftLiMaTMRoRTGEs3H8H0lnPryMFdu/27XK88DLOmwJtO4X+H4hi3KW6MSbryyxJ0VYFoNPCZrqmIv4m0fZjLJR+8ggyEhphTT/DdpuHemTnNjDPcU4sde4rGsGctHf3mIiIjI1iXjGZxaAGKwFNIaINfU8P0nZMpgHdeif6xe38zKbB4ExNTcmWLejPvX2Y34eH8akcgKajOp4lvjK+jZa/m0yrc0iPODQCWF9DKtGxoyz5cBKs8/LyqtQ5fypb6HAu8HKiPKMQ9d/HZzTM1E+WgWg4fK60Fej2Cl0rc6MlBSzT66eaQviDbWDcts/OCy34vryEvlwrkvFiJk9ADVyYcqnloFzX3aCh2pg/LS6vnFujqWmaelSOJ2Xv1GyK+y4X2YfY9/X53qxWvd9pGqnNVGhlEzU9U02Cml4pBm1r66TmMYSmssderii/o61ahqVZq3upnGpEo/b0XkaSMtFeSrKy+3iO1Nl2ZTrDTF3TshfKymofN1so7TZW1ftrK/P3ZhK8c2nxZAJrhgJlSKFdePaWeP0zl8GUha6ixIFVxvWPmw3Jch8qBVK2NhLG+NicwBri299CgfT0GrMj+jFfn3nqmyIiUrRwj1dq6MOf+MRPioiIEUSmR7OOZ+7s1CSOE05KlrKrlEUWjePB4Pf8GsZsehGf+3bBIOzqItYYAT7P70thTGen8Ll5EM+cgT6kf7ttfd11Ha/vff1LX8G22FTcCcz1NtlIx9CxranTuljgeKyyTASN0ky23RtnVquZUa3a8J67e/twbbNljLc0TewmZnG+mj3TTUZQGcK3C58B9MMPP/zwww8//PDDDz/86JC4qQxgJIw3Wi+v3WLRbJAGEojijdghG5fLEsG9mpHNFpCiSpZCd+pDEkSQBgfx5ptnbd2ZLFCHyYtg8rr6gKauqpjYxUH3dsFSf2oSOcHdCbBZdg3MR6QWkXgMrI7atZbLQCFUbK2eqxb9W22ydWUyhEGWlPBEykSCtaCkCjZrRBgdR3OBI2ISFaiTwSwU8MZ/5g0gM8kh/L1nM7crR3kM7CvGlPllMqgNE1a1cZaDCBI97+sBAuKo8DTeLcLCntE4/vjQA8iP7qJF88Icru3cAj5nrkFzc/I4BZif+6x0Wlx3gDSZD6Avr1dwb0eiMLJYc8mU9aBvh6u4133xpOyJYZtvnoa+bmkZyNKnY7g399J+/KsZMF/n3oD27d0W7uHIFjAsp0dYomAZ92EljH9/civ205tXjdWYiIiM3rpb3hgH4jeXQd8tbca9nzv2tIiIOPeCMdkh+M2By+h/U3fgGIFttIC+4z0iImKv4/wnz8OoY7QOG+9YlJb/cYyNYrwgV68ipz4xjPERilA/UoTeJULBd7FEdM/Bec/PYYwHw0AMl6+gj184Q8MZSBwlPorxd4rjNl+ijmYhIbZJfa+oloAGTiW05fIi9rk0z7x+kje2kvkdFmss2aPGIwXNbigyAyGGuaBQYGkYshH59YxcOAPNhGoprDDmqlQKk9TJs5P4O69xk4YQCZbJOXQbtKJ33nlIRETmZnE/q9QqU1IhvdTzdUUxb+/Y1+2Vb6iobT71y2ocY/CZY5Oh8BgLg8XVjVa9S52o9fwi+vfx4xg/338emRlrK2DZq9XcDX77WvtFNVYsAM/nSK1OvaFLPQuZPy05YSheyzna5oVqerb23K+yJHXHKzugRbK13zZ+2LrwHRjKCy1z/qyxyHKFBkLhOJ/jbWZoN5KmbpuTin5Xplk9UtQfYOO3rcxGzwD6conlqlZpqqWZCMpWSr0hNr0FDP6taWi/ULMlNVpp1eUZbUT0BjHWWspCGbWoV6aIxjROY6OLs08qO1imXtChXrBaIZtPXWKpyXUJs6GSfcj6CjsbJi83htkmZr3RnEMZy4Ca3bQxgG1+PB2tAYx34f4sLeEZOjAEBnCFfSM/CR+CAbJUJ6jBrNbdFl2ciEidZmsVA311fIFrGwtzZG+Iej32/VAKLFazir5wyw6swYdYxqfIjLilFTzvX72MZ0rdTEmdZoZ9DazXK2yvrovzeazrJ1aRsbTtAzCiOXkaa4xiAcfMcSzbLHs2RG24MvNmm5GQ4zTlypVxERGZXcMa7t0/in2fn58UERGDmRtNPjvsppb1QVgse9FQHwEygZFYazkZ1UZqNkCzXt8wbOI1TDJrpjuK9hsshZUhM5jLYJ6YW0zLPxc+A+iHH3744Ycffvjhhx9++NEhcVMZwDD1a2oD3jCJKpABjHRRhxPBdpk8EISIGZRqGe/RrsO/pfCmntyBN/0o7dzTkPeIV/uXxShNOu9kS3gjNrvoqrWCDVMm7VITYFf6k9AbzV8VKdeBbMT51h1Qy9Uw2MdaEycQDmOfBQEaEY/jfAINIh4sRtlgkVd1LFJkrkF3Uc13D4UtDz1u8jyyaV4s0b/ztyVaQBfotEpnog99CK58f/THcJAss2B8P3PAd/cCTd9K9KVQQdsDhkidSMYa7X2vT87xmLgXPXTsGhwEerJvHyzVQ4FWtLyTIm4AvaqN4z6t1FnMcxPuU3wPmDQaqcqOPWBi998el2NH/1RERMbfACr6sXt/QUREVrcDcU1F0G/eeQlavj+dhavl+BYgcAfiZLmJhmVGcJC+7ehPuRxQM8MimlwEwt2zsiDdZCJnruMYE3SLdbq3i4hI9BT6wP3v+7CIiDSLHxIRkchZMB/GCrR+PSWwkKFZ9J3BBHSLQ1uBMMbJpC0JWLy/KD0rV6YxPnauYjwM9QNxd1mZpUQ3YLuCNhx5HW6hp4+jP47uh57yfe+Bi9ie246JiMj1EpjUANmefB37rZlAKHNry9KkRVeVWq4c3bUC1Cc4RBxdlyUmCOfVO5RByed54p7ujHo+DvlqDvOrHcX8o9q5Wq3mMVNqx94kO7CyiE91y2yq8ya1cgG6LZ8+h/v9+b+EtqRaw1jr78Zcdt/dmMv27QairPNUMZ+WgX6wjHv3oD93UacSYmFp1RsK52B1IA3z2Da1jKp7qTfR5vFryKj43vfwObuM83eKdJA0mtJUxoX71mK9pqMOkGRx+AyyKOQOJTEA1tMsuE3tvH6qZkTLa6jmq+lsPBu8yhFaXJgMTeMH14Po2FD3yLU05lElirRciYfUq4Nn06uo/qZ9KVei1R+CatPOY9QdzDd1lpZyeYxkSvsr0hjSLEVVvYoMkxLLYjW1NIXtiKulJJQV5Diz2gvWtwmYlZF33s4VlP9ueGWfMBeqG6Rlh8Soc3x47qe8NnQr7RvAWmkt3eoinKDGL2RiDC+xgHeMJbWaanzLOdyTLd4gad0gYB39EzdRJ9/Wn3ilJDpYA1ik9i84AGfOKlMDiizubrDcgGujH87TG6JSd97EADosidOgA3+uAA+CnhTubZR93qDrf57ZEzW65d+/Dx4b2TTWJ1mWGDo1yb4yDDd0W0RSLCHX48LfoMhyWkur+O16DuuuuQzm33vvpuMoy8A565hL70J3k6kVbD8wgnWLoSVP2s6x0WjKcD/WyE98EO7jly9cwb8FWHJBf2tqYXecX8BV3wPsq0Jfj9k1OpDX8H2sRx1u8aE6dHFcLwukxgxGbV+7S3UXr3GC42eUrOzbhc8A+uGHH3744Ycffvjhhx9+dEjcXA0gy94FqXkrWXj7rtNWqHuMueXUChZW8YacMOJSJ+TuMM871KXFscmMkfnKZ+lGRTAr3oM34CDdcWwi/L1JnPrmHdBZTZ4iOg0CUOp9QKKGxm6R+Qkca6kAbVGFbMHYJqAGRSJi6yXuawG5wmP9YMS6HGi7DF7uhqf1a63b44VXRDXgFRMJMpM4lwYKHqL2pisABmOoq5vny5pZYTA46Qlc3MEBoBdTqzjBhTkgPAc2Yf9hts12omybKxE6rA6T4evvA8tTV2S9pHnGQFXGrwGd7OSaUwdc6OyKQTB7p65BI3Sl+X0REQlVcV96LuMa3nIfrvsLR3Ny9iSu570P7BIRkcQg7unm+6G9/Nvv/6OIiDRCuC991L2uTcMldCGFfT16EC6IwQHWwmGh+DQZF2sT2I8rF1C3ZvHMOdnKujcj24FCrS+hr+eXJkVEZCWKezxxhHn91NNlxsHkfeLAr4mIyNb9uA5OlOgf+/58FmNjPQDELkTh73tC2+TQdpzvqsEafDb68NYUmJxQEU6pxxZRi/D1NRw7zmt8dwDo3/L4iyIicmkGfTsvOFb3VtVK4Xch1soxJSgW0blyDsc2VX/Gwt7RKK7VXXdh/CgLUOvQOoBzV6DH0OtQJ4o7sg/3auYkmDCHLJayEqYhIqaiq62ZD6ptU7fPEJ2aLRaw1tp76oToSd1ID6zxOfGNbz0vIiLfDrzY0kbLrcrIJoy7vdsxl9W0qGkQ/b27B/t65IH7RERk37YxnC91KlYV522b6Js2taJ/9XXonq9PYV7OkfmL0NnNdTbYH88Dku3Wfbz7CYzv934IyPJ/+MxXRUQknIIefXHhOn+u7nLUOKkzqdnmLu0xOg3PaNQjp8zO1T39sKFjO8/i154OyFUdJRkNaWWFXffN+iijjX7yatOxL3h184js19kvB/px7/fuBvvx8qt4joS1djCXCMU82miYlkcFa4aR6zF3ZNzVaZPH0vHhuK19QrVHptHquBmm62mQ+r0Ys64WljPitNUvFNZk7aFrocX1SqKLrtHMXMrneG3jzOyKtvblel3b2uqaqqymKyKmTtpqPVrXbdVZlWyJfnp/71z+QxnA8NYxEREpe9o/ZM1oRtxUGde/rAbEN/Rvl/OQyTWqzYyEIN2KY0HQbCb7YY1aQHXur+TBsC+vQS+9tILn/ECK7rIF/P3AKNad1+dX5IE7oQMftsGMnz16WERE1nNgCx0bx9z38MdFRCRrgHkeuwNrjNEB7Huhycyfb/2hiIjs2Y/MkCCfPRt23/TjsIMSpX/B1Czm+vRZrLtK68xYSuAZ09uF3/bSvdwtY62TqeA6XN6mToAAACAASURBVE5jXK1TEx4rqiYef99CTaCpmSOG4elVG6rb1Tq1qlnnfbGMtj7+Azi+zh0Bfvjhhx9++OGHH3744YcfHRY3lQEMETkT1karE111WUcvTM0cTQ2lWACKNBAIS6bKt+wAUeI4fhONYZ8zc8yJp0ROEc9oFIhtjX+oMXe+UcBBzo2T+bLBvtg51vLI4XdueEWsFHKaa3wjrxtoy5nlozimAxQ5EAVasnsr0IZwDWhyowTk2jap1yOqpaig28YENsjURIIhEeYTR2y0O1AHKtLP/Oo+IttDW8DglKmbXKK+S1h3Luq5BZHxi7DmoKUuSmhjLxG6QjkrpTzaMUjBWoluaDXmcMfDQFv6kmBiGkTqK2QFOjESFq5fsh/6nfg51NpLjeDeHj2FvnQ2jT4QNKlbi16Sn/i/7hURkRdfBsL04tJfiojIya+AtX3jyBEchDrCJx98REREuvOob/fcN1A3MDuHuppfYm3Ire8De2WyD828AKZ2LIW+nq3VZF3AcPSzpmAlifZ92BgTEZHeEbh4/sHrYOHM3ttEROQDt0MTeGkcOfaZGezn8duhESxOgUlMV1ln8gD6c/Fr0PE1XpuSyzGg3X0PPSoiIlcvI7d+Szf+fvcA0L7Fa7h2dw9TK7gV6N7BC5MiIvLtc2A0rxmYN/ZXwcZ+oBc6xPkq9tvjYOwMlRpyIgp25UKciCgZ9SZZmnIJ+8oXcU+8el3GTZ06/5uJky+jP+scpizDkz/Na3wK17jWxPW0aphXHcPYQFWV2WsoEt/qXujUVcNGB0GX+h/PrRD92KBToNZYU7ax3iAiTW2GLQG5fglo7fSlN7gtj2lj/uxK4Xxe/s53sA8irTmKGxXV/e3/BGfj4TFkf+SI6qZVL0adX42ScjFkw3VQP00+qyjFmp3GeT7/DMbl0GaMNZtsSjaX4/krM6NaLmWedP/K6FBraGz8mx7Ls3T0421DtX/etePzeYP5QyjzJ+4Pf003GBR1BGytLRwga3L8VYyzLQPwIlheQZZDiYxfmA6x2QY1y3ZALNUm2hbbhyNZHqPH8+ExG011OGytXRiw9PfU8amOVLW7dOzM1bAecMTwMpVCHNuRYKuToX7G4qzxxg6ZyeBaK8vfS62g1yLPjZfHdluvm2VZHjvicOy113Az21iR9s9OjGKFmXB0/1znvc5fx3N8C+/nObpKVupv7uMOn4E277lJTXY8QmdlvYusP6nsVJMOnHXewytXMTd/dBhz6rWJSRERGaOr+aP3IaPJscKeS2c0hnXWBz76UREROfM6GPLlLPpT3zaMm4rLzMEQ1rvGCMbNypGX8O/d2K7OGsoO17Ta9zf0syK5AljGYyfAkgZttKHOuoWWi89qEeeX4BoqZHKs0sOgqtphl74DdVyf80vIHrH6cf02kQk1XfNNbsLK2utd8erWegkHrQzh24XPAPrhhx9++OGHH3744YcffnRI3FQYO2Cr+yfekKvU/oW6mFsewftonsxTiDU6mvWGNOnSpuxgqEtzzvE9l2NOPV6yPdQqEsNbeZFIU525z/E4c4Ad/CDIWmMN1kwzGoq+zkmgh9rEENqfimCfA03omyrqqBRTVhKodyN/SkREzDW4NgZdoAsh5tI3iJ436MqlzJ/WEXSkLk3mU9dLYCCSQXzvHwRyM1sBw1Lgu/zZSbgjBZq4MLUgUZkAc/EDOM9sHmhzOgtWZXmCBRQNtD0YtWTHdrgrdpPN6jfBMqpGosraMMoMlks4Vj2A69OJoSyFOvUVqrgmfVGgRdVyKyIT4r0WtyaRGGsnUWNaNnGPTp4EaxEni/byVTBdf/57fy4iIr1JMM4rM2AK3/sP3xMRkV8eATObfQn97bM9uLev1YEmh1kP8BF7TE5wn1fITjzwAezTfAb1c7ZPgSn52U+iT7w0A53rySGMn+deQv88xHpwfazxOXAN+8tfw7EWz4IhmtzOmlL3Dspt63AiK7JvXk4TnQTQJtepVZyo4/zf9cDduJZj6HdXfhT986XPg5F3L2JiOZfGtd92Gddxdw3Xep1OwMuFnMgduObbE0AIy724T5Eort38FDSOKxVqDLQUm9OZTEoxi2u5wSQRzSw9jq90V3OIxDo3Mk4qgWMWhhhk8BQZ1g2I5EtN9UDU+Jit7IlBpsvx9BrY3mX9QHVObjabYnh6Cu5LUVii1vk0+nG6xHmQOnUJQ4e7sAZ9yulT0PwlB9A/AiHOdUR5PX137YYaa4rKsp2GSUaJzT5/CdbVx8+c5vnjPB7cDpa9VtXai6370T64wYC8uU++bS81ftAGnRtr68rm4uIoA2ipmyH760bdRd7r9v+XG3WavPe84G9yDvXobXyPxzCQtNZwhWuDLNnJAqH+aIJ68L5+meAcbusiydRjkRHmb7z6xZ6OiE3hdrrOUWfkmsnfs8NqHWeT+3Ed13M+DNpt9fs8Kq/1e4CsUYjskGYHeOzdxqBp2c3GYBLv371uz7Fttddw82wV1blYn8Gd2/ktZtc0yHgVl5G501jG/B4O4pk6VcGa4S0ZQM63FjMtzDLd77meV9dYISOo03eV60WXbuT7b71dRERyeRyrzrl0y3asm1Wzn4jGpdHAfG2rvXET+779NqwJtPZ2TY9FHWuRngVhutVeuojnep5rhPV1rFd6e1v7jqc9dZty7iKYSqfOdwdmh5jUtzbpXlqrY/yF+pgJx7quqyWMowJrDjPhUcps0zq9NVyunw0+96yA6bmA6pxvuq01SDeYQR5Snwk/IDvBZwD98MMPP/zwww8//PDDDz86JG4qA6jun02TTBH1erE4c4RZ/y9L7V9UUdVKQ1y+yYdBZHnun5ksEddW0E7CtBzVGkx1um3ZzE23Q/h9ucIcfEXmyDpazB2u1SviEOFwqngzX1qksybriRWJblWLaNwi0QaWyZNudfWx8O/FDFBl2wLrECIDGCBKGKVjaaNhSiELRq9eA9NnhNGG8+eBRgxuByu3bwi1VMoZnM8ya/e5hF02j4BlubYApmY9h3+/Ng0mJ0DXupmpdX4Pi2uDralQizMwiJzscBAISJB1GyNJdcaDNq3KHO9ODAU6FaIsVtDXhyK492XqXhV5idBJzS1WJRjF/5eIvMYT1MrWiaDRCbZKBqE3OdRy7NwyWIqYIrxEbENN9k+OhUaINTGLQNzeNTgqM4voTzNE0M4y3/36FtZ8+uRjIiLy7//6WRERSWbx28EL0KRmWavvPJG5QwNglpOzmGK6k9CRPn0a53jHvXDzyq/UpX4QrFtPH8576zHWvPoRTBAnzoPZvHR1Eu2fxfWoh+AOllwCW2mT1X5iz10iIvLY+6EddLfgOh08CIvSP/nCH4iISF98swR2oC+HToFu7KmhLb23vUNERNJPPy0iIrXjGBdlAtalDiW5Gw0gxRsoOh3N1jCvWMxuCNiKTG6wcF64qnn2/sA/KzPB+nZCjRH1QYpyCudoRzhXB5RdQN9V3UOjuuE6qvoTx9FPzlGG1mbV78rI4LtJvaFjoA1ra+jXFTq7xaLKtvx/7L1nuGXZVR061j75nHtuqhuqqivc6qrqUNVZLakltYQiCi0RDBJBKBhMeJ8w7+NhwzPwZCFjMNjGoM/Pxg+bJGRhJLAlgmgLZXWru6tb6pwr57o5nrz3+zHG3Ofsfe+tgJpSq+4a31e1785r77PW2mvNMeeYUjaNn1esCbqizsaKmIKbsajtWJVRx2l7q87nz+VkKja1wxcgdskucQnhaxsG3RhAYwClPG4aBlaXjJKN66VbFX9mlnhjyIy5svbQjd9M5s8bGuR3fUlK261U4tFAip9Z1Y1Ws4HQrmmxcCY4WrR7JJU1s5lkrFZHcbsrS1IkVZxYXjoKxnBYPbXmm8nmkMkkVUCtqponTLftWltXvKqxq1bH7f1Zo4nfT/K92nsMwxCBtStTZU1V6i6baPGInv4uKPavpve/dJix/QPKf3usZnn/krFmQDcW2fKZWqxyRnl2+5Qf21SLLQbQ6ktTDCCy/Pbuu5GeDgvSoTCvCstvWtC8IXI93xdVF8t5WoHG+2LRzp47y3tK+2Jlnv31vZ+nl9Dzj9GzSUMjLC5yfy0vFtNJA0QxgP/xtz+Ch8UaWlt1Lpl3s622K1IVLY2lVuY1Ftfz9xmTrryJ9Rb3l/XpmTvJMoxvZdkLw5kewtsYymSu7djTIKYA11b4TcMzgB4eHh4eHh4eHh4eHhsELvImQA8PDw8PDw8PDw8Pjw0BzwB6eHh4eHh4eHh4eHhsEPgJoIeHh4eHh4eHh4eHxwaBnwB6eHh4eHh4eHh4eHhsEPgJoIeHh4eHh4eHh4eHxwaBnwB6eHh4eHh4eHh4eHhsEPgJoIeHh4eHh4eHh4eHxwaBnwB6eHh4eHh4eHh4eHhsEPgJoIfHFQ7n3Pudc1/9VpfDw+MfCr6Oe3h4eHh4XDw2zATQEX/gnJt1zj3wrS6Ph4eHh4eHx8bA5TRSOOf+0Dn3q5fjXhcL59yEcy5yzmW/1WXxeOHh6/e3X/3eMBNAAHcCeBOAbVEUvexbXRgPjysRMrRspH7FY4PB13EPjyS+HQe/Hh4Xiyu1fm+kj9hOAEeiKFq+1BNf7D/6i718HpcPzrntzrm/cM5NOuemnXP/cY1jfsc5d9w5t+Cce8g59+qefS9zzj2ofWedc7+l7UXn3J/omnPOuQPOuXHt+6Jz7l875+4BsALgaufcVufcp51zM865551zP95zj4Jz7redc6f077edcwXte61z7oRz7uedc+ecc6edc9/jnHubc+5ZXe8X/6Hfo8eLF76Oe2x0+G9+F/5dXHnwv2kX/5Dv4kU7AVzrI++cC5xzv+ycO6oP5x875wZ0vM3Q3+ecO+acm3LO/ZL2/RiA/wrgFc65Jefcr2j7251zD+tjf69z7qae+x9xzv2Cc+5RAMvOuaw++H+uMh12zv1Mz/Efcs79mcq06Jx7wjl3+/mep2ffjzrnnnJ0T73bObfzIt5P5Jz7gHPuOQDPadsrNWiZ1/KVPcefb7DyIefcJzT4WXTOPeacu8Y59y/0no87577z7/M7elw+OOcyAP4KwFEAEwCuAvCnaxx6AMAtAIYB/HcAn3DOFbXvdwD8ThRF/QB2A/gzbX8fgAEA2wFsAvBTAGo913wPgJ8AUNX9Pw7gBICtAL4fwK85596gY38JwB0qw80AXgbgl3uutRlAUeX/IIDfA/AjAF4C4NUAPuicu/qiX4zHFQNfxz2+HXCB7/2/07f+sHPurT3bjzjn3tiz/iHn3J/obxvf/Jhz7hiAz2v7nRq7zOk7/f6eYgw55/5a3/T7nXO7L6Lc5xtDrFs+AF/Wck5jrFc45zJ61inn3CEAd6Xu9c0aUH7BOXcGwB9c6Lk8Xlj4+n2F1O8oil50/wBkADwC4D8AqIAfyjsB/CiA5wFcDaAPwF8A+KjOmQAQgR/SEvjRbQC4XvvfD+CrPfe4DcA5AC/X/d4H4AiAgvYfAfAwOBgogZPlh8CPdV5lOATgzTr+QwDqAN6m6/06gPvO9zza9z16pusBZMFBwr0X8Y4iAJ8FBzglLWfBQUoWwA9pfZOO/xKA/6R73wJgEsAbUmV/s879YwCHwUFMDsCPAzj8ra4X/t8F68Qr9LtmU9sTdX+N82YB3Ky/vwzgVwCMpI75UQD3ArhpjfO/CODDPevbAXQAVHu2/TqAP9TfBwG8rWffm0F2HgBeCw66M1qvqq6/vOf4hwB8z7f6fft/l/+fr+P+34v9H9Yfv7wfQEvf0wyA/wPAKQBO5x0B8Mae63wIwJ/o7wnVkT/WNUsAdgBYBL/1OdBocYuO/0MAM6DhIQvgYwD+9ALlvtAY4mLKl+3Z/1MAnlZbGQbwhd5jcP4xyYcB3AdgDMCo2uW/0r7XAmgD+A0ABQClb/VvvpH++fp95dTvFysD+DLQqvrPoyhajqKoHkXRVwG8G8BvRVF0KIqiJQD/AsAPuiRF+itRFNWiKHoErKQ3r3OPHwfwX6Iouj+Kok4URX8EThjv6DnmI1EUHY+iqAbgpQBGoyj6cBRFzSiKDoGTzR/sOf6rURT9TRRFHQAf7bn3es8DAD8J4NejKHoqiqI2gF8DcIu7CBZQ582ofHcBeC6Koo9GUdSOoujjYOV8h3NuO9hAf0H3fhhkRN/Tc62vRFF0t8rwCbBS/psoilqghX3COTd4EWXy+NZhO4Cj+g3XhXPu5xwZ53nn3BzIeoxo948BuAbA07KQvV3bPwrgbgB/KovVbzrncj2XPd7z91YAM1EULfZsOwqyHbb/aGrf1p71abUhoMvAnO3ZXwMNQB4bD76Oe7zYcb7v/dEoin5Pv/0fAdgCYPwSrv0hXbMGjof+Loqij0dR1IqiaFrfdsNfRFH0gNrKx8BB6Pmw7hjiEsrXi3cB+G2NoWZAAwkAMkg4/5jk3aDB5VwURZOgwaZ3vBIC+JdRFDX0LjwuH3z9Jr7t6/eLdQK43kd+rY9qFskKdqbn7xWs/xHdCeDnRC3PaZCwHcmP9PHU8VtTx//iBe5d1OT0fIOWnQB+p+eaMwAcugOJ8yE9IDma2m8DkgsNVoDVg4+pNQYofkDy4sZxADvceXzGHWOhfgHsvIaiKBoEMA/WOURR9FwURT8EWqZ+A8AnnXMVdcC/EkXRPgCvBPB2AO/tuXTU8/cpAMPOuWrPth0ATvbs35nad+qSn9ZjI8LXcY8XO873vY/HCFEUrejPS/mu9n7zt4NM83q42LGQ4XxjiL8PtiJZ3qOpfd+MAWUyiqL637NcHt8cfP3uXu/bun6/WCeA633k1/qotpGcvFzKPf51FEWDPf/KsgoYotTxh1PHV6Moets38Ty27ydT1y1FUXTvRVw3PSBJs4Y2ILnQYMXjysADAE4D+DfOuYqjqMWrUsdUwTYzCSDrnPsggH7b6Zz7EefcaBRFIYA5be44517nnLvRMQZrAXT16GANRFF0HHRp+HWV4SaQdfmYDvk4gF92zo0650ZAt+o/WetaHh4p+Dru8WLHBY0U62AZQLlnffMax6THJBeMe7oEnG8MAZy/fL3lMpwGB/G91+q91zdjQFnrfh6XB75+E9/29fvFOgFc7yP/cQA/65zb5ZzrA90l/8eF3IHWwe8B+Cnn3MsdUXHO3ZX6wdJlWlBgZkkBoDc45176TTwPAPwugH/hnNsPAM65AefcO/8ez/M3AK5xzv2wo2DNDwDYB+CvLmKw4nEFQIztOwDsAXAMFKj4gdRhdwP4DIBnQatTHUkr1lsAPOGcWwLFMn5QlqjNAD4JDoyfAv3bzzeg/SHQb/4UgP8JujN8Vvt+FcCDAB4F8BiAr2ubh8d54eu4x7cBLsZIsRYeBkNaco4Cct9/geM/BuCNzrl36Zu/yTl3ITe482HdMcRFlG8SdFvrFS76MwA/45zb5pwbAvB/2w5vQPm2hq/fxLd//Y5eBEGla/0DZ8T/C8A0gCkAHwEnrB8EP+aTemFD0fpBml8E8E/09/uREgkABwIHQCvwaTD2rRqtERCqbVvBH+4MGDx6nx2DnoDRtcqz1vP0HPsecJCwoGf7/Yt4PxGAPaltd4LiAfNa3tmzbxtY0WdAWv2nevaly/5GSLBA61ndb9u3ul74f/6f/+f/+X/+34v53zrjl7XGIPF3HBxc3g9gCcBf65x1RSi0/dU6x8YO79P2PwTwqz3HvRbAiYso9/nGEOuWT/s/DI7L5kAthSwoFDINisp9IDUmOt+YpKjrn9a/jwAoXsqz+H++fvv6ff5/ps7j4eHh4eHh4eHh4eHhcYXjxeoC6uHh4eHh4eHh4eHh4fEC4x8sw7zHNwcp2X1mrX1RFHk1Tg8PDw8PD4+Lhh9XeFzJ8PX70uBdQD08PDw8PDw8PDw8PDYIvAuoh4eHh4eHh4eHh4fHBsFldQHdv3cXZYFCrvdVKwCAdptZHMIOd4yOjAIACsUCAODY0aOo1ZYAAJmARW62WgCARrOp7ZzLBo6MZn+V6TyaShBRq/G48bExAMCWbcy5WO0f4nkZnt/qtLXk8fkMsDQ7DQCYn54CAHRayawTkaXsUBl0CYQdppBqqhArOq8FPmelyDJWCkWeruvkszld18GpXDktI72jTCbDe2kZ5AoqglMZIj0/l5Ucr5nN6TmV4z0IdH7IsnUazbjsgewDLdun3y103K4FIv2gHf0mUYf3vOfRRx02GBqp/C0ZLe1FROtsPx/W4+hfzC/3uFKYHjrC5c4JLrewqsNaUNhzjmWNnWUzw2llzBlgNwFVdTx0/9cBAJ0mb/KqV74SABCpN3v40WUAwFe//EUAwLYd1wAARrezEOMTbAt7dwF5ZXk7/gzP+dLn/ycA4N77HgAAvOY73gEAeO8/eRPLP5Qsd+HF/TO84PjY/z7M6riO50jaoyS5rj7M+hHtCkM71pZ8pWEqA1+YumUUn+gS59k9I50QIUKoY+2cKL53lFoPU9cIde/kefF+9aMuagAAmhH79GyO/SgaDu342LWX6eeKny91j2jV9u7z8Tqp64ZhXE/D1LHpa6y+J5e/9+G7NlT9BoDPfO7xCOjWy07H6gLX43fqVE/RXUaq4zZOyWfzAICslgG7HuSy7MwK+m5n8zw+q+98Vn1Zpcxrl4s8v9XhN7ajwuX1Ec7n8jb8iEd1eRU0k2HdzGlsUKypY82wxz17lvm0Bwf3AQBclSkz88XKeq9oXVhtClPbXWq7LW0A2k6tX4iZsPt0QiATJLddLKthWbaLG6wPB4APvuuOCAA6YarPsAPiPpl/tNusd/ONLJ46OgkA6OhXK2j8Wq4wg9rQ0AgAYGx0HACQ1Ye7tshxdLHAulxf4Td3qVYDAGQ0du/oXshpsGBjVURY7NhHQcdqPWMNy0ZWaptBhtuDSgkAMDjCVH25HGtaaZBeoYtT8yxbnrXnTW95KwDgmmtvBwDc97lP4dmv/DkA4Mwkn6Ot701Zbb2oipjJJqdVBRUto7G2U0PNuGSD7aS+d0GG21sIsNzgOyqU+Y7HtzL94MDIMMswzJSFm66a4Ll6x2c0d/nB7/2+Neu4ZwA9PDw8PDw8PDw8PDw2CC4rAzg8OKy/OHvPaIbb30+L0/ETJwAApyfPAgCWF8n6RZ1ObOVptWm3aYtNy2im31+mtaBUNIubLGpiwFpNTq8LeVrcKlWa8tsZntds00rmZEHImvWhXUdLForQ2EbdMydWzWXEupn5JJDVWPdud1j42hK3m7WvIMtBIa/ryDIQtjt61nZs2cjISJXTzN6sKjaHd1q37bbbaRmYedDZImXIX8sK7dbfBcRGlq5VfN0jPa50xBZZLber+TRoBMSxQ1xWrucyq7ozBED2Pjwnw/RjD88BAMqq6yePs/09/8yjugn333Ajmb9NuseTz3C5XKc174abJnidItvZu97BtvSFz9Li/fP/z6+g4k4BAHbu3gkAKIy8BADw73/7IwCA0S3Ja88t6nn1oDfvWuNlXMFwzpiktY3mziW3u6jbz6QZvrjvcYlFt19JMYPrF8qOE0tnfZuzG7ruRdUPOjEoQczirF1+O89FVshUoUM9S1jQvVgxrO8P4NZl/tZ7rvXYufXge90XFsUqf8uwZd9x/qbGBBprHGq908Mih04eOln7Dou9yHG8kg3sO80+qS2zf7vGZb4glsA+3CGPszFHW+OTZtD1EgKAoA3kApajEJLZK2Q4VqovLAAAzp04DADYfSNZDWQ3AQDG9+wHADRUpa0Pt2WaJTgfXZYeTqQ9X6ydZVLbg9TxF8ILUecbKkxxA9IgFX1b7Y2bt0THvLlsPTSvNSLXzmBiC+uNmglWGty72OAYenbmHACgrHH9rmv50R9q0vtuYZrf32adx9dVGzJWKzT27kSsv+ZRhqiDwGgyTQiM4bNraSget79SQR6Bw5x7fNf3vRkA8KY3fe+a72V2bhYAMNA/AAAIVTPvaSyiXSdj2U41DJdcRbrWpz8pq49KtxKDPGYQxl4gGXkH2hzCxe8h6WnQaCU9BdbDBqz6Hh4eHh4eHh4eHh4eGxOXlQGMZFNymvE26rTsTzY4089q1j43Rwt+s0XGLYiAjuIEy2X68m4a6Nc+Xiuj2bPFAJrFrCOL24CsEeNjpAtCBeotrdA6lhNLEETcHseXhG20NeXPyZnX4g2NZbQJfBDHtshyqOeekh9uNsuyV0pcBjJXLC2T6SyUy7q+Zvc5h6wYy2KJVpG8WMcwFZBgMYClIo9z8i/uWDyNGFM7zxjAlqw2kEUyI3OYa7fRUeyiWSi6lnXdOrTHdyobrQ9hduPaFdKWzLR1KExtvxh8OwQoWBnTHcoexcwNsGpDxkFcPd49ZlnLFQUBjo8NAgBuZEgKHrifbfj0WbJ1b7zruwAAN9xEf/g5hVvtEbs4MML43myRy3s+cy8A4Gf/6V8CAD7w8z8DAPihn/g+/O5v/SQAoFxjQV+26zUAgD/6+N0AAIU34OihowCAm/ZfBwBoNtl33fx/vTn9Kq5wqA+PK2W6pifj8WIGzUVx5Y/JOOtHYg8CpP5I9jvx7lVxa8kSGot3PgYtdYv4IhZD3Q0vtPKvfc1Mluu1FdaHmRkGsPYpjqpc7F/FKl6q8vaFYv/WZRix2qbscWH0DbD/aZsVXUuLgwrbYWJp4wMyFRazr3pjrg7O4piIjJgVY/racdwm79Fq8lvadOxRlwJFyWkclMtqXJBjp5nvLKDQoOdUsVpSGfgcJcVkDY1OsJxWl60wYjTss21NN83arRdj11u70/VtPW7Dtlvs38XGxK9iGHtOuNTvZNdj6xJPvAIQxC9LbzJmszS21Q8Ss29xPGwHVcXJjVYVu1bmmHSlybo7ucAv+rEnGEd/7+f/FgAwKHeabVvGdC9etFGXvoc85UzfIshbnGs3MtS19bHXsTY+zmhs7jqKd82w3IMVnvumO/h9f+2bvuO872VocCixblXj8V5tpQAAIABJREFUfT/9/fj9w3/N8rbkLRgac6my2JjbvO3iIPZkzbxQdeuOIe3712Vks/mkh6N5sARiBi22cVlei8boXuheHh4eHh4eHh4eHh4eHlc4LisDuLA4q7+MQRObpdnt1DSZsnqTM+xCD9NWLlGRqlI01k2+yqbCJl/gjKllyqxVl5nHXIezBallysJRlJUhK1/9Zo3WCxPoKfZVUKiQvmg3xJqZ6Sh2WeY1m3VaJ0x5NCumb3hwVE/N88r9SVnDUExnqczthQLvl8vnYx9fY0dbZpWUJdDiBitSYKpWBvX8hEJUYtXUsJO0CDQL0sIKm7o+333YaHTjFMTQZkJjFRXnIAtFWdYXU3OttVJyRhsIZutZz6K50Swuxu6NKl5gUMzfCcXSbakCpjVnbdKsj3m9vLe9nidfe927AQDlAV3jNJdyzY9V706dJAuzrJjdo1r/iZ/6YQDAx//zBwEAZyYfxRteRTbwc196HgAwM/vfAQBFmcPHxtmu6ou82bmzYtrVRj3WDiJekyE0Ni1InhKH6tlm266YJhcm91+I1lorxi7qBiCuec6qOA23tuXUrLJ2vHO0yGYiWlyfeuIeAMD1N90KACiXK3AWJ2jP5bqWXV4zVcZuqdcsw6XgQmzhquPOGxS+MdAvRqOl+LtOm9/xto0l9C01RU7TIwhb7W79iEyFkN9C+5Z2lV95rrEgOe0PTRFQr78mpeNwiX3ZQI47dgzonrL4Ly2cw8DYzbo2z2lG8hqyj5E+yy2tS+g8bo9BiqG3j1VcE2IVcO3uid1Nf/fWwws1MrBqGrhLV/801jE4PzlyRSOXzyXWLQYwTPULoX6xXNZiVpsoF2yfdWhi6sS6jSo4cEgHDmu8u9Dk/qY8//IZntcvRrGgAUAmpsDECJrKcRAC+eSUJetaOkYMoHnC6bjxPraB/gZ1AzD7u1wOvVxX2KplTUsbjdh9FPz//H3YPkavjpkZvpPFmt6hXkNObL7T83Q6ptyPJDTACbS0rAEQe2kNK4qJxCjuN7KxqrDphqi/sRhA/a7tJcWiewbQw8PDw8PDw8PDw8PDA7jMDKDFSRhy8t9dqdFi1RB1ZsqX5YLF71VjK0DeWDPNbEslWuKNPehoOp2RRWNkE/2Nz07RAnDkFBWIdm4f0wks07LyBLblgz8wQJphsb4CFEytk9aBhhS7KkVZ9/LKgyKDQEGWNycf4LZMbnMzLMP4Jvrkn5kh41mo8MRqH9m7sTFaJQqlEpry5Z1VbEkga8JQheUbGqW60abxrYkymvqPWX7t3dbrsg4q39/KEqmYleUFPb+YxWw2VvDLdGhd6CjmIbY+tC0noeIGVZ1WOhvXtJaOiFpv/0ZBOpOU5V568ACXN90EXMvmgJuv5vILX+Xy8cdoEXvJy8QAyli3oGt84Rm2n61jVCXr4wK7qrzgqVOsvzfvvwkAsDjHAMR/+s8Z9/f8kQK+46U89kO6phnrjvPSOHCA9OKK+q7bbuXxq6x6Huugyyg5yzlqFt2wh0JAT9tIxafE+c2MSYvj9NIyxWuzVpcSc5dmD12X6kscl84LmFN89/Q5VpznnyNjPDiwK2Z/uqpwa8cVxveKldtSMssXEduYeIbzHuWxHnZv53d4UZ3V0jLbfq3G/qit2J92p6Alj+u0WwjiPJKKtVfM0vgo+43pWeobrMhbyPIGhiFvVpICYknMXn9F6uVZHt8vlXOXYxkzagvVoTGsSGnUNfK6NsvQbCflCi00ycodmcdSMoValxkM7PlS21WtL8T69SLZEr4J9c+eJnGp9dzUP4MN3ECMZYtzOYfJkYt1MebNForRbjXa6Ou3MTfrZMeU6I2NMuZOXjRlKXEao7Usj7dCgWPY4gC9bOTEh/qivPDiMT7bWblcRSZjsX685rI0LFb0Qa7Iwy8j1rHRx7bwzBLb1eKnqCNQyH2a11H2gKDI6229inGKmwc5P5iaZnt9+Mk6TgTX8twdivk1deCWyqJyDw8zt+CQlEenFxjjeOK01E9bbOs5lXV4Mwcue3fuBABk1QDnZhnfe+rUMYRTjO+tdWxcTwTGCKq/MAbXvBWallNxHXgG0MPDw8PDw8PDw8PDY4PgsjKA+UzydmYpqClWJy8T1GAfLQzmx5tFhELeVG94TjtKmo0DzYAz8oG1fCC5AVoC6qc48585KsZraZKny4+/b7Bf53NGXTeFr04nzqFnloisrAY6BVGGFoxCnTP80d03AgCmZhjzODv9FI/TbLwuxjOv624apCXkla95A8tSHYyfbeoUZ/6VQW5rKc6uorjEzVJM3DR2FcufTb/jpNU4lIWyqXc+c5ZlfOKxxwAAS40ZAECn2YzVmGL/b4u3lOkvyvJaKzJHZMXcZi/FJOixoSBRTdz1ei5PzXb3GVu4h4Y2fPLjzPu3sML2dN31PPuklEQXFVP8srfQgjaUqnfPVFn3f/vf/gkA4B9979sAAK0i5UWz1dXlm9Lyngf51yMPsV28792vAwA89jjbn6ly3bQnGUtxpeNiFDbXObMnD6CpCaeYr7gz12qYZONihc6OlSV5mpmYY0bN4t7Q9UhYL/ZtVWld0hIexy/aqt1L156eZd8/cTUr7yte9VoAQL3Zvb6Vv8t+pBRH00xgXMbkY6ZjCC+cKNHjUvC/72Yc59W7dwEAdu2Wd40jy7us+reouLzFZVrqo7AQs2GhvtP1tnIKOtMq4BigWEhW3tEq+5GRMfZxhZwYwia3B7p3BI5P2qo07biNNIB2RseEiWUzlt8NEtdIk9vGCHYZdy4yKZpgPcXni4Fdqp3avl7kaZoZfCHUPyXlgMIGbjYdxaCmgzKD2B1Dv5RYqv5NHF9mZw8jkKubCGcUxB6uSKk2rxg404SINH40Nfnt23bzmiNk2yrKuTcob7ZKgQPrhnJ+r6xwzJ5xGRSyiunro1pnrqS8dw2yb8vKedlQ5kJT5JwtcnThpMcxPMrzOw2yc/ML/N4/e9C8+/hsGZAJjIbG0ZenJ0Chpvhbe2di7Q8dOg4AOK3MAk+d5UDl+WepL4CI76EuBfGm5h5ZMP9566tUTR0f5T0HB+g14DIOS/LC+8YRfmciuSgNKMZxZNOzAIDN45tZ/oqeN3f+wbhnAD08PDw8PDw8PDw8PDYILisDaIySIZJpKV/g7LYo40PBjA9ZU9Npx2pZTnnwjG0qKO+dKepYDp/7738aAHDrHVRjC8DZ+dvezDwgX/4qc3qsyL8/q4RfrbqsGAVT0YniXBuNhqwmmvG3ZSZY2r4HAFBd5HotZNmmF+mf3JC/cl8/KYdbXvJSAMDTT5PhGB/jjL/W5D1HxO5lshksL9D6MSRf37p8nouxX3Qy7996mXfMahznD5FlZHiEVpeyFEvntb9eX4mVRvOK1czJmmC5V+AsWYxZy5OKZh4eaeRSy1OngIZCgw8eYt0eH2f9GtuyHQCwfSfruIWW3khCEPtvZLszY/q//Qjb9Du/h0zfyhSt6P/53/0yAODA87z+JMl/7LuuWy6lIERD6qT3f+1BAMDP/p9vAQD87Vd4bl4qY8EGreJpdmo9rGKzomBVAkCJxqETM4Fcjxkxi1NJWeqtq4uFOlcxFJa7yli7njIbvWFsZEpiNOr+kSx/6rmsn7Uv2unThwAA/cr/F+dIdS4+x7xXuvGDSaYyvge65/Yuo1QsIFLveBWb6bCaUrlIbGByBL/2oQ8DAPZcRzZ37zVc7t7DIOWrr+Zy1+4J7t/KMcdsrZt/y/ITF6UYWpBewK5hWvWrGm80l8lc5MuMGzIlQPPUaSkHmpP3TafN/camdGLSPECcozPFlFtDslgtC36LmXXLWahrmWK6Ddfiapf83MdKpZlLpeCwOi9ulFo3rBLn1bLd0/Yv9v5xZG1n7XttJASpd2a5WGNlTx1Q3DIBALj9dfRO+7EP3YiFqZMAgI/88ocAAFV53zm7SDvJ72at4qhOj45TCnzLbn6ALX+01duMrpcN2JdWBsjWZbMBFpbpoXZ0loxXtcH21Ffi2Lo8xPFsKWuK0yxTUard/VXF52nc3FDRMtrfECsXqb1FddWSFpDVuD9vuT0t9tFyjoPjjQNPfgMAMDtPVrHdtDg8e0/qr60d6Rtkiv8np8gcnp6VP1I2h0CNMiu1fkuNONPRuH2J3lCHThxjyewdSqH/g/hnWAueAfTw8PDw8PDw8PDw8NgguKx27GiVpYYz34r8eNEUgyQWypikyAE55QEsiakKNVvuG+SMfmGJs+ynnz0KAJibpR/upmH6xH7nW98BANi7l379B77+Rd5SrFtDuetK/Zzl16X0GSFCf1UxfibzmWEZRuRvu7PAZadBHmHrrusBAH/2qbsBAEWbZje5f9l8o80ErmcZGqI1Ixt02byonVTzbMus51ySTQ1S1uI00tuNATRW1hhWu2o+yHZjdEwNVBbsfdfTcmO5F8+epaWiFqu5NtYsw0aAD3+8OJxRFanVgP/1FcbZDVT59nbtYYze1qvYrm6TOqgZtO0dP6gUPbcrZvD9//guAMCoYvsmdpCt+6Vf/TgA4J/98g8BAL58H/fnC8CjMrLtYrcQW80//CEyf8/IfT8rCbycrJUdk0fDxooBNFwsE5jop1KJ72w1kzJHx5dWHqUwE5unE/tXqYbK2hvH1kU9OdlSxTUGJSbTTKFTNtE0MxizbKmbO0UzHTnKOO/bbnuNTuvSl7FPRsz4GTOZjkeMgwGTZUq9axczf0keJWYM4+9HFBOeaWHRNC49pvPKxROPPAQAePSRrwPo1tOJCXZEe65NMoN79u4FAOy+/nqMX8VYqe1byEQMFM5/r3xFQgKqR23l/evoh2rb79KR+rmOjiUQ9P0u5LNoK4gvZpx1cCetChvT09Y2k1+tUNSeKUOaQ08cu2r3tuHL32MUaTW0tQ4NZ1Xe2D1jG418CXsoQ6vjF+qSLPbP1D/Di+vCrkjkAtPU4Lq9z1g1PiNvtjqPe/p+toX2iWch6Qf068cxxXmLcbYxpVVdy2GdE11t48iMVKFj5i+wGFaobNyekydgs9HG1x8gu/a5z30WAPCyl98JAFiYZ0zcS2+lx9/e66j8Haofb+nDPj9LTY35Rap7mldewdwO9VlvNvSH5gUudAj0zpzaS0btx7zqzDtvRXkOI8tIYFS6qelqu/XfRZ1vZexopGPMfC4TIsxIcRjKP2x9v5IfB6a4mrH4Xn2vMucfkV7WCWAulxwsWduvKTVB2V6wucjo8FKujMoAO8p8hZMvZW3AzAJfZlYy3D/wg+8HAJw7QxnuvkHSx+WyEvbmLIEiz8/r5c+Zq6VGj62OAl1zWQwM0cUjjGXMVbA8jx2/mgPVxtOkbp966jkAwPIS3Tv+0TvfBQA48JUvqqz8Mb/6DQ4a3jpCVzfzTbCOKYMoDiY3CXWjx8PYLypJ2a/tANp1KTF0BxVGkbNMBbl3tlrNOFVEwRK966LX7bteZeI1xsdI6Z87xzQXZ86egYfH+bBZA6PNtwOtNuvT2+9Idkf5C8ytrplIrjfVpu/VxPD3/9v/BwD4r7/5E4njvusOLp+dBV7F3Mn4Tx97EgDwM+/m5NOmd/LYwqlT9BsdGq6obFbWofMXcoOi677I9ShycOnOKTURjM9dN/0DV9NuK7GrWzxJS6yyLEnPzlXljJFy10wnUO8ebmmD2K8eP8yQg7vueq8ubO5BYfpx17hWytUzNcBG+rzU9ewvGzD13i8tmpF2zV21vuraGw8FG9jGSdu5/cSRgwCA40fo7vt3d38GADC6mWIWb33PuzHST0GqN73uOwEAO6/i+pYRDtyy6/orymWrTEGMrNJTQe5jTUvZYEfL+Foul7tXSAnAWbtYXubANNCGeBJp4yxbT9VDQ5yoWom7IzXM3olUcImWz8efpataXx/HUFu3JPtRc/e366YnKkU9ageAMlddcCJa1/vIxq7nl1bmKwmW+H29bEYLGl+fXKDL5ewC62Freh5b+nnWtTsnAADPS/ykm9IkZamTO2VeH/SsiTtmLPG7JoK2nlIR7KgynDh1FAcO0DgzNsY29/WHlE9KE6RQ49etEyybjd07IbdbqjJz0a5LyAltzT1sYmCWiZ5hs+3Lyv/SJnZ5CUfasqkGk9VzWfOx9tn9NmrSpnA2mKeoHj+r8LZGFMa2mtgwEttucolrhXp3li7P3vV68C6gHh4eHh4eHh4eHh4eGwTfUgawroSQZonK5iQKU1a6hSJZvaGh0TjtQ9speXyd7NrCPN0qS3leqyHWapusEzVZv7oeMLzHkhKgm9W0I3OQzeLLkl0uFQsoSVLV3ETzYhuLCuSORVCGKeZy1SY+5wc+8AHea5J+Zt//nvcBAAaGKEX7gSFaB6uyghm13NR7KWZyWFmmTG1Bwa0ukKVD76yg8prNNi0gYNaYME5Qa9Z1SfVq+/hV27Ukmzk7ewYHn6L/W0vHVJXwvqTfxcmamRedMzREK97w8AA8PC4GKwBGx+RCoW1fPMA2fctNtG7/HfVYcPVuLWUs7heLaBa2q5RH4rf+9BMAgA/+Ipk/axNP0vsDJ0RQT03OYfsbaSE05s8somflomqW9/e9dwJA1yLtNqzp7EIyDent3f3reRmudt0yv8ukq2NKf2VVwuooZXiONS/gul5vKR+JdCqFrmhNcr371FzPym3/6DH2kSPD7NMLYmQazW4/m07BE1uAzVUVYXxs771DrO21kU4Yb26p5vIa9Dyi8Y+xZkb8LlO5JeKnSwqEbESUNA4xFspc3GKhDEu2rPWywkYeeOBLOPHsYQDAM4/RVe2tb6Eg1b69dBfdOcExwtgmc/1MQ3Ugw86sUGJHlJErW1OFchfB0VpfZeJtkUJsjFmOmRqlBDDX4Thdi7lDW1J7UfD23e+oorXa3YHk8hJ78WqV7+TECTJIJ89xDFQd4Jjn2ecofT+xawIAsHnzUOrehL3j2NFJq8beRb1NZB1RKIMIzDjdxUV6sV+RyMS5PZIst6Gp5OatJX6Lzy1yHBouBTg7pTQISmtm3VbO2G1jWOW3ayFBWYVv5SWmEuSNERPzl0nSvUHcr/GHO3f2HM4oLdqwwrKuqrKdLNdYztkzZCOPnSTDfNMo++ViLCyWZBfN079l9LBGAFnNMyw8K5NxcYUJxLplRDkb42fVLzTXTz2HMYHx8+llZ5ROYn6BAi7tRYWQ7eQY/CqJ5RybOhuLQcWMXhyaIFYym2Qlo7y1UZwXG3YY4+Hh4eHh4eHh4eHhsdFwedNAuHSQp2bXNnu1NAMlWgiqgwzAqTVDzC2Q8VtcoYWpVOQMvU/pEMYUmzNUpZXBZF3Nx7dUolhMq9XWOu+xrOs25fNbkQW3U+d1i+UySmVeOycjQS4g6xZoZp/V9nlJPjs5pZcUr3hkigGqDW3fV1TiS6WsaOm8utjLTE6WjyCLU0pgv13JMgNZJMxSG6ZNN+tYcLuBuUnLdlZWDGMSFxdpsRsYGsS+l78MAHDgnnsBAGX9TqEsPRmLPVGZCmXeY8tVY/DwuBjc9wjwasXhHRdDNzbENniaatM4c5J/3Hz7VWtewzqx3/x/Pw0A+MxffBIA8Bv/8p0AAGV2wBNPsO5/9+tZbz97/6CFVOPP7mYj/s43sw4/xFAD7LmGy8/+Ha10+2+gpVpNFtv3XeSDXiFIM59R2mS/CmvtWNv0HsfbxYIsa98zLWiynihMHEsXSAgGvfLzKTrRkrR3LpBaIUzGJ544+jgAYLs8TrqZLswjI4rvETN3KaYvsNiZtJhLmCzDenF7abYyFpmJOrHggpOpu30Bai9mWzewSH6uT2mPJPQU58yO1l5O7NkBAHjmueew/xq6KRx5noHIn/hTfk9fdefrAAC3KgXU3mspKDMywO/ugMYKq2HeUFzLyt+hruTzC4vs3VyQQbVSXn06gGLJdAPk9aO4wiCVhCGMxSlMlAM6z0Rh5GvRG1sLpo2I1L4KGlfVJKJXUZn27WdHquEXhkdfCQA4KUZnfo5jn8H+fE+JsCpILWZZdMD89DyqenfZYh7nQ58ImLlG8hobERZ/t4o0Vf9gQi99IV/WwUPUtViqVHDHHbcDAGZmTula1rcmX2igsWWrzbqahY3vWScs5i9Iib+kvw7GVi4vLaEgEbZxMYCvuXYXAKBUZMVaVJzhqNLxmMZHnHLI7qG2EIsnBsmUDkHMFGqcnM3G6SrCwOp9qKU8A6Wjkc2ZVoiuYLG56jBCefg1JjmmaKg9lvs5z8hI6ag2y/3ZRhsd1XGbQ7k4dYuew1hTY+dVJhOtWw+eAfTw8PDw8PDw8PDw8NgguLwxgCn/VbO6dqRO1ddHlq4kJawwkDWptYxIM9+qfH6LUsgxidhMjhaAT//lXwEA3vnD7+EtpIqTjYxV4/Klt5Pd+pu//BsAQEMJH6cnabEb3cok01GQg5M6aUFWgYqlpKhIMdRkX81CFuk5ZcowtnGoj5aPKOJxrVCMYRzrwmVBloROu40VKaQuSfa0rHeUy7Ms9Qb3F5RHNm0dXi/9gzGHxj6aQlN5gLGGtXoNp5eo6mlWEkvBkbYadC3TXM/lvF3B4+Jw+NAZvP5m+rzLmB1bZvcp/cO1V5P5S9u3Y3UtLTeNTQAADjzyPxLbLdrmXWL+ZrX+lpcDh1nFcUKW6C98eSsA4KxiCZ5/nP3BE88z8SwiWvL7LHHzvuJFPeeVgnTczDoimqusuFGvJCXWPqa7JUiups9LM3xGrKUSVxvCcI1yxzEUOsZ2WBxUJ8mAxfEoIfe3WrRqHz/8KADg1tvfCABom2y46/nWuWTcdRyPbWWJy5SypKfYuJj4TMUAxs8U/9W9X1qRLx3jF8eSxeE7F2J0r3wEFWkVSIvA6kKg3zZriZ0VILz7erJ+X/zi5/CONzBh9ic/+ecAgALIoPzRf/n3AIBfP0M14R95/08CAL7/+74fALBrF/udHTsZ95NZ1zbPelWsSNVQ44IQETpteRBlafU/+BxVS6cXxfiJSSlrPDKxjZ46ppsQIxI7FCbVaoO43okZFZ3ScQ7QOCtOBq4K1OlI52FFauNljiEGlKqnr4/P217mxeqLtcRxUaqdxrCxVaEQe3V1ClJ6X0Ulqc7rd6yvkv/ceOOVsJ2iVuNXojqi8eWy+o/rd1N1c/vINswcJfNXrCp1mOlJtJMMso3va6pflao8xTRu7qatScb8pdm4SBT84sxZbFO93zdO77mqxv1O6dKu2sJ2NLaZrHwuawyY6nJcn4zpSzFpcRnSDGAOFbGOff0cUdRW+A04O0lRgeUmU8+ZEmfW6lmj1VMCICcmsKb3Zaqn5gPQVDqJEyf4nkM49Mlb0LIApMsbrvIK4aKuucl62Hg138PDw8PDw8PDw8PDY4PisjKA+aIlKdR67IKuJOxS1ewXC9V0tMQNb6qgqVl0J0wmVwy1vqB8JWZlPXtWebsGpC4llnFJakbPPEWLvkg4M2BhSUo8u5U3cLEeIKOYv5wltLQZu9Q7G/KT7rTNmsBrdZq0fFQVlzh21YSegfudJZPOWeL7QEtev9aooyv3w2NWVnjOsNi4owfpm12apPUhW+BxZbGUA4O0HBSLZlFLxtk0VcZlvZecLA1RvYWi7lHMKteIzp2bIYcyOJTM3WMKqxejUOaxMWHxeGZ/fMtbN8f7vnIvlfNuvPkGAMDhU6x3qqLYSkEvjEvt0zqvrytZ+3vfyeSv6fSBn2UOW+xTvF5ZpN3BaUAEOn7qH9NyeN8jXP/e1yg3J7j8zD0s01tfdYkJr64w5CzfUIoiSq8Hq0z2XcYule88RprwS18inQssXk/mQ191PecQ51ZyXapPZTDL8PmpTStTR33hwuQRAMCJY48BAG57+V0qWzImI3GPOAQwee8wTqScZAgDZwp9SXXPVdbe1PP2vo5uXGDyqFQYYvczEz/+xu3DBxS3VlNS9pZJUeq7HxVZB7ZuZZ/RmGNS6U3VKvr0/V3ReGR0mP3Gc08zVrRT57EHn2b+srs/xWu/8pVv4n5V7pER6QTI42k1lLe3ZFLIy3FsX0PqgpvHyPBlcvy2T02zTM9JqXRuikqc+/ezjHFl6FjuQWOSk7Fa1oAtpitCN2dil6FQHKGkNweHCrpG8imq1okPWL/KMYcRVK1UAsRufe6ymabSarntIjVyl0m2iExeTNWcWJN1s+Bd+ciU+DFtt4whsnehse3wNgDA3GmOLwfkf/Pks09icIDfxIGKPBPqYupSqpcaFqPRslzdGpuKQbMx/LpxePrWdNT/bR2o4NZX3MhrN1mnBwr6jTP0yGmIBS6W5PdTsBhA9cuh3Yu7u9+aZBmsTHHMXeBQEzNXtvg6ec1ZBoGpc5xzDFq+TR3ftmBhqehv30Il3El5FBw6w/H0Ult5AzXGN+8+l8ug1JKSqgYvGf1O6T4/dp4JrT2d35XDM4AeHh4eHh4eHh4eHh4bBJeVAdy7dz8AoKXZ7MIc1THry+QFKoqr2bGTPvXzdVm0Gk0sL9FSUVdMXK3G9VLRlIY40z169AgAIPz83wEA3vqWtwAAjhzi9nOT53gd5TfZtZOBRrPLnIWXNcM2ZiyX6UNgFmv5GecrsjIon8mC/NZtOl6QZSCyPCmi/PpknZidp/KoLoesqaHmC4nrdDrt2GrcVqxeKLa00+Cxp/VcrYDLnFjUch8tIJu3Mn5qcJisahw3kzNLJS0pkPUwJ7NNMV9CbWpKxWH5KrI2DlQV/ySY77dZeMKNnETK47xQ6Ac+9VXWs+++sxDve+MbbwUAXEfDOiZFkEsIF/m1Re7wla+Qhdmzm9ZBiejiGamI7hCZZ83LeOuhTcC0/jbW0BRJ09jozJ8hVv5NsfwxkWSs1eoEc3H8Rbp3SIlZYo0IwsTWBLOXLBzvHSZZPRf2qAeuc890PEpMFIZJunJlgazJU4/fAwDIyao7N08PjEAxJ1Eqzm8tpOO009boLusRn6H/U9eOku+JEd5zAAAgAElEQVQn6HmPYYoBNO+MbphUlFhPMy0bEbuv4zhlcZIW/doKv9c1WeRXxAzu2Mm4qOOHGWt3475rcPw42bUdO9iJ1Wr8vualf7BVMUqdFY4Z/vJ//jEA4FN//ocAgGyZ3+n3vPdHAQBv/57vAwBs28Z7DfSvE3OcrSCbVT1QbBI0/hit8N5jg2R1bn/J/vM+/7LqssWJtTuWOM8WybgjAHCq73EcmLZbrHRwiYlTzSOrncpn3FW8NcYxEzdW05LIxl11us9WPGKT2y+UI+1Kxu2v/1kAQLvB+rmyTHZ4cYVj8XMt9mMjdTKyx7/xKQBAuX8Moze9HgAwNM7f+viBzwMAnON67IEQ8n0H2l4QI1vIKX5VDGAmldDVBcl+MZvl8aViGVsHOYI4fvQpAMD0Euvq6SmO63ft5ni+VKUnW1v5wbNtTXXMEySOr4Zt4O5Mss64VIwgACwqh3jG8vtpYFKXZ9wNQ2Tt55e4/8wiyzC3zOW+G9gO81I7//iZLwEA+iJ56Wk6Ma/chshn0bG4Y1Or7iZ2ZRnMMyeyuMxkrOB62MBNwMPDw8PDw8PDw8PDY2PhsjKAd34HFbLaTVr/n3+aATcz504DAIYHaf0a2sT8f20xZflCG+UKfeLn5ynbt2mUPswzUzx3pUarXEaxcjcoH899X/ocAGB6mmxWURapwaqURjtS01SuElOKKkhWM5uroCyW0fJ8lBUD0BaT2VL+nNDUqDJSLZrlPb90990AgK99jfn0rtt3CwDg2v1c1pb5nNMz5CP2W86cVgvzs3zeg/d+heXWuxl6yUt4boPnOsWm5LVsLNGyc+wgrZPHj3BZV77DQHn/qopj3LF9J59JaqidIIuckg+VTNTU/KOV38WsJzHzJytdmFKn8/CYZDXFqEJajPmbioARGQCN+XuMApy4Uczd6I7zX/vNbyXzd1Tn3aDjN9HVHk7Mn8J78cjzrLnX7HU4doTb1Gxw7hytyG8Q42cROFb+lLESmzaWCGic8ymO49P2br48WezNc0EHOkSILG5hVcyesVBrM39xqsF0nF58xyTP5rp0V7weKy3bQWGKGeskYxtX5eyT5uzUGcbEHD7IeFWL+bPl6kIAzszOMQMqBi/1zrrlTj5nOvZvFUuXYhLjGMKom4PQFjlYXI3KkCQbY2Y3ukDsyJWMl/6j7wIAnDnODmVRsT3Lk/yeL53j8juV2++//sF/AwC87g2vw+c+TzZk62bGNi8sLOiqfN+bZPXPOJr5t47ze768zOOWljgGuO+L/5vHK4b/5Xd+BwBg+w6yB4ODa+UNFBugvMWBkzbBitgDS14a6yKv3Z6KotBa8rKKc6hZo47UWfbER1k7D1s8xlTF24rhMwajNLBevsO10VIclbWVOJ+xhq6NqB2rqjeW2ElndQ9Tp+w+Jsc8g/3ccM4SAl7eYfCLAlNSeg3yrAtZLav9YpCaXDb18pam6flQ7NuEbbe/GQCwZx/reFn9zZFvfAEA0AA/iq2IrFzRxs1S/7RYZ4sptT4pkvdEKKYWNbaFJ59kX7u4vID+G28DAMw+8yTLLXawuJntaFzj2GzBWEb1hWG3rvYiWsU6ptVAu/GI6WOsLmY1ls4tcS7y1n1k69t5DmB+/3NMKvz4CbKsy7vo0nTjds53vu8NdwAAKqqHB56gqMGJOfYzuYxDTgMUi0lENlnuuO2nOL1O5/xjcc8Aenh4eHh4eHh4eHh4bBBcVtNHYGqXmnb2iX3qK9EqtmmEJvui8m2UZE3KNBpYll97oFl3syXfXl3TmL9bbyYzNiCloaPyST83RSteocjr1OqyFsmCsHmM97z6+msBAFu2TvB+QYC8ClyWOlixIJZNZbD8eGXF39XqvMcJsY7lohSwZFUtKRbw4EHO9I8cfpr3VLDS9fv28gV1IhRkXcjLylVULEHYsZwrSUtARkqitUZN70vvx6wXYinbimNYUFzDkhLzjI7QqtN2AcYGyJLOKBagFccCmFoTV6MobYHfuPEjHmtjNCVmN6WqtLgMjPQn97XauCTI2B4rev7q7zA+a+9utuU7XkHr4D/7eebi+sWf/zkAQNgEHjhA6+KnPv3XAIDX3/FqAMC1E7S4X8sQWvzZJx8EAOyUBX7zFt5003WXVtZvd2Sz/OGMjYuM+bI8q6ZwrOPjniHs6SeM0dIfnZjYMiW6dLRfEqv7G9tuByRZL+d6LLo6xO6UjmWEFN2iFJsWKfb6yEHm/ZuePAoAqDdZsYeGx9csDPtCe1frWWPXoCzRw1rG7y15fieV4w8pdVDnXByLGMf+6TkyKcXRTvqdbmAl5zuUy888Xs6KCTRGcF6xgcsVegmt6Ht+0y234BN/8FEAwPVvJDv4tfvuA9BlPcybyBjloU3s/EJ93/OKKZ2fIZvw0T/6LwCABx/nGOENKtt2MR67d7E/Gt26ZdVzuBIZhnJJ3/xlKpA25JlkegFBuZo4L1MRg7jA460RG7sQmceT1edMpkdxU/VJY6NI22380TFPrVIxcXy3p0jGYOWURxCKaYriOm/9UBh7AMwoWLw/d1XiHFj+abX6hQUxo027dzcOfcOgJaVOU4PXZlMvbrcV01ygp93ojtsBALXlBbg+vq8h5QYcuIax++1H6KWW6fA3riv/X1Zj875hXitjbFbMBNrNGVvXmSfbeObgEwCA+eP0Xhus7EC9TEZ8KWC7WZxkOxnZzN9y7hz75eER1oFKQZ5+mieYhLTNI7AG09eLXtbPviE257Bx8Mws+4P5BT7vg8+Q+axU+Twv03j+gceYc3zfNn4rNvXzPbxKLk7LM2yXf/M1MoblfrbL4cHBuJnEnzZrV5nkFC5ziY4bngH08PDw8PDw8PDw8PDYILjMzs+cbzblD74sJc6KfNZLYgJzmmEXi8p5kenOU3M5sW/yDc/KEuQU8JPPc2Z//4GvAQC2T5AFWI5opatIHeih+xkbuGmQ99qxew8AYNdNtGYMKAchoii2dAWahjtZpapFlne5rfJl5f/e4b06sSwoGcKmfPDvvefLAIC2sXFmpTVrg6yx7XYTgaxWZXtuKZIdepo+0HEsjnztg5xs28baaRmYBGLblJp0DzGBjz14Px9Bv8WOnTsx0Ee9xIZ853PKJbjKui8E6SQ/Hh7rYFAGuL5+4KwqkgTzcNuuS7uWkRfnaHhDXex3aZDt82OfYCLAX/k1Mn/7Rda0Abz7B9je92xnksCBMs/9xoO0Pm4Zp2LeB953+6UV6gqFGRzjfKJiLqwzCCLLcScvAZkss0EUs4OWx6+jk0y10vI9GTMYJgnDOC7NCC/bb6qfUSpGDj1xeatyDhpplibf7JSUAmdO/fDJI4xbbyyz32xErGNbt+1OXX8NBi1mIdeOdew5kFvtAS1G0N6ptlr4h72X0L5PdtUo6oYT2vOkEwCmLODOKzijXTNVb76TTePMpzeqvH8Tg2T/nzpyDADw2veTKTvWXsb4fo4jSmLRbLxSlpfQ4IAYPzWG+Xmeu6g8vCPyggqc1GT7eJ1dO9kpDpX4+xx6mv3T5/7mLwEAr3v1Hbjj1a/guZVkfl4b5mUrvHY2EIO5yBircHFGxxkrjMTz27gsVH7AlvpXy6GMKItMx/QB1P7jums5FLm9YUldF+uJeziTblY7yweWG1m51pTvuauua9Kf7fjv8S1kmFbkCtJfsnjDZJ3evpnXfOJgHRsV7T6OrV3cGasv1v7ZFpm0bIlM2u7bXgsA2LK1irJU+o1AXZCORrFgLC/rR0Pj/Kx+y4rye5tmRBB7WXAsbzkHp45Q1TtqcX5gKvmT02eR0Xj/R378JwEAH/tjxt+ePEeWMPfUw3w+sdS79zO+rn9IsYFG3mXEICMZG2hxiV2POsWchlG8zcrf6lgbZvspi2nur/I529IV2b2Fbf4115Dpe+oZtt1tt3Ju4nJ8Xwcn2R5PSZm/OEqWf3ZuCYUV1tUBvdPqxDaVL8VYWtpMm7N4FVAPDw8PDw8PDw8PDw8P4Fskf7S4SN/Y6WnOeM3CZM7ATrPwWF2y00FOqlLmu2vWg+YK141NO3iGeXg2y2K2/caXAQCqOziDPnOSVru8/N8zRd7znPz4C7K87RczFgQBspplh8qrY8o6mTi+rpDYHvvGm8+wyn7jbVT9bNRp8ZhSXGJO7NzEXqp/hj2z9kjPOy3rAJb5nH0BrVthWQzfAMttFraM+eSbRVc5chot+Xy3dZxuVZOFIavkhPd/9QBmlljOUj9/n5GrtupSlpPEqk/KmuxjAD0ugGzPsmjVpbrOwRfAkBHtUv98//veCAB44OtnAXRzcD1wPxMDnp2gVXNuegVPPkp20EICbr/1egDAXd+9/5sp0hWLbNZiyaBlaj1Mrhu757IBOl2jPZf63SwG0HWS58bH67cJUl2andgN/VM/3UkeF/UcFKWWcU8VM4Ep5iKkdfbQM/SQmJ5mzElHXh6btygORvHsgSRn21J5DMNMHMvX5fOS/WScO/Ei1ZPt/CDOZZUMEOnlO7qqdnaPxKFdZef4vaTY1A0I844x1dWoZXFtrLhjOu7TM+xP3vD2uwAAU/VZfPct7FuaR7nv5gF9YM8wR9iwxgKTiiNcWFhM3Luh3McF5SMeVtzUrgnqA+y/hnffsZnM4J5dZAk6nTYeeexxHnstA5MHBxn/BI1pYpR4zUqJ+ztiAOvLcsFQfaoO8tooDiffzzz71cYMFco7tSaiuvQNxFjawKLRYjswnYeW1V0xe6b82z+aimHUGAnST7DIwOYsY9M6LY5XnAtj1r6k/MzWAUR2jPLOWYLASCr0UccYwFQQ+gbAYo3vwBwC4vjOIn+/hSUy0/v2cswalfg7DY6PAGVzA+Fi9x5+KyeVJ3pBY/KWlOiLJXrdlUZY32LWSn3QESl6PnmAXnk5KXeGsVoyr7ewsIisvO+eeJIs4XyN7cf6/umzrJtLM6zT1SrZ8E0jkhTPJBVIbSSSSef7sxg7U7rNAXnNEZzOWVJdnDzFtt4Xse1WmvTSq/ZzjD6nLAfWux85w7Y/tTzB808fAQB8/tFnAQBNqaHW53j9bCaLqnJv23MWpHKazrndFQVVjKc7fz/uGUAPDw8PDw8PDw8PD48NgsvKANpstSU/VrN45vNmoTI/5KSvbbPZjP82AR5jAPOj9GvfNkbrwpRy9AQlWjJs5lxv89oNxeENDvG8UkEqmFJHWzEfYK2H7baFzcUKSblUrFulzJl+Q/F2Gfn933TzTQCAq7fTX7djlimdt3UbrYWbxmjVKyk34co8rYVor2CHLH/Tc7SmDGUZfzAwzec41aDFAzletTjEdxlY7I2z/CCyP+RMyUjHqTR9BVqwT03Tqjc5N4+yrJWz87SyPPQIrS6Lilu49jr6MO+9Jhn/4uFxIRxiM8XVIy/8tc3Pf2VO+TVP0kJ34MvMx3nnHay3J08dw09+4KdZDsUFTqtxyiehh7XxAIBMNzQ6sYxDciweL84Zpu2dHuukCbBpNauDjBEMda6JwcZODOm0XnZri2Mx1lHWWysCom6s9Hrp7WKFzqSAKBortCQ/JwZwRSxJKNvp9u27dFnFogem3NmjlJzK25fO15d+om58Ymr7qkInd8SMYo8aaBDHD1pMCI9NhRfGTIzP4Nr9LXMwpUTCfg4bND18gire33MbY+/+1/FpjI3QS+Zonde4/r1vBwCM9vPbXnuEbMfS334JAFAW02I/REux/EaT79p1NQDg2j0cIxQKagT9ZAAGlFewE+ZxdoElXWxq2xzHCMMDbLTpHGiWFy9T5ZiilKGKZjSt8YeU0tMMYE5lnZTq5mC5gqwYh1ad7cNYnoKWTV3L3qXlTgussxbTDnk2GfOXRl7KiY1zyq/oAgS6aEtxXY0lflz6KspxZx2JFN9dmfcuKvZqI6Ka57jYxqSWF3R2hcxgoaw8uI5xbO0ymbRCf3/8GxqHVqnyIz4X8dyclGytX87kdK0RHpeVvsZKnmze4eNsE+emORbPF8xFhHfKZqRBkQG2jnPcfuI0FXn3XsvYvuOK56xLI8OyBMRMmJXamD+5/OTkxRYzgvp22Ag/zvWXDbqKm/oQ1Wp8N6fOkuGDGPXlFve351mmr5/k/ocPcRxy9TYy8Edm2FYefY5eiY8e5DO1VKZQ/VDGNZEtWJ3luWnmbz1ciOHzDKCHh4eHh4eHh4eHh8cGwWVlAM0yaQpZw1LunDnFmW9HDJr5cGcVu1PNVtGQslNDs+6BKq/RV6FVISfLa03s4mKNVoSOGD8z8LZatFKYImdB6lIVxc4NNHn9VtQNWAlsRm7WBFk27NoZxdNBvsoWA1iTGpItzSqWMWuDM8aT2/Oajq/Ix37x1FFk5Ao/sYUWmGxI68l0m1a6KVkR8oss46YhWnbyokrN0muKRWZFb+u3yDmzZvAZZqXOVW810arz75bM/vOzZAdP6tpN7Z9VLOd1+xl7MKD8gR4e6+GciOvRkRcuzs78CLazW8H7f4jW85MLXMK9FQCwRTdcy8a8aR2qz6JF7JyNygjmckmVv1iB0hLrWfydS1JMLoji2L8gjrfQsdH5ma445K+T3N6Jr+0SZYjZPvV1YdRlwCzoxViD+NoWlxKr4Sl/7CnGZTz7DPNFdhQjbQzfyDgZn5biw4KMKXFa7F1vjOLa+fq6DGFSgjQdSh3bfC9g/Y2ZwN5rhKl3bGWAeYhYXCH3p/MCbiQcUwxPUd/pkr7/Vw2ShXtc+gB7d9I7x95ss7mCXIZs2dlTZKhy17AzmleespdspprwUysc8yx0+B0P1bO4GX7wMyXee98+xldtHmMcVS7PSm4xQCV5OLXaBZxU3r7jkxwTbN+q+MA5xcKpF+sr8HlKlWTPG5RZl1HmM4RnyUw0nmcO1IZYlDOHyWQ09H4Gt4+haczLEu8RK6AHFutHhGJYOnKriuRlVD97BgBQlDeUsY9Hj/A99Q3wWTaNssxRR2OqyMUxxB0FC2ckT7o8TyZwwAZWqR6/GbOvY9hoeP6c9CfUHxbE0hWyUpkskD39+iOMkd/58ldxewg0RAwvzvOPpw9Y/WAdzXb43vPStigpPrp/kPUq07LYZdbltmI1d++iJ9lTqm/VIse8Cwsck28areL5Q1RhPnmEeTFvvumlAIDTz3Hd6p2Gpjh8lPXn6hvVbhSjamNw6yuN3QuyNoZX/6j3k8nk43jJluIQywHbeL5FLxHznjuhe5QLPG5ZitFRyBHKN45QrvyYvPpMSqO/n225orY9X+P7bcPFHbnpg9j316VcOsJUt+0ZQA8PDw8PDw8PDw8PDw8A3yIGsL+f1oWhYc7wp08w50hbfruRTL15+YdHziFvqlj9nF0Hsd3NZr5mXubi2LEj3J4jGzW6dQIAkMnKIqWZ8vKyGMUZWkSKc6QmOv0sWybIImcqUrIS5JUApSVWcjmiFa9QooVjYY4xcwefewYAsDh5UmVlmYcGaSkZlfX4xDFep6o4garj+srSIhrKMZgFLV7zK7QeTDneY7ZFK9ZQSCuLxZVkc8lcJZaTpRGawhKxonyKZ8/yGU5P83pR0LW4W67FrBn1ZGoPlQ/ozMkTvHab19q1axc8PM6HO/a/cNealpBeyxgiGtIwpN5t4gUQeSum1u1WmfSBVzgadSkgqzMwhWSF18SW/rbUhtsty6HqYKlVV6l0WuyeMXZxMKD2h0lGLEqxWbHZ036MmPVCt1BmOe0kT3E6JhZNtvxPoitPHmFeqRkxFNnAvjc8rlhl5QoyRT1DkLi1CyOEFttu+yzva+obZuupFH3x/vVyGSKON1wdwRetE/SYzvsXxbGC3B9sWI4bmFrktzArz5eCxgzbtjLH3xOHGbN0x2YygItgB7R5uIphjRv2TPAbeEbf7YmI3/qZFlmCxvWMXeofp3TxyyPG4VWeo9X/8YcZr/zSl5LhqFbFRIQWk6QYurxi5sIIX7iHbM1Dj3PcMTzAjvCWfVQXf+kt1CTYuZWKm/nksKYHqsvj1C7ItaguunhMbJzjOO2aG3m9YGx/HDO9/Ni9AICFGY4jOqHl+TPxBsUKWnpAxTZBnlfBDL2JGouTKhuf77R0CB7+KlnJ8X6OoXbtvTr2NghaluPSxkxyn1JcIgr2wFz255rpB98weO455twz/Y1hKb3v3MLxdWWAv/3AGGPOtm2bAABkImBRHm3Hjx8BAEye5W+1dRvrw7nJvwUAFKS0PziiAHvr9/UzWU7rnD4exw9zHhCPzaVU2lBcbBgC3/jGQyxfWfk0oxWdY/lopfXR4Dk1xWzn+vgcQcB7BmpHFp9o+StNsb8pjw6n/TlXQBSxvrSlHru9yjr+0z98JwDgAw99BQCQL/G5+6WWeu1mPv8zMyzTglR3c6bCW+c9hofYn1QLpkzK9jZfm4/V+vsqGtxYzLbeqZU/Ho/E3z6fB9DDw8PDw8PDw8PDw8MDlz0PYJwsCkCX8avVpTSkOLzA8n9omc3nYmucsYNmn+xovaNrVUpSGFqipW1ezN6W7RMAgLExWuKmR2gRmD1Fi1NnXlYi5d5wBVry6mE37qNPNEBDzGQ7S6uJKZK2ZNltK1+gqWjmB2itaNblty4f9foyy/jE80dYdsVG3nQ1rYFnJ6eRzfGmY6O0eM1LgXNuhuWs1/ncobEcUs8qVskIVmUpLOu4+Tnec0nWl5osb0ty7K7HeQQzyMo0uKL4wGzB/KRlcZM53fz/J4/r95xP5jby8HghceYQ6/DcGS4HR2lNX1Q7NLauMPgPV4aNxvwZGg3ZDJtmYhTDpxeSz5uCs1SapVoYRT15UqXIbHn+urF7ipvLcH/WaDmzcppAYhw0p4URhsbuBSlmLewyWx0VP4jptFQ8nvrm2alDAIDJSVqlG+q7c1J4zpbITCwvsu8rSXXa7h2EXdZuFdPn0gU3ltTYwzC1e53z4nyCWrUcjM68PKLuJ9cUSIHkufb72SW0DIKNaxte1vvLhvqeNVgnNmu49AXlgnzTrjcDAO5vHAEAjA+MYPo4PXSyCkzbNcgYwLLe/zPK/Xv2CNmNzR3Wo723Mv4p6iPzEBbIQlaHxUyofeUcy+CMvlOZVlYWMTnL7+6Q4gInz9Cb6W556Bx4gAzhu9/5gwCA3deQCeyssB8NFUO4+/q9vLRy92W3MRfcWIV1f+4Qn3f6GFmkwtRcnF+t3dT4KVCuYDXyVk2N0/JSajzXaZNNaTaVY00xxk5M6jYpjW8LuH7DK5FA+/RzaClHsmk0WB8Ep3x/S3yXzpRFK3xnfZVUUPEGQp/GmhbrVpc317l59SH9jIvcpLq0ovFjJZ9HU55vMzOKsdzCY/trHEOf0Fg8J/ZsaIBtIA4L1/ixpdzTK4um3Klc1Dmp6iuOz/Qqao0aOiF/050THL+fOMkYbfNwMxVqY8RCMWAFxc5antaY8bOc43FOa9UhY9TiXK01NHVuQV5349p3ZpnLuVmW+67vvg0AMPv8QQDAX82R8Vss8j2NDdBTcIdyX85IDyErhf9l6ZIUiyxTo3EIk7NkvrdIF8UFyRFIoKD2TGi5MPkbdJrnr+Mbt5f38PDw8PDw8PDw8PDYYLisDKDlimnLztiJkpbMliwHxgSaD3EGYcz8hVKPihlAKXEaCyUDUqyCWSrJV16xG/PKDbZSo3nBLLaNRTJhnRVev3aK0/Kzk0uxotuOvZyxF7ZKzUhBLR1Zw53y0ExO6lzLk1NMztbNkttp0poRaMYf1mnBazdIXQxuGkVkanI6d2GBFrb5JcuDI4tFkc85In/jbWI8K8qFk5PF4OAzjA948vFHAQCtJu+dkUUuL9ay3W6jLmvLa17DPEcW8zel5xsqS1FJZcuI2W0uLcFjY+ILn/5jAMD2nbRo77n5Vd/0NWdPsa0eO0aL2tEjRwAAi9Osn7uvZ+sojzCv5jf+jtYyfAfjcLaPftNF8DCYGddiCyxHn6gjIwZblg/P4nMCFyuqmQSnWVftGGfxyaHrPQzZpEBnl8UKU3+kVDQlyoxE+jMd2jCWLJI1+/RhAMC5E6xjtUX23Yvz0yqjvlFiL4tBUmU5MHpSDE0U05NdZi+Ox9ODReuwcy6titoN9ksev0beP24Qi9LlShPKoGveyxY+8WWsAG7p48ar/N5L/BAdUdKKvsP9M2TC3jR6O5pTHI8MyQPnhkEGO1dVB+ZLZAD7MlS1vG4LK+kW6Qfce+TLAIDrJ9h/ljSgMW+buDLrO21f39PTyxjdxG99pcB9JdGGNbHXSxo7/NGffJT3uIaeE/t3czmR5RM+M3kEALBr3z7eajtjHzF0KwBg8DZ6UdWeoFrj1KFvoCl18pHtjGMaGidjFy5wLDA/RRau1pDHkt6HaTIEyrcWitksmACBcrhiuw1Vk8qlrt5AJAapkeP3oNNUfJe0CzIN5Zuu6p1VOK7sKMZsI6ImmczAGFnVp7Ajhkn1ysbdbY27G408StLjiMex6renpjgubBXIGmZrbAtBgePEjtQzA8X8VXI8brjKujLZPAKgm3vPvg9tsV6ZXB45eZYcPc42Ny8l0rbG0C3F/rWbykEYWAyfMWHyYtM9LBY6Zgx1lJUhnmdEEZaXyfy507x3c5jv7PgU3+X41WSrr97B5zlWYnu55yHG8xaqjKvcv4uM6MNf/BqffyfjfJfVNoIMnyXr2MNsGZvAqROMQS8NcG6wOkbbPsDcPlAk63jLyDLOB88Aenh4eHh4eHh4eHh4bBBcVgawYfnzlEfHmSVAFoAA5hvMWWuhTB/0TqdrHe4o0KNtpldZ62wmX8jR2lCQulFfH2fRGTFlNeXWqEvJKFJ83+y8mLWztPguRPSbzyw6tMUW1rfRYjFWoUWstsBrPHT/PQCA/k204l29h9a7inLVzE4yZiDS6262zc7A6w4PShVVQYaBzLDHTpzGyDhZx6KseZuGxA7OkrE8N72gd0cMyEKwSQpekSwgFk+59Vrm6puX+uepA4wLMDXQSj2IGAsAACAASURBVKWk91RDy3IqnqAVbkw5BqfE1E4q/9+A/MTNj7pQ8HaFjYrXfdd79dffP75icpJt9OCTjL966nHm+FlcpAVu0ybWt13XU92uKVv9Y/d9CQBQHSYLfv+9jOM6PMp2+ZpX/gMGBW4YGNOlhalrinXq5gM0ZUqutnskLF0m6L0E0nRTtCo+z+I6kodnYfeQJVoUYWzsjcVEV9NZWfNbELNSX6D1evYcrbv1Rca3ZOXl0devOBbFAA6MMF5ji/rZwCX7PMvXhCDqUn0XiPXrMnm2jsTzGdKhgKvVQZP5Bde6hiGOcekmGdT/G5cCbGNFf7GObBshW3dihXVk51X87b8esX8aEOPUDtoYuYruBpuqVPgbVj0Y0hW3t3itGytiYMb5nssV1q9nFFf3rle+DwCQV1ksZtXZtzWOWWJfOLJlG27cz2MPPsd+z9jDRY1tiqrrs9JFeOQhKipOniD7PT7A8djuEY5Hzh0l6zC2jbnXXD/3X3Mn86mWbngjn2ngQZx5lOOISlbsxQCf+LlpxmidFKt4VT/75o5kAhqWZ03LpuLGWkscCzUXxcAfpedSdpDvdUgK6pmBAoozfIfNhUVdM8moTy1STXF3v8UA6jfa9wJKUX+bYWSI9bDd4RgvJzXWkpRSnbzTBjW2K1c4Fs/kgLLG1KbmPytdiZnTRwAAnYbqrNpPrmwCFarD0pboH+BYPdJHo1+/rcXcLmp87SwbQOgQKk58VOVfWuCxKw2p67bF6KmZDAyRMTbF/mKpv6ckXf0O806xftylvDJC5DA/Sa+Q8CyXzQF6GB1b4NXGdnA88miNsX5/eg8ZvnqW6xOqf3uv4pj+mX4+Q1NKpYGznJkt3ZPfh8lWA9uvv633FXaVpVXOQsR39JIRMn+vG+T+Wy5Acl/WCWBHri8rx9mRhnJJKPXxR5lTUGl1UUlUi6xoc/PzKEpatSS3wygwtxoio0qSyWjCp3vZRLAod9KtW/ljnDvCir3cVFC/BF0Wj/LHnXasfFsqVYwoQWNbYjWTx9mpnTrCSWK5xR+wEvIHPnv0eV57xVwhzbWJP6zTjzW3okTxkkpflBvqsjq/E6fOxY2humtz4nmM2h4c4uB2fCvlpKem+O7mlh7VvZMuPtaQDx7nc56d46C6KbfbfFEy6NkAixak22DHuiL3gNBctjQY6mhybRPGZlqm3eOKx4rcP5559AsAgFtf/w7tqVz4ZPUDD35DghuScTl+lG5DJ5SipF6XWIEkn3fuYV0eGqHb0ewDlOw/JSPOFg0Sohbb2S/9q88AAP5/9t47Sq78vA68lVNXVefcjQYaDWCQBpgBJhAznMQcJZqmSEoiKVHiWmub3lWw1j7Srs9a0tpHa/vQsrxeSVSkSAUGiXE4gZMDZjAzAAapkTrnUKmrK1ftH/d+r6d7BqRM+szZw37fP6+7ql76ve8X3ne/e79Pf+ZjAIBdXf/w+3NNtiXt0MkYtIxQe/ly3nneoAyB7WvFa50MTk3CWxZwTrFeK0Oj8ceGGXvh8+lzE3pxykig4QixOAXQFYScHTsPANi9gwvWfIY+NZXj71dT/F1zM32pdwdf+Fp6mG7cqsLUFWjO8iV17ZLphscpW+GUhnBeCPWBpYRqHLUyEdYwW1M8bXHT2PLmtzVV1OOQF15TjN7a/gaF3p0jbOMhvCAaiFeUju4ofeP8HANRB3sYAD63yBetgF7KXrj6HJIRvsg1dx4CsDH62ULrlk4KrLRFGIx6eZYlJaaqDLJG2niujg6uOYJKcYPmWgRM5IGbqpy/M5nAu+9hkfnzvSaQQUGa8WtMa15VzmqtwEViRGW4BnfRl0c6eLVXznH9MlfmuLs3z2toVuAtnXye/6sMBAZOoHuAaaT1K1x3FE5zHhiIqpzAHVyfRCSln7vIa0tnOabXKnZDEppRgD5UZMsFauxnO++6n7+DlY9YRmQn27JykevHVIprGhMyivp5X8VFzlHhJj4jrKlt2/Zju1k2zbayAuitrWzfsiO4Qr+am57T91xnJpJJR2glqVIcngafWaaoUmoKyJaCShUN89gBrdH9An6iyuYtaX5OK5XXAn5WnqSmUSy3VoDXx0l7K5hSNYEwXb9fY2EhRV9fXuG2r5c+XHfK+Ug8RqCTN8zx22clQzSZNHx+BCQqFInzu5KCnuMpXsOpUa6pr09z/bFc4DV0dbLtygKC/var7F/eAD+PGPVBwkk1CTM2lNYaDHvRFI5u+gyvmV8AoCfGz9/Ty/mqb4LvJteL/FxJ3K8zF6pxzTXXXHPNNddcc80111zbJvamIoDlK4xyTatIe6BMZMkbYyhgPsu38LjERkKCksPJmCPXatoDhvj5lTJQUgqjZSiFgoqmqqCvpUB2dTL6U67a50IOY3wb9zYzshRngASLy7MIqRhmbYbHTKX4dt3bvwsA0HKAqQRppVaMT1GEoqJ0U0e4RhFev6J4NZGzw0I680VGAOaU1tnwBdCswvehMKMsHVHCx7epaORyhm1k0fNzF5kqMS8J6JLasqoQiRW67O3j/kN9jNDNSbAmrUhIWzKBd9//VgDAyjRTKJZXmBLi0UMIKH7QJNJ5Sqmy1YpFKVzbLhZVsdejtxzSJ4Z+3wgBFAJRrWF+Wig0XRVpIc6lCreVKr8w4aYVRfcmJhlRO9w5xP1S7LQmU12W/PdajtfWo9Iv/+f//tsAgI9+9CN4130j/133ud1NQ5gzBlgKqAm3OEjg5gx9fudsnWrj2mxOQ6xtQRG3Fop30nN0wEDAs+kaTDwm8JrMS4sUV5Rek8tzvFuevggAWJ2hz2TyHHvrUp7ZtW8IAKDkBjQrDd5b5++vXjqvzzmeeiUw4bfC8AAaFm12aAy6TmssK4NhmRXWpnafauuGIYJqGEsRsnapv6atnf2sOPwWFHGrgIzzve17gwLy28EuPfw4AKCzn3Oj78A9AICXXiFa954eijZ8Jcf1zEGJpDw/Nok0s7qwS/SOl4WGVDP8v03lDBDieuXIyEEAwJMvEDm7dTePFbJsIVE/PD5D/qwTCOnwvn4Jd2CEaNxebS/1CCGT8MfLL9CHK2scXw/fwjFwtxCZP/rTL/G+/fTh9QYRsr481xozS0TvWp9jitvgoSPYcedtAADvCEtjREZWdDVcG4QtCbaR0rkfAQA0L/I+1/JK91PGE7SeK5moVJH7zUpkrPcW1oOoBppx+kU+l5AysUJKXzRBo7gQm7IyrMKS1MfwUWxX88kvbeywEgxBrasDStPvUcZcSOI8lco6SlrPtsSVuTfPZxOV4OBSimhVQiVAovIjf0hlRbz05Yb6yKpQuqQQ6XCEx19RSm+ah0exVMP0nFL1NdeXtNY0IRqbO5ItXHdkF4l+P/X3fwAAeOfHfxYA0J3kWsBKiBRTHPeju24GANT9rWoo9r+a14Oxq0zPvrnbUr55X+MZ+qxP9z9b4LUUJdR4/wEi7GkP/XN1kdtqzcSm6K91zU3Vitb0updqvYZAdTNWZ1NHXROTJ89GWp1gu1RX2bZW/uJG5iKArrnmmmuuueaaa6655ppr28TeVAQwt6iIvKKSsSC34Rbm3XbVhwAA41eZIxtTBCfR2gY08Y0+YJEIveqXJDXrcBuqVjiV/589TaLztRnl5S4w53xxkWidceoMfQjsadHn3L9lPoHuYV5Xq2ReveIhepWXb8RTKwTfnFSELcRIwcQkEcOkisKeu8Soc1DnNuEWI6gWlP8Ovw8ryosOpRhtGNpD4mkxxzf8tRmicrMq9rom/mRF5TGs+HJFSKjqeWLsOqMO/X3MzW9TjndV7doSj2NumscsrfC5dcbYNgWFH7JCWjwVPpPmJkZ4yo0fXgDEtf9/28q8+p3lqVfZry5feBEAMNxBHx68jZFDeNLaUxyWCvvO6AvjAIDUWhnT8xQhmJ5lRL0gn/WpD+fX6KvVAvtTKMZtMmphMAkF+Oncvc08x7xQ7ZPPMQrYO0Bxpne+lRH9r/7xn2Ju8gEAwM990rglrn0/MxX6hiMW4jDbtHljwZLGRk1yBxY0jo43sLlEgSM9bsXUJe7iIINb45YbaincKFtEwVv4Ag34JB/ur6o4sYS3wk2EPXIpjrm9PRwPw1GOu0tLHPu8yoyoVuWLEh1IK0r9nW9xrNy7924AwJHjb4NzUw7HUciftZWBOSI32n3Wa7oPXX/9B3AIXy8mo8N7vGg0rG2tjWqbfgNnn8ama/RsXwAQvmvjAIDOANclT/3V1wAAa1eIALzYRbGp66Msg9DtlWjK/Ap2xDmXmzBVmzIgTo5zDKrl6E9Prn0FAPBP7v1nAIDJeaJS954gsuWR3oCJvXgcRaPNiIfPd2NhK1vcHdy7OcuhQ9zoZx97iKdo8FyZrBDCUWoc+IRcP/kK7/P+O5kR1NnMdUB7jA4cuzqN4Svc530f/cc8SbDzjS/Kw31bb9Hvqswuyk2PAwDKWXHTana7HNPzq+pvK1y3ea+Q791959tx7H0/o4Mzk2T8218AAKyr9MRahohLQ3oPsTT7sC+n+kCJHW98rT/GtrDA8apF6z6fENqostEa4mqOy2+bJdASiUSRL4iTr+yySpVj4PF3kVu/8me/BQAIqVBKTWiuib9YAZOSOHIDQ1xXz146CQDoU3ZaLKbSFEJ2szmgIE7bPXfTF2fneB/LZ4gCx/WeYMi4ZQPt38M+ENO1FDLsb0mJHpYLVe3PQTcTkJiRAGl4g3j4UfrczT9N1D6dp5NemuK6JVNS1pN4g/4q/z+msg/T+n58nL68vMZrNPGyclWl3YSweo3f6KvCH2JqgYlg2tjv1R+TWbZlys+xaE833y3y1Qy+n7kIoGuuueaaa6655pprrrnm2jaxNxUBbLuFb/qjzzGCkJAkcHMrFS67uxiR8Y7wjTmfVeHFUNDJgS+IB+S38K7Cwj7jTQg16BniW/rYPN/aJ84QdcupmKNPxyuKX+FXQfTaEiMfUas+jABapAAX9BHhWl7l9XsctTahbXo796u4qSdskV5ea0RKPnndg4VsoxFFXRSlDaiI/fHjRxER4llQIvSKlKwujFKBbGGB+fhW0F1BO/iMV6Nz+BVtrxqPRAhhJs38653iHuSU57+yuoJqgZGdFpXjyEk1q6BosVdKSbkaoyoBtWnU68YVflysIWrtyZeINF+9zohbRfzdK5cYDb/nBPvb2Qv8vgz2GRNU90vZc32d/W9+gX68VsphYZGRr7KQv4K4Kckko451cQCjIUa3PD7+bmqKyN/BY7cDAI4dJBd3PcOoMkoxXRuL0T/4yAu8l+dfBQD8zC/9Iv72q38IAHj+nz4OAHj/+34KAPC+d29fifDvZ6bMZmRsB2EygMLAqS28NkJOhg5KTdggLQOnHH63OMYOt834HaamXNNWEdTqZh6blM2xIUbs2QAmVai9WTL9J+6k7+RzHAfDEY5pJ1+SOuMso7YVRDedw24lkRSfu0H/Doqr7fGIB+3xOWqnttMNC7gbIuoodWo37xZuoI7njLJb1USd5vBsKK5qnvQ66nEbSqGvPbaVjve+lry5zeyjH/8lAMCOTo5hzzz0OADgjjZy/l967GkAQHBZKpujjwIACnPTKC6w/a6rPMPyMNc2K2mOcbU1johlKWsG8uIYLdPP9nfw98Ec+c1Vzf9e8yGPlLaVySNx9P8us34RjnPnuNYr50eJinT2ExErZIhU+MRRKopvtaB7yRS5bokHq1g9xVIRBw8wQ2mog0g6uqS4qXUZ/OIy5ni/68pYinXwnPGhu3WVWiOBN9pqZVvM2XNUjE5NnsfKItu/p28IANAyQO5mUB2klOZc41f/8BqfyuYJq1KwjayS5lxYDnDdV1pjI4S09jRNiVDI1t3011JhDamM1gLXqIqbaGE/aY9xTG1S2YdaRbKzGr98Psts4HNoUoH4o8eoXjt+nllEWfHxmlro+zu6eZw5FFCr83qWCry+inh4QflVXKUVQnr4c1Le/Ouvs8/+8wj5eNECkWT08f+QSrxFPZwH4uv0r2gb/XihsIKg+HQJzSHXrjEjIK3KAT5plpQ1v8WkVt4f53137+AcMjfB9vn2i/S/os0Dfh4/6FGWo8jhjVoZESGVxgOvO0ra7BdZL7/P1aX5Eefn6ymXA+iaa6655pprrrnmmmuuueYa3mQE0CtFq+4e5ofv7Ge0yydFtfwyc2kDKjBZkSLUUmYWnaq7FJCC5kZNJIuKKvop/kRzK6NAZfBNvlxjbrnDhfBaTRKLRqsgZoPn7lLufmSgA2tZRvry4k9MT/OYOfHz9h1UPRyLfpeVn68oRCzGY3nEQymq8OOuIV7joYNU2QoKCSyVxfsL+J0HlFEA7Mwo+VLpVaIflgNsiGhVEV0LuFnNtIbDP7FixLSsUNYVFfM8dJQqSC+dOol6Ve2vczUUgbdnEFKbpxXNDMfJp/ElfoiwpGs/2IqMGFVyQootyvrDmlLO4cMGF0h+dm2GUd9HHmXNp9l5Rsa6ehnRfeAOFj2dvPwkAGB+gb6ykqZPrJxhVKyrWxG6Mn1oztRpxecbHBhApFNI8jqPsaiO1C+uytw00e6a6mzOpxlpi7Tx/8lJ9selafEAFHh879vIpzl5itHOoJcR7q999bsAgC9+8d+jUwp5t99CTsFffP73AQDPnuT9/c6/+V/eoOG2r5niZlU8hZpBf4IGDfhyarZr3GnUPRtI1RZwyYAuh96wpY6oVbOzQvBer6kqKwoqxeeKxquKcU4USQU2eBY1ndwnjktF5O/mZtVZEkNlRUrMJdUn8+iOHGVOL38XUbT75pupDNnaTiTDJ557reFxLnxDkPMGNfgcJHBzHUQbq2tW7MqQUWsX21/Pot7YqB9oKKvXMmacxt6sKGqXZAW5aze4xu1gnmaiCDcf4lx4tsB2/8nbWIz5z776NwCAO/ZJwfMhIlDFfAWNGalcPkN04OpXOT4uKeunEOIzessdVEteHufvjg0MAQB8ynao+ehfXgfONUlAHqfu+eHn2Ol5js3BCM+RbCf60RXlePmTP8sx8blnvsd7UZZQayvXMU3qb/EY+1cgHMeRQ+RYDR1SwWqD4aXICalK1nOm0Mn7yS0QifnKX36RPy9yLdHfPwQA2HP4CABgYN8+AIBf50Scz6gl3oOWPUIZhRZa3knykLWR0MQc105Y4/2jYkrV28/aomz/MXEpL41yDm3p5prUFO5H9rDdu7qoPr+6NINTLzLrJ5Vhps4tJ4gi1qQkGm7hb7OrHKdDYa4X/X4jZXNj2FRKWWfBBHmIKfGty3qeMWXO7R/uR2c/M3MOHKHq7Pg4M/uuiZ/b3MWswltvZWbHN7/6ZZ5b/ejChXHe57rG9xfJXa128l3E4+O9NfJEDm9WrcvuA3tw90GuR15+gee0dfxIjWvnWfWLNY0XUaVPVTM8V3eA9/WL7+Yc8euf/AkAwG//8V/zWlRvdHSM7xtzC0RafZ4gYtILyRt/3uZIqwuo8X0my/HHnyS3Mz/vIoCuueaaa6655pprrrnmmmuu4U1GALMZogArM1QW2tnBUH1mmciGJ8DoTjrPN+cro3yrH9h/HBGpE1n9P7PGFq5CQ1EDv6KoVpEuIL7a2ppFifhNNMhoV1UIYEaRq1XVEIt3xzF2iVGCeB9VBDu6GWXoHGDkLCv+XSJsNXt4jkyaERJT0xzewTf/T3+CqlWxVkaufCHeWzrNCEprO3OpvfUKCkJBJxaJXiyuMupglICA8vd9qofoFKyyAIFFeE1ZSBHwqkPAYQxgRSjfkZuZj12rNfCOt90LAHj+JHOzZ5YYrUtEifSVpSzqVyR+XWiitYNrP6yZ15qvM3I09vQ3AQDRJvpsJM0oVUSog1cRXV/YIqKbbXaKx82u83mVVsyXUsiXea7ZLCNGF85SPXdVSEg8Tl8f3kHf/K//hbX09isy65H/jY+xL5c66Puo0bd72xix7e1l35maYv9K5TPIqZ9EdY5CTn1UwavBfkbepsfZDrEEfXZs9BkAwKunWBsqP8+abLsGiYzGPEQb81KOO/PSt/U9/bfhiaIpzuji6ZfZtjOzjL49f6pL981I2r/6lU/ANSAiYl7ZwCoNIzWNw3VF+o3P5yhVehoOT9CRmKxv/rfmsXp3mxFAM69Cx852M8XagbG8uqiglJzrDQ9QEaqmiGlD0Wqf7ayLW0nRFwtShas5w6n2E0pnPO1wiCjD8O6bdBzOM8ZXrFYrqGmcLKv+lUdZKrUtiOgG6GaoXGPrF9zfyX6xSxc6WbfosB2lAY9vcw3CRmMzaujsY8fS595tzONePsdx5MIAx5Gs6vuqTB5auoYAAG89QRSicw+RwunJq2gRKS/aJH6dlIg70ozmP/Z33wEAfONRcpJ2KnvoM7/wKQCAX/O0r2zFLM25OSauSx4zFO36oe8vu8Z5Oh6iD/e2EcVuDLIu3v1J3l/eI87/uXPcUX4YDdPHOxJSSm+N4dBhzgMO9K91CcSJNe6tN2wDAre5Eu9rdpkoY4vabXKMa8RLUnHPCG1ql2Lkrt18Nn5fFS0t1I7o6CF6NbiP6BVCJuGo+TCuz+PD+vxGNWp//C2T5xiylKYvZNeIYp2/9DIAwCOuXavm7f03EbGemr6GjPjSRw6TW9/WRPSsGuUx1mucUxeXuG6OzXP9W1XWRSRJpKuthz68MM/+tZzj8/JrLF4vSpFTUpw+zzpiCdVwnKcP7tI4dWKPlEOVVXfbCWZkBLQm+vLf/i0A4OsnuWY4foTI8p33U/27fTf9Kqxx/uyX/4zXIL7r3LUprKlywKSud26W7ZDOs61yWekjQNzFDrZDNct1VimweZCdG+W6+tAg+1+kl/ewo0U84mn2kZI/iDVxuG2dZbXFrbt5dc55LbsuXOZ6Plx2EUDXXHPNNddcc80111xzzTXX8CYjgMuz5PLsHmDExlOVao54FnXVE7k6R3Qg6OdbbVt7G/zKgTXbiI5uiRYrGlrIMhrR3MS36+zKqs5JdDGh+k8VJ8Web8qTGXIFU1kiGcG1AGJNvI6eVnIXq0IhPRXVzxHC0KXogzfIKFarVKl6RpgXbzX2DIOcGROilmSEKicuXbKZERXUqwio1uD8HNEeqP5aQvflRHY9Ftnl96YsVBdvxMFJ65sjwsbRKa9zv3QmBdvh+tPMDy8pSuRRDZb2fqJAYfEL16UIWdBzzEgl1bUf1rZ2S0Yq+/vFg+1W3aJmRkEnX3wcADB4/J0AAHUrPPM8o2Ury+wLGSHVkRAjU3v6iCSuZvM4c4WRrp0jjAJnVoieNYWIgAV99E1Plf1oqJ+RwdY2fv/SaUb7urr4+e5Bft4Uof+l1a/MAurbr45ewk4pcaUWGS23WjyPPcZaWbFm9tnxMd7Hxz/GGmtf+yty+Zqa6H/v/AA5LCE//bCnh79/y1vI73v0cf7+PR/4EADg4e89gbr6y8+rDuDkFMeo8Xn6uIv8bTafwpchvylW8nOjp1W31KIzxUpPfePLhgM/mZLZa34DOGFJpy6gPjexT0O4vOIRrQmhUVAU0SY+u1LFahNuKB8KoITfL7SwieNrSBkm2Sn6aUUn8wdVi6pqSqRSapM6Xksro9hep16brk2RaZ8v5Iz7dhFVQxWNs6evHY7fFq6gM8bjjZFRow56Nv7Y+NLazFFU3aw4apy/DXW57av+aZa9RkXLr/zHVwAAXvn6l9PkSc0scly5ElEb1jhedYXCSCQ4pnYrC8E/Qs7SygrHttwa1wZ338ux6uJlKikeHCY65Str7oxKfbHO/yNWG7LE7USJn3eIb5/wvbFvvNZMkblJyF93mBkXCHM9FpBGwVpZmSFaS4VjnC8CctTmOOejng6uoZKJIAZ2cAzf8L0t0r6GVaifwCsO2gTnjR19PFZfM6+pplpqVbB/GY6RyXP1NDPFZ1HMF1AvsW50dwfvY/QF1i2cUeaV5dN0aH02OMSMkuFhXnNi8B5sN9tznPyzC3NEunoHOF9HM3w+2TTn++lp+ufC/HX9rhcRIcB1y2zQXD6luovffoyc+/ZOZdddJ5q7NnmW+2mgz2vAvjxNtCqreo2moh8JSDsjwPnfW/dgaYznOr3KZxvz8xi9rRzzO9qV3ZPjOYcG6Yc//5F7AQCz87yvzmEimnuPcr3jD/NcRfH1yi1E465McQ01WPRiSHW9jx4nWnh6lGvyixNE8rx6t4gr82SohWv0Zi991lMS71Xr6IKH99k3TP6sT0qfLVH66e4eIqUTmTU8tqra4DXLsDFuPf8PCl21tktJ06Pd//253C4C6Jprrrnmmmuuueaaa665tk3sTUUAm5v4vtnSTMRrbFq5v+cYBdp5nGqYxXW+zd6i/PhIKIAqjM9hPA9xUVQQx5CuddWuK6je39wMkTwLkJWLfJMeHB7iNSjgVqvxzX9NdXq6OnmNwVAEMR9DtokYz1mqMlIRDzP61r+P0T5T7KoqcpavsHlDQvHyqqOXVZQhoHzeclEImyICxjepl/N45ewo9y3wGpqijFQEVZuvqvuuiPtXFqfF6gCaOmhdUcqAwudlQwBNNVRR6iXx/N77nvfhG39NZa6DO6jqFJ5m205cp9pit7hmI8ephjY5Oc42zFus0bUfxU5++fMAgNs//GkAQKCFETVILRPNjBANHn8PAODCSTrzVfFOllfpZ8Z3KsvfMovsEzf18P+XT5/DSxeJ+KHC/hGP8bvmJCNqv/wvPwYA+Oef+Z8BAG+9k32zWmGkenlRdaxuYh2oxQVGzvxt9JHePkZdV5Z5nlSKUb/06gJmpKKVCNEn3/+h+3gfF6nglQzS1zvj3GfyOtXcBnYM8dgM/GJlaUWfM7JbByNqf/KFvwAAdHTxOL4A+1exUsfBg4wE5rK8j9ELRDh7d/yICqs/prZRP44WUMTfRCZN6M04yj5T36xtcPvqQr5qDieQmw0emiFf+t9RvbRzi8uaI1r31JPkVdWKqhss8QAAIABJREFU/L67jz44spfcrFg86SCAxTz7SMjG+6z4HOIgjV5m36gKputoFpqoaHWxLO6gap62tRkXa3MsdYO+V3eQOUMfJTTqIJv1LYrWjZrdv+ocejfXPzTEtLaFIujUFzSlUo8HHquNqH09r/mO+26GCA1dxTZGAiVaiIcfol998ANES777ZY5HkRjn/QuPkzecUi3enQeOomdwCMBGZlFNzlsPcJ9qgKjASp3/7x7iOBMMWmegn1UrHBMjjz/EjzuIjC2OMFNhWLXXcuv023OzGRSkSp7WGubEneQ5RVUjbUaZSkHNAx0J+ldFOggGlWWy7Bs+q42sTu23esYR9V91+nhrB7xal0DquBs5R1s5SNYwPPf8JMfym0a4xvgvX/gGAKBZaMjNe8nXO7KPSpOtTaql3MbjV2o1VISoFHPsm3Pz7MN59dV1KbBeepio0Io45x7dzx9/81FsN1uSr8T3Es3yzigbTboUvQPMMuofIhJm624fvDBHSWU5xweFkO/eT3/73Azn+LRcuipE+fAePruI1PShutqrU1yvZIUWV8QLNdV5E3H1hYNoOUw/WFkmAjx6jSicd5LHSMyxPxxKcx3zwANE2hP7qepd0qTz8CNE+V9+giq9PUNDAIDuQa4d/tGvfhYAcPLRRwAAc08/jkSBc0dbmG3xjkMc+99+mOuvefEFp2bZd8NC57xao5cksT6+xGt8+hq3a1Wimp0dHBv8et8orRFRXAsG4I2pWgHUFppvnOHa1HZ1zlpF9UUd/PuNzUUAXXPNNddcc80111xzzTXXtom9qQhgXspq6RQjBBdHGZG55w5G/D0hvuoP9jMa5FeNpXJ2CY246nRYlMDULhVQMiTQG2F0x1djFCyX57mSccEE2r+tkznPU1n+zu8Tv62Fkazbjw/xPNUCEhFFLqJ6u5ZCkl9I3/o6z+kP8k0+llTus97sr1xhjnqymZ+3SFmppHtIq85Obo0IRr/Ug8KNCiYnmOtsanXBKCOBMW3LFb751+uM6JgFxZmsGdKniK+p9b2WJqID8H4rbIdSsYybVAcpPcPc7RO3kKfQdxc5WFMvMwI0cNtxnYt1g8al4OXaj2aG/JlVxWv1N7N/5OaJgLw4Sr859QKfx3Ka/+8S1yGf534jB6jadX2VSl9e48uuF9EmXsfUFJV3bznOZ59bon/91r/+twCA9gR9ekznfvUl5r8f2U+e6+ULjOju3sXIYtHPa/HU2EeqJfr6B9/LyNzc1PNIqLbcu++/CwCw8xijj//6l38aAPD1b5HTEQ8yOndlgn26TZkEh48Q8VlX3r8/pPo70u37tX/xKQDA1XF+f+EqEfeOti5cuMC+OTYmfvJu+ng89v0jZ9vVStgC2xmCpKht0PjRQpRUChVVr8foC6gKuvKKZ1f3WHaHIV12CqFU+tcUjItSaJ65Sn5KKcPn+eRjTwEAlsT3bgiNOHjzMezdR39MRoV6CAkMqy7bpOqRXbvOWrRzc+wzPo3pVpesrYM8cONmd7RrXjI+o6GUmmf8fq+DhjpgiKFy+tfn82za5/W6bZu5fdZe3i1ZMRugXcPZbK056Nny2F5Xk3ALQrgdzac6t2MT9IU9uzge/enn/wgAcNc95IxducSxo6B6cpFYE0I1rmFSqxwf81I0n50nSujzckzKv0qe1Ec+8SkAQLCfflULqq7vE4/xYv6EWSD1AY75O36b4ybSnJPj54hkHLzrp2Hx/GfOUrVz5hUeY7mD42N9iQiFrbMSyqyYzUsPQUjLpPhfPiFn7XH6+qDqrtZL7G8VIYRt3Uc3uH2ObfRa2mZuYGqaY3HC+qMylJ65wjavptl+Dz2pGq6CzY8eIJLzkQ/ey3O3JgDVX6vGuV4sloiu5nKcc1IZ9UVxsyIJznUZISzb0WIBjjK3y69OzxBJqymzoSHUKiKV45IeY9YHhFJE16JRorSXzjwOAAh7h7ivOH7pVSrfpgNcey+VVFtbyHHIpg5Tf4bxkDerIBsHOh6N4pvfIip/6CZmnY3JV1PL4wCAZWXMfeUks9S+9vBJAEAxx/H8yE283zWhxatZXkssxv4X7iDiObyHa/RL48xOTKSzSNTZz1NjvN7mca5DOqSKH5Oi+K44s0YaJXH9lLGVVn96cZRr+mtZ/t+tuSWr17F8lnNpaU1q7b4C8nouAfFx/ZZNo8wUq5lezNPn16RP0tcrnu8NzEUAXXPNNddcc80111xzzTXXtom9qQjg6AVG2UOqeP+hj3wcAJBrMKJw+ZFvAQB23sQofFF1k9ZmLqMlyihCVUo7Fh2olvi/qbOFY0QFFotEDW49yvo0r7zCaLG9OZcrfMu+fZ+QQb1Jt7Vw/6awFBOjMUelrdHgW7bVrfIo8pQIq46OEJVKtaAtz9HVzSjxwsK8TiWOnw5cFerWqPP3c/OMxuzua0FdedAxcf6CUqULC5WsSQnPIs8BfV8ub+ZKKjUYHtWv8hRNcU71qpTnbxEE+L24vUPoopf3V8ow4nH9yQe5zfBce1qI4N58O6OT2TwjJa79iKaoFbx8JuenGHG78hQjvEtrfGY5ocAKmmJugShcUvy9jo4hAMDe3fTt1Ayf53Xl/TfFk2huZl9bkFLvVz//OQBAe4yRpPFrjKz17Ge9s71J5r8PDjAnP+BlpLApRJS7Lcxrev6pr/K4rfQ3v/ys6zM/BwD4P37vf8PCSSI3Xbd/RDdOdKb7GPku8Rd5P+1R9qOpzKtsFtXbjPh5ruFDjHRH1S8/90es+bOnl1G/k88zGijBW9x54hi6u4kqfulLrBNUrfA6axXXh9/IPA1THd6MWj3xMHk0TUlGP3ffRD53PMH/Pd4NnmBAfCdTpLQxzpQ3rfaeiWJ69LuaeHmLs0S6r11jdHZ5aVn7qV5Uw5HVBABcOfcCZq6zz7S1cu7Zt5dIwm1v4ZjV1s3nvThPdKPzMH2mUqOPlYVwFvM8l9Wb7evl8cpV76Z7qdZM69nj8KIMbLMatcZLrzrI3WZenkPP07cNcSc9VmvRnoHOucHaMyS1vqEC6rGp3viDmzmBZnbu7YwAXrtMdOrQfvrwpYvMimgS/2y9QCSttMYIvl+IRzToh0+cm4KyebI5/r8iLQKr4ZrKMDIfT4iXbBk8QgAbL5ObtHCJY2HyADMyTn2XdfH2PsaxrX2U2Ryn3n8VqZuJTO718NxDrRzTR4aIYJ6Z4DF3xDmf5Ar8/DtTRDLbhAheXBWysc5zJ0K8tg8c4veFApGcF2bpU329/a9X/3wNW2nz57TZKalG9zIT67kzRGzyy+xfCe0W826uK2oq5RVx1TvaBhEN8jrKWuTk8uIw+td1JZybvD6hq1JBDUe2r48HvJwzK+KOGVfMr1cCW0+XtL4utXLNEIiHUJVOREaqn+e/9wUAQP5VrROL9K9YhChaVPzQlXXLzGG7Vyr0q0UBsZY1EpG2hiGBgVBA11JGRzvH5dU1jvUVZcb5E1zrxOULmRI/vzzONXdHkuh1t/j/XtWwnK7yHpbkV0+Osn9NrXKdMzPDbJKRtijuPkCUsFFmv3r0WfbBwSQ/7+3g/cakklor0f+qQsdHF8Q797E9fvXX/icAwLE7mflUaPB3VhdxYZFZA1PjEyiptnZN2TO2Tl8Ur3dmnONLSPdtvu3xb66esNVcBNA111xzzTXXXHPNNddcc22b2JuKAI4cZs2NcIxRsKsTjOx763ybr4nHl1nmW3ujnejcjuFdWC4a8sdjVayYkiIVpnIW8DESUMi06th8sx8c4BtxvIu8qKSPb+Ntygtv7mR0K646NFZesFCoI6DIREiRCTgKPIpCKOIUMnU7Rbg9yvf36y08rP0tulrXtRuqaWpK2Syj0NWeZvh9fESm5unTtip00WPXsoXr55h3s8KcXxydimqTOOimInaGTvo9HpQUZclUGNFAkJELb4bI1E0jVIL0iejT1EFU6PDxO+Da/wCL0y/OPMkI7YUJPrOFRUal2jtZP2ZFiEhnM59PucT+VZEi7vFj5D+lFhktGlfEtzlBvzz55NfR183o3fwY++R9Rxkd/ukPUQHv7x95DgCQ89OH04pAfefv/woAMNjG/uMJ8PN/9Rv8/AOf/ve6GUUaxxj5RYrcQbQMout2ngOzjAIjRKQTOuaAFEQ9AW7TWSJ5fV2MvO0aot+NjhIZvHiW9Sv37vvHAICHnmfOfcNDBPRTH2O9xN/7q0fwc7/AOn9//hd/pnPxWPE4fX5c7TG0cydcAyLYzFNblgLi0489DgB4/gVyduqK/A/voe8dOHgQN99yKwBgn5CVZLNqNsFq0gl90qxkCsWmerk0z+c4fu0iP9dccOIttwMA5mf4fSazCjsCAFTLRWSlCJgV33pMtanOX+Sx2ts415x7lUhhvkCEoUXKu+3ibSdVb6pQYPT6L7/wJwCA+99DX0skOO9A2SuNOjaED43TonnCgEonpUTmqKE6NCrjAG6u2WcHNmTQu0XRE9iYx5xjmTqrZYYYotvYPH80XoMnbjcbuzYOANgvFPvsWY4rxuFf0jjaqItLJ92AWDiA9ZIUlyUDWpIitpY2aFaGTof4y63i9MCj2nsljjuff5oqh4Fx9pGhR8mvv/uzfwgAeOmJ8/z9TqIH+doQLgtF29XGMfjcI8x6Cp2gL+7OEyWInX0eAPCEhJ/HhTSsKxuiP8i+EAHXYb3Gh/ZynRORvkDcT953ONm5Ift7Q0jBOgE3CzPMNBnu57leepHZXwkdJ2mZSo3N483uEXK0uvuIBCVbWhAQChSX7zZFTIqYjV4pC7V3+o3Wc8Hti3/sfeBuAMA3v8oMnfUi13otWuNVVePZ1oO3DTPTodGo4/y4sszEoTzQwXZcXh3TPuwDVlu1XT7eLUS2FKKPL66qtnBDnFOP9QU+x4DWulbzLtHcilaN08Wi1fPWmrTBrfGpc0IbbdhrbuG6vlUZIDGtf3t6OJ5PrHO/56bpl+t5tseeYdbxTKCMYo7+v75AnuqLE2ybV/wcD4bbKzo3+19zVFxH1RhcrtKnkzH+/9RDVPidnSJi/+n/lcqjg6r3/P0sqyyEpTn23edPck2UeYJ3nF8mkplWje4b2fbtAa655pprrrnmmmuuueaaa9vM3lQEsFBgHOfpp8khm5xilEup2WhRJG1EJJDwKiNabfu74FNErRxgVCFQMx4ddw5IxdDfYGQtXCKP0FtkFMtXlfJfP9/4Y36dVDU3EGBUoqJCSKU1RuRqtQDiyr+1+hyWm+wROmcKnYW86qwVGV0oqJYayowml8QjqTsFrsR9UZ71upDEJuU8F8uV19Rr4i5VoYZ11TO02oFmdYvEORHfzap0jTrbzZBDiL9QEwpbERqZTi0houewUrEoMb9rEicnJoRp8gKjkcMHWQemZ9BFS/5HWO4qI2qzWUbGVlJC9O4k1+PZp4l0XT7PfnLb7Wz/wQH2ge52Ro+ffZBR48lJ5ZRfpnLcL3/2HwEAlk+v4Oc/9S4AwLe+yWf+i7/wAQBAaPfbAQAdV4muREWg+7uv/CUA4J/+HPmfI33sPzOqQZga5TW1HP0Qb2aFiN/SCn2oyy8OUmIN8DGa+/TTTwMATtxBZNmjGmuHVasot84+7hH3T6JbmFe7PHeKynj7h8nrevkC/19do8/fvJ/XuCbfvzS2iEunHgcAhEM8109/iGjS7//h1wEAT36Ptb+GPv1rcA2IaTxaU03T8VGiDGuKelpGhkepBdc1NkxcvoynhGK0d/B57ztAJbeRvYyy9qv2YtQerHHehJ5cvkh0cXqSSMbhm4lA3H47j/PXX6JPGqoF1V3yeDY40lvNEN4xbY0D59O4mM8xg6RutTTF6xjaJS7sHqLwGrJRF/evKj6H1+eFqOMbnD1DNj2bYTkHoND33i3JHPZBfYtSpx2vKpzEWFeemgc1mw+sSbbUcfTqrzqMV2hj/faNDZ87zbHr+M0cT8+d4/8J8aBWVMfL3Czewoi9NxRH1jj0Xq5TskKjPX5lICVVO7iffuMrcs5HWTyp5zgG5pbpb31CrpNZjoG+Ns773Z/+DAAgqCyk4coiju1mhlVcyrUvXSHKvXuQqFn4FPsqxohYFCPkbw+BqMf4K98FAOw/wNqonS30pMEOZT5p/q+vEoVpa1et1LoPkI4D6g58r+3mftdQ+6xn2S7x6BAA4NWT5FO1NbHdkk28h5KUnD0BIjh7b2JdwM52orF++FG3+pmmIqxTRiK87oTWhjYEhIVg1t94SNgW9jdX6dPTsxxLUWTbde5iVk1KSstdXZwX33KL6gUWFtAh1e7zVzjeFMWpn89zEEwk+GxC4v55la0Wlcrsop7h5VbOIQ0hgb4if+DUTRXX2ca7RDKOeILPtCKCeFUP0ae1uEfzT8hjW54zFKP/zNbYhz3SALFB9+ocUeKIdBMiYXF0VY+yNRjE3CKRv8nrfKfwa1ANqVpBzs/tlVX2L2/GMj3YQMNDvO9qw9ZKfMeYmKZab17vFx/8iXcDAPr6qXlQblQQkgqoD5wbExEeI7GLfXhYW3xMyum/+ysAgNookcEb2fYd5V1zzTXXXHPNNddcc80117aZvakI4Le/zTzvpWUmnxsHzqs3/YKirPNL5JjFm4k6JBL3YmaOkeSWtn4AQGcTIwGRJN/CWxK8laWJVwAAtYoS3H2MetUazLNdXhcHLqKQraF1Qshy03zLb1MO+sDQIdQKRE4qHoutqv4fLJfZr+tkxOPcqzzGifuoQDq/zgjHmurShJQDbSHEkHgB+RyjGetSUaqUSk6U0er5mURe3bNZXcvy2+tCRmtC+hobBZ/4vSGCxjOpbY4IWz3FmYUlvLjEv2tS0WpWOOVYG6Mj84rEN1b4u7Dqn/QM7IBrP4IVxwEA8d3kUl5+8AkAQHqRUblZRatKyoO/7638XbbEyFN5jWj3bXcRCfvCs1TZ7JUC3cBe+t+xE4xIDTX/FNoPkJ/14QS5TqEm+VeKKNoD970VADA1Rb5hTxd/197F6HJnF30iVxMPsWH+KWlSoejdx4iwWTQZ5QLgUz9PMrLs0TXUxNOKhehX5y5RidQUzBqKLE5OMMJ2x92sc3ji9vcCAAZH6J/ffohjwrvfwfZ48FvMva+sF/DUs1T4KynC/C9+k5yuqJ99cmLiVbi2YQ2pxb34ElWWl6y2mCUU6LE3agY5mapqFellPq+0lP4WZpgBcv40kezBHUIkdnHc7JV6a1F+vbzEyOvOISKGtx8jUmzqyabsOT4mzqdTP6+xVYTQsfrWOnhb71dIZmGd0dp1KRzPKHvl6mVG0vcdJBq5ey+3O/cSPWpKtsOjRmlsqYXm1OSrb0b07JKM9+SM2dgCCVo9QON3w+pp6WvUN+q/GrKnx2JqpR6niTZfk3cbh4Zva+F89+wXiSgPqFZde41jRLXMtru8wPVKpMYxcagpjmqKY+8lIcp5q5mm9UowQo5oTwu3EdUfRo1+deplRuwDZY5pRr+vH+Aa5Mwf/DzPPU3f958lhzUw9wIe+OPf5aEOfAoA0L+H83B4lfPGQicRiq5/y4yQd8SkTv7fqMg8tkiUof8Is0GeW+O4eUtSaElRCEiV/axlkGsx+KuAx1TF9VuHyGsLDbbZ3CTH23bpO0SE9H3rO6yxeOpF3s+1MY7dz53kum9+meu3AdWta5Fau88XRWyECqlIMftkdY59s57ns4gKLfFobVQOKNuptn0hwHuinEMLezlfLyaIdO09QBS5fyfn9WiMbeeTemZn8wBu2kU/2LmX8/KTz3KeviZf1LIeYa03ihmO2xk9h9SSUiJW2X+qqpfnrK59Tg4DgI3xrSmehMejGtx+rW/1G3+AyFhFmXFQndaG6W8EeJ+FCn9fqvHz7Dp9YG5NfbyDHMH1dR4nEOR2/94+TIM+eEUIYM2jtbaQwBWtx+w9wZTR7b3m+hzV/X2aU+LiL3qUQTj3FWZGlks83oc+xHXMruEh1NI85/NP/Q0AIKSMgv4u9sEm6SREujgufODX/gP+IbaNh3nXXHPNNddcc80111xzzbXtZW8qAri0zCiWIX+m2BPRG3BIUSO/atxVy3zD/t4TL+C972VUKpdldCfmIH+MQtROUr2tLr7PxCzftlfyqhtT47vugpR80pbWLqRsQeqFEb3Vt/Yw97kpFkU6K65e3Woocd9lcfYefpCcqp2Kmuy8Sfn9PkY8AspP7upk1Csv/kzRakspulwRvySi2kChUGijqo6pfNa3qLYpTmw1Besbsp76HbdeUwN1+CXG+aht+t9qaVXW1xDIM3KxrDpzRSGVVxuMnnQpTzyzzv/PPkeOT7lwHK79CBYeAgD8+Z+QF1FYIyKe7FZ+for9yF9j+/c28xm++OUvAQD2DjJKGq9we8de+nItyYjc8OBtPE8zORzt+5tRyYkLqn5idWXSa4zYxprJc0mnrc4VfWL3Ll7r6KVnAQC+ENG7bI4+3QVF5KR85WDNJd7D/Onz6OrlsfcfZGT5whmi9rmVcQBARePAuGryeKuq7ySlskyGfrpzhPe5ssi+b2jlffczKlasMFL5jreT4/LKtVl87xG2cT3O6HhSaGNAPJqlhe+vorXdLJfnmPXSSxxv77mHHNHJEUbqL18kSls02cNNA1Ft02c51V7KajsmXsrjj5B/ef87qdba0sZn4/dxXBzezXG2Kc6odaPG+WP37hH9joi58URsrOTfypjYgqY1boAEbqXhbdTo43Z+lvPRwhxRx8cf5LXHYvSjoX034yOf/CUAQLLV1N2kEOqoeG45p07q2wwYOpylhhV1NY6kZ0tNP/H3fD4vPF5xvp2ai5tPYvfh8A01uW1fDVDg00eYGSExb9TLqolWVy2uISJnVhNzcSWn/4uID/K5vy/GZz0nzvO1Vf4mM0Z/eeVvWXe0Pc+x6p77iRyfUm2xpIQRvDrHEy/Qv86+xHG5u4X97+i0UJbVOFaExHQPETGvlDgGX1bNvcUc0Y1nVrnv7/8m667eEuE65sRh9qveadUUu8JrO+0hsrZzgONuvpnrmJ6S1Hbn0kCMfdTZBuSjqoEJjdlzU+MAgJYWrfGk/NvWyrnq3rex3e5UbeSP/SzXIzOznC9a28jRqokT5U10AF7ui7Zu/YZoyOoTbGO/Msws02wja8rGqO1nQ3OcYxPDUqbfy3afFSI20E8NjYaP35dy7AOp9DX4l+njBwe5zvvid6kQfvdHfgYA8LU/YF3AapHrllWtH7NNRL0rS/SFn3onlUi/MU2/LeZN12MjhwEAvD76UMAfg4n/+/2cn/3iczY03+TXhSrq/5pqcoeURdJW0lipDD+5H2ZU/7dTdYyXVF+vL85zd7aGUJVCeLCZfTi5llUb0a+WcsxsMfzS1twqfYyq3jWK8u1imgh1kzLnKhl+/qU//3sAwNwksxh/6ePvwPUnv8Nj6F0hoFqW603s21HVIBy+mfsk9t+pq1B/vIG5CKBrrrnmmmuuueaaa6655to2sTcVATR+mvELQnp7D2obDTHaEAvz7b7mYYQ3U1jDI4+Rt3PsANXXYkHmgGNevLpRKQkpyhySGlU4xGhQXqpT/pDe1sUzTCovOR7j5zs6+SYdVU5xLreGmiKtAeUTl6Ug5PUzurBjN/c5coLISlcfc+/9at5igW/tBdWWsm0uy2u3mktFqSCFgwwZLC3MoSF1znKJoYpAY3PU2CLZ9S2R6ZqhrIpCVBXS3Von0PnfiTZLValexa4m7uQTT3JBdVqqUh5tUl2krhEqRD3xLFGg3AqfBX4Lrv0Q9vSTRK5yWfpJfzN99OyLjwAA1hbJzYgIIug8+g4AwEffbjXXyOdru4ucuLta6IfPvspIm1eShbVR8vsQTKIoH/T52C8WVxhJykm5d3gvuQEvv8pnnIjzmMurjEyP7Ge/HBkhPyswsFd3I45Lg9GuxjS5Y7PiULU198LTTGTTX6c/XR/nNYxINWteEcLDUpldXGE/uWkfI9Yh8WoSSY4JsTCPs7iQ1jUQfTz2ADmA3/r8XwAApmcnUKuzbUd6Gd2bydDXczW27aUL83Btw555hmquVaFQ3d1s80OH+Py/8leMXjYamznKr/3bY8qAloXgBHzZ5kEpMgfCipRq/Ln1CNU+d+1kxN8QMo/GZaNJhzR/lKXk/Ia2te6dgwzqmPaz16FzVifPeHmbUU2b3NbynG+unj+JR7/FNjp+glksO8RxrFXFTfLYtfAYFakuW7ZGwG5UaF62wAh6KBjddO3YwjEE6hscP4fbt/mGtoiEOhHhLeKN28q646r5KBTXK45S1RQ5pQwYFgq11iIdgUoRMXGPfBrTKkOq8SvOc1KA2OgKx/brz5Of/dR1co1nJzmeGvkvKoXFjnFew1yS41QsqlqpKc61saIHVekeTD9NtGDobVRyxmEi432LQobjRMqC72AmxNNfJf/uG9/lmPyFBuvE7lXf9tY5dk8/OA4ACDX93wCAt06x9mVPXxeeO/UyAMAjlOfoIc4Xx+/UXDRCtGhygse44wT7gE+ovtcjjqTWgr6IkGn1r5h44Q0hiTXxrOIdnANoQoFmyVHLqY4xlCliSrcNr3Fyt69Nv0x/i/ZKdTXO1rAypsU5osQNP1HjGdXF8yMHb4a+vb5CVeY2Kdr+6s/8OgBgR5wHmZggkjw7TZ/2Jzifxyp8DpOz9N11KcMG9USqWjd79byiygj0eHyoN0yRn78JBeg3RdW7W1dWnd9Z1vJ3TVH2pzYf+3BN0sxFQYrrRf5uRTUJ8372V39YPpXPoUU1CHu6pGI9wvs5d4XI31yKWTA+1doOal5q1KRuyktyFEYrVsnAUgr9pn7K+330u2zf6OIl3L2Tz8Fv6swa8ytVznGpJfbR7z1LPn0lwjrM/Qc5Z975iV/BG5mLALrmmmuuueaaa6655pprrm0Te1MRQHtr9StKuqGpyf9DYUYjmpqVK1yxAkpeFNf5Jp5KM7LNrfNzAAAgAElEQVTqS/HtGqcYtbowzRzxl6SmhXZGufbtIWpQCXL/zBqjQ1cKzN/tjLEJelv5Vt4iNaRIRGhfcR3+ID+rmdKVXpuTTcxHP3KkQ78VwrfKKNXZU0RLQkImLl1i3rVXEaj2DkZS8kL+Umvcrz0pJcX1nIOa1rfyRawmlKIHFeU8V6tCXGRW/89jSnGKTji/M55DzThg2hYLCClyEReKs6KoScMv9SJF4apSnQwmGaW7/e33Y9uaXCQvEDT2/VOwN5k92q/8OVXann7iKwCAE8eZj79vD9v3gU/8EwBAIsFIVNdRIn2nvv47PE5M6LhqKIUPUCV0r7gszYoe57LsM8n2ZqRSVLhKL5HvERIav5xif5tVfb/2XkanYk2MkJ0+Rc5KZx+v5abDqkezwEgUuojAo2rICH287xZyXlCPAD5eJ0KM5u4YYkSxXBOHIEv/27mHyF5nJ6+hu53bkmpX+nzcrq1zP2+Evj68n+0Hcc3e+5OsXTiZyuPyKD+bn5GCqvpm2cNjlRwkyzUAuD7G8fPAQfIqQ2GpJ+fk8A6WtJU99/rsA0eSUp/7Rbp64O2sc1kWEtbbqXqQh4kyRqIWt7T6X+w5t95Kn/ryl9lv1tQJG43G67h7No56ttTFe/1VY8t+m8nVG0qjzl0C2KgjWCvm8cKTRO7Lup7Q2x4AAKyqxmChYEgf+5xPKMjZV4n0m6zejp2sr1rROQ4feRsAIKwIuV1LrWbXVt/IFHFu2Hg14g8at1xXX7O/foA66o+zlUviLUel+hlj+xalaJvwcf67tkrELaKMiqg3CAgVjES5T0DIn3HsC2rvvbv4u71S1v7edXJnDZ3K6hqiMR47U+OY3VPiumX1rLKHfJxgAk1RnBmlFsF//pfkYO0Z+UUAwE98+MMAgHvezrqvpWWe6747paabeA8AYOIqVRxzXcwoyST3AwCuPP7/8ngaC/e8/yd5nNAQAGAmlUJ7J9ds1RDnnivj5CZdvvASAODYHUSDkuIPeoNSfFYmV1593biC/hDbvibV85pPmU1aErb1aJ7xcw0FAItTRK2uv8ptRbw1f0AK6UqDcmiwelbb0VaznO+LNfrpuhCveJsU+tMc5xNaR3dLdTUUboKvS+qxafr0W4a4xvY2iFLdcecHAQADA1ybX5Nf9XVyvx1SwA1LNf/RrxGda5SFuumxBMLsG8lmIvHVoh/1kFDCsmqFS0lzPU8UztbDIfW/uM753Dn649H7eK3pdePmSslT2W2RO5jFd2iA52xZo5pvpVgClPlm9b/3DhMBfPol6iRkxelL6P3F6iDCZ+Ox0GuhjolEXNfMfuUI/WvNHwqybzcnoihqrotEm3X9nEvqyiIMFfl9XD4dDfNa69O8/hvZm/oC6PG+8TQbVNpEWC+A8TgbxuRgV1OrgOS3SyJQlvXbwG4+4GheBeEvcfFXauf3LXHKpJYrTOVaK3LRu1uLyGSM50okOOB4gmyShlWnb5SdF0BzrrKIpKZ03tlJx/e2c4Fy5SzTIbIq+3DtHAekguDphkitORW3j0sgwwY3n2DtSsOLjSLBVuaBL2VWHLOixUFZL8tOkXkrNgwrbEwHqSodwspGNJQDVHfSkLh3tVpFXm1tqUhJDaSWTlpTOstClp2vEWEb7ty3B9vVriu1wiuYP3Zo5z94X+sV//E3PgYAOPtOpsnUfXzGvUMchFfmOEjPLKlY61HKYPd08vvR83z5kno5oirke/Ik0/fuPsb0nA4VCL50dhaZMhcc6zluG1X67uw0n+1ymqmbAS2GUOe2KcaX0g++i+ltHi1Ixi4zbXOnpKDhp58FtaiC+i/Qh+wlpqIuLHKf6Wu8P3+I/SUgYZazFziIlyscC9olCFDRi8Ssitp2tXFxUCooBdSCQkqtSM/xZS/ePIx4JwfSXJnFkKuSX68WTViDA+qKBtw2G9S3qfX2crzdPaIi0F6209UrnGi8mhytbM3rXvrewHxiyQ8M0B9N4t0vIZ7b7+CLnQXl/E5+osYw/dfWxol3eVky3bbSewN7fdrk5s+3isLc+D7shWnz93WFN2sAqiX6zoWzFEsoZ5kSFYqwD8zMs48tLnFRVpFYgEelSELaLs1yIdW/m+PrtWv0xcNH+EJpaXE+p+SD73X3sZHqauWATBxsa3v84Of242oNtePYNBewCYlnhSS8UGpwXAkkOZZZOZ466qgr7diE7OpK2QoHOQZXFYSrqRh0xcvti4sKQNX4f7iD7d+uwO2H+5SKpmLaz1xWoEqid+utNZx+jn61qnXE+WsqYfKlRwEAixM8x0986H0AgFg7/Sd2MwNuHbcqbX+QL4qvXuMc0D4jyfwp9vnHnuEL5Ds+QhGPcK2Eyxfo47ef4Mvh9SkGL65d5Jg8fpVroluOcQFeK7MtL03zhTae1HosYsFlvRRoDVip8saDCY4/3tgQNozX+e2/pgBTLstj7hjkOJKMK1Ck9VvNiX1v3+CeR0ELSzus6uVkLV3WluvkgF7kmyUyEk8kEIrTlxNKR75FFKg1D+fv3gG2uwUvWvTC16qtT8KNmSxfmP7df/hvAIDxVxks+Ms//QOes5tB5eH9TGO8PHodoarSQy2NVw8zk+V1R6K6tk4GBq5fYVpmR0h9QUGclxbYSV68zLG3byf7uH+AL3VrRc4hyZLoBKhiboEvxXkFsYN6R1iYI2UnpvnKKF1le6ENiNoQ5DlKRX6/pjIlAT993+fV2kLBZ6MXvHxxFUN3MRhTyvHc8xKtOXyQwf3plymg5vfw+4Bc29fYDAhtNTcF1DXXXHPNNddcc80111xzbZvYm4sAOoUdNxd49AsBNLMITpOQQK/X4yBWC4qWvvpdvqEfnGfkqaubaV73ffJDAIB6J5E/I3KnUnzjL5UlxR0QOiC5YiMIl5SKEAhZymQA8IqUX+V1F9KMOKXWiJYk+4jU+KWxvTWuZLxNj+RiA4oGDg4oiq79SutEdIJNjCy2Dw6gQ6IUczNMwbOIR1ntUVYqp5P246T8bJYGt/aztKutkWGvItgb115DWamfDYXS64q8R0L8VThCtGdKMvxeoQE1keW3oz375J8BAHbuZCSpsU6i/c6DKoAeC7zhfq81zxCjPTskJJFao7998zHK2x+7mce8fIUpxbtHvwkAaGkmojDhZxTo0hijQr5pRphGx4istcfHAQAdrYymZtaraKjERyAktNsv8n0T+8/1ce5z4FamDSVa6cM7hhmli+6VL4O+29xsObCS6BZB319jRBhpoXLNKSSUCj23QNnxTJp9s7dXZSv2E/E4rXQif0hpVkKEEkpXffQpIpwPTzP6nGiif555+XEe/zojazcdYPpioiWJ2rgI6ObTUW4TcUbzcipxcv0C0Ze2Y8ewne3wYUZjIxLPMonrpRWiV5bFUWzweW9kJDRumFbY08M04VYhePl1+u9tB+nnA/0cs73ezWPXFu0YJy3+n332swCA//y53wPAsdEZ/m5wDTdC/rbahgjM6w4AAKhbmrz9X/chKAQvk6Nfv3yG/TomNLmqLIxqVdkaisq3S3SgV6Ji7R3sr3mJimVWiPjk13i8aJS/t/mq7vegITEjj6UZOeN/bdN9WEaJZyvVYBuaCapFNQ+XSpzPYkrJKkvEwq950dAIb6OBrKglM1pedLVwbCurDEdunf4xt8jf+ZQ6+eoEn2VUyJennT6zVuX+6Saes2uNv2tt4f/ZrMpbhfKYmFT6pIpkr4PHuDCh4vTXmYr8dw8+BgB44DaO2ffcSv86cB8RPbTyvvYrzc73SaYaI8w11a0z9CF/hNfW1gbcfQ9R+mKZPrg0yrH8PW/9BAAgJIGMZj9RSJ9EN/Z0EnWcXWYaXcXDbCqf2mEDo1CabTSJrTYnsRETi7IC72sqCRAJCWERsuvB9i3/YBZrlaiOSmFU81zLWnaFT2t1j1KYrZj72uoU7FkktMZOZDkela9zjvQ0cTxvH+CafKifzywW5bPtaOd+QWXxKqEOqjmP3S9zfu4b5Lr6/T9BRHp+cR7zM/QrK5k2O8d1cVmlojxzvM4VFV2vqeRaS5LXMLbE/8+NE+FcWua81dfP7JPDl0lpicc5NpdULq5WrjjzUkLlhxaXeIyhQd7PtTG+m6zX1WZFtoshoeWSUkg99tplfVjvOzFNpl7t7+H4M50rY6bk1X3yu1NnmHGz/9Oc6155imvBrvVxtmGIc6ZRvm5kLgLommuuueaaa6655pprrrm2TezNFYGReRWJCStyHwgwyh5SZD8U5ueGWsWb4rBYZVH59lZYOnqCAhiBIUYNYopA18Li8imS2dHEN3wrjroq0rUHkoUVoc9nNSoUVfb7NtBHn5DKRIKRjGCCEbBk5xAAYC2j/FwVoR+fYmTKpwhwQPft9evNPyPOR1XcQJ0zLOWQjr7dSK0yYjE1Pqn7UWRZ0ceaih07UrIm6tIwyXUTBuDvTPzFRGUs8lt37pffR7xVeC3abUVXxYUM6frnlhnNOzfO7b4jzO/fiPpvP7MSH+kV+sDCFKNEo+fJDWwfJLrXuYvhL3+IbfXgN76FrFCUj77zLgAbkcoXXyTylVYx4XKD/lQF+8np8+SvHVUh34OHeY5Min0lINGebkWb1yVWdP26BJGuLaCliz7XLu5setVQeH6+fy9RuNTqkrbiCBgEJH9aOHMSALA4z2tv2cvomJH7Z1XQt7nE6Fa8qRmISRDhMFHTNpGjyw7vQxFcjQ/eAkOFJpdcU0A3nzN0nL5+0z4eb2cv++lgL/fLK+Q4M/MK5mfIW4iqb+/o52/vewvRpwsX+ByvXSCaenybI4AJ8TdEa0ClxvFpzwijvadfIVe0DkOYNrhlHiEl1XWT2OYYvf8A/dUoeyMj9LWdO4cAbGSMeJxjee2Im67NyvSMjY3zGqzUg9fjaNNsFEvXThv1dDbZ1vIQW82Ky2+9ho3vN47rlJ92kEp+si5kqW5zjvpSnxC/gWGiI03JVn2teVMo4+oSEf2sxppopE33S1/2ouGM61bx2LM1A6RuXED9bGv7bEPLi+8bkshbWWsDUeuQSRHes8wl6VshEQ6iVxLxeSESaaFQPglIrGY5Dmb1eanMZ9gqEYrVOrOAllNEUUKHWbrmNx76IgDgaIC9pGXfEACgL72ka17H28RXigkBvqKyIbmaeLnqYIMSVcoscy302DPkeZVC4nR1EOX3iTcFH5FntDL7o0sidekFlotAkw9o4/WEVzm33LZDpXn2q9GayFPF+ScBAJPT5/WF+tlF8sDDR3kcb5rtk2gherkMIhqlFZ4zVOd9I7kLF04T/SiVhZ6KM2/jSVU+b1wsr+bPRm37rlOCERWAFwcV4rdC68iCeG6NEsfqhLjwQS8gaQhkFyf0W44/PpVHg4SAvKvkliY6OZ6hRWWbwP7Vs5vjW26ez7Ka43GOHuTnnTtYOqQkvmi0qRO793M9EVKptMEU+8vOPdTfWBd3Mb/GtdRjTzwMAKjn2WfHV+nz6WVmTVQK4tbm6G/T15Ul1cR2SfZxvvP7PcjmLdONbWScxgN72S8ujH2P91EUcq71isBKRzwmavolxvFOMNPJ1uQlE6HSu8nk2iq++D2uq1pVNH45x+fy61/8cwDAUDc/j7zE+5uI8j4s++RG5iKArrnmmmuuueaaa6655ppr28TeVATQOH+WM28RYSucWCpITUsyk5uQQIUy7bvlGt+u51T0ub9FyqFOEUlxGZTTHJSscOcORpfrimxkZy9rPyu+q8iulNh8jRryKs/g8/N66ipU7AuqOKO4AWsp5iMvLjMXOK83+aCQmrqirZGolWJgJKRQ5LliYUbWTDFsbmYRZ88QeVjJMBIWkwqjo+LZMHVQC3FDW7Z1rb4Z+bNtzfazIJjP1OC0e6Xi/F338X694vwVxYVcVDQlqzIWISlLebZxXKG9XZwPleWoSbV1XaUyxq4wT35W+eO9UrpqZFMYP88o6EMBPqNdu4Se1dlPCoosPfjt7wIAVubJB6kUGS0eGhDSNSAVzHX6Y0ES/Z0tjDy1d7KvFITKvXDqNPYfZdStvYvn7B1g1K6U5m+8CvslvVLNVbHkkyepOJpook936n72H1SCv8XNxamtyD/TUsiN+wOAX7UyJPvcntC+Dbbd0kVGGudmGdXLpYlOXrhMFP/oEYaZP/Belh/59iPPAwBqUsAaPKhiwep3uZcu6dIaCCtKt7JINNTX4HhSFv/FH9Y4sn0F4zbZvJRWm6RgWRfv9/bbiVrPK5q7ktJ2mRHa1GoGKaHKERXSbuukD4V1LF+QY/Rtx8gnisWlzKxzm5S+MZXrGts2UDh+fuIEr+Xk8/QDQ52BjQLnr1P5dLaaZ+oORLjl955Nv9tqhhz6pPQMr9eRsjd1OBsf61YyQuN9/w6iqPsPHNMxpF6ozJGGIs++CNutnOOzWFkYBwB09+3dfDPYUDpsNOy6NQdbGzoNovnEs/m+t6OFVCYnpTGqWUhgtiBUKsnxaiFHf66LExgK+XF9gX4flgxfzU8fbkoKaYmptIRU/Gxd8547yBF/TuuRVIHz/MQFjnHji/ShfCePOzIlpVvx7W+JR3BC4/pRrW0e9BC5K7Zz3Lu2SISlX+OgR0WklxY4xn/ti38DAHj4QSIZx24mqnLkNqpMZxu8Fl+J95+TfxZ6gZ4BrnVWzrG/r/DykKhxvpuafggAcOVVIi+lDt7HpS/8JQBgp1cqkxO87y/+J3IC33ULOYXHP/c5AMDVr7EkxUOPMdPgvZ/9v/C9J9jPo0I7mpvZ1pZRFpA6o5WD8NTVr3zbFwHMrfA5xU3hVir4Ufm+lQ9raL1dzxBBq+TXUNecHtB45jd1fPE2K2s8dmOC6vfr05xvfVrfXtZkOriDyFlH7xAAoNVLP7xpkP6V3EVEuq2NftyUiCEttU8b45qb+Zv9+6lsHjQ0XmLjP/eZ3wAArKpfnn2Jfjj9u/8OALCwzDXSzhGuOTqa1G9VJiOzrnVxIIwVZWD1tGrdr7I9eZX3CQY0V4StWLt4iTDEWah/xRSypQnit/QLvRdU2H+NnxlLJBGKsx9E2thP2hM8Z3GG65YuP/vy2/fxGSylxJWsugiga6655pprrrnmmmuuueaaa3izVUCtKLne3g35821RA42qiKPvNUXL/Q43gW/Z66qpV1C9Lr9FOrfW5d0iFdcU59t77wijW17VCMvOk2NXUx6zaBZoVMsoCn2rq9B0tI+R2miSyILx7WakYji/qmL1Acv1Vc0z1erLZsQTy/FcQeVfS/wNa0IcPd4cUoq8rCn66DfepOeNI9HGv7Noc7VmW3H/HA7I5nbyCY0NBaO6Ji8gxKlSN/SG0YXZjCKH4hCYopxfWfcVqdRtR2tuV055htEiq19UVjQnm2bUvjVMpGx5lhGcvu4Y4m8hn3VFkeWCosMV8TzTq/SFvXvpfwd2khfR20v/OnuOfLapcfGyhhg5qwvdHd5NTlxQqE1Aef07epuwUzUGwxE+64vnGYHtb2F/iamoerfUw3qF3gz0cr+jb7+XDRCyOnkif+SoyDl1gRzIQp6RNb8UPFFcA5bYX84+zwhzVQqSt4gPMjfJcPK68t6bwowQZlUEFW38v7ubUbwjM2y/WXFwsSJJvjJ9++J1IqenTl1BLE6kc/cw76Ori/eZ0rmuXOf1DkbEGVDgfmWe5+juE3q5TSwQ4hgwr5p10Sa26fikODle+ktrCz9vaaaf1+slh+tckaJZyK9MCm0PHqDqW1RZDo4ipVNs3VTUVLtK6MeyrqWoTIQ9e8g9WRan9rVg343q4r3etiKEm+uqvv7Xm9UzHbTS49lQ3LQaq/pRu9RvB3fSbweHhnU0cfgceVPj6xnBm2N4RDyeiQmiRoduJooUipCbUq95UdXYUXXuU+hj3RQRxQU3hW6bR9/wLreHpWuGhnIsKIqvV65wm1nn/FYLcKzzi0e1Um6gLr7qutDDqsbacpXjyWqKc79PNQRFV8Nb72cB6vzTfKbnJ5hFVOvlHNy9QnRvVx+fbWaZqHb6sAqhd7dg1BQOr/Ec/cMcY/ftoV9NpIj2XJynv0BzkpEDI+KgNgkZPP0cM1JGR3muZRW6vpjmPbZqHF65sohsN/tsVGrS6SkiK598/60AgMRF/vaLZ1gP8F0fZ6bJ3hCvf/IJjqePnWTGUyrHa1npfjfb43Eifg//ztMAgMAR7j+TqSAcVZFyP+8nHGMbRsNCADVm+VRfDULnf5Di74+zleTTAY/8VFL1MY0psRjbFEH+rhFlW1bzTSjk+ay8mgwDphEhsptHfhWS8rABXCjxXAGhinOnqUqbn2YGU6SVPlQSQnjmGrUN1qSeefieD2B45ICuj/5U9/LcVttRACZ0e6jp3AO7iKD5ffcCAHrb/h8AwGwHfeLEJz4DADj+FmZfPP5lItO3DNHPPLUGfAny8Hx19rNUmf0h2s77OSg13ZQUzvOq0VnJ08/Sy/y9rW98Ulwv6nhBAdJ9PVIfVqHtULgZIzt47GKe3L5Mln18ULzfEaHbFbV9e4zzsBWVv5G5CKBrrrnmmmuuueaaa6655to2sTcZARRqpeioXznkYXH9QlIDLQsp87+mNpMhW/ZdVVGrlSVGnrOrymlWLanXhzA9m7bhBBGM5j5GX/Mp1fDQtqLU2Uq5iFiCb9n1EN+qE+18G29qIwJYUA7wyor2VdTVp2iY5QIHBPFZJNjUQANqh6BU7DwBtke5WnNQUON/+YQOxqPiFVqOtsPp47YmDkvNQQBrmz63qLSZoZQSUkTA00DYqxxnRYeCHn65lmeEI6dn0dPGyGJXC6MyBdWt2462cxcjtbOzDEFNTJO/NruoejslPo9cRv7azAjo/oP7UVNu/ZnLRC4GBhiF6+9lRGhmkc/j+TNE1Y4KCQyJj3fqEiNmI72Mag0Psi+Em7l/xFQz18W/C9ER77p9H3YdIWriEQp/+bQQwE6qSwXlu1cnGT2OJ5THPyh+XUhbp5Kk6gCqNlOHuAb9PbyHgviJSM/gW99h1PcP/ugbAID77mXUeHAX739xkRHommoSxVVX59Vz3K+vl/e5+2ZyVSpeonU11UOEooKvvPgCAODMOSrQdXXVUQuwrVMrvO7lOUbWEklG2iD0O7dMdP/LX6Dq1tXLfK6/+du/ie1ksRjHo8uX2T4j7UR6p2aJqloNOo+yBbwNbX1N8AfYtjHVWmxSTa+b9pG7dvAQo7vGfauKd+oocvqkWKnBfWmJPvToI48CAM6fo29+6MMfBgDcdpyoypNPPoMN5VDaRi3BN0b6fhDit9XseBUha+GgRabLznehMPvfyF4inZ09nEfCUfaNmiK+ft/meXLjJJZBo4wLcZsqqg11XXU/9x8mB9Lv98G0ED2ezZxxq03rUMdrdh/avjHFcVtYQ5kR6yVmDhgSaxoGy0uq1xuSmm2Ez3WuVEBMSn9BJ7bOcTBTImoSTxKJqFo/0bN+4WVy5QLrnD+ONHMujd/PrI5zL9Kv9u8jIvHk01xrhCWBfC1RQlpqgrd94O0AgOSa0N4ix8HRZfa/irQHHGVwPfSgrsVhxmlNcGaCc9mTF3jOulCGvI9jYsHfQOdltpVP6oztrZzLFr9J1c78On3+Upnj7b6vCU3U+qW/QN5v9wFmtRwTx+nwDp5rZYzoy70ffT8AoOktdwIAyqEmDHRznigWNaeKYxxWrdKA13QhHFUDAEDD8/3RkR9n82lcqFfoE/V1ts3aAufOlQlm3XT1MosoqrVdtDOKZki1smp1n+UPfLRoZNWuPlvoanAxDqaU5k2BtC5l/vziqn7P/tekdWaz/s88MYOrF+gfA7s5V1SjXD/lq3rmSfaPriGu6xORzfe9Okd/vPsYj3PgANHxyyWuMV7N8b4PSOn2rjvucfY9fOReAEB8yzFLUhW/cJ1ZTovKBFxdZYNMjHJufO7p5wAA42Ncp9XXOd7HYpzH3nqMfvyee3ntbVJBvTRRQXaONY4vXGCmYqrOi1gtsZ+t19lW4S62aatUTOuNIL6fuQiga6655pprrrnmmmuuuebaNrE3FwFUNNWvXPOQ+Gw+1Teq1U3RUpwJn/EUgICQMI8U43IFU7Di23M2zehPrIXRCWxRbTP5t/+vvTeLkew8swO/e2/ce2OPyLUqs/aFVVzEVZRESTTV7nZPq90tNcbTgGHZY4w1HtkwbPhljIEx8zaY5xnAAz+4vcB22+j2TEOetuXuliWxtZASJYpLkUWRxazKrMys3DNjX+7uh3O+mxVJkeyn8kDxHwKMisiIu/7b/c53znes1cDnZWpUKmRiOgd4au8PNJ+8nOfRpszZFafBbTFKnJKVpH5w6QyiC+rutk8GQyOIKjAsUGNQ9hFBnF9ALrRLHd7O9nZeV06hOhfXntSHHLuCkvlTBlCvqX6udQRVq0LtX7mMiMfMHCKUZdeWXhtRvXCE460zlz6mfqtI109NwD6Omk9v+PilV+B8dW8LkaSVFTAloxHajkdHVc/FPV6+wNpdhaKMIrDZng3t2uuvI6Lv+2QVSohueRa29cRjYABrCBrL2h8hWvzEVbBx80vYdmEGDOBL3/o+9pXg/nzuWdS6u36tLHfuIkrlUgPwSy8gF763i2MZ9dFgTi/DdeuVN+C89v4dsGkXX30Zx0pWosYIlGpLi01qCorU2HbQb+POoZRraEfFGfTB+im0Qb/BfpbXKEM7rNGFb3UVUeTdbUQOrz6P/jO7eElERL77g1UREfnJm2s4b2YafO1v/3Vcjx+8KdsdZgwkiOZHEZjP5z8LFvK7L+IebNPB8oi1jw6PyGBOGYIQY/PeHs7/+nWwd+1D3AtRx0rrJHNmiwijkqp1OHteRESuXQf7XGHkvkPNdUA3wypdGH2yaq0W+tTqGu5VnGCMDGO8rtxBhFXrC65vbso+I9vdrrI6yqaptlxZN749qRVUJvCEFv2k9i+L8Y+Y4q5URBLQP/EAACAASURBVObm0K4fexJ9SmspFqgRT1mXzM7nu8ljytNZ+N7mvOGRoRlxLtzaxZiz3MX1q1VnxGYUvuDq+VLPTSc6O9J6sD//fKYRuiiKqVl1q5gbmy7+UrFx/9QV1BG2v1FHnDJ1qh7WIXMVtN0i586jfD7HfXCYydO6h89HQ2rFxxxv/gDt6PLSRRER+cmbYDB6I/SFPrNDnnvqCXn3m2AeLAe68qfIsIw4f7y7jb/bZINpnyC2pcw6s404nxepzT3t4pi++hzWNTepf37zLp0hx5402a6uLuJ8Frm2m93CtUqq2PevMkPrUQvZVOd/A/PJa1/HNr/4WYwnT1u4bsEcjv21PfT5rUXUey7QpT0N++L5OP4ydWFlrkscj3dS3T5T5Ts0G2B6rZ0LOc9LvZ6P+yTUp426WItssx2Wm2jPlZlZqXA9UaPVZqWBtUxVM+VizN9JQMd5Zq3FrI3J8pSScN1sF1TzTLaKjG2Na33bVhfNQMJ9zL8bXYx1FrMGo5Ta/S7mjDcraFf108iyWL6IdjXch8/Ar72gBSqBzQwMfHAL2VFPfPbaxN+PBiJrdzCHPPIY2vDWJr576Tzddx9DHeyDM2ij802bx4Rr/ZWvYpzutDgXHeFa/+4/+2siIvLfPo/3V8dg+/79P0LG0sqRJ+WncK33mWEwexnn41Tx/NJ5H3pKdQW21YTa+ui1uGEADQwMDAwMDAwMDAwMpgQPWAOokSatuQck1HqkJ9gqjc6mWZbrItIYUYPDHUSUCvP6W7weM2Endq6MWf452UiybzPn8US9uYVIU6uNCHc1TKTAqHafLoIOa6ikcW9iWxHZuT260j3zaeSpX34I0YaIDKHWADraY71AarK8MiIJ4xHOZXN9TYb8m0ZyY9a+CrgtR/UxPJKT11BfY2o/4lQ1OhrxptsWz0kZQLdclT7rnigDozXTjngPHNURMpikkRxlPqcRf/IiGEC9H/0O2sQcI2hPPA7W7eY7YJZ6P4ar2e3b78ut26iXc5FR3XYbUeFeiAja4gzaRb2K+9BlDZtBF9f90YfRztbW1kRE5B//zj8VEZGvffUrIiISM9f+6JARuRhtvnnpupQ7iIC98jq0Fr/5F5B/PqJW4PZt7KvEGksF6glT1rYRG5G35lXWIhsgOrx+FwxiycU+j/aRJ7+8iHMsXH1GZldxXGWyhsoexdSR+XQFHtFFbGsP74tltNURWal7N6GN/PGriPINySAdbuF61Ctkr4T7c0P53ov4zdmrF0VE5JlruE/f+cPviYjIN+lM2mAtoj1GINcPEBmdNliM0l65DJb1vZtviohIOkZbtVgj9QNJALaVf+byO+fPgwGsNzDuqT5tkw6CLjXUszOIuPpFvF9ZwT3Z2ADTUS7h90d03u32cX9rdCh98unHJQi0Hicd3Oho2NI6hS38tt8bnjjwPxsVpoyiy/pvyuYtL1+Qa9T8VWpkm/ldm66mDiPe8qHRWtVmhXynTCF+71I7vrmxJiIiz3wS51hwmsdbyCY1gA7nQ5KJkqbqDvpnOt1faNh0GC8x2l5m9lHE6Lq6GtaLdCnnSqZYsKXRZFtsYRv9CHN8Vgfj1SUDWBStCYy5MuBYHyS4d6GuiUZ0VqTO0w3BRtbpE2AVsb97a6Fs02E0iLiNuvZFfHcYsi6xjfk7pF616upKDMcUxGg3l+iu/N//eezzj96mnncVDIZVwPa+WRzKp0dgXH79CTAusorxfXAd1+yRh7CvM+vYdt9Ffxuyz27UsM1/92OurZ5DXbf3X7vN64Y+fcDMgzd/+JKIiMxcuipCRv3hJ9HP1DdBM5Ei1j7WJWBe6/hj2JFfaGgyGq+ROqWG1IeWyxgIPLZH4TpmMBrI4AD3NmJmw8I5OmwW6XpPJ3G7znm8jvYzHqItp12OscwsG1FLqIStL1zLk6mOOLe4BTf3y0jYL5yY9UxDssDsu5WEGv4YWUDt/Z+KiEiRa4xoltkXXMLWed7BAPvU/J7KNbTno522DOl+f7SP86+z9vghHwOK7G671DIWXHgz9MhKHhzgi4unMO89/ijOZbyDtdYP/vRfioiI1UBGzNJzf1NERP6nf/B35caPkGl1p4fxhNOZSB3HsJqOeNz0RKG+1aNvAldlH4BhAA0MDAwMDAwMDAwMDKYE/1XqAKruTGOrY7oIlZnzO2SkQOMznufl/x4wgtvp4ml8nnXKVm6AeVlbQcRohrnmC8t4gq8xiux6x7rC+4+hskD3oGvI3739Brbf6gdSKeI37x0yOrIB/VPTxfszF8G85A6cITVzdOU6cwFP9D3qFNfvIiqhbN7cAtiFmVkc4+1biHCvr6/mOdkpv5swmhVRT6g13hRpOsn86TGpC6jFqGbIYwzpUJrwQqyuguFo1KsyZlTY97WZsKZRQKdERi9rZKSKVUQM7cIJm6QpwqCPqPwZauWeehhRoLdvoKbSym0wYKv34OakDmad9oG8+hMwgPIs2JU4xm/Xt9dERGTu09RClXEfNllDMEtxHw5bYN3iIdrZwjy2vbGNqFFzHhG7N99FTaVvvYQo6hd/Y0Eefhz9ZG0HdZq+/e3viojIzgGiWXMz6B+XqFl0POSeH+6BiX/lFegLrx2B2UxtsC+P/vJXcU7dF0VEZJ9uqOpOK05DHr2OfPvlGVwjm1G9b72I98EAkbOLyxdFROT1G2AVH70Ozd/nnkW0+I23sO/DDWhun/sENCt+gut3+TL66elPoAboyr//lhx2cW0WW4giV2P0xSJ1kgEdbTt0Qa3NINppV6eTKilyLNQxbHUFeru9HdbiG+G6VChMLVHXVyh5YnPsXZhFOyzzbxqIb3N8HJDBW1pGG3PJULQPEN3c2sIY1Wphn56DcUfrf737LuaAhx6iTsIpiae6IGpwKzXc7xnOH5oxouNli67SR6zp2m6jHYyGdM27z6FaRMQhlaaO1kvn0IfPXbwiJdaezehmZ5GKSFWHmAsINUPm5zuQJjLJ1Cil4flkW+ise/sdRLsv/cpZCTXjg8xfwoFe5YVpnmVj379lSdPpZUe2OmAPqtRohnTaC0aIpvtkmKosOpZRM+fPzMruHjIDlJFoU1vcodviMEQbKJfRDjsHGLN9to2DDtp0Z4gx7FOfwdrh5k30HW+A7WWsy9rewvj18lYgEceskDXbkhGzhQYYo1MbtEHANqB1e3U9lrJomvoGOlwbHBySFfk+siV+aYDx+m/6WN80zl+V3yNL47P23tYa1nC/+eWLIiKyXobGfHMF+7ixg/718BK21X0Yny/M4Lx3G/h+UR0hXbTxRWY+bbYxFuy8uiPv7OKafeNf4LyuPQ0t1ic/Dx33Qw+jL3rMYNLxZpoJQM0E8/gIoLri3oBtWcDaeR4d7UXHpkjsiOM07+Ess4L6TIrRmtNlzgEe9aDFJrZZbfLC61jENa3DNW7K9+qun3TQlrLkuBqAbWf5Z/gN1xXKYjNTxaNjrR2gnzj8PMlwLIHWWCajmGbUFt7FvPbTG1gPlZvzUi2ive/RsdfyMe84+qr1V5mZcYhuJxXqgJ95CusRxZDkatrF9djpYL77BmS+cukFXNCuiDz8KbieXqR+knJ3Sck62k/DRbfL0p6jI+w80IP4EBgG0MDAwMDAwMDAwMDAYErwQBlABiHzKKTmZo/iMf+OL6jbW0Bm0LIsSRlVGPKxecic+IDOQp19RO0suhDu3EMEqdfDo/K1x8ESFApgJj4Q/OGj8JlLyCPPyFCs3nxZuqxHEiZ4IvcEUbvmDC5fpwU2R51FxxHOp08ms9ejS9sOolvdLrZTYgRxbgER8YAawo0NRLi7/a74nkbnTkTrVOundZw0opt/PskEas2fvNYgo8+B6vnIKGodn1KjISmvXbUBNqt3iOMOuoz2M6p5jgxno8FI9xTHFUY9tNnWATRGLus99XpgEA4OcG9T1vx69lEwSr3DDXn7bXz37jbadnsIpuv5T0I3+H/8b39VRET+79/5tyIi8jv/+o9FROSpp1A3ZoM17Bbn6BR1Ggzit38Ih85TZCU3e2AOI36/9//9KwmoDzmkLrXcQJt86V1oFH/lC9CzPnQVzqOtLbTtU6cR3Xrlh2A2n/7UF0VE5Cc/ARv3b77+d0RE5G//dUSoskTbHS/Y/m1pd7Hv/+YFRGrTCH30n30dzlanT4Pp+9KXfk1ERL7/EkJkh22tG4S2HVE30x1gLHj6cfTlhge2p8N6PXKE/nvl0hVpzGGc2N3Gtf7xm3jttXEvmjy/LseZ9hB9ud0+qRWbDvzJf/i6iIj88R+j7X3qWdTae/pZtJftPUTmxwPWGOM4vbu9Lz7dPB+5hmhmRWuZspbZzg4YrJTa4mqVDCH33WxifLl+DePNPsd8v4g29eVf+w0REfn2d+Gelql42SrnNWeFtTZTtvd2Z7K22cIC+uPCKRxbk1kZwyHG5l4PY2CnSw0T2coowbh5+TLa3OklMObFck0yav1OxlvVkTnXJCkReIL5y999iEOpuuRppHltg861O4/IOda5zZ30tF6sKCPI+5Q7cPPYppgBVKfmOpnVWOvesWbdfoy+f7WC73XJTLdGgTSaGHNDaqIdsgG+hTFtkKrLJxkLzvkl6qeuzGGMts9inE3a2E6jzKVaDTeo46Ed9/dYf8+uSEi91hY5vDF1ghKjjSoLzC4gvjvJBo8jssB0W94eYts3foz53ma91dTC+TfGbIfnW/LZNn77e69gbgkFx/2dH2KsXjmD4+6fRabJH7+Ha3a0g2M8oLZxkzVwnQDn+ySzPGhoKm4d53BG3RBrsXziNJiVzgAncpva+Dsv0in+9hqOdwHZK7OLk6/TCBJfUqJWWetIj6mbF3pqlOhqWyDrbTue2M7kOGZxnHb522Ad8/KA7p8JM8MsZsQlei+51rS5bWXWtYbqDLffPVK99oHEZNT1EFK6Lkd5vVMylnRhVr8Rj9tWLxF1rhfVxfLYtQat6q3H+2AEh52VvO5qwMXLkJkBXp1u6yXMgU4Jc0jHx+f+AjR//S7GYq39vHUH4/TKCvZ1/TLWWN7D2M+pK+graVckQfeQlVu4touLzHCs8jR4rWgfITGrG8QXluSjML0rdQMDAwMDAwMDAwMDgynDA2UAP+BMSQawWEJ0TOvkKfOXu2bGsSQRntBL1AkWmVfcGeAJfmkG23SZg+4wZLS7jYhUbRbaHs/HU7XN/HabtX0c1ibUGiSlMnUVo7FkrPdTqyGaVWPtkTBBROPmHbAGm3ua549jvfkWtBgOXY3GrC3oc5+1OiIjHqN/Peb/a62/JE1kxCiDhmYrrNen0KCe1vXT+n+ZvmpdJ4aPR9x2HGudKv0hXsqlY4b0uHYgdSR0JJ1j/Zd91jdRzYreR5eRkmnE0rLqfNAGjtqMNJEFWDyFKP3rbyF//rCFC99sXJZRH+zgqTO4x5evIBr8P//9/05ERAp0mLt0AXX+zj/OemfPwEVq5T/CuTJlVK9SQ6QpYXxodQ3tNxwjitTroh3+aHVdig1855Hz2PYco72nNhFdvbWO3xaX/zL2fRp/Pxogkjt3Cvf+iPV55ul6em8PbXrnCNHpJj9/h3WdvvHDF2WJDPhjz0K7ceMl6MuunkP/uENX3e+/jv60cQAmPfHQlv/wT3G+b7y1JiIijVlGLRkl39lDu333Dv6+WAfrd+b6k3J4iGtRKuC7gyLO48rnPoHzGiD6+JM36CwaIqo87DFSOmXY3UObG47ADiRkdJMC7tXcAtq/v4y2laa4Tve++wMpMwI8fwr3u0hXz5TZFvPziJzOkEVR18+Irsk6bgZjzYbAuLNHVn33AG3q3Dmwbxb7XGZlkpBVVN3dmM6xMXVTReo73II6Z1Lb51BD4rD+qoexulIBqzymHZu6J799Ezre5fMXuR3nuK4fYWcaZZ5k4/JvfYiD9cfV5nNZt21ArdrW1oacPQNW/aTu3tbzo5bGzwVR6jb90fv6RUZC/V7IcbMfYPxpziKafo/19MazuEg1aq2P9jtSdXH9htSsdbiGsRy0xZkaPQioay2ScSiT/m1U0H/2qTUN2DZcso6NErbfmEM7tTPWUB6lYjs4jtsBWOr9DsY236d3gY/fJmzzvqfO6jjvjMeYcB3W6qMdDbj2GDs4tj8YsPZyC30+WOvKThXHt08X3OLnMT+8PcZvVg9pW0itYIXn2eY8kAY4llky56cvgy3Z72Ksr/bw+0bEetBcrziS5K66vsNsrzmOK3RADNdv4ZXu0bt0KF0v4fXL/+M/lGlDpjWp2e9HZMDqVVyT4SHa/GjIgYAaat93cvfUvH43x2fhGjpVt09mwo3p4JpwfUuZoTh0g144jflA6mUenc7fXIvPol2XF2YlYc3XjJlrI66px33uq0MHXPYXzWgTavhFGUA6jKYxzttKsD2Xbdzm9z32CadgiaM1VTlf+cwKsgKs5bIE64y4Aw16SGp9dBvHsBtwLV5WbpNMp4N9F+noWeC1DvbQbr/3//wTiRw87zBxQA6XwCoOL6KftKgNXlnFs0idtZK15uwXnr8iPw+GATQwMDAwMDAwMDAwMJgSPFAGUEOcCSNKA3X5STRPF0++Hus/aWQ0Gg9zdtChlkE1HbvM0T07hyjBTBGPyPtdRL/mF8BgxNQ0hHT7uXsLTj7nH4KepMIn5Yihz7d++hMREem1OzLDGikVC2yHb2Gb723QITAGYzOgRlBZuO0NRgLoKiYuIhwX6UZou4iSjVlfaEw2s1LF9uxWQVLmPKu7nLKmmjctvC6q5dAosbp6qtZPr3nMSLjm/Wt0usCc+nqjwd9ZEnCbpYgsJCM6R53uxLUKGQXX6Lq+TiMOD8mM5G5V+Pz0Mu791StwPfveK38qIiLf/ymYtcXFqvz230AO+BtvQFO6vgc24Sv/yz8QEZGZGtiHJEAf6HbQNv7kj74jIiIOu/O9e4hIbZ/BfVk6x/ZkMXwUYTuH/H27Fcv5OXx26SJYx3fvYt+LNTSk6iJZ3T2wcxKi70Yh+sDS0gLPmy51rC+5Saewt1ZwTkvLaMd9suLnr1/N3eZeuolrUi5D9/HoZVyzn22Cxf/ey7i2AdmbjSP0x+++gvfLF3D+LUYD36YO02nh9eJl9PWH/zy0hIfrqxIwauwx+Dh3AZkC/QznWcVpyV/6y9DkHOyiv/ze/4u6PNOGXoQo7twZRCtf/NE3RESkQOffZhXjx+I82JImx9VxayBJBdd6no7MGkEWMhKz1FKkmo5gTaY3JNR77GxTkyST84UKhRxGSzPWy0v5H/4NRIz8Vqr4ju/rNsgw8LVAzbRDPZXroi8lCdp1IdV94PuPP/ksNuMoC5GJqJ7O0Vp8J+rcWifpNuu+/x9fBmXvjsk6/EMd/OwCjk0j0evrd+X6Nd4vOq+qA6luXXXrqgHSvdhTbJE4ZnZMK8M4UvHRhtMMbaHho+3vsa6XT+bDqlTE9lQrhQHlXA1Mnbpu9wOOm5wzizNgXFz2gUzvKdlue4x9qEOiRFi/dO6CVZkr4p6Xap6MWFctZFvdYY3WsTI0HGddxv2V5dW6hmmK3ycR+lHuHE6tY6eDtdZWjOvwpb8HLe/h7Q156Rtg4cd1/ObpeTDPr76ONdBei6xQiPnuuaehlZ2jpjFq47p0exir91awvVkf1/ZohLnr3hjn3XDIxHupOGQyfa4NHRXTUuNY5DVNyApFrPUZt8AuTiXYvSMuFO0THhFjHR/YRgJmoh2PHyIO2+iYa+ois/M4JB7vitkHalhRSjR7hjpYZiINqFUNXLBXJdbFrs+gLxXcggjrdiuRV61j2w6PuxCjDWiGw5A6VtV8x/StUI8PR89vgPHc4ZxR4HnqflzPyuehjH23wP6k2YZ6TYue/sifOLYBr1PQx/OAR7axVmSdQ/ZDz0GfDjMypu1diTTrJca2t+7hOO+8hGPq0vE3TDlvlZhdyNcvPP+/ys/D9K7UDQwMDAwMDAwMDAwMpgwP1gVUa3Qw37jAyIC+T+jiF5NxygMH2bGvpDJdGmRQ154D1pAqMAd94RS0TI05RPS1bsi4jSjWrRU48ISMrF26itpgAzp+tnbBOhTEFiXbgiHogLCKqN7r74IFmKGGaUSnR62TF2pqNGmgWdYO215FPZ2V9/D6yOOIGg8Duo2RGfUKBblwFbm+h/uIZBwewLVufgFMTZ91yvL4sEZuc7c36i15nhpFVs2jMqtlap/W1xF5Wzq9JCXmyO/SnU/6YFUTXnM9r1oNEQuNANn2gyWW//+Ec2dRe6ygmlKyGc153I/FJbSVr/2tvyEiIkOSqwftQ2mPEGkuN8nChoiG1mJ1qOK9JJM3SBDB7DG6Wq4j8nv+AvLC9w7QjtpjMLard9FePfaRmWW0571AJNnAtl98BUzdU5dxL2evol2UF/Ea0NF2MKY7Jmux9ei86fvIb++PwOYUCzi2m+/gXHoZfv/bX/68iIjsrm/I7g7a9sIZXLsaI9A9jgsl1iJa28Bviz6OLaKdGCU78tjTuG6///vo23/wh6gj+JUvgnGvqHXYFvR8/+SffyN3EdY6ccUattmcZR1ARhAPj9AHFk5DQ/zkE6pXmC6MElwH20cUdPk8+nqjimt7tAt91K2bayIiEo5wHUteSUoUGw130W5HrBNVoHY4rz+mWQuZasHHfMXf9/bx+yxTBzt837XpemhN1tFLskwi1d0xijugTsXzWPuMY1dma/bJ5DZSsmwWNSUOI9E+2cZqbZb7LvB31AJnjoz7aPvjGGP1wiLGbr9Ivc2QBbQs1aHTjVHTND5M+5eLAidr+akW+/ad9+SJPdRCK1E7XiAzqZpAmyyssoj59JFNrwiwWqQLKF1VQ2oq+yx0ZiXKAGBcqtWot6nWJBijXbWYndClDtWn43ef19UnK0C7ABl0WeuM46VTwTZDzu8Z65edmoU+dp5ZILFHFiVzpE9tVYNaW3+E76j+LgypNfWoceSxOGRDIiXcLZx/wsynIfV37SHOV9dY/+fvwgl4b2tBQuqtn1zC3yIf57++g7b/7FMYN+vUcJWrOLYqdb7LtC+klElCdvaUuqnDIc4zYxZAkXNCRTzxyXBmpGsC9gdlt7T/qAysoBYFHC6mErz3kTJ/zACrsC5qgWu44ZFqvenwGcS5i7/wNzHX7VEB34nYL3yOZ+own+b7xL0luSuxstzUkw8i7LPD8f6IY3Op4kuJWVAumW+Pnh9+iTe1OJnRUSYLp9kicaQZHDjGRgPrlEqD7NwAO81cnFOmay8vlSTU2tuseakNKXdS1WKyeClow6Pzr7L4FudBn7U8NUuszH2WPGVImbUosXQirOX6XK8Itcaxo7pCHEPMOSNI0O9G0UdnchgG0MDAwMDAwMDAwMDAYErwQKkafeo+ZvNUJ8XIQKTvmbOdqjtaKnGskVj8usmaUhnzvbuMzqlO6hw1DykjGS5to95941Xsi1GI2+9CCxgwf35AlmtMDWG1XpQx85+1dNmQ7lIDrZlyRCdBsmip5uszABBp0RVGQIqMVNke3m++B8bisc+gptbM/GdEROR73/qWnF2G3vBwD+en2pIR7YACagtO7jNXdDDibTNaI4zOlOiiGsXYTpnRzhYZnVEU5HUJQ14TdU6NuO+UN/LRx58UEZFaDQzMMfs1fajVEWFSV9YGnZ3eevs1ERG58TrYp7YyaB3cj3arJSrVCKjzbM7gOl6/Anbhc38O17lPRuE//0ew1InDaDHbQGIjQrV3AMZsvI/tpamGPHFwe9RSOW4mNvPRA1IAm1twezygJuMr/8NfERERvwT2ohWACdqiW9iv/uZfEBGRtQ1oPKwGjuF//79+i7tEBGtAJ9lbt6ExjAe+JA6Ob4b9Z+V9ntcQ13LI9uRSp6hOjEUK904t45hPnwajuUTnw7V1HMPqAWvnXMI1/853wLy//Prt/JqxnJv85+98U0REfusvgTmp0ZnMLyo/hUjh00/MyzTi+adeEBGRtbtrIiLSHSLS3xsgaluq4F6VqR31PUY7JZLhEVzNXv/+n4iISNTHNT5/BQ5lhRLHdDIwQ9VGs/aiukKnjKDq/ODYk+0iUl2eajay4+hz7kBNhmHMbI1ShU7UpAVSjvYWp0hLJjVylSoZP85mqrW2TmjnLMnkcB/ObN0eWPKFJtp1ja6+9+5u87c47rN04h2T/dDpI2cC9bxUU29NUoTK5hVdX977GeaWh6h117+pnlIdWI8zSLgN+6Mjx7/IGLEuqepXD/fAOM8ugqXyinT67KINlZjlMdpr59kIBeoxbTLGCRnXOttqQM3OgNdf72WRY13YxbibsL5kmW1bXLx6nLcrdawHdtuhFHl8LKsslwqs28d+tNlFXxWLTA3nGZ+awfYQ+xpzng+5vukO2B8DfC9gppI6i4vTlbkG/uYkuGY3XkSb/uw1jMlPXAcDWLQxV927+b6IiCxxHbLAfqlrC0vrzrEdDqmjrbFGXNVTF9U0z3Ky+J0C15NqRaB/15rJ+VLpY1x1f5GhY6Nmhvk+MxZYA9OfQ/t1ZzDPZSPWgO4cSMp2MWT6UiVQppWu/WQIdRkYa01q3oAoz5LADdLnAUcz5o4HOvydbT0bRjLkeJ1ZWE9Y1CGWuc4qNtAHSBCKR01ukULXAoV2PueYQh1tpjyLdhhyra+1/tLcLV8kGJO9Z71MvQ4F1s3MVDPLNuurS6pmU9C51FaNt7rX+qpd1zGX+9ShOR2KsMYnyXspMwOly7q0hYLW8OSxke7OPmYtbhhAAwMDAwMDAwMDAwODKcGD1QAmmoRNLSAT4DUy4LnM02VkSp3aCvdFI30+4VaKqhPCE+58c4F/pzPlGE/MmaM1pBASWLsNV6pTdAS8uwrNU5/1aQY9sAUFHlPYHYrDSESoEaY5jcziOGvUskSMtGmETCNQY+btLjTLfI/ttbt4evdnEX3xfOpp8nzlSN575xaPC1HwMmsG7u0ib+K2FwAAIABJREFUmqxaPr1CGjvR6NYM665p5LtzBA1hyNqGqgF0NYLN6Ho8GMkR66/NVBEd6Q7HExtX1vHUElhKjxorm+zqNOLV1+Eeq6GVuVm0jXYP1/35R54REZF//LtgQS5cgz7t9KlTsnEPUdGzC4iSPvEk9J9f++qXRURkZQ36zH/17TdERCQIqVdipEg1p6ub2FeFNX0qbCM+GZbZedzPS6yzVyslkjEq/PAjvJdCV1C2B9cn212k8908TvCZS6jdF7joP06TbZimipmNY4kYueuxTs9MA/21F3q5lu+9t9DefvwS9mGzBls/Q7+pzzPvnX27WMTxP/cCWEkJ4Dy5fQ/s4nYX23nzPTKeR9D/vvx16IUHtohfwrUo0+nvi38R7Hur/ZaIiFTnLoqIyEjFKYJX15lOhuRME85/52fwWiiRsSDtcG8HbNcq2+qQWrteqy2U7UingzqMr30L3/3h76FtzC6BJZh/FHrsxatgBs+cQZtcZR3HKFQtCdpmg2Pc8lkwvwEjtAeHGF973YGEsdZ9wmuV7oxxitcsxRicUCuurIGtDp7aoXP3ZOoURZ09Tzh3MoqbZIlskE1PqOOw1LmZ9W53tsB4z86DsQkDOibSbfEDtnoKDbyrqyjnSVvdEG1f2tSiHRyBxZqfWeTZMGKcx4BVd8hdJtNLj1jUUI4DzfmhbpR1f+cauFZdrlP2ulxrBIk4ZOgCaqYczuXneG+9lFk1AcbFlSN8T3X0ITOR9J5Wc9dD1nFkPeS4QDYcXUcqcSrxOJr4rs9b+Mx5zCPnxtB3bXbQFjZa6H8j1kDd7qH9jNnmAwvnV3Awb6j2q8Yah1/8Evbz2o9dicgCDTawrSXW+Tvtcu20CUbQL6A9XqmRaQ/w93Ve67w+JdcaPsfZM2T+Knyv9Z6741Hu2lomkyJ0SlQzRmVXNWMpcTSbTKYWmWbX5XppYGsT6+FKA21l4RTmVqtCx+WSK2EP7X1MvatFNnZI/4zcWZQpTQ7X+8KsgwIdPHwh88UboY8HqinUdU2BGQ6ZnYnFbafM4Ii62MewjQwUr4K26VM3WCCTXGC7Ueav0kCbrtFvwWf2keNyfeOgn6l20LJ9EY6rC6fhKzJmG01DusqSEdRXTcwYkaXTDEjXnWT8cu2gul/LZLaeZcuxjrWg7KFMQH0Rjtf/nO/Sjx7HDQNoYGBgYGBgYGBgYGAwJXigDGDKyJSlugPmaieROlnxCZl6NX3KH2dJzi5FLqOiDM4tNuHiU2adPGUVR3TPqjG3N2E04up11GET1vJpM9e+xCfmdgfvy1rPR1KRnAFkfu0BImhJnqQLVMkE5i51fPqulRFl0FpAO4wI2AVEHZb42tpFVPAHPwODMRiNpDccTezj808/h/PkNTui/rBLB1INJhweYltzrI+4eY+upnQfU3e7godoRJ+Repcs5OH+gcSsnfLoI0+IiMjLPwG7pVGjUwtgcUqMmI56YFasKY4rBAmuo4Ydo4waBzpuLi8jGntqkS6ZNlnUhYaUfNS/OzWL6/lXf+tXRETEC8H2zpfQ3gZjpSFwL+eXwIQpy1gpIlJ98TwdLWvoE4t08jy9jL9fvTTHox7KwR7Yt6XT1OnSzTNLEFlSl7o7jBo7NbrW+WjT7TH7URN9vNdFNNC3wDiUXUQSowraSLmMY9lc3xTKPuTGq2Douoc4vuEIbVpZFgbpJFFn1UX8/cIiWMg775PdDhkBJlN0821EB9/OcIwpuqnYkQilweK5OK5d1u5Up9TNHo6px/5VKSFSX69Pp4Wc1oNy6G445hjtUJu8NAcWbmn+ooiIZKJ6D5Ej6ovHvJYdurGuUFd57wD60aeO0J6HK3Qf5O9efxPMd5uOzxpiXV46KyIiM9SBZHTDrNF5cWVtS7rUq6QJjscrkpGxJ+tdxdRpaIRVtYO5O2au2eLvTvxeod8P4kAS1ShRZxurQ2jq8nxwfheuYGzQOpcBGUKdD+0T9VUDZpwMR4jI1xjVVi2h65VkZw+My91V1LUsF9BubY5PBc6nBdZSPN7H9I7hykBkzBpauAhGemsHLNYgoP6M2TizjOgndpQ7vGZclwzoIlym0+HdFu5VvYZxUQYYk4q8Hz0y1PNN9AHNROqwhphLl0M719/pvU5lRObYp65Oda79e2gDjTq14tThjWysS3bYHz3qEx2bGlUm8owz6mB5jB6p/CtnsZa69aN1qbIdlZu4Np99AZktu29gLXSKDN4c+0t/wMyAEa51lefBZZck1CHGZPPKrKE8W1L3Q86FgZ3rn0acJ2JmIHlkmpy8Xia27fL9FJcrlpBMmmZ+RWStNHtrtI0sjW4b7bMwh/GyOjsj1VmsZeIQ69haDfdmj3P+cR4EkDNfauvPwVI/T3hjlDELuba1OU7qmCvJMRWWkAm3yP56ZBN9nofLtbfqxsfMZOrz2A6peVQHapfs8ewCzrPWBJM4ijQLIBCX6y+7QDfcMo+rjP5Wsio8e+2jrCnIufIc+/aYa+2ImYIBa1vGzBDJeMxaKzQYpPm8pRl/Ojco0g9xbXY+Rsv9YFNA2WnTgCJH0pmaSqiiZI9pm07eQ63cIlvTfoSNQine3PKX29rfWhMREZtq0GINhZwXzuP1kPa2I6WxWYR+xIc0Vy+odV/D1fQgLYrJpy0VKt+9C/q8xEVikWkKCRvpvRYtkJewuP5sjMYXct8/fR1GIbssJu4VXHH0/DiYDzhYa+qann+JD8CaBtRu45hv38GCVtNsndz2m9eW79vcbqPJB+xRKIUivvPT116d2IZCB16PE2GHaalTnFkh44ipOrxU7S7a6wEn/j88QimGIQe3Jgem3v6GzC2yCDlTFYMu2kGD5ieBz/RnGpOcY3pGsYi2cdGBecTFs3iAeuwT+F6ppGmXmPCrZQxuKZ9+2r1Yyix/MOJCebGENqrjXxKinY1YLHiXi1ZLWKy1gX0uNZjGNmQaEdMw20M8nBUrOJf3bmFh4Nkl2T3a5b5w0QKmfo+4EB4zbUrtpfncK49cxzHvM73t639EYxldNDB9KgzUXEqLvHLQt1JJBX3yL34R6YeOgwlv0Ma2tQDrsMfVEAXpIQfnaUOiRdltfaDiE7QK+DVVTR+gLB13PJmZQ4pu3ICxQHUWbaXjIfW5fQsPKdeeQsCp5Os8gG21zqDd3g7wAHh3m22KY2GlxAcsHtPMDBYqt26vSZxwUmVQIE//0sWgNTkVfmAM0wfBE2YpJ3Nx8nRMvg/jQM5dwpyTMsdJ+9TuIfp5ianals3SAJzsk3xfTIvTVCiOw22m879z600REfncF2DQowt43y1KwvN+680fi4hIlYuXCoOSLhfWHg2V9EHQpRxjGtHgA91rdzHGPVZFX59j236LVvHFAW7kmCZW5xo1iZiePl7D2G0JvtuydRGrbYCpyAwADka63mB6Kdu2T7v3EgvGW1wAjrlIHIwxVnqBK/sHGLuuXaAp0hyDeDxuTTCbp+Gb62G+cI4wRpccNQjCMThq889cymFGaQrLYHzrP/xURETKdlEemsdcdOpRPPh2N/HQWbYYmD/APjaYNlfgAt6ncVxJl1hMJbc51mfs23lKtbrg67rH8fIC2wkX3HTrlz7t910NdjAw5LF3usn0mtVFmhqoc26HpARNVUYRHkq6KsNi0fYgHkv9NIIXPg1VQgaiUi0xw33oWKgPJyqNUtMTfQBMuY7UdHs1h9SBXwN6UXL8EJ+XNeMzQ6apkTL5cB/y+G19PODvtLRbIeZ6mAEHyRo8epzL4R76VDDsS6XKwEiVcjOuoyw+1yTsy447acYXCckZlUhVcf7lGvpnzcJ8mOTlIjjO8zr0B30Z91mGgyVpBgPer0Fv4vgzvS6TFVA+FFMcAzEwMDAwMDAwMDAwMJguPFAG8MwMIr7bR4jgKjOmxJIWRMzLJuRGC2luje9l3sSfAjJjcYanbYtP0Zp++YklRJ1dUr150WEXkTqXqQmakhAmGtXj03qWSao7o4h1nA55vJPR3zlGtFXUmRe8J8O3SOr8iyEiCbsL2Pe790C3a2qoQ3aoXq/LkMele7p1+/2JfarYU4uvJxoRydScgHbsjLqoKFTTOFOeb8pIcaOGNMR2564s0TCg12rp3nB8DJz5TJPTCrLjcDpZkfsxHGiXUj91Ft1lCnJviKi/rxmEyio7xVzIe+YaGINKgywUWTevhm0/dRE//tVff1ZERGJus8p2kzr4/pBGA5UCIr7xgOYTNs0LWojSur4nZRbVLgTYx9E2IoClJkXQTJnoUGxdqyB6FZIZ3N1CKvLqypqIiLSY5lCuTBZenZsjI8rC6o9evyYFG9tafR/ppZrS1mbaae5mYE+y2PUKos03buL8tw6YJqGpJSwsq2ysx9+FGu2zWnL9OsaDShl9NWQ15VoJ0b2ALGlaU5MCIFTma8qQxirYnyw+njJiP2QKtEZ9HUaUKyWRWMOSHKtKFYyHzzyF18V5pI9usXA1kxqkTtv9pQWMR0WOzXWmfJ4/C+Z8dh7taDhE++kwqv3DV16WlTtoWzMcgxt1jNV1MtaeV+RriYeoRlda3kHLQeQye7xPJhm/YwNzpqPFkQSk/Cplpk5xftvdQx/QNHw1r/E5EebbzDOgaH5DmUOP6YOlKts906BsLQhfLMmv/PLz2GaRzIqgz4+YmjgSZfrwWiAj6DL9cRrhUiYxjpk54WlmAtrIvk8JRMy0s1mkVPolSwZtXN/TTPFfZfpygf3mDG31mzTVuHGA7x8x80JN2apsSSRZZMR0eLuoWR1M5aWRnBRsWWKaXpFzfXii4HTKwtUO23Jaxusyp+0y27yVm6iQ0fGxnRHHvL2uplyi/1X8utSqOK4azeVSVvHWjIEBMymUhWsyE6RWx2sY4lqXyDbO0pSrzkLxXob2uB6wtFE8ys9bUz4dltwoO7g2VV68iNdBS/70OJ+KzgNTCCXZdF2tS+4aTVKUobYLZLdYBmI87EjYwbpCjdzcRYy7TnmSgc2NSBJ95RzKL2i2mpqhaMZfzCwbYUplIvcVkk+0ZJyeCF6U2bO1FBZf1VDS4zpfP1cquZSTwExT9VUixbbSYemUUUesMRlwllMpFLFG0NI6HhnBEtu2bsvxqjwfLZ/CWYKZjppabVPXFsS41lrUPfM98dh3CzTZKwvW6SddH4+NaJg1xbTwD4NhAA0MDAwMDAwMDAwMDKYED5QBfOQcRO49Pn13W3jS1bxVTUvOdSX3WZ+6av6isCa+IlGs9sl4r5GyGnVPCQ1NDrZhhtJkhH9mhpG6zS3unBtkBGSmVpE2GYk8pzlWFg1fHbNgelcNZMh6NJuIjIxG2HdIqvM/kX2U24iktMheRmTSMuprRsGxgYCaDmgUq0A2Q4tJ5hEPjYSotsXJwzH8vpoWaISaNv/upPbDFlsunEGEZ09wfltHPG5GR5ZZ/iEaISpX9DR6Mr1InEldQajtSQ0g2LS1wHQYaXXaVO7dhSnG2WfRT7Z2YIqxQi3K8jx0dr/2eZQwqdjUPjBq+oPv/KmIiFy/Dvv8cgU7q9LGW6NDnX20O2E0teHVpFhq8jhxXHd30E96PRrFMJ+9zNILXRb+7rJN5L5OFOg5PL9ei2zjAfa5VANbc3EB+pOihLKytiYiIhmtxPs96mJ6ZBEd9OXE1VIleH3pJWgHQzKbKd1kVEvsqqNA7pc/ab9/4aonjz5OrSI1fgXB+RW5Ly054coku11wplMjleUDrLKrHJ94jcfUJHXauG6qtU7iSErURNdn0NZOn8brTBMR0+VFvG7voV3u7DFyP4Px9DSZljr3eYp627V3kRWxtY5Miuostrt7qCVItsUi87VPs6PtDY5zDnUdNbAnamjVnKXZD/WKfh7t1d/hMli5hlC1J1peAfDcuizMU3ObajYA2t8hMysKjBRH1F6pPiUvKn8sWOR7jdvSMGR2idtn1ooWIvZtcTLch1naog8GanLD9mxphJgR5IjjVDSd7VtEZJ9s1YKvzAVu9p5qyXg/yqpD00i/Y0lIjc5rexg/owjtqUzWdm8TZZ2efOyiiIiMQ9U1YxuX2e7UjCilVi4l49fkvobUAKn5SpREIpy7BwOuBXg+ytKXffxdS6L0D5A50SzpeoXZENQZDnpsIzaPgW2/WUOfOHMNrETr/cPccKa7zWLgZCzUsCthtlCVjJ6yIYMe9Nu+lgAbssh4qrpXnPceTf0itu2QHaxWLkpbP1MzQTJ+iT25Riox86Cix3bC02CawCYgI/pZlGhkkpfLsZQxU63gMVuqJckyHYfKbP+8t5Gu31WIR0YvJfOqejyLeliHjKCunaJwkiEL+T07va+ETm7RobU9uHY+oZ/LdYYn7rXNdbH+XbOKXHqGRBxb1UymWqvma26Hv41Z6iVOuH7P0F8yZlHpNi39nU3fjjzLhH2D+kWPjLzDbJSYBkkSJ7mxjOrBQ72mOh/r/OPqK3Xg1Jd/GAwDaGBgYGBgYGBgYGBgMCV4oAzggLnDNY32MKrV6zOhV1k9PpamfNr1Cq64+uTPCEXIvOGi6vD4VF4q48n3mc9/AftsIcoVpYiErq+8IyIi1UtgSZZPgeVqa9HzMqLHIctDLHkl8QdkABnROFIrY0YoVKc3Ozs3cR59FsxUB0+NIByQDUlZQNN3EJFycxchtXoNjiPuCr7XzzWKpZEMZfQKjH7Z3KdGQCx+ru6i+juNNt+jHjGLI7lx420REbl4HkxfnZEZl65Nn3nucyIiMmb5AbdQkqnHCWcxTWf3qIUq0HXSInv32FWwfVcvnZVnP/mwiIhsbcLN8nCXtuN0pq05YCmSQ7TVcBXX3SPTlYSIzHY2cU/r59AeC2MwBMEQbaDXR6Q3y7C9nU4qowwHOssyDtkI9/KpR66LiMhb78BFMBmTCSyBWe/Rwn9MlluZESHLVmcfF+bHRy2WcGC0ubWxJSEj1ofUfnFTuT6k4Gv0i/2DAca1NfZLBspSaqQadLkbkvUfJ6pzoCsci94/9+xFmStjLFJ3rYi6n8Pc0pqHrxFSjTKH06kBzE6o3ZT50/El5rVWDYkyA4PhUPZYkqD7Fhi7MrUSs7O4XzbHv+VltOOLZzHu6P20yEAMHdyjgyOM0esbYBFKZYyjNVrob+6h/xwetiUM2KhSdQRkJkQEJqXfQl9qH2Js29qkEyKLXs/NodTEwikcU6WGz1Vboq95JNpWh+v0uIQEr5zWRSYpJFGs1xTnl6i7ND+18urseNESDkW6nlbIXmaMlCfU3tSqvsw0lLmkFpzRZ3Xa08wRzQgRS8teTK+ee58u5Uu+Zvxg/KEaSIpcoJTIqKkmNRr1Jcyd7rm0YnsbaVITnQRfWYFjeJli8NO00l+km/IGNZoLfpX7Rr/aeBsMYvE8xvQ69X5+xZc+1ymDkWr/yOhREx4z40NsWt5Xsa+ITolli9+LmHlBt2nJOK9TJ15lhlK5TTfUii0H1E4POD6sH4KxW2Y/UQfIkFrhtIJt1fi+zgLe4xY1hOorQDY2YjsNo0ntcRL0pKLlxdRNnuO8cj4ZJ6VE15COatCm1wVUx4iQ85jP+W3MtYZmnGk5Dh3XwMDRnZtjau4UPmbmWphbtYrIsceH6vVUX+eo1lnHR02P4p2z6TYbs+yK43v5NpLju4u/nWD8Qq4/9L36cajGVucrnc9L1OlFHGqDIL7/kMV2HLELWoqBvgiaWHRCd2fRLVe9PGzViwu+N6a3gcXC8jGPpbYEJ92U62ifZSHu3t0URzWAHHP0WPT4tIxFbqSimX4fY8lvGEADAwMDAwMDAwMDA4MpwQNlAO9so85Tm5qdRh1P3TGfqENGH211JOTvnIKTM1bpiZzePEdZNW58e2aW+rsOdUJ02GwwRzbm524fnz9cRZS5yJz6lkempjuQh0uIKGvycpu55FkfT/JtRgsyLfTIumXBWAv1Yltas6/MqJhfolsX87Bd1mBSNsWxrDzHV883d/fUAp7hJHuq9f008pHljKFeS621ou6f2J66hGqB2SuXr8i9Hegib28hwl4l83f6FByIZujqOtq7g33I9OpGFCVH7alwwcs1XN+r1+h0SOfKxSau9+WzYNK2Nm7JqRnUoqsWEQmq1z4lIiI/+M7LIiLy/W/+TEREuoyUnTuPQryf+gwKoV+9/riIiAz7iCCtH+JY7lBLpXnzBR8MSxgiSutIltNqB4eIUtXK+M5bN1jH6ojR4giR6SefRhsY7EGnOBqBQSnRcS4ZIl6+WAbDuUgdV4+aD6sJDeDKewfiFNH3HLJxzVPJfVfwONdekVGDq+6J2uyUxRhSR6Ish804l0V946//xnX+viWtFh19rcm8/X5/zPfU+ZQYrYvUiXFKla4arc0zE5iJwDYZR6olUxdA1VElEuv9yN3fcF/6fUQ6NzbAirxzc5W/RbsvUve0fBqsx7lLaJtnL0FP+tgnUXT66AhOtK1dtNHNdUR3R+2hRGRF7Hyc1Oh/7us68U5dPLMQrEl7H9scDzBvVOroz46Hdq1argpZl1m6jfolP2cHLU5OvWGf1wHH0ORcpUV+VSuYWSfaWH7snBMs1QyS1SPblNEV0XdFbIs6ylDnEfYd7su1J8fsNHfQnV591P4Y1+yJOq8F3f+UAaxyfVJmuyxzTo3aQwkdnW95XdkvtM2PqOnTdUudzEuVbWTtAH2hQZ2sOioKa8IuLmPcjLXeagfb29zflSuzaAc9/qTIDKMC3aVTdVduY33Ror55aQ7jb0Dn5k6Esa9SxnWYodtuQbWlnjri4nU7aEtA9k1rWZ4ps34hz4tLIRnRNXd3FPH8ufZLqQ8uY63lam3PMo598x5cfCOLBbq5ZpKCLaNIXSOx7QozzDSrKcrU70DrTSvLNb38xzExpOOBsnXxxN91vamMoZ1lOWMXcR5O2Q4C1pLWzx2ZfFVmL+UGCnxv5fX/2FdyjSCdpJWNjJNjo5B8fOKrMsKqS9SkidwVn88H6uivRrD83KvidwPNBGKb0u9HkknCcVUrBRSZTaG+Gco6WmSpHWWe87U4a19yrT03Rz+FPvpjSvauWcX22mO223FPximeU5JI6xjSkXh///7TFZ81ubX/+NR+X5Gfj+ntAQYGBgYGBgYGBgYGBlOGB8oAqnNe2eFTNnV2c8z/7mkysdaYUpYvToTptFJQ/SCf7INE89bxZFzkGbXXwUo5dJsKD7Avt40o16CLYxgc4vMiI6GdNqIYHqPTRduSgBGKlHERn9Guq6yZcofHud1DBLrKvH518qrwKXzICFvkHMeZecI4RkbNHVud5Y7zpDUSc5z6TJ2INfE2ZwDV/TPN3T/5d1u/RxaTuip1AouoE1i7/b4UXOq39MfUJpbJvIRtaNE0ahTnTokfk3j8C4xPfgJtucDIUZXk8YWL1Hgwmlpj9Phzn/60iIh4zgtyexVt8e77cAdcX4MWsFCG/ujK46iTFtItUzxsc3Ud97Dfx71r0tBTmWRtA7MzYBr0djqO6jOCXIc6Yl6/R1ZhfYs1yobYd62CBvSz1/G93R3sc+kCTvT0con7Rlt59Y3bIiKyuc9+lSJyfbCFNnPl2nU55HEmGfS6qp9Kcj2ZOpBNtisvl+Gp46260KooAV/wGImLKMRZoWvks8/UpUJ9WUCtSZt1DrUuXIE1pY6oaXEZKbWnVD+i2pA8s4Cfq/NefEKLnDNKaZKzhKpLVqdidUk+jkdO6gv7A1z7d97D+PrOLZk4hhLH13PnwUqfWwIz+EsvfFJERL78peflzZtw2H33/bsiIrK6itete2hzWpNKa04dm6upVgTtPOpgjBt0oS9MM2Uz2X9K6GPjLuu/uRURzi1Fsh029baqr8mzMmLVqagN6AmtktbV4nVpkEXP+0umjs46fyTyox++JCIiV69CX6ztV5ltZQL1vWpkHGd6Y8MN1v3TmnVHKa4RA/IyT/a4zLWIx+s/GA8kZFJCEpMBZF1MK3cOZE1X6qPqXEOMOP4UGcFXF8JBqjUkMT93YrTDoMX1DN3MvbIvh3TF7Y7RX/oBdYQLmD/shPXKqJW+RDZxEIJdUN+ABp1vqwn2VWd79JgFEdLp96jLzCbblmXWgCs6yjhTJ0bWI1StLJecY7KPB0N+b4RjPcPsqHiANr62A4bDY7vtUDjboeNzfaYqLjO0AurTe3zNx391ONcMLK5Doyn2Kz/Zux31EWD2TD7T5kW6f8422F48tuF+a3/i7zHZOp0T1O2fCX9SyEXOWv+PjJ+ut+3cMh3fD0Vsrbuq9fw0U0OXxznxrvORZgayzp816YmhyDnKVHWNHJt5XTw/E4u/dXhA+ppxPlMWNTlRi9DJ3WiF26YDLt3zhQy9Ov7rkdVYTWCm2ZQ4w7hxuIc5MKLe0DtRDSAjcxnwddzuyEdhekd5AwMDAwMDAwMDAwODKcMDZQBjauWas4iq56xATOZoyL/TmXPIfPk4inM9XbGI3+oTfMCn6cvnoZ/65c8g6msP8CSvEYCt95BD7nYZwWVkN2CkwGLicZxp3jKejYdpLGM+yRcZSXIYTajwqfsso3k9ugEds3XYlur0lM1z+Lu83mFug6QRct6W+wJUGpnV8ICVOwvhtzW6iy2fgUtdt6c1FvG94RARN434atTdJVt5gc579Ro0Wy+/9Y4EPG43dxRlvRIygdEQ0YggoJaM9yKvXzWF+Ptf+20REfE81SHg87OMtm6vI0p24w3o+b7xn1AvquA1ZWsb96TdoX6iCEajx3vX7fT4XURTZ+ikOTc3x30xX5256C6/l9d2ZLQoS9Rl8Djvn91Aij626ZH9TekYJ2hectTD8Zdt5taniC739qh5SnGem/e+KyIis0W0J3eBrEUVx3hqHkxgv7cteztkevp05AzppiV6vPg8TSYZ8xGj6cfOXtSCaB/m14vcTppHAXFdi25F4gD9pFlvcsuT13AcTGpl2/0OtyXTibyOqEZgGbVUpi//VOgeAAAHJ0lEQVRSxkw1gMeaMo0Eu4zAKysyZsRer312wun4A07IJ6Bj2a33kPVx62fQELocl2u1kiyfASt4+eJFvF7Cq+quB3RQvLMOHeLdVbgh7+21JvaV5vVWtY4eP+ccZlHb1CNjGMeZ6DVyGektMy2gz4yQzR2wiQsziyIiMkc36Sp16SVGgjV9QyPvWT4nkClk+/YqvL52Khfodq1yp0h1LMxOkRHnEW6rkEezp1fPvaj1/9KPcf8kcxuPcR9DSXMGOZ+nc6ac2mFe9zKZPmUA73bpUcD0jR6zaTRCr2zkPucCa0B/gSLm4ILEIkWmfrjqGAh9eZRpP6P+kG3T4lqoTC+Ggk89K93LV1pg5S7FmEd61HU/8Sg0txt09W2WbRkPqatjlkmBmsVmlU6IrBVXYfvr8rq0qOUbsAbmvY7WRWSGUoZ5SDObopSsCbOQtvYOZHYB5+2zRm2tif6jGVjhiE7VvE/a75QRnEZYJ9jPfExR1uo+NbTIfZllE0s7fOZyTR7y+hY+kAH28/XEJ7NItG8kpPEyrsm1Zp8di6TMIlF9oGY9eKzbm7tPqyZdWUVuI6NGN6VWXX9vcZ53ueYN6GTq8JhiKxWnwDREzchQc3Iev0+9qmbw6V9Stnl1P9XHg8MW+q5U0GcqdLHeP0D2SH8HfglBt5un+gW5xp71NpXhtCavcX4E9kevxQ0DaGBgYGBgYGBgYGBgMCV4oCEQK6+NpE+t+l4m3v/8H0/+TfUh+qI1pWaaiJqO+CSfMJIf0WmnyH11s0l3II/PzAGf7kMeY9Fy8lqDRdXZieol8Or7eB1RB/LGIXQlTqZaDL7qITN6UXBVhzep5zu+IHZ+fOrOqdcwy5lKZeUQnWjU6aJFlk7Zn2N9GKKCh4d0beR1u3QFkeI7dxEBv3L5IRn0e/wuIhHXrz0kIiJPP/kYjoE6KY14jPvqPza9CIdkg1kjKUBQTDZW0CZ2d3CNDo5wH4JU67k4kqRoP7GNaPBQEEV1CnQWXARzxVsrI0Y2Ox2wUlrrRp0sa2X8TtmYDvVtLtm9nKiOU4nZkWJGvuqs2xczajwaIlrcKCJa1R90eIxkx1kqar0NrVWo1pxMQV8kS6mOWIuNJ/F+3JSde6+IiAilXnldIU8duzJ10zpZE5P5/4zq5TUt+Wcnd3oENNqlxHuW3jcmZRopJLud/0r7sImV3Y8P1v9Thz11A43492OGMGU01vZwTdUZcRyoyyvHNtV15A7Ifza69VgHPTlWRnEi+/tg8gYj1n2ibrDO8XKuiQyIWbbTKxeviojIOzfXRETkgFrxMXUbQ/a9KFQmkOM05w+tORmnkQjrP6Vs4CRcJIlwTCnnooNdjMm9ltY9LPI8MC7ML4JlV/dlZelK1erE+XoU18zOlGVxYY7HSUab2qmAzIu+1/q6MTVnUaC62+nDgqf1/3Ad9/h5iUxamZkVWv8v7IIxC51MMrpZCjVE2sZDsgAuM3DqRXwvoHeBajDz8UjdGVWLyj7QIsuyzNEsULloEItH108vVKYPv1Flvs99xhl1TWTZhiHZSXU+ZCbFYoPOzSO0W50v3n+fDDtrFM5ViyLUTe6znuZ8Af0q4Rg+HpAFYb+oWOh/QYJjrvP8NtuY+0rz1CG6+HydbEnCeUXb+EylIQl1hMrsqYNvgesyjwxVfRbHq+sV1QxOJdQRPGdaT9S1Pfl+gtWjiyfXnDqzJpGycd7EL06O3jqXWjJZg1rHeU1sOEkkpiK5+6c6heq4pU8yMbWnqoFTVtEXjKGauJJZ6qZPFp/1myP2x2GftTF5TnGa5vtOuM5Xna6yi5pNogyhZhzFOq+pSzPnzEg9RdRTg/uO2I6HXLOPB31R0/EgUAZwkuHUx6P8kqnnR/LR6xazqjEwMDAwMDAwMDAwMJgSWB+nrzAwMDAwMDAwMDAwMDD4xYBhAA0MDAwMDAwMDAwMDKYE5gHQwMDAwMDAwMDAwMBgSmAeAA0MDAwMDAwMDAwMDKYE5gHQwMDAwMDAwMDAwMBgSmAeAA0MDAwMDAwMDAwMDKYE5gHQwMDAwMDAwMDAwMBgSmAeAA0MDAwMDAwMDAwMDKYE5gHQwMDAwMDAwMDAwMBgSmAeAA0MDAwMDAwMDAwMDKYE5gHQwMDAwMDAwMDAwMBgSmAeAA0MDAwMDAwMDAwMDKYE5gHQwMDAwMDAwMDAwMBgSmAeAA0MDAwMDAwMDAwMDKYE5gHQwMDAwMDAwMDAwMBgSmAeAA0MDAwMDAwMDAwMDKYE5gHQwMDAwMDAwMDAwMBgSmAeAA0MDAwMDAwMDAwMDKYE5gHQwMDAwMDAwMDAwMBgSmAeAA0MDAwMDAwMDAwMDKYE5gHQwMDAwMDAwMDAwMBgSmAeAA0MDAwMDAwMDAwMDKYE5gHQwMDAwMDAwMDAwMBgSmAeAA0MDAwMDAwMDAwMDKYE/wWK9GQPlIs0nQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Display a few images and labels from the non-augmented and augmented generators\n", + "\n", + "batch = next(train_generator)\n", + "batch_images = np.array(batch[0])\n", + "batch_labels = np.array(batch[1])\n", + "\n", + "aug_batch = next(train_generator_aug)\n", + "aug_batch_images = np.array(aug_batch[0])\n", + "aug_batch_labels = np.array(aug_batch[1])\n", + "\n", + "plt.figure(figsize=(16,5))\n", + "plt.suptitle(\"Unaugmented images\", fontsize=16)\n", + "for n, i in enumerate(np.arange(10)):\n", + " ax = plt.subplot(2, 5, n+1)\n", + " plt.imshow(batch_images[i])\n", + " plt.title(lsun_classes[np.where(batch_labels[i] == 1.)[0][0]])\n", + " plt.axis('off')\n", + "plt.figure(figsize=(16,5))\n", + "plt.suptitle(\"Augmented images\", fontsize=16)\n", + "for n, i in enumerate(np.arange(10)):\n", + " ax = plt.subplot(2, 5, n+1)\n", + " plt.imshow(aug_batch_images[i])\n", + " plt.title(lsun_classes[np.where(aug_batch_labels[i] == 1.)[0][0]])\n", + " plt.axis('off')" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 300 images belonging to 3 classes.\n" + ] + } + ], + "source": [ + "# Reset the augmented data generator\n", + "\n", + "train_generator_aug = get_generator(image_gen_aug, train_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Train a new model on the augmented dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "# Build and compile a new model\n", + "\n", + "lsun_new_model = get_model((64, 64, 3))" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train for 15 steps, validate for 6 steps\n", + "Epoch 1/50\n", + "15/15 [==============================] - 11s 761ms/step - loss: 1.0921 - categorical_accuracy: 0.3900 - val_loss: 1.0440 - val_categorical_accuracy: 0.4500\n", + "Epoch 2/50\n", + "15/15 [==============================] - 10s 700ms/step - loss: 0.9937 - categorical_accuracy: 0.5300 - val_loss: 0.9592 - val_categorical_accuracy: 0.6000\n", + "Epoch 3/50\n", + "15/15 [==============================] - 10s 700ms/step - loss: 0.9181 - categorical_accuracy: 0.5933 - val_loss: 0.8677 - val_categorical_accuracy: 0.6000\n", + "Epoch 4/50\n", + "15/15 [==============================] - 10s 683ms/step - loss: 0.8742 - categorical_accuracy: 0.6167 - val_loss: 0.8353 - val_categorical_accuracy: 0.6333\n", + "Epoch 5/50\n", + "15/15 [==============================] - 10s 694ms/step - loss: 0.7762 - categorical_accuracy: 0.6633 - val_loss: 0.7767 - val_categorical_accuracy: 0.6417\n", + "Epoch 6/50\n", + "15/15 [==============================] - 10s 700ms/step - loss: 0.7298 - categorical_accuracy: 0.6733 - val_loss: 0.8494 - val_categorical_accuracy: 0.6333\n", + "Epoch 7/50\n", + "15/15 [==============================] - 10s 689ms/step - loss: 0.7398 - categorical_accuracy: 0.6733 - val_loss: 0.8149 - val_categorical_accuracy: 0.6417\n", + "Epoch 8/50\n", + "15/15 [==============================] - 10s 680ms/step - loss: 0.7302 - categorical_accuracy: 0.6900 - val_loss: 0.7711 - val_categorical_accuracy: 0.6333\n", + "Epoch 9/50\n", + "15/15 [==============================] - 10s 693ms/step - loss: 0.6948 - categorical_accuracy: 0.7033 - val_loss: 0.8223 - val_categorical_accuracy: 0.6167\n", + "Epoch 10/50\n", + "15/15 [==============================] - 11s 713ms/step - loss: 0.7074 - categorical_accuracy: 0.6967 - val_loss: 0.8049 - val_categorical_accuracy: 0.6833\n", + "Epoch 11/50\n", + "15/15 [==============================] - 10s 693ms/step - loss: 0.6856 - categorical_accuracy: 0.6967 - val_loss: 0.7708 - val_categorical_accuracy: 0.6750\n", + "Epoch 12/50\n", + "15/15 [==============================] - 10s 693ms/step - loss: 0.6761 - categorical_accuracy: 0.7233 - val_loss: 0.7492 - val_categorical_accuracy: 0.7000\n", + "Epoch 13/50\n", + "15/15 [==============================] - 10s 693ms/step - loss: 0.6434 - categorical_accuracy: 0.7100 - val_loss: 0.7391 - val_categorical_accuracy: 0.7083\n", + "Epoch 14/50\n", + "15/15 [==============================] - 10s 686ms/step - loss: 0.6191 - categorical_accuracy: 0.7400 - val_loss: 0.8185 - val_categorical_accuracy: 0.6333\n", + "Epoch 15/50\n", + "15/15 [==============================] - 10s 660ms/step - loss: 0.6553 - categorical_accuracy: 0.7433 - val_loss: 0.7689 - val_categorical_accuracy: 0.6917\n", + "Epoch 16/50\n", + "15/15 [==============================] - 10s 673ms/step - loss: 0.6007 - categorical_accuracy: 0.7433 - val_loss: 0.7495 - val_categorical_accuracy: 0.7000\n", + "Epoch 17/50\n", + "15/15 [==============================] - 10s 693ms/step - loss: 0.6047 - categorical_accuracy: 0.7400 - val_loss: 0.7563 - val_categorical_accuracy: 0.6833\n", + "Epoch 18/50\n", + "15/15 [==============================] - 10s 680ms/step - loss: 0.5955 - categorical_accuracy: 0.7633 - val_loss: 0.7878 - val_categorical_accuracy: 0.6750\n", + "Epoch 19/50\n", + "15/15 [==============================] - 10s 680ms/step - loss: 0.6175 - categorical_accuracy: 0.7400 - val_loss: 0.7690 - val_categorical_accuracy: 0.6917\n", + "Epoch 20/50\n", + "15/15 [==============================] - 10s 680ms/step - loss: 0.6363 - categorical_accuracy: 0.7567 - val_loss: 0.7529 - val_categorical_accuracy: 0.6583\n", + "Epoch 21/50\n", + "15/15 [==============================] - 10s 673ms/step - loss: 0.6516 - categorical_accuracy: 0.7300 - val_loss: 0.8479 - val_categorical_accuracy: 0.6333\n", + "Epoch 22/50\n", + "15/15 [==============================] - 10s 693ms/step - loss: 0.5844 - categorical_accuracy: 0.7667 - val_loss: 0.8102 - val_categorical_accuracy: 0.6500\n", + "Epoch 23/50\n", + "15/15 [==============================] - 10s 674ms/step - loss: 0.6111 - categorical_accuracy: 0.7233 - val_loss: 0.7905 - val_categorical_accuracy: 0.7250\n" + ] + } + ], + "source": [ + "# Train the model\n", + "\n", + "history_augmented = train_model(lsun_new_model, train_generator_aug, valid_generator_aug, epochs=50)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot the learning curves" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Run this cell to plot accuracy vs epoch and loss vs epoch\n", + "\n", + "plt.figure(figsize=(15,5))\n", + "plt.subplot(121)\n", + "try:\n", + " plt.plot(history_augmented.history['accuracy'])\n", + " plt.plot(history_augmented.history['val_accuracy'])\n", + "except KeyError:\n", + " try:\n", + " plt.plot(history_augmented.history['acc'])\n", + " plt.plot(history_augmented.history['val_acc'])\n", + " except KeyError:\n", + " plt.plot(history_augmented.history['categorical_accuracy'])\n", + " plt.plot(history_augmented.history['val_categorical_accuracy'])\n", + "plt.title('Accuracy vs. epochs')\n", + "plt.ylabel('Accuracy')\n", + "plt.xlabel('Epoch')\n", + "plt.legend(['Training', 'Validation'], loc='lower right')\n", + "\n", + "plt.subplot(122)\n", + "plt.plot(history_augmented.history['loss'])\n", + "plt.plot(history_augmented.history['val_loss'])\n", + "plt.title('Loss vs. epochs')\n", + "plt.ylabel('Loss')\n", + "plt.xlabel('Epoch')\n", + "plt.legend(['Training', 'Validation'], loc='upper right')\n", + "plt.show() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Do you see an improvement in the overfitting? This will of course vary based on your particular run and whether you have altered the hyperparameters." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Get predictions using the trained model" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 300 images belonging to 3 classes.\n" + ] + } + ], + "source": [ + "# Get model predictions for the first 3 batches of test data\n", + "\n", + "num_batches = 3\n", + "seed = 25\n", + "test_generator = get_generator(image_gen_aug, test_dir, seed=seed)\n", + "predictions = lsun_new_model.predict_generator(test_generator, steps=num_batches)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 300 images belonging to 3 classes.\n", + "[26 14 27 55]\n" + ] + }, + { + "data": { + "image/png": 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VP4h+a7AX+BLwf1NKX7V1v42+ZtvR/S9962gXmd+D2va2o0rup9FvISZCF3pu7kUvdN6fUnq+SvtfQc/jR0TkOHrML7C5fBm1J37N2nxtrE5SSo8BP4beQHcM/dvmauefA+8QTaT4i1E2/1lU5NmGvtafRT9Ejku116viWK4BdgGdwLts9WfQ4/4AepwHyH64mTDV3hfGmdekX+Nq+2TfpP8h8E/2Wm4iG/040f05hH4Y+z/oh/VfBr7bv/2n+rkdnGYk9yH+zAyqVcA+iCopT9nie1NKd9v6Hv+Ky1Srt6Fv3kPAPSmlO0VkB3C1n0gisha9GaMd/brqnejXHr+UUvpua/Nx4PGU0p1jzOsW4I9RNXs98NMppUFTFP4G/UT/qI17k6jn827UJ/mz6C/QZ237LwAfSim1iYgAf4T+AiX05pHPV1l+U+W8gyA49Yjeh3FHSulvp3suwSsfe1//h5RSx3htgyD49mRaLqqDIAjONCLyHehd/93oN0l3AOellPZN68SCc4K4qA6CVz5RnScIgnOFC1B/aBua1PGOuKAOgmA6sW/Cx7LIfDuXiz8nOSeVahH5EpopXcmvpJS+Mh3zCYIgCIIgCL69OScvqoMgCIIgCILgVDId6R9BEARBEARB8Iqiqqe6tbU1zZkzZ8z1YnUU/NFV72JRi4aNjGik5PBINlqytqYms53HNdfW1pba19fp1BqbGq2F2LphAI4c0poxTS3NANSI96k9NjU122PjqGMVChrhOjCgRcV6enuwndBxGxv9GADQ0NCQ2d739cgRnUdfXzZWuq6uPtNPTU12fv4FwWjfFPhcfZuT12f7yB9/f+776K+DL6+ry77s/nppLCocOHCwO6U0f9TBg5dNe3t7Wrly5XRPIwiCIAiCSfDEE09M6Lqo6kX1nDlz+MAHPnDSxbNItmZJ/mLNH/2Ctb9fK18e6NYYxa6DBwFKF84jQ3qhfPyYVe2VGi44/wIALr30Sl2k16h8+T/uBeCQ9bHqPM2sb2rQi+hmu5h+3eteC0Btg16YNjZozZaiifOdnZ0AbN68CYBdO3cAMKNV4607OvQG7RtvvBEoX1TX1epEtm3TCNgHvqnRoH39elG+aKFWPn322Y0AtDTrfBYv0aqu8+a16z7bhW6h4Be0lI5ds21TW+sX1cme11rb7MWz48t9rlu3btV9Lury2bP1A1JTkx6LkYIe94EBLT7pr9Mdn/zr8UqmBi+DlStX8vjjj0/3NIIgCIIgmAQiMqHrorB/BEEQBEEQBMEUmVCkniugeYU0r5QOD6vy6SqsK9azZ88GylaI9rlaRLHrwAEAvnD3F7SDgva/fNVKLjhfK6gODKqKun9vFwCbNqmyvGLZMgD6enR9/UxVZ1evVuW6ptZn5VYI7buvX9t3du4CoKfnBAAzzObR1qaPK1ZosSlXjZ2hYa16vq9rv87rgM7rf/3we/QYFa3doLbbsWMbAM9s2ADAosWqWHd0LLVjpPN2q0d9fT01NflvArJ2GVeefZsRs8S45eXQoe5Mu8bGJnt0K4z2O2z7krfCBEEQBEEQBJMjlOogCIIgCIIgmCLjKtWVanResc4r166c+nJXrl0JdaXUH33929/2VgBeeO4FAGbNm82qVSsBON6nSvJzm58DYJl5nf1GwGLSvltnqMK8aPEiXW4+5IaaOhtL1fO9+/YAZVXWReG6OpW2Z8yYAcCaNWtsefYQ7encC0Bn5w4ALrzwIgCWL7fY6yQ2T53/I498C4B771Mv+IkTxwHo7laF2j3WLS2tpfHKxzyrSPsNha5kF4u67+619m8IDh/Wmyfdp+03W3q/Q8NDABQK2n7QVPXjx48RBEEQBEEQTJ6oqBgEOUTkVuDPgVrg0ymlj4zS5ibgY0A90J1S+o4zOskgOItY+av/Md1TCKaZHR/5rumeQhBMO+NeVCdxV3I51i4fA+fPXZHOK9eusA4NDWWWHzp0CCinVbzuJk3aaG5pY8QU6KNHVXXt71UvdEuzpnO4gtxtiSJrVquyfND8xLNmzwKgvl59xn0Dmmxx8OABm7POacASL5otEeM8SxNx1dfn6kr3/v26/c5d6pV+73t/0va9mDkmPr+W1jY7Jrrckzf27FXFvFDUfj1qrVgcodYSRtxLnceXFyy9o9mOSVdXl+2btmtpabFjYNEppnyPmJd6ZMS91HZsDnSPOt65hIjUAp8A3gh0AutF5J6U0uaKNrOBvwJuTSntEpEF0zPbIAiCIAjOFsJTHQRZ1gFbU0rbUkpDwD8Bb8u1+SHgiymlXQAppQNneI5BEARBEJxljG//KNaQxKuM5PKRxyhgkleua3LFXp599tnMcld1jx9Tv/Gs2e10H1bVdN+efQC0z51n22gfPT2aC+1e5F5TW1/auRuAFTalIVOQT1jftbW2vXmbh009X7xIvdjnn3++zV1V4MEBfTxgKu72HS8BcPWr1wEwv7199GOAjvvNBx8AoLlZ5/ma11xv+6zq8RNPrgfgySefAmDpkmUsX67JJu49977Lyn8hc9zcH172RIsdm5bMdkND2m7EXpfBQX1+5MhRAI4dO0HAUmB3xfNO4Npcm/OBehH5OjAD+POU0l1nZnpBEARBEJyNhKc6CLLIKMvyZS/rgFcDtwDNwMMi8khKactJnYncBtwGsHz58lM81SAIgiAIzhbGuagWNQOnbF3sySrX7qn2tI/1jz8GwLIOvchoaNBpXHONCoJHjh/jmCnL2LatbZrKMTKsKmtPj/qHPaXj2AlVWVOvKta1luYx84R6mmtMOS6aAt1tFRlntOn6Cy7QCo6e+ezOmL4+9XLv61LFfN8+Tf94/etvtvZZH7l7sR96SCvneRKHX1Bde+11QFlV9mOzaZPmWL/00lb27FG/9dq1um/z52tlTFe33Q/u/uzOzj22XGfuaR+uZJfLxg9l5ugVFA8eOJjZh3OcTmBZxfMOYO8obbpTSr1Ar4g8AFwBnHRRnVL6FPApgKuvvvrkmvRBEARBELwiCE91EGRZD6wVkVUi0gC8G7gn1+bfgNeKSJ2ItKD2kOfO8DyDIAiCIDiLGEepTpCKSCl/2hXpnHJd44p1deX6xS3PAzB/vvqjW1tVae3aq/d57TA/9MFjh5k/R9vU12syyPCQZV6bMrx0seZVL1q4WB/rdMy+fk22GDJVfNAqKIqpu4e6D9m+6T7NnDUHgLnm2XYVeGRYH3t6egHYaV7qyy67HKjIyTYVuL6+LrPvDz/8EFD2RV977bWZdvPm6XjXX3+D9afK+u7du0s/b92qY+7bp6r8ihXLba5akfKEqfO9veovr7Eyknkv9eCQHgNXqD3t4/DhI7a9eqtra+MzVkppREQ+AHwFjdT7TErpWRF5v62/I6X0nIj8J/AMUERj9zZN36yDIAiCIJhuwlMdBDlSSvcB9+WW3ZF7/lHgo2dyXkEQBEEQnL1UvahOQDEVwRI0aiwf+STluujt7XlOuR60dIqBPlV9B3v1cXOnKrCXXH6lTqZeVd3ZM2bRaNnVhSFVV92P7ZnWrtZ6okUDqhy3NOl6V2tNwOboEfU2HzmiXmavQuhK8t6u/QAsshQQi8ku5Vqf6FGP9+tvVi+1+5VdhS8W9fGhhx+0+ap/+YILLgRg9eq12mHuPjjfH/dRv+pVV9Lerh5qz/E+elTn/Nxz6jBoMx/4kiVLgbIC3dCg+1ROCRnJrPfHwQFV8/Neat8uCIIgCIIgmBxxFRUEQRAEQRAEU6S6/SNBKqaSkll0Sbpgucm2vKxc22ZeXNBE2f/576/qcvNFdyxaAsCsGaoyt83QZI8Lz9cEjgMHD7Bvnyq3PrYry/78+DH1A7cMq9I7Y7F6qweGVIVtN0/2wUOqNB8/ob5jz6V2jh7VjOb+F14AYMiU8bnmed66TX3gF154EQANDaps55XqY8e0n8cff9T2XQ/Cq1/9aj1WNa5Qe4a3rn/xRQ2M6OzsBOCWN9zEiuXnZfZ5+3at3vjMM54Qol7rp556EoDmZp3TsCWXXDdXfdpFy6MupX3YNwYHD2rm9oBlcPs4/hgEQRAEQRBMjlCqgyAIgiAIgmCKjJv+USgWyl5qV6ZLVmrzThfdQ63tXJXt69XUiYLlI/ebKrzdsp+Xrlylk7AkjQMH1dd8+PChkvLbahUTXUV1ZXigR5Mveo6p2rrN1NwLL75Ul1sixglr12sVFL0S4zKrWjhisvrB3aqMNzY0A7CvS58fPaK+5isvu0LXNzZkjoWz4Zmnbd/1GKxdq5UZPWPaqyCW1WAd95FHHgZgzRo9FvPmtpfyp31fly3T1I+Dlq390jZVqr/7u7V69lFT7bsPdtncdISCjel51CfsGBzIeam9yqSnjgRBEARBEASTI5TqIAiCIAiCIJgi1dM/zFNdMH+wK9CSsl7qcqXFbHXB1lZVfRfNWwDALkuzaKjRdp7ZPHeeZkUfP67q9DPPPMW8earwDpkH2lM6BodVeZaCLl+2TBXnQ6bCNjWpsn3shPbVY5UZXa3t6FA/97IOTc6oa1BVuHZtVoHe+Mx6HbfBs7TV8zzL/N9ts2cD5azozZs36nwH1dN93Wuuz/Tn+1HOoN5q22uyx2WXqWe7vV2PVeW2XsVxzx7N8b700ksAuOqqqwAomHf6kUce0bGG9ZuAkid7m6r4hyyXOq+We751eKqDIAiCIAheHqFUB0EQBEEQBMEUGddTPVIolJMrkl2De66xe6rJ5hy7t9rzlR99VFXfi85TVblY1PYLllhix7CmUmzZqgkcff29LKzVde4r9pzqVpvx7AXtAGzdrv7iFavUw+xVAbstj/q4KdUDlnyxapV6l2fN1qznPqsm2NCgvu6uLlWF+80PPt8Vc6vM2L1f1++3dJIdli/d16tK+BVXvEq3m7/AjoVkHpMFYK9f/xhQVtrnzVuI48qz+7937doJQKcp1e/78dusZcqsF/tGwZNJesxP7tUlb7n5DQCcsCSUHTt22HiWDmL7GARBEARBEEyOUKqDIAiCIAiCYIqM46lODA8Plby27r3NK9diz5OlgNSYUrpvn6Z5LF6h6RXMUR/yisXqZ54xW73U3ea1fuEFzWxe0bGspJ4mS+fot4qGw82qKLcvVKWaGh1r1qxZQNnjvGeXqroD/apEHz+u29977z0AfO/3anJGa5vOSerUU713zw7bFx13yRL1YDebkr1t+3YARmz9/kNaqXHIMrivf80Nme0l51PesGGDtR+y/vVYLFyoSnWxWCylf2zdqsejq2sfAFe/+hoAZs92D7ruk1deHCnoHFqaVYV/8UX1Ur/+Jq0CefHF6sWeP1/H8mqRTz/9dKa/Bx98iCAIgiAIgmDihFIdBEEQBEEQBFNkXKW6WChQdAW6Jpu1XFurm9cgmeeHLE95+w71O8+aOxeAelvfZRX9dnaqP7muXlXguabADg0NMWSVEetsm6H+XgAa5qhXecCU4dWr1wBlH/KB/aqOHzygj67qLl+uavngsPqJBwa0/4FBVZq7u3XOx48ds/Xa/3NWaXGB5U2vtGztHeZjHrEqhuuuucaeq8K+b6+qy0uXqtLdaFUPH31UKy6ed95q3ee58zLHFDSnG2DPHj0+nkP99rd/f6btgQOqNI9YEoonlRw/rmp9nanvF118OQDNLZrGUhhRFX3hQp3bW9+6wo6d5lx//OMfJwiCIAiCIJg4oVQHQRAEQRAEwRSZkKfa0ySK5qF273SxoIpnrWUvNzWpEvqCqbsFU20HrbLfoGU4u9LqiR7Nlowxd46qyidOnCgpz/WmYs+av0i3seSQomgfPT2aWNHbq33vtTxp93+7N3vN2lU2xwbrV5XjA4fUR7xzp+ZG9/VoiodnOj/77GYA1q5da8egaOOpcu7513PnqBrfY8t93/JVEOvtWDabb7qzU+e7zbKkr7vuOrabb7vLkkbWrbsOgNZWzeA+apUpjxxRlb1o+9jaqr7yHTvVT37F5VoFsqFB97nRHhusKmStHaNkx9SPVRAEQRAEQTA5xr2oHhoaKlka/OLaH5MJ3XWiF7577ALRrRReLlskW2Lc8Ys9j47zC9X6+vqKCDrdxi+y7YHduzTKzi+e3YbhNzZ63Jz36Req86zQTFvrTADD4uEeAAAgAElEQVQOeoRen97Q6Dccum1k/361WOzatQuA88/X6L5jZslYs0btJyM2sRN2s5/HC7o14/HHNVawvV1tJK1NLQA0telNhfPNmnHw4AH27N1ju6D7cNWrXp05Bl1dXfZc+25t1T6O202aM2boxfWKlSttvV6M+wW9xw46e/fp67b+sUcJgiAIgiAIJk/YP4IgCIIgCIJgikxIqS7ZPywmzpVrt2a0tqjquvk5tUq49cFLcrvC6rYP78fV3MHBfutPx2lpaT5JofY5VM5Nxxoade6NjU2Z7Y5aMRhXxX27ocHs9vUWnddpN1HOtZssr776at0H26cRs0ostci9Opv74SN6k+Ecs7K8sOXFzDzaTDWuMSW+zgrlNNl8n312MwftBsR1167LzO3wYbV7eGnzgtlqZrXqWC9tUzX9sssuA2CGlVR324er+K7G+82gu3frds9ufo4gCIIgCIJg8oRSHQRBEARBEARTpKpSXSwWGRwcHNNT7Uq1x8S98Lwqna4il4vG1GSeu/rsnusFC+Zn2g0ODtLdnVV8vaiL+4NnzlRPtM/N1XHvu7W1JbcvOqfjx1SptqQ+RkZUrR025XmmFYO5+OILAUrz8CIwGzY8AcBqi8RrbtZxWswT7Tcg7tmjPuXnTL1fsEALrnRbsZj+AVXI29p0f/rshsdDhw4xb57G7F1y8WWZfThyRH3crjC7d/rAQT9WqqovWqQ3dbY065xcofbXzY/Rli1aXGbzZp3jQJQpD4IgCIIgeFmEUh0EQRAEQRAEU2QCnuqBUlEXVzhdiW6z5ApXrLu7taiLq8bunT5ZoVaJuqFB+12wYEFm/c6duxg0r/N+K+biYzab+nrJJVpy+6WXXsqM4X0vXarlv3t7VX11H7Gr4TNn6tzFntfXaMRenxWZGRwayMzxgQceAGBkROd1/NgGOwbqW169WpVr9zEfOtRt26ufecT2x8uXv2iFVmba9o2NjaX5+TZf/epXATjvvPN0zOMapefJJl7s5fBhLURzxRUaodfS0po5FjZkSdV3b/amTRsB2LnTCtlYmfMgCIIgCIJgcoRSHQRBEARBEARTZBylusjQ0CC1tZ7AoY9NTY2ZdgMDqup6cZc8rkBX9AyUfcauqHohl6NHj5UU5SbzKLvK6n5h9xW759rVV2/vyrar5/X1utxzqksK8oiOU1Pjpdb1+YAnkljax9Cg7qOndNTWabuuLi1HvtIyof35iy9qMZmSB9w83l4wZ+V5Woymy4rDdHfr/GfMmEF7ezsAW7duzezjwIDOacZMVbcPWrn3jo6ltm9W8tyO3dDQkO2Tp67oMXn66acyx2bY2s2arX7yIAiCIAiCYHKEUh0EQRAEQRAEU2QCZcpHKBRU4XRF2VXdOlNrn3ji8UkNKp7NbGXNvdS3e7cbGxtLvmx/dDwBY86c2daXzm3xYlWwd+xQf7D7va+77nqg7KnevHkTUFasPc/62muvBcqe7UFTptev12ST/gFVxr2U9+7dWvXwxAn1OQ/0mXd7JOvdnmEZ3q5we+RJfbOVCrdjWFuny4dHBtm9W8uMuy+8mFTpb7AM7bpafTx0WFM/1q1bZ3PPZnM7/k3Brl16bLyMvCeqeBHK1edfZFvcQxAEQRAEQTBxQqkOgiAIgiAIgikyjlKtKqd7cT372SslPv64ZjYfPaoV/lzJdj/zWHgqiPugly7tAMre4c7OPaU2/ug+Y0/XcF+2e5mvuuoqm4sqxwctu/mmm24GoLZW53bLLTfZPuiur1p1nq3PVnscMGV6eYf2v7tzOwD33KMqbsGSMo6acr6nodOOgaWJmKpsQjq9pmTPW6iZ3AcPqJfa1WL3qTc3NZNMmfbj0T+g2dqLF2lW9r6u/XbcVMn2BBP3tkuz5YPb6+F54Bs2PA1An82laN9ArLngAgAuusiV6iAIgiAIgmAyhFIdBEEQBEEQBFNkXE/1yMgITZahXFdn6qupsU899VSpXeWjZy57YkeL+Yp7enoyz1et0gSMCy44H4DDh1X1/c7vfEPJM+0JIZ7VvGPH9sxz9y57KojjivPx46oEu297piVndHQsz8zZ27uHu69P86pb29Rjff0NNwCw7lr1L997778B8Mi3HgbKSrd7pJ0DR1QxnzdXqx3OadeEjl1WcdGPyfJly3R/autYtHAxUPaLe+708Igq2P7NwKuu0mPwyMOP6BiW/nHppZrh3TZDq05u27oNgD171AfuqSDutb78VZpvPX/uPIIgCIIgCILJE0p1EOQQkVtF5AUR2Soiv1ql3TUiUhCRd5zJ+QVBEARBcPZRVakGzVWus4qJjeYT3r9f/cArVqwEoKFBs5ldoV67di0Al156KVCuBNjYYFUDLeXjwovUy7tgvvqlPf1j1qxZJX/wiROq5A5ZBvby5aowFyzv2T3WNTXmI7ZkkmPHVM31PGtf7hUQ89UhXal2Rbu3tzeznaeBNNTrHN94y60AXHfNawH41kPfBGD7TlWFR0a0P1f1e/u03we/+SC2AoCWFlXCh03Vb65rYq4pxkeOaI704sXqnd63T73UHR2qah8zxbrF1PRt217U7Y6pV/3SS/T4P2Neas8R9319zQ3XAbByxQoAatFjeC4jIrXAJ4A3Ap3AehG5J6W0eZR2fwh85czPMgiCIAiCs41QqoMgyzpga0ppW0ppCPgn4G2jtPtZ4AvAgTM5uSAIgiAIzk6qKtU1NTW0trYya6Z6c5csUcV03bWqFns+8oIFC4BykkWpAqCpvsPDQ5l+ay09xKsfuse63hTx4eGRku93xDzORQtTtiALGhs159nzn1157uvz6o4Dtly384zn+nrdzhVqz712ZdrV3BHLm/YqhgO9mphRb6khLW1WIdHSPa69VlXf669X5frxJzUZ5fkXntMGKauM+361m8d6oF/HXbxoKT29ehzdEz0wqMei39T7jmV6/Ddt2ghAa4seA1fx9+7bC8B/3vdl2wft2/3izZbict11rwGgzqpIelrIOc5SYHfF807g2soGIrIU+F7gZuCaap2JyG3AbVB+fYIgCIIgeOURSnUQZBntk0U+I/JjwK8kzz6sQkrpUymlq1NKV8+fP/+UTDAIgiAIgrOPqkp1c3MTF198USlZo7m51R7Vw+vZ0fnUD/clF4rqE052TVJj/uJaS+yoMw+1V/Tzyo0jIyMMDmYTKrAxPGWjwRRnx1XYAVOaXZ11PGu7u7s7094TSoatUmLBEjYEnVTXPvWL19foXD2ju3+gz56rAj5kynj7PL1wuvVW9VxfeaX6mu+//78B2L5jh/ZvqvAR80V3LFWf9Lx57Rw6pN7pBfPVm75ly0u2TlXtBx9U//Zjjz0GwJrVqwG4/PLL7Fjp8e2z12FkWPfNs7MX2MWdp63MnatJK56VfY7TCSyreN4B7M21uRr4J3sN24G3iMhISulfz8wUgyAIgiA42xj3RsUgOMdYD6wVkVXAHuDdwA9VNkgprfKfReRO4N64oA6CIAiCc5uqF9V1dXW0t8+nuVk9uE2mUDc0qEqcT9Bw9bdoqm8qZr81N4G6lNThVQ29lavGQ0ODJc9xchnblF3fpt5SOIpF81KbMu2+4yFTnFPOa+1zLVV/tLF9eW+vpo30mcf66BFNLimYx3vePM2bHrTnnozSZj7leks4abfUkfkLVOW/8kqt+Njdfcjmq0q3Z0cv69AEjuMnjjFj5ixte0hTPIbMk+4e9JdeUuW6YMd3w8ZN+rjhGQAuuvhC3UdzMniWt3vW3T9+/39/VdtfpO3Pt7zwc5mU0oiIfABN9agFPpNSelZE3m/r75jWCQZBEARBcFYSSnUQ5Egp3Qfcl1s26sV0Sum9Z2JOQRAEQRCc3VSvqFhMDA8MMGRe6War7JfPdnbF2pXmgivVrgqbYqrRvmVftFgGtKvN7nseGhpmxPrwZbWeUGEq99BQ1kN9vKfXlg/ZWDZmaWdy++ZVIO25p4Xs7zIPtXm2mzxH2vobMq+3K+BHTxzX7fap4jxzliafbNumedUzLTllXrsmpFz5KlWsv/CFLwBw4oTOe/169UfffMtNzJmtaviLL20FoNG+Gfja/9yvc7FEE+xYYF5194V7ioe/HnV15j9P+rzJfOEzrcpjp1V33LJlC0EQBEEQBMHkifSPIAiCIAiCIJgi1e0fArW1woipuI2N6sl1ldczoEsK9Ygr1LluxL29+ug51d6urHCXFe+i9Z33QHsf/f3qSe7tH8hN2bTpVFqQfTR87IKp5J6l7b5wV7w939qTMZqaVLn2+RWSq8GmoLs6bCp+Prv7TW96MwBLLe3jk5/8JAArV62weQkvbVPP9FHL0G6zqpGenb11i1ZOdO91a6t+g9BmlSn9ONaagt1ox6wOS1Lp0TnVzFGluljU9avOK91/FwRBEARBEEyCUKqDIAiCIAiCYIpUVaqPHz3Gl//jPq694XprrWqs5yXXWs50oeR/tkdP7DA8l9r90I4ne7jiPWw+6cLIcMln7dnWXuxvxFTYmlr3Z9dk+sgXBcyr5o6r6u4Ll1J793VbFUjR503msR4a0eX1lk/dNkszngcs7SNZT2U1X+fb06OpIp680dbWBsCVV14BQIt5t7dv287jT2g1xhUrVwIwa9aszDZvf9tbANi4cTMA/ZZ80mTfJJT2sejed6+oqPvc0KpjdXerEr6tU2OYt5hCHgRBEARBEEyOUKqDIAiCIAiCYIpUT/9IieHhQfosuzmZIj1siRupXlXesv85u3059SPrh3ZdeMSypEteauvf1WjdKJsn7cpyWWF2D3U2zSNvonb13IRnjh7V/Om6Ov1cMTig1QdbW5usO53DiRNa8RDzNc+YMdOOhe2z+8RNte/vVyXaM6V93/v61AN+4MABa6fjrVu3DoDnn38OgOeee46eE3q8d+3aBcCqFSt1zsNaDfJYs/ZdLKiyPzw0YMdEvdXuC6+vNzW/WRXs4X49vgePqkLd1KYHo6VFVfhDh7oJgiAIgiAIJk8o1UEQBEEQBEEwRaor1UAReOpJ9fgOmUJ93urVADS3qse3sVGVU/dWuzrrBmcxX3RZVM4me3j6hz8WC8Wyqm3KsivT+Yzs0o6Y39sXl6o92vqufV0A9Jti7J7txsYGG9Myns32PWuG7lurqcK9tl1fT1aJLoxYjnaNqsGDruKb0i1Slzkmhw5pRUX3gntlyEWLFtkxKJT28eB+VbXnztbkkFZPHrExZ85U9XzYFH/P/x6241iDzrW15Tw9Rq2a9rH1Rc2/rmtSRXvBfPXI15gnPgiCIAiCIJgcoVQHQRAEQRAEwRQZR6lOjIwUOXhQvbYLF+rj0qVLAKhvULW2rs5U4VTeEspJHOVEDv3BkzpK/mhXlXPqs/aZMuuKrlyXBvNHTxgh03evpW5se0mTLZqbVe1ttPzp4WFVlkcs1aNoKu+MNlWBm02RnmnPfbh6W+7jNjZmc6y9eqTk40iMQqVvHGi3iou33347X/va1wB48MEHM30/ueFpAC656GLdyLr29QOWJ95gPvHGJvVYL1m8EoAFCzXHetESzcju6u6yfdN0kbn2zcNX+Oqocw6CIAiCIAhGJ5TqIAiCIAiCIJgi1SsqJk3imGOV9/YfOAjAww8/BsDFl10KwMKF6gd2xdSTMErd5DKbHVesy5UYy9UT/WeP6/DM5YpOfYrlyVL2FftcDh7UOdeYhO2VE0fs0ZXto8fU64xVFzxo+zrTMqJn22OjKdT1yaoU1vm+lpKuR913x5XrfDVJ96UvXLiQ17/+9aWfAe6++24ABgZUid7btQ+A5cuXZ/r06o7NDeoTP2/VRQDMnaeebE8cmTlLE0wWLdbXrb9Pl7+wcRNBEARBEATB5AmlOgiCIAiCIAimSFWlupiK9A0N0GDVA13Wre3XJIxjhzXveME89eqOlKoc1uX6MVnWvdQpm2+dr8AoFRnT7qV2a3Ledl3OvvZUkII96nNXez2tY+uLWwAYGnIvtbavr9P1rmCL7evRI7qPfebNXtyxwiap6m5DQ1Zl7+3NVp2sq8tWOSztV4UqX/l8cHCQjo4OAGbP1mqNjz2m3wzs2LEDgH37VKn2rO3VqzXdw33iBVO9a60KpB+bJ598EoCrrroKKKv3x4/pPh48emjUuQZBEARBEATVCaU6CIIgCIIgCKbIOBUVVSketgznjoXzAVg4X5Mqnt28GYD+AVWuL7n8MgCkVtXXphqrTmg+5aJkM6aL+RKMRrFYoKbG0zPKcxl9jtkUEM/ELivWOlZLi6ZyuJ+4y9Re17lrbbtaSzRxBXzIlrt32hVuH9efF3Oeb+93nqn4tXXV7eu9vZop3d3dTX29Ks4zZ+pc3/rWtwKwe/duALZs2ZKZQ3OzV4HUvk6Yqv6k5Yuff/75AFx++eU6N8/M7lbfeF/vCQCOHDtadY5BEARBEATB6IRSHQRBEARBEARTpKp8WltTw4zmFoqmAr/wwgtAOVHjwAGt+OfVAdesXgNAo/uIzVtdyvxw/3DOGO0Ka+kKX8oKs5SqMma9x2PlP3vfdTanEfNr9/Vrcsb5a9cCMMvSPHZu3w6UKyqWp6r9e9pHTa1XbNTxh0e0GuHQkCWY2Lj1tu+e1OFVJustkSOfBpJ/fuTIkVJKyrJlmie9YoX6uG+88UYAjh8/DkBra8tJ20I5AaVQ0L43btwIlKs5XnjhhQA0Nejrs6tzDwC9vX0EQRAEQRAEkyeU6iAIgiAIgiCYIlWVaqkRGpuaGLaEjBmtWlXwmKVOuJq874BW5rv/a/cD8PqbNGd5eIaub2xSRdV9ySK6PKWc2uzR1CJQY09MyS1605LJOrO6QtHG5pb1WNeYcu3tFy3SjOYZLTq3TRufAaDfFObSMfD+zSXtXm2nlHttedP9xf7M8rmWAtLQmFXex6JYLJaU6L179+rcLaXDUzs8n/ob3/g6AH196sd+yapGelVHV7o9+WT//v1Auarkgvnq9962fQdQVteDIAiCIAiCyRFKdRAEQRAEQRBMkapKdaFQ4PDR44yYfzgly3Q2L26beXqTqa8nTDHda8kaa2ZoznI55SOXR20ysJQ81RUqrinNJTHbN7WPAUn884BXVsz2VR7DkkiswmIJm1ODqbhz2lW1Pb5NPdae+UxWGC9Vgayx8V0J9+WlNBDzcnsO9oQ94SmV2rhn3ZVmZ76lr9xk3wj8+7//e2YOrnQfPnwYgFWrVgHlhJENGzYA5dQQn3MQBEEQBEHw8gilOgiCIAiCIAimSFWlemhomM7OPcxfoL7gpkZVb+ssR9llZPcZF8xX/MzGTUA583nlSqtCmHQ7z2Guyam17puWlK2qaBtX/A9SyhTJBlmnXLB1svWee10ay5RwX36eJZcsWbwEKKeCeGIGpX58kvroSRu+3EX5oUH1VB87dgwop42MpVhXLvd13vfwsH5T0NamnvY5s/X1uPCCizLLveKip7N0danXvbu7G6BUqXHmTG3fY3nWTrk6ZRAEQRAEQTAZQqkOgiAIgiAIgilSVakuFouc6Olh5qxWANraLLPZqxaW4jrswZTOgWH16G43tXfOHK0MOGf2HG1nqmydKbGuSteUojZqKjzSpuCWEkOwR1d4s58LSlprTrEumil7LAXcaW7RfV21RpXrQfNE9/fnMpxzSrMnm5TUZfOh792rGdCuEvv6PPm86tGordWs64YG9YcPD+s3A3PnzgXKlRNdHe/r0zkPDQ1l5uipLa5Mu2o/kTkEQRAEQRAEJxNKdRAEQRAEQRBMkapKdV1dHe3t7ezvOmjP1QvdbtnLRVNjXSx2/7ILzgcPai7yhg1PAnDNVdcAUDt7dqadq7el7SsiPJInhJSCqX25PeYU41SWsjPLS/25Al5SrKXif0iW2tFgaSGuMO/cuTPTTz68wxVzqcnOo5Rf3a/51bNt371qYnl7Oelnz6f29I+mpgYbQ9uNWB/zLW/6gx/8IABf/vKXAbjrrrt0X6yaY3MpRcTU++S+8qxiHQRBEARBEEyOUKqDIIeI3CoiL4jIVhH51VHW/7CIPGP/HhKRK6ZjnkEQBEEQnD2Mn1N9+HBJET16VL26ra3qO66rc0+0X5ubwuq+YRNfDx3UvOQ9nbsBmN2mSRjFGu3XQyeKJclbTlJ88xUTc8J1ORUkpyCXKyvmFO2cHzzfgSvZ80yVnzNHFWZPA3Ffsj96woYfK1eoBy0FxP3NM2fOzOyX46q0iJSUe09JcZ93vSnORcsL9+xuP37umV63bh0An//85wFYskQTTZKltLgaX0pfKSWZRPqHiNQCnwDeCHQC60XknpTS5opm24HvSCkdEZE3A58Crj3zsw2CIAiC4GwhlOogyLIO2JpS2pZSGgL+CXhbZYOU0kMppSP29BGg4wzPMQiCIAiCs4yqSnVKqZQcAXDs2An7SSsmLlgwH4Dm5mbfAigVQ6S2lMyhjy+9tAOA1mZVa5ctW27tzcdsVQwTNaRS8cWsoluyTJvyXEoMoTaznlzKR3l5yjw/KZu5lG89+rhz587LzNmTULzK4b4uPTa7d3dmtttnVSZdNXbc7+zq9MjISEnFPnZcj3ddvfq7GxvVE11O61A1PJX83Nm5fPSjHwXgP/9TPdbPbHja9tm+YYhc6tFYCuyueN5JdRX6fcCXT+uMgiAIgqqs/NX/mO4pBNPMjo9813RPofpFdRCcg4z2SWPUOzhF5PXoRfWNY3YmchtwG8Dy5ctPxfyCIAiCIDgLmeBFtSvOem1x3BTUtjb1+s6YoRX63Edc8jsn9x3rdcrgkKaFbLckjdYZbQDMnZtLxKiFGqSyqzKS/aGkijO6ZzqdvGGGfOKF24zLXuy84u3pIdYuZfOvO5YuBWDRIlWkBwYGAGhvbx91fO/fqyYWCoXSNg2mTPtx9LELhRHb1vLCXa2341csuHda+16zejUAW7a8AEC/+buLSbevrckq3ec4ncCyiucdwN58IxG5HPg08OaU0qH8eiel9CnUc83VV18d8SpBEARB8AolrqKCIMt6YK2IrBKRBuDdwD2VDURkOfBF4D0ppS3TMMcgCIIgCM4yJqRUuwrryumsUoVFVahdZS23N0N0ciVVnxZwr7CmiLz4ol6PXHHF5dp/vVUMbKyH2jrry7oqx3Po/6WUDiWvGEs+f7r0WN1H7B5oyVVkLG+fz7nO52TrY329zt+TUsaqpDg4qNUny1UOa5gzRysktrS2jbqNq+g1Nf5NgM3E0j16enoBGBjQbOz5CxYC8NrXvg6ADU+rt7qrq8s31H0ojj7Hc4mU0oiIfAD4CmrU/0xK6VkReb+tvwP4LWAe8Ff2uo+klK6erjkHQRAEQTD9hKc6CHKklO4D7sstu6Pi558AfuJMzysIgiAIgrOXCV1Ut7SqQu1ZzTPMC10oFDOPnqSR9yEXTUEtFHT5EKpsHzyolRp37twFwIqVKwCorasF1Ddcm1OsS8pxMasgl3Osc8pyPue6lB7iPeYdMNl0ELFM6LIyXpPp/yTG8GCX/OZGPufac6rrGxpobGzOtC0llOSyrYsFT1vRPoYsE9sV6pGR7DcIq1adB8B556nH+oUXngfgicefAODYsaNj7VVwFhN3vQdnw13vQRAE5zpVL6pra2tpa2srRef59aLfUOjFRvxGOr/PsFwpPGUe/QIyJdvOxtm2YwcAra0tACxatIiU3IpghUn8JrrSxW227IvbLgopWySm7OLIFnuR0gWqzSl/sVyyk+T2BbsJsFSoxvbNtqwZY5+d/MW0lAqv6P7W1dafVMCmNCWPLMzdXFmwC/byxfRIZg75suc+9gUXXAjAhRdeBMDTTz8FwPMvhE04CIIgCIJgMsSNikEQBEEQBEEwRaoq1TU1NcycObPComCxcR1aQM4Vay9s4ohklWzHFVa3i7ii6o/PbNJK0AlYvGiRjmmKcOlGv5S3fXjvJV+HNbPHkmSdrWueV4Pz5c+lOJJbb0q2K9SuOHv7Glfhs+PnbR+l2Sa/2bAm9ygn3UrponpJ9fZoPXs+PKQ3O3qhHo/cc7zceR6fm/d7xRVXjtouCIIgCIIgqE4o1UEQBEEQBEEwRca9UTGlIrW12fLXHR1a4OSppzSabciKuniMnKu6fqNcOQJO1ebaWivcYll7w6awDpnium3HTubMmaNtTR1PQ8m29TGyPmFHJOs3zt/cd9LikufabkhMXlQm6932R3dIl26UzEfvpWzMXUnhznmt8wq1lEq6S8VNkjn1O+epdi+1x/LlFWpX932M/BzKnnj/BqFAEARBEARBMHlCqQ6CIAiCIAiCKTKhSL2ODq3a7EryE09oSsTwsCrM7rUeHvby2R5Dhz166sSItXNvsD7kC7e8+OJWGkzNvvJyLQzT3KJFVIqibetOKqaSTQEpF2Xxp9nlZaE6H4GXTesoZYSUIvxMic57sE+K0hu9v5MV9pMc1BXJIb5NTqE2RXmo5KX2bwSyY7kSXY4bdL93VrHOtw+CIAiCIAgmRyjVQRAEQRAEQTBFxs2pnjlzJjNnajnyzZu1SIsLnPn0CH90xdqV1L6+AQAWLFiQ2c4V2NJzU237envZtOk520ZLbK9YsVIHNZVWGhuBci502ZuszcrFX8iMlUpxHVmfslPySuf6LSnSpnjXuHpck1WVXZkeK2s67wEvjVtK+Eil8uMpZRVmV6iHh/W4Dg4O2DZZL7QrzmON5YynXAdBEARBEAQTI5TqIAiCIAiCIJgiVZXqhoZ6Ojo66OrqAspqqudMn1TZz5TUI0cOA2XF2lM/ent7ddCSd9fzqtWb7Spxf98Ah48cAeDw3V8E4Hu+R8vwnn/++TaWqdt12eqMY5EXYUvVBnOeZveH5yKhR/FkV08VyXupy/1nkzjKXvCCjSuAHq+yl1pbuofd86jzGdiuPI/njc77v8dbHgRBEARBEFQnlOogCIIgCIIgmCJVJU0RoaGhnkOHDmWW58TAfEYAACAASURBVFMmXLnu6ekBYHBwONdeFdXjx48DMGvWTF2etJ17r4eHC9ZfOQHjxAntc/Nm9VivWL5c59bcbL1r3y7OetJIXnUdS4P1fXHF2nOnS9Zrb1hSqouZdt6ipDyXPNXu2fa0j9psu9z45WM6QmU2CZSPryvVw8PZ4+vk0z5KUx/HKx0KdRAEQRAEwdQIpToIgiAIgiAIpkhVpXp4eIT9+/eXnrtimnLVA93b60r12P2pwtrfr97q+gZVb4ctZ7nSIuxK8bAlWzxu2dgFU3TfcMvNAMyePRsAj60uFzj0nGpXjLOU98GeUzJD6/iuUZceSrUUs9sVUmWzyhFGbe+Kdz5xI6/+V+LH1x/HypduaGgYte98ykcQBEEQBEFwagmlOgiCIAiCIAimSFWlulAocPTo0THV1LyXuorYmmFkxJVp9VIXqmyXckrz9m3b9fG8HQBcdtmlmfa1JlnX1bmH2bOe86kbJ41k/5un2isylpTrfHNXgUf/XFKuwGh51zZsbSmD2rzUBe8u2bzrTvJZe6pKOd9bcvs6ucqJ5V0I5ToIgiAIguBUEEp1EARBEARBEEyR6oHGhiumeS+1p1F4/vR41NZmfcrlrGhVXAulyoBlh3KpsqE9P37iBAD3/ceXdW6m4r7qqlfplqXqgzWZMcu50dlUjlL6tCvH+eSM8XYqn+bh47hqnHNbu+pcwlJBaqz9yMhIaZ+9bf4bAs+6zivVeUK5DoIgCIIgODOEUh0EQRAEQRAEU2QcpVrzoguFbPJF3ks9UYGzrt59zV6uUJXWaskXeZVVTKUdtoqKz256FoDlHR0AtC9cYH1alcZUr2PX2thjKNFJzIOdSwEZj1TyYudX2PJsIcZyVUpX/0vKeVnBPjm7OounfLhSPVEijzoIgiAIguD0EEp1EARBEARBEEyRqkp1Slm11H8eHNTUjr6+gYkNUpe7ds+ptp6UUYn7hr2xtxk2n7HYJlu3vQTAPffeC8Ctb34TAIuXLAagYNUcpaYhM6j4JFwJL5dO9Bnknlf3IbuCfrLCnfUxF4pWYbG0776/5XbJk0NM0a/Jeaf92PhjPo96PMbyWgdBEARBEAQvj1CqgyAIgiAIgmCKjJv+of7pbE71eJUTHRdO3UtdLHrJxLG81GUF1VXUoiWCeF/5Tdzu3bX/AAAbN2wEYNasWQC0tLYAZXW9rk491nklPJWEa5tDynmeS3nXxYqtyqQx/M9CzhPu+1USwEfxT6fSxtm+cgqzbzOWQv1ylesgCIIgCIJgcoRSHQRBEARBEARTZFI51f39/QAMDAxOqPNa81KXVNzkCqv3O7qXV0QqcqVt01xTf+qZGb396u/+5kOPWB869uteewMADY2NAAzZmLWWBiLmV/b2tV470Ssl2jxOslyXJ6v7MoYvWSSnhOf7M7yCY63UlGX5Uh/ZCoqlqeRyp8dSnEOBDoIgCIIgOL2EUh0EQRAEQRAEU2TcnGpIk/dS26V6XV3WF132UhfyW+j/JaX1ZL/1eAEVRS/WaI8bn3sOgBUdSwHoWKY51k1NqlgXClbB0JXrGj0UkquI6MkbXrmx3jKiy9OxOZcqN9rSmpxv3CdmDYq4Yp39XFOkgCRbVjO6wuyVFt0Xnleix1KuwzsdBEEQBEFwegilOghyiMitIvKCiGwVkV8dZb2IyF/Y+mdE5KrpmGcQBEEQBGcP4+ZUFwpFenv7ABgaGqnWvNxpnVUndM908vSPcr9KVjEt5y6XlewJRyhnhWAOHz4CwJfuvQ+A669dB8CVV1yu7dB9aWnRdJA0YgpybryR4ax/fHhYKzWWMp5HdKdqG/RQDpNViWtElxdsnxrqTRF3Od/a1ZmynQTq6poy++SclCCSS/8ojZnLrx6PUK7LiEgt8AngjUAnsF5E7kkpba5o9mZgrf27FvikPQZBEARBcI4SSnUQZFkHbE0pbUspDQH/BLwt1+ZtwF1JeQSYLSKLz/REgyAIgiA4e5BqaqaIHAR2nrnpBGcJK1JK86d7EtOBiLwDuDWl9BP2/D3AtSmlD1S0uRf4SErpQXt+P/ArKaXHR+nvNuA2e3oB8MJp3oVzlXage7onEZzTxDkYTDdxDp4+JnRdNI7949y8sArOaUbzwpwUpDiBNrowpU8Bn5rqpILqiMjjKaWrp3sewblLnIPBdBPn4PQT9o8gyNIJLKt43gHsfRltgiAIgiA4h4iL6iDIsh5YKyKrRKQBeDdwT67NPcCPWArIa4BjKaV9Z3qiQRAEQRCcPUyoomIQnCuklEZE5APAV9Bg9c+klJ4Vkffb+juA+4C3AFuBPuDHpmu+QYmw2ATTTZyDwXQT5+A0U/VGxSAIgiAIgiAIxifsH0EQBEEQBEEwReKiOgiCIAiCIAimSFxUB0FwxhGR20Xkl6Z7HkEQBEFwqoiL6iAIXjFYIku8rwUZROSjIvKsiHx0uucSvPIQkTutcNip7vcmKzZ2WrD+r6+yvud0jf1KJf74BEFw2hGRHxGRZ0Rkg4j8fW7dT4rIelv3BRFpseXvFJFNtvwBW3aJiDwmIk9bf2tFZKWIPCcifwU8CSwTkR8UkY22/R9WjDXW8h4R+UMReUJE/ltE1onI10Vkm4i89cwcpeA08lPAVSmlD06ksYhMezKWiNRO9xyCM8M0vtY3AWNeVJ8KzrXzOC6qgyA4rYjIJcBvADenlK4Afj7X5IsppWts3XPA+2z5bwFvsuV+Yft+4M9TSlcCV6OFeEBLwN+VUnoVMAz8IXAzcCVwjYi8XUSWjLbctm8Fvp5SejVwAvgw8Ebge4HfOUWHIngZ5D+QicgKEbnflt0vIsut3Z0i8hci8pB9GHqHLb8HfX0fFZF3ich8+/C23v7dYO1uF5FPich/AXeJSK0p3OttrJ+ydjfZB667ReR5EflHERFbd42Nv8E+/M0Yq58x9vUmEfkfEfkssNGW/aJ9CNwkIr9Q0fak5fYB83kR+bQt/0cReYOIfEtEXhSRdafjNTrXGEMkeN0o515GaRaRj4vIe+3nHSLyWyLyIPBOEVljH+g3iMiTIrLaNmsb7VwbY163iMhTJhx8RkQaK8Zqt5+vtvN3Jfp++r9FRYrXitZneNjO1d+t6FfsHN5kfb9rnOUnncfnCtP+aTwIglc8NwN3p5S6AVJKh3N/Fy4VkQ8Ds4E2NCMc4FvAnSLyz8AXbdnDwG+ISAd6Mf6i9bUzpfSItbkGvUA+CCAi/wi8Di0lP9ryfwWGgP+07TcCgymlYRHZCKw8ZUcimBRS/kB2Q0qpW0TmAn+HfoD6OxH5ceAvAP9wtBi4EbgQLdJ0d0rprSLSYx/EsD/0f5ZSelD0gvwrwEW2/auBG1NK/SJyG1rY6Rq7OPmWXXADvAq4BK2k+i3gBhF5DPg88K6U0noRmQn0ox8ST+onpbR9jN1eB1yaUtouIq9Gc/CvBQT9YPANVBAbbfkRYA3wTuA2tJjVD9kxeSvw6xXHKngZjHFO/imjnHsT6G4gpXSj9fso8JGU0pdEpAl9jZcxyrkGPDjKvJqAO4FbUkpbROQu4KeBj402cEpph4jcAfSklP7Y+rgH+GRK6S4R+ZmK5t+HChFXAO3AetFvD68fYzlUnMcTOA6vGEKpDoLgdCPoBe1Y3Al8IKV0GfDbQBNASun9wIfQPyxPi8i8lNJn0YuDfuArInKz9dGbG2+seYzFcCqH9heBQZtDkRAfppOTPpAB1wGftfV/j17IOP+aUiqmlDYDC8fo8w3Ax0XkafTiZ6aIzLB196SU+u3n70Qrpz4NPArMA9bausdSSp12fjyNfvC6ANiXUlpvcz2eUhoZp5/ReKziQuRG4Esppd6UUg/64fK1VZYDbE8pbbS5PQvcb+d2fEA8NYx2TsLEzr08nwew829pSulL1udASqnP2ox2ro3GBehrv8We/x0qGkyGG4DP2c+VNr0bgc+llAoppf3AN1DxYqzlPu9z6oIa4o9FEASnn/uBL4nIn6WUDpmyU8kMYJ+I1AM/DOwBEJHVKaVHURXue1Cv9CxgW0rpL0TkPOByYFuuv0eBP7evO48APwj8JfDYGMuDs5fxPpCRWz+Y23Y0aoDrKi6etbF+45H/cPazKaWv5NrdlBungP4tHWuuo/ZThal+QKycW7HieXxAPDWM9TqPdu6NkBUvm3Lb+Gs90dfTz7Wx5jUWlfPIzyHPWOfwZMfsrbLuFUso1UEQnFZSSs8Cvwd8Q0Q2oF+VVvKb6IXwV4HnK5Z/1Hx6m4AHgA3Au4BNpvpdCNw1ynj7gF8D/se2eTKl9G9jLT91exqcBu4HfkBE5gHYB7KHgHfb+h9mlK/Cx+G/gA/4ExG5cox2XwF+2j7sISLni0hrlX6fB5aIyDXWfoboDY+T7aeSB4C3i0iLbfO9wDerLA9OP6Odk2OxE7hYRBpNELhltEYppeNAp9g9Hta+ZZLzeh5YKSJr7Pl7UOUYYAdqbQL4/optTqCihvMtsr9bzgPAu0TvD5iPKuCPVVl+zhKfWoMgOO2klP4O/TpytHWfBD45yvLvG6X5H9i/Sg4Dl+a2/Sxli8BElrdV/Hz7WOuCM0tK6VkR8Q9kBeAp4OeAz4jIB4GDqLd4Mvwc8AkReQb9G/gAesNWnk+jX7U/KSpjH6SKHzmlNGQ3av2liDSjFqU3TLafXJ9PisidlC9UPp1Segr0xsz8crv5LDiN/D/23jxOsrOu9/98a+vqqq5ep3v2LTtJIAGSIILKRZBFJfi7cFn8qSAY8Seol6uC60UFhesGXtBcRIxBEBDBGzFeVLyIrBmWLCQhJJnMZGYyS+9LVdf+/P74fJ/qqprunp7pZHqS+bxfr5nqOuc5z3nOqdPVT33qcz7fFa7Jldoe8ntC7gRw/2ptwUnw/zKz3wZvtn75aY6rbGavBfC3/mFuH4AbffVvAfgLM/tVUMCI/AOAT5jZ9QDeBN5E/hEz+3kAf9fW7lOg7eoOUMn+5RDCMTNbafllpzP2JxK2ZCMUQgghhBBCnAmyfwghhBBCCLFOZP8QQgghziJm9mR0pisAjHF8xkaMRzy+cNvF3q7FbzmNm2HFY4TsH0IIIYQQQqwT2T+EEEIIIYRYJ5pUCyGEEEIIsU40qRZCCCGEEGKdaFIthBBCCCHEOtGkWgghhBBCiHWiSbUQQgghhBDrRJNqIYQQQggh1okm1UIIIYQQQqwTTaqFEEIIIYRYJ5pUCyGEEEIIsU40qRZCCCGEEGKdaFIthBBCCCHEOtGkWgghhBBCiHWiSbUQQgghhBDrRJNqIYQ4A8xswcwuWGcfN5nZ29fYdo+ZBTNL+fN/MrOfWM/+2/r+HjO7r+35ATN73qPRt/d3t5k959Hq72xiZs8xs8NrbPs2M/vrx3pMq+w/mNlF/vONZvYbZ9jPuq/tjcLIX5rZtJndttHjEecXqY0egBDi/MXMXg3gzQAuAzAP4HYA7wghfGEN2wYAF4cQHnhsR7k8IYS+jdhv2/5ftJZ2azlPIYT/AHDpozEuM7sJwOEQwq+39X/Fo9H3GvYdAJwAsD2EUPdlKQCPABgNIdjZGMe5QAjhDWtpZ2afA/DXIYQPtG27odf2Onk2gOcD2BFCKG70YMT5hZRqIcSGYGZvBvBuAL8LYDOAXQD+FMD1GzmuUxGV4icKT7TjATADoP0Dx4sBTG/QWM4YM0tu9Bgep+wGcOBMJtTn+u/CuT4+8TiaVJvZ7/tXiL+/0WMRQqwPMxsA8NsAfjaE8MkQQjGEUAsh/EMI4Ze8zXVm9mUzmzGzo2b2XjPL+LrPe1d3+FfVr/DlP2Rmt/s2XzKzp7Tt82lm9k0zmzezvzWzj7VbL8zsp8zsATObMrNbzGxb27pgZj9rZvcDuL9tWfyqvdfM/tDMDprZrJl9wcx6fd3fmtkxX/55M1uTamtmSTP7AzObMLP9AH6wa/3nzOz1/vNFZvbvvo8JM/vYSucp2hnM7C1mdgzAX65gcbjWzO7xr9H/0syy3udrzKzjm4R4LszsBgA/CuCXfX//4OtbdhIz6zGzd5vZI/7v3WbW4+vi2P6bmZ3w1/21azlfbXwIwI+3Pf9xADd3jXebv8ZT/pr/VNu6XqMtZ9rM7gFw7TLb/p2ZjZvZQ2b2c2sZVNux/aq/RgfM7Efb1t9kZn9mZreaWRHAf/Jz9Qdm9rCZHTdaOnrbtvklP0ePmNlPdu2vw1pkZtf778acmT1oZi80s3cA+B4A7/XX673etv3aHjCzm/14D5rZr5tZwte9xq/1P/Dz9ZCZrekbFN9+p5l90vuebNt/wvdz0K+Dm43vGe02qJ/w8zJhZr/m614H4AMAnunH81u+fLX3hQP+u3AngKKZpVZ7jY0Wn4/7mOaN85JrTnVMvu4nzexeP1efMbPdazhHy733fLeZ7TP+vu8zs+9ua7/atf024/vRX/vY7zKzS8zsV/w8HzKzH1jr6yeWIYTwuPgHYA5Az2m0T50DY05u9Bj0T//OxX8AXgigvtrvKYCnA/gu0Ka2B8C9AH6hbX0AcFHb86eBX/0/A0ASwE8AOACgB0AGwEEAPw8gDeD/AVAF8Hbf9rkAJryPHgD/E8Dnu/b1LwCGAfR27x/A+wB8DsB23/d3x/crAD8JoOD9vhvA7W393hTHsMzxvwHAtwHs9P3+X99nytd/DsDr/ee/AfBroFCSBfDsVc7Tc/zcv8vH1OvLDre1OQDgW237/mLbuXoNgC90jbX9XJx0TN7f8/zn3wbwFQBjAEYBfAnA73SN7bf9dXoxgBKAoTVeVwHAlQCOAxj0f8d9WWhr9+/gtyJZAFcDGAfw/b7unQD+w497p5+Hw74uAeDrAH4TvKYuALAfwAt8/dtAK8VyY4vH9kd+3r8PQBHApW3nbRbAs9pex3cDuMXHUgDwDwB+r+13KB5bHsBHVnodAFznfT/f+94O4LLu62iF1/NmAP/b978HwHcAvK7tWqgB+Cnwuv8Z0Gpjvv6tAD69wvlIArgDwB/7+FvXLfg784Cf3z4AnwTwIV+3x8f35+C1exWACoAnLXd9YpX3hbZr83Z/rXvX+BqXwWszCeD3AHxlDcf0Uj+mJ4Hvab8O4EtrvKZb7z3+OA3gx7yfV/nzkTVc23HsL/BtbwbwEPjekfbX8aGN/NvweP93dnZCpeBOv9g+BH4981lf9lkAu7zdTQD+BHyT3Q/gZb78FgANv/BfAb4R/x2Aff7vWW0XzPsB/DP4BpME8Pve5k4AP+3tngO+kXwC/KP1YSy9CVzr+78DwG3gG8my/axwrM8B//h9BMA9vuzN4Bvzt9A5KThpOfiG8W3w0/a3fGzPA/+o3Q/guo2+aPRP/9b7D1Qzj53mNr8A4FNtz7sni38Gn5y1LbsPnLx8L4Aj8ffc130BS5OOvwDwP9rW9YGThT1t+3puV98BwEXgH+FFAFet4RgGfbsBf34TVp5U/xuAN7Q9/wGsPKm+2d/7dizTz3KT6iqAbNey7kl1+75fDOBB//k1WN+k+kEAL25b9wLw6/o4jkW0fdgCJ0TftcZrJL4mHwDw0+AHkz/3ZcHb7AT/nhTatvs9ADf5z/sBvLBt3Q1YmlQ/A8DDXfv8FQB/6T+/DaeeVOfbln0cwG+0nbeb29YZOOm+sG3ZM+GTHgAfBPDOtnWXrPQ6APhfAP54hXG1rqNlzmMSnLBe3rbupwF8ru1aeKBtXc633bKG1+qZ4ITvpA/W4Lzg/2t7fin4+xg/YAe0Xevg3+pXLnd9YpX3hbZr8yfb1q3lNf7XtnWXA1hcwzH9E/zDiD9PgB8Yd6/hmn5u2/MfA3BbV5sv+3Gf6tp+G4B/aVv3wwAW4AIgON8JAAbX8vumfyf/e8z9OcavOn8NnPhOmNkwgL8C3zz+yr+y+hPwUxwAbAVvNLgMnEx/IoTwEjNbCCFc7X1+BHyD+IKZ7QLwGfDTH0B169khhEXjV5GzIYRrjV8vftHM/tnbPRXAFeCn6i8CeJbxTuGPAXhFCGGfmfWDb/CvW66fEMJDKxz2dQCuDCE8ZGZPB/Ba8BfVAHzVzP4d/IVabvk0+Gb2cvDNfB+AV/s5eQmAX207V0I8XpkEsMnMUsFvKOvGzC4BVb1rwD/WKVBBWondAH7CzN7UtiwDYBv4h+JI8L8czqG2n7cB+EZ8EkJYMLNJUNE7sEz7djaBqtCDyxxDEsA7wN/nUQDNtm1mVzmWOKb2fR5cpe0vA/gdALeZ2TSAPwwhfHCV9uMhhPIp9t+9720rNTxNtqHzWLr7nuy6Jkrgh5zT4WZwMmEA3rLM/qdCCPNdY7imbf1K5303gG1mNtO2LAkq22thOnR6fbuPvX2/o+B1/3Wz1v2V5vuL42z/fVjt+tgJ4NY1jrGdTVj6lqd9P9vbnh+LP4QQSj7WtbxeOwEcXOH3f7lrJAXee3HSfrH6NbLa+0LkUFf7U73G3fvOGv3Oqx3TbgDvMbM/bFtm4Llc7bXrHl/3uQGWXpNTXdsAv92ILAKYCCE02p4DPJftxy/WyNkwvT8XnBhPAEAIYcrMngl+/QpQuf4fbe3/PoTQBHCPmW3G8jwPwOVtbzT9Zlbwn28JIcQL4wcAPMXMXubPBwBcDKo0t4UQDgOAmd0OfvqdBXA0hLDPxzrn61fqZ6VJ9W1tE+5ng+pa0fv6JOhhsxWW3wIqEXf58rsBfDaEEMzsLh+nEI93vgx+DflS8Buj5fgzAN8E8KoQwryZ/QKAl63QFuAfnneEEN7RvcLMvg/AdjOzton1TixNhB8B/+jF9nkAI6C6HWmfkLcz4cdyIfgNVzuvBm+8fB44OR8APzivJYXiqI8xsmulhiGEY+BXtzCzZwP4VzP7fFg58WOlY2mne9+P+M9FcLIH39+W0+w7nuu7l+n70eI/QIEmgN9IXNi1/2EzK7RNPnZh6bWO5719fJFD4PvzxWc4riEzy7dNrHeB30hG2s/dBDjJuSKE0H4dRtZ8fYDjvnCFdau9XhOgQrwbwD1t+1luPKfLIQC7Vvhg3fH76PusgxPCHWewn2XfF9ro/rB9pq/xascUx/HhM+i3fXzd5wbg+fk/OPW1LR5jzsaNioZTv8m2r690bbscCQDPDCFc7f+2t11A7SqAAXhTW7u9IYSoVLfvpwF+wFhprKv1sxzdY1iO1f6oto+t2fa8CcUgiicAIYRZ0LP4PjN7qZnlzCxtZi8ys/ghuwDeS7FgZpeBfs12joN+x8ifA3iDmT3DSN7MftA/cH8Z/D1/o9+IdD34jVLkIwBea2ZX+7dRvwvgqyGEA2s4lib4Vfwf+U1CSTN7pvdTAH9/J8GJ6O+u/Szh4wB+zsx2mNkQ6E9dFjN7uZnFycY0+D4W1afu87RWftb3PQx+Q/YxX34HgCv8XGXBr5TbOdX+/gbAr5vZqJltAq+DRzXb2T84/TCAl3R9O4EQwiHQ4vd7ZpY13rT2OtBqB/C8/4qZDfk5bVc4bwMwZ7yxrddf6yvNrONmxlPwW2aWMbPvAfBDAP52hWNogtf0H5vZGACY2XYze0HbOF9jZpebWQ7Af19ln38BXt/fb7wJcLv/TgGrvF6uYH4cwDvMrGC8se7NeHRer9vADwbv9N/VrJk9y9f9DYD/amZ7zawP/L352Erfap2C1d4XVhrXmb7Gqx3TjeB1dQXQugH05WdwPLcCuMTMXu3vZa8ALSifXsO1LR5jzsak+rMA/ouZjQCAv0F/CcArff2PgkrC6fDPAN4Yn5jZ1Su0+wyAnzGztLe7xBWolfg2+LXPtd6+4F/pnG4/7XweQJw05AH8CKiirLRciPOCEMIfgX+gfx30IR4Cf6//3pv8Iqj0zoN/GD/W1cXbAPyV8Y7+/xJC+Bqo1r4XnFg+APoMEUKogt+OvQ78WvP/BfBp+AfWEMJnAfwGeK/GUVDVeyXWzi8CuAu0a02BNwEmQBvCQVApuge8QW+t/Dn43nMHaE355CptrwUtZAvgt10/3/Zt2dvQdp5OY/8fAd9r9/u/twNACOE74I2E/wre59H9/v0X4DeJM2b29ziZtwP4Gnh/yl1+bGstgPOrZvZPa2kbQrg7hHD3CqtfBX7r9wiATwH47yGEf/F1vwW+Zg+Bx/+htj4b4GT9al8/Afq3B9YyJtA2MO37/TDoW//2Ku3fAl7HXzGzOfCcX+pj+SfwRsZ/8zb/tlInIYTbQLvhH4PfyP47ltTO9wB4mTGR4k+W2fxNoFC0H3ytPwJ+iDwlq71ebefyIgAPAzgM3jMF7/9D4N/Jh8Bvgt60TDenZLX3hVOM67Rf49WOKYTwKfB94aP+Wn4LndGPaz2eSfDD2H8DP6z/MoAfim4ArH5ti8cY6/oQ/9jshFW/fglUTr4Jvsl/EPRrjQN4bQjhYWPRgE+HED7h2y0ED6Hv+nkTeLd9vIv28yGEN5jZ2wAshBD+wNslwDfrHwaV4XHw6+anAvjFEMIPebv3AvhaCOEmn1D/T/Au20Xwa9vScv242tZ9rM9p79uXvRm8mxkAPhBCePdKy81sj5+DK71N65x0rxNCnDlm9lUAN4YQ/nKjxyKe+Pjfhr8OIZyufUEI8TjhrEyqhRBiozH6qu8DlacfBb+OvSCEcHRDBybOCzSpFuKJj/y5QojzhUtBf2gfeIPiyzShFkJsJO6tX8ki83guF39eIqX6DDGzJ6PNa+dUQgjP2IjxCCGEEEKIjUOTaiGEEEIIIdbJqvaPbLYn5PN5xPQ36w6BCx0PrYy4YF1L/Kmt0MGq0/rTnfO3BsGHZpOpUvHDQ6PB54lEYvkN/HnS18ftEsnu9l3DDKzpkMlkAADZbO9pDvzc4eDBgxMhhNGNHscTjU2bNoU9e/Zs9DCEEEIIcRp8/etfX9O8aNVJdT6fx4te+ILWhDJhnQl8ccIZfEK6NKmORcN8Ytr0iWoyHVv49j7hbRUZC23bxTads2o7Vc2EOHH37UrFBQBApcbiYXMzjLPOZtv9BwAAIABJREFU5ZiI15os+6QYxmJVuV5Oiut1xmLywwVgPtnuHkWtwv637eQ9KJdf/pTWiB9vvP71rz9VdSdxBuzZswdf+9rXNnoYQgghhDgNzGxN86JT3KhoSCQSCE2f2Ca71voEdmly7cuDL7eWRA1gSTVOJuNum759934DWup4a8naJOs4lrhdLk+ff7LMfTYLoWvsHEPWJ9G53nzH+nqj3nFsCV/e8El6cZqVPKtFPqZTPEnTWzm5Hhwc7uhPCCGEEEI88TgbxV+EEEIIIYR4QrOqUm1Ge0S9RrW26aputw0kWjLCSe7qpvfjSnYzKtOd9pDW+tBuA7GlQXBlxz66bSCh5d+O2zU71vcVWAypN5cFAMzMTnOEDbbPZHp8d539Lqnq7q2uVQEAi7PjXDw/x/7dRlJZYL8PHTkEAHjq4JBvL6VaCCGEEOKJipRqIYQQQggh1smair/Em/nW662O7RoNqsiplO++tV27urz8DYpLS5MdfSSMN0FWq1TVt2zeBADIZKhMl0q8YXFi8jiXp6lMp3rZz9JNlJ17j2NO+n6qlUUuXywBACreb26gHwBQL/JGyNmjjwAAxse2AgBGR8c6+hNCiCcKe976jxs9BLHBHHjnD270EITYcKRUCyGEEEIIsU7WplR7jFzD0ztO11tt3r6VBuLPm+6xTrRi6mI2dLPNG81tcjmmeFQqNQDA6OhmAEBfvgAAqHv+9OBgwfvm8/4Bbjc7Q+/z7Owk+83mfF9dqSItIZlj27nrIgDA1i07AQD33Hs7AKDkm1Wr3E+pWPGtOL5EbhYAcPDQAR/vWMf+pFgLIYQQQjxxkFIthBBCCCHEOjm1Um3W8jxbwpXo0/VWd1UttK7c6kSCfubeHqrMjSYwMjLIZdl8xz6Gh5niUV5kTnS2l4pzsUQl+uDBBwEAs7PMjY5FW+bm5jvGsJRMkvRj4vN0D/urVJjykfHnSLDdjl17AABHGvRWlyaPtcYMANUyl9cq9FpPTzIl5Kh7rLdu3QYhhBBCCPHEQkq1EEIIIYQQ62RNnuqYFX2St7rLE91qvkZvdTLB3W/evB0AMDhIdTrX29PaNulqeLlMz/L4xAQA4MhhVozM9jDFY25hvn2oLW/03NycL4iyesNXJ729p3ukMwCATZvofe5xz/XhI4cBACMjXF4osELiwCYqzqFExbw0zXzqgnuqS66kN+eLAICHH94PANi2bTuEEEIIIcQTCynVQgghhBBCrJNTKtVtluoWLcW60eh4vrTN8t7qKB83m8ySTrvKvGXLJu+Py4+dOILiAj3J9RoV34Uic6FTmYL3SSV5cuoEACCZodIcR5J0r3TTlenooUZX6ob52PdecCEAYNOmzfEgvX96oo+foHd6bIzr+wsjAIDFQSrh89NMFWm437wwtoXH2E9lu1iiYn3w4AEAwO7deyCEEEIIIZ4YSKkWQgghhBBinZxCqTYYbMkb7SpvjKeOFRNP5a2OynUUiYMr3NUK1dvv3H83AGChSHU6mUiiUY3+a7aNanc+NwoAGOijUnz4YfZRWqSnOpvt9X034iA6SHslxUaT3udaveb7Zj/9AzH1g/3s3L4LAHDgYXq4o1KdL7CCYrbANJLeYY6rMMTHZIb7KbtCXQtMBXn48EMAgB07drBdcm22diGEEEIIce4ipVoIIYQQQoh1siaZtKUwd3ur7fS81bGjZDLt21Elnp2lLznpWdCNZqOViY1EFgAwNLQHAJAvbO3Yx+DQmPe16H10jjEOOQ6h13OrY371ocMPAwCmppgqsnfPBR3t+wcGfP9MJpmZmeJ+B4f9kYp5rUrvd3edxJwr1j0FesFnyu6t9kqLF+y5CEIIIYQQ4vGNlGohhBBCCCHWySmU6oBmCEhYV+50l7c6GTOgu2XaLmW723ttxt0nW9nSSyHT9TqTQGol5lOntwwBABZKVKSLM8yPrlWO8kBSPoZW4gj7SrtSDE8DueSSK9k+7ekg7gufmmSKyCOPsN+t23a1xgIAY6NUxB8+RGW7MMDx5PuoQC8uUtEulegLj1Unmy6dp9yj3ee9HjrAfrZv3QkA6PEkFLHxmNkLAbwHDDf/QAjhnSu0uxbAVwC8IoTwibM4RCGEEEKcY0ipFqINY9369wF4EYDLAbzKzC5fod27AHzm7I5QCCGEEOcia/JUR4V6JW91y//cjXU8tLarVZmwkfRyia26i7aUFlKt06ddq1CZXph/BABQb7B1pXwcAJBOenXHECNJ+DDg6Rx7LrgUAHDkEeZMT03SO71lC1M8xkb5WK9xTKUiU0TgVSPhVR97eujtzufoxT5xnP0VClSsC/2upPv2rVPkx7TovvOM99PrJyMq3xdfdDHEOcF1AB4IIewHADP7KIDrAdzT1e5NAP4OwLVnd3hCCCGEOBeRUi1EJ9sBHGp7ftiXtTCz7QB+BMCNZ3FcQgghhDiHWZtS3ez0UrdYSbo+BcVWJjR9yHFm36rAGEJLKTb3JM/Pcp6T8MKIaR95o061O93jySGucKe9wmKs2rh5MyscHjjwoD9nFcd8ni7nPle2F4tMIpmeYYXEwSG2q3u/Pe6Njqkh09NU0vfs3sb+cvRYx8xtCxxwLstxRJV+wL3YR13x3rqVqSZ9+ei6FhvEcl+7dF/g7wbwlhBCw7p/J7o7M7sBwA0AsGvXrlXbCiGEEOLxiyqPCNHJYQA7257vAPBIV5trAHzUJ9SbALzYzOohhL/v7iyE8H4A7weAa6655vQ+fQohhBDiccMpJ9VmS0ka3aJcFKhbnuhT9eWPfX19vn3nHMM8FaTZbKLm6R+JBBXnlO88kYrbsG1vjsrx5q30Rqe8YuLDrkhX3JPd25sDAIyMsOLh5OS0P2fOdMydLnkFxKnpGQBAoZ/51NNTbF8uM1s74/sJoPIclfKBFL3VRfdWN33/ZX9eb3DchWG26/V0kocPsmLj5ZdfAbGh7ANwsZntBXAEwCsBvLq9QQhhb/zZzG4C8OnlJtRCCCGEOH+QUi1EGyGEupm9EUz1SAL4YAjhbjN7g6+Xj1oIIYQQJ3GKSbXBYICF1jMAaHjedKtioj9HYvX7HqMyHRXpVCrV0V+r+iGWcptTXn0xhmL7nlAYoLJcXWSOdT5PT3TLG+2K81H3Pu+58DIAwPAwlenDvnxwkL7u3qznSOfpdY5507OzVKxzfTkfK5f3p5gCMjM9CwAYP8E0koJvn0px3GU/5lQvl/f25v14uDzf7znXC0Xfbwm5XA5i4wgh3Arg1q5ly06mQwivORtjEkIIIcS5jdI/hBBCCCGEWCerKtUhBNTrdSSSsWIi1dWE51JHS3RrfauaYSet/Onudp4qEis2Nn15MplcyrBuGbcb/tw9yX1UpHtHqTAfPsR7ya58MpXnLdt2AADuvvMbAICFeSrO+T56pAcGuP2ke6XHxlgxcWiICnj0RM/OUonemqfCPOxe6PGJcQBAJkNFul6ht7qarvh2VLSj5zvRUtrrflaS8eywnad+jE9OYreUaiGEEEKIxxWrTqqbzSZKpUX0u0UhdE2XT0rUi5PnkyL2vGR4OsbKcWIZb1jctYsxwPsP7AcAFBcWWgVlYlcG3rCYTNIAcugQb+y75trvAgCU3QZSdBtFj09Mx7Yw6m5inNF12R4eS8En1wcf5g2NhUK0Z2T9OSfnc/OcVM/PcZI96JPuAb+BcecOTt737z/o23MSHW+ATHs59PmFWT9ZST+e7nhCPhRLRSwsLHScn7XeCCqEEEIIITYG2T+EEEIIIYRYJ6sq1WaGVCp5kkLaXQtmKWqPc/SoLsfCKuk01dukl/weHOLyQoGWimPHjgBYKgrTbiJp+k2Mlz/pKQCABVd8p6enAABHj9L2MTzMIi0TE1y+czeV50xvn/dNpXmhyO37vax4VJyjnWPbVirbAwNc31KqF7h9ob/ft+tvP2Tk3Obx8KHDAICx0VHfD8dR9BsfG16uvFX6vevsGgyTU1PeJ1Vz8/MmxVoIIYQQ4txESrUQQgghhBDrZFWlOp1OYWxsrJWUV6nQt5zN0q88N++luJtskHU/8ebNVHsLHm8XfcvlKtXeYolq7f333wUASBh9xsmodCeA4U2bO4YYFeBLLrmUbfyGv/kFlhXfsoVlvoPL5ocPH/Z9csyWpCe7WuGYk0kqyWNjLF++/6HvAABOuPd60BXsQVesZ2Z5Q+PsjEfseTGZSonFYGI84JiPe3CA2ycSoeNczPmNj92Fb5b0eUO5VPJ9cZ8D7uM2S0IIIYQQQpx7SKkWQgghhBBinayqVCcSCeT7sq3ou2y2xx+jJ5oKatoLtYxuYuLFoYeZhHHw0LcBAPV60x+pUC94QkciEXcfXcLRrB0wPTMBAHjSZdcBAO6+95vc1ou65PJM56i5R/nECSrMlTL7qNSoSPdk6EtuuoK9WKYKXHRvddx3jPCbm5vxsVLh3r5tN5e7t7pYikVaPGUk25n2EY+xUil3nLO8R/LNeYpI8IjAbn+0IaDW4Pk6fPgQAGBwaKjj/LTKxkMIIYQQQpwLSKkWQgghhBBinZwi/QNIpRIwL1RSrdI/3GjwcfeOCwEAk9MnAAD33PlVAECxzEIoIcQga99ZstMTfJLiGnOukUCmhx7ohdIkAGBslFnWBx9ilvWVT74KwFL+c6NBZbnZ9NLnNfZVqnF5Ku3lwrMcw8wslfAtm6lEx4I23UVn6jUea0wJmZ5hMseUP27fujMOnvtbpBJdKi0CABbL7C9mTzcb9fbm6NabDU0kUtymWKLaffz4UQDA5s3bvVH0Y+szkRBCCCHEuYBmZUIIIYQQQqyTVZXqZDKNoaHNrRLhs3NM2ojq7R37Pg8ASCR9bu6JGKHK9mi0yiF2clLyxcnUylR0y4vc58jwBQCA8XHmUh85wmzrpUSMpu+L2zU9kaRQoKe63qSCnDCWFW/U2W56mvnUPVl6nlMNrp+d8fLkWzn46Gte8LzqymLZx3McAFAt83mtTpU+llyPlSHrrlDHcuXxpHRXqQQSMF+WzfX5sT4MANi8mdUbWyXbW8q+3NVCCCGEEBuJlGohhBBCCCHWyRo81cmWN3pokFnLMzNUjzMDTOBIuw95aj76hqkaN5pUVJNRTe5SqE8WsKPanAD85+I8UzZ6s8xs3uE+7kMPM1d6756LAAClRSrL6bR7o1Ours9yrIkEleLMYKZj72XPrd66Za8vdS91nY9lV6ALBSrYuV4q38UiFek592bHtI+YNhLVe/NDSsSjjT5zO+nol/7305ROc6zlEhccO0rFekvLx+2dQ/nVQgghhBAbyaqTaiGEEEKIc509b/3HjR6C2GAOvPMHN3oIa5tULy4y2/noUfqZZ12pbrik6jHWqFSp3i66apvzCot9PXw09xN3+4ijeNvfx4SNVKIHdfdjz80z/SNb5OPoJirVaVeEx8eZPJLtpaobqz9ms35ogQuid3rrdlZSbLTypJkOUlyIlRLpYx50FX5migr5YpGKdqlI5bxc5mMjuAKe5ngsmfJjdM90tJVb9FB3475ob9hEEzU/jw1PLmnU6WE//giV6k0jrAKZynhSiaeByFsthBBCCLExyFMtRBdm9kIzu8/MHjCzty6z/nozu9PMbjezr5nZszdinEIIIYQ4d1hVqa5Wqzh44CHMz8cqgC3ZFQBay5vuf260PNNc3/Bqh4kUd9PbQzW3XKYC26rUmCsAAC659DIAwOFDh2GuJPfWqRyXF5n7HHOrt21nvvSB/azamExuYl+90VPNkQwP0wee73Mv9AL3vWXLGADgxHFX32eZO111dbhec7XY0zxKrVQPjiudYaXEngRTQ1rKdDx5LhrH/Otms9mxouEJKuXFoq9n/5MTkxgc5PlI+3lLujF7apJq+3333gkAuOKqp3mPUqgfLYxxMu8D8HwAhwHsM7NbQgj3tDX7LIBbQgjBzJ4C4OMALjv7oxVCCCHEuYKUaiE6uQ7AAyGE/SGEKoCPAri+vUEIYSG0PmEij+VcPUIIIYQ4r1hVqW40GpgvLrSpr1RE02kmYWR7qNYuekJGTM6ID/Ua/caLrjKPDI1we1d5y5713PC0kBPj9C8PjYxg/AS90rkCleBGhQpxcZHe5pERKtADg8MAgFqNffUalyc8rzqAivCuHcx4vuPOr/nY0x37bnpSSXMh5knHlI6YyuGKc5KPiSb9zFF/jl7umENdWqDvPFaRTKR5zHDFutngeCtVtjt+/BjHUwWSvs/BoXgs3KavQNW+WuE25TLPRTbLdnZSoog4A7YDONT2/DCAZ3Q3MrMfAfB7AMYArHh3hJndAOAGANi1a9ejOlAhhBBCnDtIqRaik+U+mZykRIcQPhVCuAzASwH8zkqdhRDeH0K4JoRwzejo6KM4TCGEEEKcS5wy/YNfcsd5RufcIpdlqkf0VJdckY5fjDfdU12p0qc8O88s6YF+pnwkE1Rxa15t8ISr00960qUoV9hJkoJyK2UjWeNYSiWqtZvGtgEAHvzOtwAAo5s2e59F3zfHdHziMABgaJj7XnAluZUv7QklrfxoV5yXnNAcT6LZnWDCFhX3iU+NM7e67Gkh/aP0evf381Sn/ZhL7t2emaEvvV6LqSENTM8zbaUwQGXaXB2P3wgslPiYuI9Z3VdddQ3Eo8ZhADvbnu8A8MhKjUMInzezC81sUwhh4jEfnRBCCCHOSaRUC9HJPgAXm9leM8sAeCWAW9obmNlF5l4bM3sagAyAybM+UiGEEEKcM5xm8ZdOxTomWwT3CbcqIno2dFR9a550MTtHpbovTwU25REdCZejY971iRNT2L2bHuiJaSq5tRRzpNM+5FSKffe4N3pwcAgAUFpk+2xv2sdIVbdaLXUcQtK9zvm+QsexLeVJe/azK+SlIrfP5+lfjn7xRILHXHel2jwdJOfVEFFlu1qD52DSc7WPHecj3MsdfD+pVAqbRngslQoTQY5MMJmk5qp62r8hODrOedyFnp0dz2s8RjmsT58QQt3M3gjgM2Cpyg+GEO42szf4+hsB/GcAP25mNQCLAF7RduOiEEIIIc5DVFFRiC5CCLcCuLVr2Y1tP78LwLvO9riEEEIIce5yykm1WZuTOsZUx0qKTaqrPVkmW2RzOQDA3Cz9xA33SseM5mqVau2c51sPubocSypmXOA+fmIaw558MdDvlQ3nuG29QsU47QpwM83nfX3cd7VGD3WlEpVkrzoYRfSoQHtcx1Jihj+64Fgq0nM9NUGbbHGB+xkZ2eTbw5+P+uOI74c7eujBB3hOXMGePMBAiXyeaSbZDMcbEzyizhkCMDXNfde9kmLE/FjSqVg9ks8PHjwIALji8ss7jqUzNVwIIYQQQjxWyFMthBBCCCHEOlmD/cNaGczdrtEk6Fs2N0PX3GccvdJTk/QC93nWdLnuKnKNqm+j0e/t2U/Kkz7yWWBiih7qTSMDvq9Or3O5zL6KJWZbJxJefdAHGy2ubllGCJ1+8JL7kKcmWKWwMMD9FOepHC/MUU2PPuWRQVZgXHQFu+gJHAMFponUUtxv1dXlhHuqy67ODw6wXbMeKzJ6nnXJq04mlj7fRA96PAbzbwQyXmGxL+PquyePfOc7rCo50M+x7tjh4RWmz0xCCCGEEGcDzbqEEEIIIYRYJ2vIqQ5tvuMoVSe6HvjDQonqbhJUUqtlqrCTi1Sde/vpvc7nXLmuUu3t86SOSoX+495sGtUyleTxY3yMWdhLRQM9xzoZDyFWRkx6u05lu+rJGeOeulEuUekedi900ysrTvj6bVu3AlhSjedmqWh7TDUyGY752FHmX09OM4nDXDGPqn66J+vj4YK6V1JsFD1ruiuNJJvNougqurUqWLLPMc+8Nle7N+Wo9JeMY5k+wbHs2LHNz0nM1CbyVgshhBBCPDZIqRZCCCGEEGKdrK5U21IWdTvBPdQV9w9Xq1Vv7su9WuDAIJXUyXH6nhdm6KXOFbxiYJ5qcaNG5TV6q5OpBBLe15IXumtoLfGc2wRwLE3PiU64Wt6s0488NTnh+6RyvNt9x1PTzM6eW6Q6nC/QWx2rFzZdAU8XeCwZ90pHVX1mlgp1ujfn58DVZT+WTA/V+fmFWR8wxxdzrmPyyZxXVqz0ZlvVHZuenhK8zxMTPI9JV+37PCO717jv6VnuY3aKqvrA8BY/WfrsJIQQQgjxWKLZlhBCCCGEEOtkdaU6AM1maOVMxwzmRJJz8YRFzy5V5UyGKm6j6V7pQaZRFBeoSC+WqFCXZvl8vpdJG6PD9ArHvOtk4uS5frere+mRCnXDvcrphPfhLY5PUUle8GqOqUEq0QePHOw4BbH/XI7bw5XmzWNj3irhx0JFe36GPvHonY5jLvQx5WNsZDMAIO9+8a/dvg/AUmZ3xrOm02lX55McQV9fDhXP4K40YsVKz+R21TwKz4/M8JjmS+M+VmZmHzl0AAAwMDzmJ8n7cXl/JW91gIoCCiGEEEKcCVKqhRBCCCGEWCdrqKhoaDToA56bY0ZzyhXpHvcL92bpUy5X6TMuuyJab3hFwITnLbvEWppzpTpLpXrIM5yXSjYu4+MO3RkWbSUIAdQW6etuhJjWQY/y5ORkR/votd46xtSPI0ePcjtX0aMH+tIrWJ1weJjtDrn6O1viOaj5sdX93Gwdokp84d4nAQDSnkpy6PCDPmqOu9ngOGY87zrl+dYxbWRmZqrlUe9xn3bK1fd8L897Lpft6HNktLMa5MIsVfSjRx7m2Lbv6Tp3sXl8XXythGohhBBCiDNCSrUQQgghhBDrZFWl2syQTCZalRLTGfcru6e6WKS6Gyv7BU+lSHnmcr+nf0wcZ/JGDBLxZqgsUtleLMXMZt/Okza8U3/0h5bYGv3BCX/ktsddeU74GAaHhgEAey+8DACQyzNZ49477wAAzE+w6uPevUwDSbqaPuFpIfv3H+CxlmOCCVXgsTFmQW/btgcA0ON51FVPBTkxfRwAcHScfudFrwCZSbNdMsHx1Twp5cDBhzjuhCGfZ453pUQl//KnXwcAKC/yed7Pz/wsxxTqVLZrrnBPTHH5xOzXAQBbd+xmO6/uGJJRmo6fqZRgLYQQQgixHqRUCyGEEEIIsU5WVaobzSaKxcVWJnOPe6eTGW5Wcx9yxLqyORbcgz22mb7k++69HwCQy1JpXZiN6i3V45xXWmQORWdShbka3kqy8F1F//H4BJXlildA3DpMn/boGL3KszP0Vh/YT+W40FcAAFy05xnsL81+H9jPVJDpYyfQvqMMhWVkephoMtRPD3WvPy+7En3o8H7u56EDAID8II8p28tjnHEVOY4/49USN3m1xGJpHglPEpnzbwLuu/cub8M0j8UqfdzlItM/+lzZrgVut1hj5wX/RmHugUMcSw892o0eJo6gj8/TWXq1TaZqIYQQQogzQkq1EEIIIYQQ62RVpTqEgEq1inQPlcyqV1BseMpHnJJHNTn6kYejSryJfuY5r/Q37t7q8iKV1maz0bF808iQ99RojaHhfuHjx+iVHhhkm2yWyu/CPH3GI8NUjod2U4E+dOQAAOAbX/+yj5V+8IJXTNy6jR7qTJpjfvAAFeZjx48BANIpnppeV5Iv3kqV2AZi/vSAHwvHN+P+5uKip3r08qxMTtKzHXO0ky1feeg4vmqFqnFvNoe5eSaX5AtUwSfGeX5Kvjx61hPG8zQJV55TPCdPu/RKAMCFAzwn8NfLKszYDq5gVytU/6t598pnXY4/zzGzFwJ4D4AkgA+EEN7Ztf5HAbzFny4A+JkQwh1nd5RCCCGEOJc4ZaSeEOcTZpYE8D4AzwdwGMA+M7slhHBPW7OHAHxfCGHazF4E4P0AnnH2R0v2vPUfN2rX4hzhwDt/cKOHIIQQ5z2nVKpr9TrMfcw1T4+Int+oaw4NUbWNSnN5kf7io48cYTtXsHft3M7lR6nqzs5Q1T1y5LCvpwqcTgLF4rwPgmpqv2c0DxaoRM94DvXEMSrLF190MQBg/NgjAIDJ6F32UW7fQm/1hZdeBQAoFqna3nv/t/i8zGMreiLJllGq7Zc/ibnTtTLbJ1JUhaMP+d77uP2JKSrpPZ7hnXHfcqrs+dl1noOmV0ds+c/dBz0+TkU729ODxQrPX/yGIO3Z2VUfQ1+O26T93Mw16KnuAdttz/P1aPg3BK2MjzqV7aSr8BkfQrHOMZU7LfLnK9cBeCCEsB8AzOyjAK4H0JpUhxC+1Nb+KwB2nNURCiGEEOKcQ55qITrZDuBQ2/PDvmwlXgfgnx7TEQkhhBDinOfU9o8EWhUVU0mqr4UCvbsjI1RzY9by5DgTM2LlxIF+rk+nqaBOT1GNveyyPQCAI0fYfmKCyRyHD7ECYL0yjy2bmBjS64pwveH+3woV5bSr5Zc/6RIAwPwcleuZBarfmz1NY6Dg1Ro9buOuO7/BsU7Tp5zN08scc6af8mQq2Vdf/VTutx58cz7efS+V6W/evg/AUupHrC7ZitX2sJKsK+wLC2yX9BSTVEs+5gZNP76YZw0AwasvxjFm+/kI90bP1fkNwgVbdgEArty6FwCQKbk67r7tsld/zMZMb99H0hXqTJXHXs+kIZYN7V42FsXM/hM4qX72ip2Z3QDgBgDYtWvXozE+IYQQQpyDSKkWopPDAHa2Pd8B4JHuRmb2FAAfAHB9CGFypc5CCO8PIVwTQrhmdHT0UR+sEEIIIc4NTqFUB4TQRCZNX3JfjopmtpeK5twMPbuVWC3QKy7mc0ytyLhC3XTFNe851ImkZzOPUUU2T/tIeMnFzWNjCE0qt8cm6I2OaneuUPOxcf3+B5na0ZPh2C7dQ+XaGlRhjx6h+n3MVXRkqLKns3kfIx9HvPJiXy/7Kc5TDZ6ZYwXFu7wC42KJSnjSpWbzMVdrVIcTrtJHaTNWdiz0ewa3e6qrpWLHcXRUiHStNFayjAknyHtOeIq+8sEhnr+LBphMknX/dsXPd9FN76XAfhLu307y0+kTAAAgAElEQVS7gl2o8xuGtGd7J3tdCT+/2QfgYjPbC+AIgFcCeHV7AzPbBeCTAH4shPCdsz9EIYQQQpxrKP1DiDZCCHUzeyOAz4D34n4whHC3mb3B198I4DcBjAD4UzMDgHoI4ZqNGrMQQgghNp5VJ9XJRBIDfTmkXI2NudK1MhXOUpGPi57VPDpGhbrqymfZ2+3fT7X4wr27AQAJcHnN1drgimlUWA/PzcM8g3nHZqqw07NUax+4/0EAQNarO27bxuCFoUEqzUcfZkXEEw9SQOx3zbjfzc7pXirGRU802TLCxJGd7netVRiBMTHOVJGjnlt9YpyVGPO9lH+t6Uq1nytzj7dPspBOUfXND3BcD9x7JwAglYoJHFT7m42ovJNmaCCZcInZz3fKM7aTaW5TrXD59gTPQS7Bl7Hh7YueSz3rodhlH1vCx1b2KpR9/rzfE1LqLfX8/CaEcCuAW7uW3dj28+sBvP5sj0sIIYQQ5y7yVAshhBBCCLFOTumpTqAJtzcjny/4Y58/UhH95jduBwCMeE519AAXvP0Fe6gCF+fowS4t8DHb05k2kUwxKaO/r4BcD4e26JnXNU/9SLi3+pKLLwWwlHH97QfvYx/eV9rV2n5P1djcxzFXXLlOjjEdZGjvhTxSV3NnZpgKcuI4c6cPPETPdqNBdbe0SHW3zxM5oqqcSHPsAwM85skpHuNgH5/v2E6VvrroarCbrsdPeJa3q8rJZBL5vn42cX92rO5YqXAMNfewx8SQb7hf/GJX66e9YmLJU1sWPV/c3N/dcIW65H7uySo918nGUiVLIYQQQgixdqRUCyGEEEIIsU5WVaoTiQR6c/mWQh39wjHZIuMV/6648goASxUXd++kKltcoCp74gT9yIkG1eNmk+0qNSqluRz7b3jPlVoZtQrXZbNM6wjugR4eocJ8zwP0TC/MMp3DPI2jGZVoTxrJlajqJuaYelaIvuJFbjc7zrS0B45QMT585ADH4l7nmRlma494HNq0V4HMeVJG0wOp817psdeTOAaDq8vuU97lSnXas76rVarCpRIV7WqtDADo6c0j6Ukm8LFGr3TKVfqUe6t3794DAJgo8bweK9IP3nQPfNmV7opnYTe8omJwVbzmHvaUv6C1RY5BCCGEEEKcHlKqhRBCCCGEWCerK9XJJPrc37ssHn3RV6Aq/MB3mMxRLlHxnJ5mxnTTs5kT4GNvlv7mfq+4WCrTgz07TRU4lcki4WkdY4OexXwZ1fDZWbaZ8vxoS6d8KG78dpU2557pHvcd2zy3q7kHerFOJXri4Xs51nmvjJhlf1NTrNCY9Eztuiea9OWpnDddRS4Mb+ExlKgSpxJcn+vlY7/7z3vTvp2rwvUG249s2goAOHqMqSXpdAbB1faGZ18vzHMsfQW+FmmvfHjP1HEAwI7tFwEA5jDl7XneG03/ZsG91HU3x4caFeuoXMe0lsWK0j+EEEIIIc4EKdVCCCGEEEKsk0en+Isr1gODVFInxulftpRXCWxQWTX3+EYv71H3M8fM5nQP1dxMKoWRfiq8ZU/L+ObXbwMAlMpUwZOethH93ea+4XwP/cjDw0wigSvNNU/rSLgH+tgs+y1nuV21zn7HfewNr2booSAolaimD2+itzqqwOUyFed6lY9FV483j45wXK6MN6JK7FUNs1n6oxuupFddNW6EJtKeYeKiO/o91SOZiC8X2z589BAAYNcFVKqHRwYAAPML/g1Bwj3qfs5C9GZ7v2lX9+fn3dddj9UdhRBCCCHE6SClWgghhBBCiHXy6CjVrsL2eiJGLh891fT4Dg5RNe7zRI6ZGSqj9TI9wylXna1JX3NoNDE9Rw900f3EDVeY056M0XR53FyrTrkivHfHDl9O6p62cdz3OTlDL/Z8rDo4QcV6och9p9x/bLGKpHu7ezO9HcdY9qSTcpHHmk16dncvVeVEsqfzHLmk7lbsVpJKKu3KdSa2N8z6WPv6eb5KC1TBM37s2aznexvPyV137OPTRVfLF6lI77r4EgDAoRkq17HyZdY/SlW8euTcAlX4YFH3F0IIIYQQp4OUaiGEEEIIIdbJGSnVTXQqmlEV7u2l2horK/Z7dcEeT8IIngIyM0P1ecETM6KM25ejApvJZDC34D7gpKvYrhg3W58DfK9xuduBv73/AAAg6wkZdc+Djkkko1u2+lj8WJpUadPJWIuxk6goZzIZP7bNAIDxycO+ey5/8hVXsZ2nfBg8UcNzqGM/cdx191JHVTrp6+uVais5JJngMWSzXFepUmnOJahYx5SQfI+r9U2v+uiVK+93BTv6uCuuqpdchY8JKLUaVfvedByjEEIIIYQ4HaRUCyGEEEIIsU7WpFRHRdQDL9CMSRaNhi/g+lhR0Tx1IpOOGc9UqKPHulyhf7k3R0V2boHK9WCB1QgnT4wjnWeSRTIZvc2xmqPnTgfue+lTQfDn3OecV1KMinTCfdsLRXqom67Spn15HHsIXb5iV5DnPef6xPFj3M491nAr9N333e7HzAVJV6SHBunBnvN87ax7sqMXfGiQxzk1VfP1vWj4GPyQkenx9BQ/r8EV6Usv2A4A2D7EY5g6xmNu1Ki6T8/wm4BYnbIRqEiXyjU/Bz6GPLffMUZf+De+AyGEEEIIcRqcclLdtNCaRDe8aEiI5a49oq1crnRsk+vjxHF+lpPlEycmAACDBU5Ed21l3Nxs0YvB7OTNhekk9zPY34dimetmvdS5+b6bxiH3Zrh+yCfmC2WfUGa47x6f75eLHEPGI+xiWfGE2z2CfyCIz5v+QSEec7RtxOIvRx9hjN3mrbSR1BscR2nRxzPKYjBlv2lw8kHaRNIpjrt41G869Ml8n5dhz/RwfMGs5WxZMmN46XWPBxzso/0jWWP8X3Wek+7ePG9sHPVJed7tONUKbR/Vmheg8Z5n/AbGnZv8A4yXnRdCCCGEEKeH7B9CCCGEEEKsk1WV6hAC6rUamlGZ9pvr6n7D3My0x9N57N3w6GhrOwAtW8iWESrTPanOgio5j4Yruz0k+h2SaKLoxVZiX8mUR93F+L40t13wGxEXGlRvreJRd7FseHL5zw0xMq9lbelSpluPrfbsJyrZc7N+g6EXril5afb68XEAwO49FwAA7p88AQAY9HZNP4ch5YVXFmgLiXaUnr4+BC+SE29eTLgF5tIL9wAAxgb4soXFE35OGv4Yb/h09dvV8PkFjrXXj9XcPjLUT7tNLJCT9nMqhBBCCCFODynVQnRhZi80s/vM7AEze+sy6y8zsy+bWcXMfnEjxiiEEEKIc4vVPdUhoFGptdTZGY+lm5nio7nCmUxRJa7Hm/1cde3L0dNrRiV6wf3RyZTf3eeRcQnXg2t1vymwVEbJPckt77O3jcpwLeE3OXqJ7aQXf4lqd979xbFEd7zhsHUsrjTXKvSDxxsI4/rgHuqVKPkNj5u37vJDoSrc4+cCdarBw4ObAADF+ZmOcdW8/x73VM9Pe7RfPYuEH2O8UTHrN3z2JDwCzz3sSVfXm033Qjf9WP2zUjJJZXqgn57pqMrHkunVGseYcb93KiOl2sySAN4H4PkADgPYZ2a3hBDuaWs2BeDnALx0A4YohBBCiHMQKdVCdHIdgAdCCPtDCFUAHwVwfXuDEMKJEMI+AKt/8hJCCCHEecOqSnW9XsfM1FRLoY5FRJKZzlg4eFGXhqdM9Obd3+zLJya5/dw812/ZTI91Ie/KqavDx8ap5mZSCaRcnR3yxJBqjbJtuUaVtVSiUpxytTyqrtGz3ONpH1Hp7stxzKXFznLkFT+EhvuSCwXGykU1fiWiv3zyONNARryozNhmpn/ECD23Q6Pi3u9e95Hn8jnvh8t7ctxvpbyIbI7HdKF7qAc8yiSXcp+3jzVa10OTynUzJpnE6D1POqm5ap7tca91hv3ne/wbBv9modFUmXIA2wEcant+GMAzzrQzM7sBwA0AsGvXrvWNTAghhBDnLFKqhehkubKSZ/xpI4Tw/hDCNSGEa0b9Rl4hhBBCPPFYValu1BuYnppBwj3QrRm4K9BpT+SI5chzrg43XDWem6HyPDNDv3Ajlsuuc7ucz196Mny+bWzYu68inYmJFFxXLnsJ7il6reMsJ3qUrdQ5F0p46kfWFepWHfMQt6MyvLjoGc6eaBIV654sVdzoyTZP5OjxPOlSkeOICnQsThNLfjdi0oanjMSkk+gj781x3HVvHxV41Gqo17nP/jQfsw2mqzQbPBeJmEzSKt0evdKeAtJKLuHydDq+zJ480iouw/6SnkTSqMrNACrTO9ue7wDwyAaNRQghhBCPE6RUC9HJPgAXm9leM8sAeCWAWzZ4TEIIIYQ4x1k9/cOMSR2BynTKVdf+Aaq8+Zx7p1veaiqeCxW2L9apylZchY0K7MzkpPc3CAAo9DG9Ip1x5brQj4r7s2PFw5iAkevxyoRecbESkzDcI11zf3AmzbFVPWO7Vo5pIq44u6o+OMQxROU5HkrOFe6yK9XRnxzzstOelJEvMFljcNi/2nf1d8C92UVvV1qkWo+Yjx3V/yQfh7JUrvPpUVyyjds2Fye9jR+bq+LxGKIinfAM7VQqvpxRx2f7pK+P6R/wEu8hbh8V64SXoTyPCSHUzeyNAD4DIAnggyGEu83sDb7+RjPbAuBrAPoBNM3sFwBcHkKY27CBCyGEEGJDOWWZciHON0IItwK4tWvZjW0/HwNtIUIIIYQQAE4xqTYAiVBHf4EqaiHf68ujEsrHOa8mOFeialwqMWGj7LnUDVePe5NUbasVrk8kWNEvEe3QMT+5Um6FNEf/7+wsfcUV77sRqK7G5JBYPTDbR393KpXoaF+vVnw5x5DOUCHOegLGtPu/q17dMdfLY826tzoq1tF7ne315a6onzjBSoqbNm/uGFc8tHw/FfGyp5aUPYc7m+/3fnnsw9kA1Dt9481GK+YDAFCrRU97yk9bTPuI6R/u6nEfuG+2ZPYJ3d7qqHR71rcQQgghhDgt5KkWQgghhBBinayqVKfTKWzbPLKkJLt2Wlyk6jvjyvRiVKZLVFjrrubGLOce9wRfsftCAMBkkdUM8z1xTu+qbvT+dgSYeZXFeXqSR0aYcT05z30l01590JXrio8hlaNyPdBHRblR4fZNTy6pLrqv2FXcnOdEz8669xlUoIc82SS4ip72/cXHRqPi+/UKjSUq3LVqrG7oFR77Oe7o7a65cj5ISzYu2sLkk2xtHPUy+zL3OkevdPQ+V91P3qx7PnhMNrFOr3XcPjT5TUECq3urY3shhBBCCHF6SKkWQgghhBBinZzaU23AomdEz7Q801Rhoz+4XvFqhrHSnyvUTZeBB4apwsKV6bHeIQBA2hM45utUZhPNqLQmMDU1BQAYH5/gtu7HHp8pdfRdr1MlD0kq08HV22qRy7OB+8hn2PdAP/3hUZOdXvAc6sDtK4s8JTXvp+zH1legYp1MRp8y95/xdI/ZOQ9+GNvcdvaAup+LcokKeLaXqSLFOXq4aws8zm0X0Fu9OF1DpemKtOdGt74paHmm/annTsd0j5rvK3qqbQVvtcXtW95qfzTdtyqEEEIIcSZIqRZCCCGEEGKdrCpN1hoNHJ9ZwEKRam70K9c8CSOqxcGTLpqu3kZhdfdupo7t2s0Cdc0q1d9YCbBcr/pzDmPOEz7Gx8dR9/SPVJYKcSJNxTm0DNctuZV9uIe5kKXiHAMvBgtUhvs8zaPuSSRFV9k3Fdh+pL/XH+nBXihxbMcmXIGO8q73HJ/GxIw+r9BYXGB7S2W8AfudOHGC5+TCCwAAW0fYftsAle7FOWZSV5EC3OOc8dzouleoTGcynWNpealdwfYT32zE12F5b7WFRMd2S0p1PGtCCCGEEOJ0kFIthBBCCCHEOllVqa7XG5ianEZ10ZXplmc6Jme4d9ofd+zcBgDYtYvKdKxa2MpN9hSL6LluUIDFsUPHAQBF90EXRrYguLo6N+vKbytsOaqvfGy6om2uIPf1egqI+5LrnvFcLFIFb2U5W+fniaie9+e5vNBHJTmTZv+1Jh/LVVd3EXO0/Vga9Y5zghr95wlX45OuAueSPHd7tzOju1mip7rulSEDDCkfWzMqzL6Ppp/36OtuhM586ri8VndvdTw3CXeQx35b56DzGEKQUi2EEEIIcSZIqRZCCCGEEGKdrKpUh0YDlWJxSZHu8k5v2cqki13unc7nmazRUqZjP83OCoxRDy0WSz4KeoX7humfnp2bxcz0dPsmGBod8ee+wJaSQgAg6Z8Pqq4QD/T5WOI+W2NohW53jClmN8exx+fDg/RkHx9neoc1PWUkQX9y9FT3umc7sVQH0Y+d+9u6ZRQA0J9l/4tzTDVJwTO9LRqim2jG3OiYT11zb3WsHuneakvEfcTcaVes3Vwdj9ncOx0V69CoeXvfZ5e3WgghhBBCnB5SqoUQQgghhFgnqyvVCGjUay1letMmqsXRM90/ELObqYBGDbjZpUzHNQ1XWqdn6JOOedfjU1SlF71SY7PeQCJ09rC4wLSOsU1Uxxvu76436cPeOsjlSSy27xItXdz9yc3oye72aPueore64ZnN4zPM0J5fpKKcyHC/idbnEU8pSXVWI4yC+vAQSyZeuGsrACCXpPe6Xit2nJNEK7pjyfOc9DHHSoqJOLbWNu6tbnZ6q+Pyk7zVMZ07ngs/B0ve6gaEEEIIIcTpI6VaCCGEEEKIdbKqUp1MJDHQX2jlTA8NsxJiVEKjwhmV6dClTMdnk5NMuJh0Rbrq6R81V1YXPfkCsSJg2xgS3veVu/YCAEb7WZ0xqtx1W/C+Kt5Fp2c6eG9RmV7yPEd8rFEoNuZGT86w/9l5KsvpLD3aoVnv2G7JId7pR05lqAaPeJpIpsoc6lSe6j6SMaUk9ufnNBlaOdPxfFqKY+r2VifX6K1uxPzw6ENveatdmW55qyGEEEIIIc4AKdVCCCGEEEKsk1WV6lyuF1dd/RQkkl3KdIiJGt1pEVw/PTMDAJiYpDJdrlGNjcp0rUx/cq1E/7O5AhsL/SEA5vtMuXqaSfKH+XlmWkf1Np9invR0zfsCs7GbnqoRky1anCRUu1ruVQuPHGWe9ZxXkezppUJd80qM8RxEC3RoicP8IdPDU/qUq64AAKQrPBehyvE1at6PK9WpBNs3PJEjabakfbe81X7+vW1U7xv1ekdfrdzqZqe3uuHe6paHulVRcXlv9fmOmb0QwHsAJAF8IITwzq715utfDKAE4DUhhG+c9YEKIYQQ4pxBSrUQbRhrur8PwIsAXA7gVWZ2eVezFwG42P/dAODPzuoghRBCCHHOsapSbWZIpZItFXYlZXp2jmke0TNdqrgS7YpprUIVturKdL3K5yFGfLSiPpa82FGv3bSFiSPpHP3Dc1Wq2nPT9CiXyicAACODWQBAT6rZNrKlyocr0ag3fOxlHzvHls1RoU6m095RzMeOSRmd/vGWKuwVHE8c57i2bRqMO+K5qHI/2Xw/+096RcZYrRL0VQNY8lbHVI8kX66kt625H7uVvhIztj3FI+ZOp7q81YlTeavPb64D8EAIYT8AmNlHAVwP4J62NtcDuDnwhfmKmQ2a2dYQwtGzP1whhBBCnAtIqRaik+0ADrU9P+zLTreNEEIIIc4jVlWqjx4fn3j7H/zpwbM1GHHOsHujB7CBLCfXLx8Zs3obNjS7AbSIAMCCmd23jrGJldkEYGKjB7FR2Ls2egQCugbFxqNr8LFjTfOi1Yu/hDD66IxFiMcNhwHsbHu+A8AjZ9AGABBCeD+A9z+aAxQnY2ZfCyFcs9HjEOcvugbFRqNrcOOR/UOITvYBuNjM9ppZBsArAdzS1eYWAD9u5LsAzMpPLYQQQpzfrKpUC3G+EUKom9kbAXwGjNT7YAjhbjN7g6+/EcCtYJzeA2Ck3ms3arxCCCGEODewpRQLIYR4fGJmN7jVRogNQdeg2Gh0DW48mlQLIYQQQgixTuSpFkIIIYQQYp1oUi2EOOuY2dvM7Bc3ehxCCCHEo4Um1UKIJwyeyKL3NdGBmf2+md1tZr+/0WMRTzzM7CYze9lj0O9zzOzTj3a/Xf1/9yrrFx6rfT9R0R8fIcRjjpn9uJndaWZ3mNmHutb9lJnt83V/Z2Y5X/5yM/uWL/+8L7vCzG4zs9u9v4vNbI+Z3WtmfwrgGwB2mtmrzOwu3/5dbftaafmCmb3LzL5uZv9qZteZ2efMbL+ZveTsnCXxGPLTAJ4WQviltTQ2sw1PxjKz5EaPQZwdNvC1fg6AFSfVjwbn23WsSbUQ4jHFzK4A8GsAnhtCuArAz3c1+WQI4Vpfdy+A1/ny3wTwAl8eJ7ZvAPCeEMLVAK4BC/EAwKUAbg4hPBVADcC7ADwXwNUArjWzl5rZtuWW+/Z5AJ8LITwdwDyAtwN4PoAfAfDbj9KpEGdA9wcyM9ttZp/1ZZ81s13e7iYz+xMz+5J/GHqZL78FfH2/amavMLNR//C2z/89y9u9zczeb2b/DOBmM0u6wr3P9/XT3u45/oHrE2b2bTP7sJmZr7vW93+Hf/grrNTPCsf6HDP7v2b2EQB3+bI3+4fAb5nZL7S1PWm5f8D8tpl9wJd/2MyeZ2ZfNLP7zey6x+I1Ot9YQST43mWuvQ6l2czea2av8Z8PmNlvmtkXALzczC7yD/R3mNk3zOxC36xvuWtthXF9v5l904WDD5pZT9u+NvnP1/j1uwd8P/2vRpHie4z1Gb7s1+rvtPVrfg1/y/t+xSmWn3Qdny9s+KdxIcQTnucC+EQIYQIAQghTXX8XrjSztwMYBNAHZoQDwBcB3GRmHwfwSV/2ZQC/ZmY7wMn4/d7XwRDCV7zNteAEeRwAzOzDAL4XLCW/3PK/B1AF8H98+7sAVEIINTO7C8CeR+1MiNPClj6QPSuEMGFmwwD+CvwA9Vdm9pMA/gRA/HC0FcCzAVwGFmn6RAjhJWa24B/E4H/o/ziE8AXjhPwzAJ7k2z8dwLNDCItmdgNY2Olan5x80SfcAPBUAFeAlVS/COBZZnYbgI8BeEUIYZ+Z9QNYBD8kntRPCOGhFQ77OgBXhhAeMrOngzn4zwBg4AeDfwcFseWWTwO4CMDLAdwAFrN6tZ+TlwD41bZzJc6AFa7JP8Iy194auiuHEJ7t/X4VwDtDCJ8ysyz4Gu/EMtcagC8sM64sgJsAfH8I4TtmdjOAnwHw7uV2HEI4YGY34v9n787j6yqr/Y9/VjM0HdIxbWlLaYC2jFKGFmSugIo4gAKieJ0V8SfqvV7nERyuOFxnhYuAiIoTCgKKqAiUuS20BcpU6DxBOrdpm7bJ8/tjrac9OeQkaZMmpf2+X6/2ydl7n72fvc9Ozt7rrLMeWJ9S+m6s4xbgipTS9Wb2kYLF34IHIsbjQ6FPNf/08IQS06HgPG7HcdhjKFItIrua4Re0pVwHXJJSegVwGVAFkFK6GPgi/sYyw8wGp5RuwC8ONgJ3mNlpsY76ou2V6kcpW9L2+qJNQEP0oQkFH7rTS27IgOOBG2L+r/ALmezmlFJTSulJYFiJdZ4B/MTMZuAXP/3MrDrm3ZJS2hg/vwYfOXUG8DAwGBgb86aklBbF+TEDv/E6CFiaUpoafV2bUtraxnpaMqXgQuQk4KaUUn1KaT1+c3lyK9MB5qaUHo++zQLujHNbN4ido6VzEtp37hX7PUCcfyNTSjfFOjellDbEMi2day05CH/tn43Hv8SDBjviROC38XNhmt5JwG9TSo0ppReAe/DgRanpud971QU16M1CRHa9O4GbzOz7KaUVEdkpVA0sNbMK4B3AYgAzOzCl9DAehXsjnivdH5iTUvqRmR0AHAHMKVrfw8AP4+POVcDbgR8DU0pMl91XWzdkFM1vKHpuS3oAxxdcPPvC/olH8c3ZR1NKdxQtN6loO434e2mpvra4nlZ09AaxsG9NBY91g9g5Sr3OLZ17W2kevKwqek5+rdv7euZzrVS/SinsR3EfipU6h3d0m/WtzNtjKVItIrtUSmkW8A3gHjObiX9UWuhL+IXwP4GnC6Z/J/L0ngAmAzOBC4AnIup3MHB9C9tbCnwOuCue82hK6S+lpnfensoucCfwVjMbDBA3ZA8Ab4v576CFj8Lb8A/gkvzAzI4ssdwdwIfjZg8zG2dmfVpZ79PACDObGMtXm3/hcUfXU2gycI6Z9Y7nvBm4t5Xpsuu1dE6WMh841Mx6RkDg9JYWSimtBRZZfMcjlu+9g/16Gqg1szHx+J145BhgHp7aBHBuwXPW4UGN7H6a/25lk4ELzL8fMASPgE9pZfpeS3etIrLLpZR+iX8c2dK8K4ArWpj+lhYW/2b8K7QSOLzouTewPUWgPdP7Fvx8aal50rVSSrPMLN+QNQLTgY8B15rZp4A6PLd4R3wM+KmZPYa/B07Gv7BV7Gr8o/ZHzcPYdbSSj5xS2hxf1PqxmfXCU5TO2NH1FK3zUTO7ju0XKlenlKaDfzGzeHp8+Ux2oRLnZKllF8Z3Qh4DZre2LH4R/H9m9lX8y9bn72C/NpnZe4E/xs3cVODKmH0ZcI2ZfR4PYGS3Ajea2dnAR/Evkd9gZh8H/lSw3E142tVMPJL96ZTSMjMrNf3gHen7nkTDlIuIiIiIdJDSP0REREREOkjpHyIiIl3IzF5B8+oK4GUcj+uO/sjLS6Rd7F80+TM78GVY2UWU/iEiIiIi0kFK/xARERER6SBdVIuIiIiIdJAuqkVEREREOkgX1SIiIiIiHaSLahERERGRDtJFtYiIiIhIB+miWkRERESkg3RRLSIiIiLSQbqoFhERERHpIF1Ui4iIiIh0kC6qRUREREQ6SBfVIiIiIiIdpItqEREREZEO0kW1iIiIiEgH6aJaRGQ3YmbrzeyADq7jOjP7ejuXrTWzZGbl8fh2M3t3R7ZfsO6TzeyZgsfzzOyMzlh3rG+WmU3qrPV1JTObZGaL2rnspWb2613dp1a2n8xsTPx8pZl9aSfX0zODAhQAACAASURBVOFzuyPM7D1mdl93bV/2fLqoFpE9jpldaGbT4k18aVwontTO5267gOgOKaW+KaU53bj916WUftnWcu05Timle1NKB3VGv1q6UUgpHZZSursz1t/GtpOZvZBvPGJauZm9aGZpV29/d5JSujil9LW2ljOzu83sA0XP7dZzW2RX00W1iOxRzOwTwA+A/wGGAfsBPwPO7s5+taXwgm1PsKftD7AaeF3B47OAVd3Ul51mZmXd3Ye9gTldY+1ldosXPCIQ5+2C9U4ys9s6e71F6z+hlfnrd9W2ReSlzKw/8FXgIymlP6eU6lNKW1JKt6aUPhXLHGtmD5rZ6ohi/8TMKmPe5FjVzIhyXxDT32BmM+I5D5jZEQXbPNrMppvZOjP7o5n9vjCiamYfNLPnzGylmd1iZiMK5iUz+4iZzQZmF0zLH7X3MrP/NbP5ZrbGzO4zs14x749mtiymTzazw9p5jMrM7LtmttzM5gCvL5q/LcJoZmPM7J7YxnIz+32p45TTGczsM2a2DPhFiRSHiWb2pJmtMrNfmFlVrPMlH83nY2FmFwHvAD4d27s15m9LJzGznmb2AzNbEv9+YGY9Y17u239HdHmpmb23PcerwK+AdxU8fhdwfVF/R8RrvDJe8w8WzOsV73WrzOxJYGILz/2TmdWZ2Vwz+1h7OlWwb5+P12iemb2jYP51ZnaFmf3NzOqBV8Wx+q6ZLTCPwF+Zz6t4zqfiGC0xs/cVba/ZJwZmdnb8bqw1s+fN7Ewz+wZwMvCTeL1+EssWntv9zez62N/5ZvZFi4vQfC5EH1fF8Si8oWnrmIwysz/Hulfk7bew3A/NbGH0/REzO7lg3rHmn3atjWP0vZheZWa/jvWuNrOpZjYs5t1tZt8ws/uBDcABbZwT7TlnP11wzp5jZmeZ2bOxvs+395hI19gtLqo7yrrvznsSUPKiujN0476JvBwdD1QBN7WyTCPwX0BNLH868P8AUkqnxDLj46Pq35vZ0cC1wIeAwcD/AbfEG2JlbOs6YBDwW+DNeUNmdhrwTeCtwHBgPvC7ov6cAxwHHNpCX78LHIP/nRkEfBpoinm3A2OBocCjwG9a2edCHwTeABwFTABaC2h8DfgHMBDYF/gxtHyc4vE+0c/RwEUl1vkO4LXAgcA44IttdTildBW+f9+O7b2xhcW+ALwSOBIYDxxbtO59gP7ASOD9wE/NbGBb2y5wM3CKmQ0wswH4ReNfipb5LbAIGIEf1/8xs9Nj3lfwfT4Q3/9teetxMXkrMDP6dzrwn2b22nb2bR/8fB4Z673KzArTbi4EvgFUA/cB38KP/ZHAmHjel6MvZwKfBF6Nn18lc+DN7Fj8xuJTwADgFGBeSukLwL3AJfF6XdLC03+Mvx4HAKfiNymFNzrHAc/Efn0buMbMLLb7WSsRMIv3zNvw37Xa2Lfi37lsahyDQcANwB8tbvKAHwI/TCn1w1+zP8T0d0e/R+F/Dy4GNhas8534uV8dfWjtnGjPOVvF9tfn58B/4H8TTga+bN2Yoy4tSCl1+T/8l+cx/A/Ir/A3pB8BDwBzgPNiuUnAbQXP+wnwnvh5Hn6S3Qe8Df/D8K9Y56P4L8Ek4G7gRuBp/I+ytdKv04HpwOP4m2jPgm3VxM8TYp21wDJgMTADP8H3Bx7Ef1G/BqyP5xjwHeCJWPcFbUyfBNyF/5I/2R2vkf7p38vxH37BtmwHn/OfwE0FjxMwpuDxFcDXip7zDH4hcEr8DbCCefcBX4+fr8EvBPO8vsAWoLZgW6cVrTvF37Me+Jv1+Hbsw4B4Xv94fF3uQwvL/hu4uODxa+K55fH4buAD8fP1wFXAvi2sp/g4TQI2A1VF0xYVPJ5XtO2zgOfj5/cA95XaRkv7FOs7I35+HjirYN5r8Qu83I+NeR9j2ovAK9t5juTX5Gr85upi/AJnDJBimVH4DVt1wfO+CVwXP88BziyYd1E+NvgF5IKibX4O+EX8fCnw6xJ9mwRsBfoUTPsD8KWC43Z9wTwD6oEDC6YdD8yNn68FLi+YN67U64DfYH6/RL+2nUctHMcyoAE4tGDeh4C7C86F5wrm9Y7n7tOO1+p4oK7wtS6Y95JzrGj+KuL3DZgMXEa89xcs8z78WuWIEvv81YLHbZ0T7Tlny+JxdRyD4wqWfwQ4pz3nsP51zb8uj1Sbf0T5BfyNZDzw8Zg1HDgJj6Bc3s7VbUopnZRS+h1+wfzTWOcJwNJY5ij8TfNQ/I74xBL9qsL/WFyQUnoFUA58uNSGU0rzgCvxPyhHppTuxe9sr0gpTcQvuLO3sP1O9AzgO2Y2vJXp4HesX0gptRS9EpGWrQBqrJV8XjMbZ2a3madOrMVzr2taWedo4L/jo97VZrYaf7McEf8Wp3iHCwsLfh6BR6sASCmtjz6OLLF8oRo8SvV8C/tQZmaXx8fta/GLy/yctowo2ub8UgvikXEDpphX2nhfK8sC1KWUNrWxTPG2R5RacAc1O9YtrHtFSmlrweMN+E3OjrgeDwq9JPUjtrUypbSuqA8jC+aXOu6jgRFF59jn8e8EtMeqlFJ90boL971wu0Pwi9RHCrb195jeVj+LjaKF87MdaoBKXvp6Ff5ebHsPTSltiB/b83qNAuYXvdYtMk8Heso8vWk1HoHOv0Pvx28ono4UjzfE9F8BdwC/i5SNb5tZRcFqi3//2zon2jpnG+PnHA1/oWD+Rnb8HJZdqDvSP04DbkwpLQdIKa2M6TenlJpSSk/S/j8kOb+vGhiZUrop1rmp4JdwSkppUUqpCY8o15ZY10H4nfqz8fiXeBRqR5yIf9QD/ouXnQT8NqXUmFJ6AbgHz6crNT33e+4Obl9kb/cgsAlPqSjlCvyTq7HJP9r9PH7hWMpC4BsppQEF/3qnlH6L37yPzB9Lh1EFPy/BL5gAMLM++EfGiwuWKVU9Ynnsy4EtzLsQ/+LlGfiFQG3eRCv7kS0t6uN+pRZMKS1LKX0wpTQCjyT+zFqv+NGeShjF214SP9fjF3sAmNk+O7juZse6aN2d5V48ADQM/0SiePuD4v2osA/5tW7tuC/E338Kz7HqlNJZ7ezXwDi3CtdduO+Fx245fjF2WMG2+qeU8sVZu8+P6HdL52fxNostxz+xKX69Fre8+A5ZCOzX2o01eLlH4DN4atbAlNIAYA3xO5RSmp1SejueXvUt4EYz65P8OxqXRcDrBDwQWJhrX7jfbZ0TXXHOShfqjotqo+VftoaiZcA/0irsYxXN5Tvz1t5ICtfbiEegS/WrlMJ+FPehWEv7VmrdrW2zvpV5ItKClNIaPC3sp/Glnt5mVmFmrzOzb8di1cBaYL2ZHcxLP5F6Af9UK/s5cLGZHWeuj5m9Pt4oH8T/rlxiXmLtbPxTpuwG4L1mdmR8Ael/gIfjk6629qUJ/yj+e/FlpzIzOz7WU43/bVuBX4j+T/uPEn8APmZm+0ZO8WdLLWhm55vZvvFwFf73LUfOio9Te30ktj0Iv6HJ+dgzgcPiWFXhKQ+F2treb4EvmtkQM6vBz4NOre0cn0i8EXhT0acTpJQW4mkB3zT/MtsReLQz57r/AficmQ2MY/rRgqdPAdaaf8mzV7zWh5tZsy8ztuEyM6uMi8U3AH8ssQ9N+Dn9fTMbCmBmIwvyt/8AvMfMDjWz3ngueCnX4Of36WbWI9ZzcMwr+XpF9PUPwDfMrNrMRgOfoHNeryn4jcHl8btaZWYtfUJdjb+31wHlZvZloF+eaWb/YWZD4nitjsmNZvYqM3tF5G6vxW8OGmlBO86JXX7OStfqjovqO4G3mtlggPjDWsp84FDzLwT1x3OeXyKltBZYZGbnxDp7xh+DHfE0UFsQhXknHjkG/2j1mPj53ILnrMN/MbP78fxu8NzObDJwQfyhHIJHwKe0Ml1EdlJK6Xv4G/QX8TfMhcAl+BfNwL+EdSH++/tztl/UZZcCv4yPxt+aUpqGf7nvJ/iF5XN4biYppc14Gtf78Tfe/8C/JNUQ8+8EvgT8CX+jP5DtfyPa45P49y2mAivxiFkPPPVgPh7xehJ4aAfW+XP84+v8/ZM/t7LsROBh80pGtwAfL/gE7VIKjtMObP8G/MuPc+Lf1wHiU8Kv4t+Nmc1LI8HX4O8Hq83sZl7q68A0/Ps6j8e+tXcAnM+b2e3tWTalNCulNKvE7Lfjnxoswb/A+pWU0j9j3mX4azYX3/9tn2bGReYb8XTAuXgk92r8U4j2WIafm0vwC7aLU0pPt7L8Z/Dz+KFIH/oX/mktKaXb8ZKU/45l/l1qJSmlKfiXC7+PR3nvYXvk9YfAeebVO37UwtM/igeP5uCv9Q34TWSbWnu9Co7lGGAB/iXBC1pY9A78y77P4q/LJpqnbpwJzIpz/4fA2yK1aR/8e1prgadin1u7EG7tnNjpc1Z2T1Z0s901G/XRuj6F391Nj8m3pZRujPnr80dREV06G/8juxm4JaV0nZnNAybkNBIzG4t/aaIGv3M8H/8o5ZMppTfEMj8BpqWUrivRr9Pxb9uX429iH04pNcSd/zX4nffDsd1JZjYO/+Vqwv9ALML/MJTjb6JfTCn1jY+Gv43XOE34lzx+38r0SYX9FpGXDzN7GLgypfSL7u6L7Pni/eLXKaV921pWRHatbrmoFhHZU5jZqXg1kOX4J1RXAgeklJa2+kSRTqCLapHdx5424pWISFc7CM8P7YtXQjhPF9QiInufvTJSbWY34TWlC30mpXRHd/RHRERERF7e9sqLahERERGRzrRHDFMuIiIiItKdWs2prqmpSbW1tV3UFel+/qnFI488ujylNKSNhWUH6fdJRETk5eeRRx5p13VRqxfVtbW1TJs2rfN6Jbu5zQCY9WxtWFrZSfp9EhERefkxs3ZdFyn9Q0RERESkg7q5pF4qardG28T26/2KaFsb0Vs6R1N3d0BERETkZUmRahERERGRDtpNItWNLbQNzadtWeJtxZFtrDNHW/P9wuZoK3e6l3sPlVcUERER2RkaUVHkZa72s3/t7i5IN5t3+eu7uwsiInu9brqobipqW4qQ5qj1Wm+2bvG2ooVFm61jgzcb44uaq+Px8Ik70c+9xabu7oCIiIjIy5pyqkVEREREOqiLI9U5mlwcqW5sYdmyaPt706t3PC6VI23Np/eKGt3l9TvV071DcU678s5FREREdoYi1SIiIiIiHdRFkeriiGhrEWrwqHPPomk5cr0m2jxaZHG1jxxtHeRNRd7FyMkunZS9FyquulJWakERERERaYUi1SIiIiIiHdTFkepSEeuWqn9Y0bwcuc73ATnyXCq6mnctItbbcrHz+vbmERrzyJVRGYVe0eoeS0RERGRn6CpKRERERKSDuihSXZxDXRyhLn7cUhS5rGjejuYB51zrHOHO69kbx7/Jo1XmiLXnmS/d1NTi0iIiIiLSOkWqRUREREQ6aDfJqS61fGvTchWPHc2Nzs/LUdq9Mcc673vzyihrF83ult6IiIiIvNwpUi0iIiIi0kFdFKkujgLnCHVxBY/WrvGLI9XtycNuzd6YS72l6HG1N/WLAfj+X+7r2u6IiIiI7CEUqRYRERER6aAuCteWF7U5pzpHTouv7UuNtFi4jhyhLs4P1n1CafXR5ij/OgCumPw8AFubWjvuIiIiIlKKrkBFRERERDqomxKL+0abc6lzxLqpqG1JnmdFbZ6u+4SXytH8PKpkbwA+8snPA/DrKS8A8LZXj+zifu2ezOxM4If4CXp1SunyFpaZBPwALyezPKV0apd2UkRERHYre+O39URKMrMy4KfAq4FFwFQzuyWl9GTBMgOAnwFnppQWmNnQ7umtiIiI7C66+aK6V7S5dvSmorYlxTWvs45WA9mT5ePZfDTKp9b386mNcwBYXnlizP9+13Vt93Ms8FxKaQ6Amf0OOBt4smCZC4E/p5QWAKSUXuzyXoqIiMhuRbkSIs2NBBYWPF4U0wqNAwaa2d1m9oiZvavUyszsIjObZmbT6urqdkF3RUREZHewm6R/5G7kXOtKYEP8nPOAc/51cdWP4oh0rmCxm+zabmF9tIOjfRqARUtXAjCssgGAnmPGdXG/dkstfcRR/LFIOXAMcDr+ccuDZvZQSunZlzwxpauAqwAmTJjQ0lChIiIisgfQladIc4uAUQWP9wWWtLDM8pRSPVBvZpOB8cBLLqpFRERk77CbXlRXxj+AjdE2RJsrhRQH/XKEOme0FNev3hvlCLUfwy0rnwPgU1/5IgAn9D/EZ4/fD4AhfSoRpgJjzWx/YDHwNjyHutBfgJ+YWTl+oh7HXp6ILiIisrfbRVecq6PNX5AbFO3OXLT1KmrXRttW+ke2N5faW+XNVh/k5fPfuxGAX9/2DAA/+v57AVj4oqeDzFm5rov7t/tJKW01s0uAO/Cco2tTSrPM7OKYf2VK6Skz+zvwGH6CXZ1SeqL7ei0iIiLdbW8O44q0KKX0N+BvRdOuLHr8HeA7XdkvERER2X3toovqBd6s9nQDesQXEPvVxvyx0e5M2bt+RY9zesiWojYrHiSm1Da3FM0vdWhWRpvTTwaXWK475bSP+LLnGo/el1UP83aoF7O4/h8PAzD9X38B4A2fPLLruigiIiKyB9kbcyJERERERDrVLopUR/S4KvKbI6eX1Y97u8VzeCnr7+2gCfG8PjuxrZxrnUvu5VJ8xRHrHFnOu1z8hcacq+1DdrN5mT/r+bkAWP8hPn3EwUXPzznbQ3a047uQH+/NS5cDcPlNUwG44dZ/AjCwxr+YWLHPeABGjvZxTYYPre7SXoqIiIjsKRSpFhERERHpoF0UqT7Om6potw3g4jm81K3wtjHapXd5myKfeVAtVB22g9usLGpz5DlXA8kl+QYUPS9HsAc1n1zuz7dDcv53HKqm+ubr7ZFzuvN685Dr3SFH571v02Z7tP0Xf/Xv3FVVrgHgoFovw7zhBS+r3H+Il9ZbtSbvm4iIiIjsCEWqRUREREQ6aBdFqhdFu2+0OXp8sjdDlsbjqGe9zHN6aYzHq7fAPstjmdHR7hdte+8DiquE5Mj1mmhz/naOVOeqH1HNo0fveDwn2k0lNp8PYY6Ed2ekelmzPixf5RHrw/t59ZXhRxwDwKDxZwJw3wyvzjKi1l+fr7z7NQD87D1d0VcRERGRPYci1SIiIiIiHdTJkerInd76iLdl0drAmH9KtMObt/sc5G39jdFugTQ3nhvVPDZFxLgq11IuyoFuU3HkuqjP2yLZuYpIjmTvaG53d8h1qb3qx19+fTcAt989G4DaihoAXqzzSinPT/c64ue89VwALj7G88H7dkVXRURERPZAilSLiIiIiHRQJ0eqPTd36R9uAWD4Gaf75H6RK12Vo8WR+5sin7lhdfPV9C4rGPgwqms0Ri503Z3ebo2aytVRN7pvbYf6vH2DOVKd60/vzvcdkee9OqL4A/wTgatu/BcAGxr9eK+c45HpAYf7JwP7H+657ucqQi0iIiLSKXbnK0YRERERkZeFTo5Ue1WPF+7zkfvSRq9DPeL974r5uWb0MG8sItdVudJGzmtewPYIcuQ894nIdK9YtkdNzI6qG6s9Okt5PK/vMfH89o7SWFy1I9ds3plRHne1iPCveMLbDR75/9mVvwLgmad8+rCz3gPA4vleSaX2lKMBOKzWj+X+XdFVERERkb2AItUiIiIiIh3UyZFqz93t2eD5z+uWRwWPbRHqZ4oe5xzrXMnjoKK2UJ03PXIt5iHeVHpFizwCIutilMa6B70dPDSel2tmt7dqSI5Q51EKu7P+dAmNEU0fMhKA/73BR06ccKxH6R+p9H3vEVH+n3/kbACGdmUfRURERPYCilSLiIiIiHRQJ0eqPXe3R8/+AAwbVTyiYo765soaUYN6W63oldEew/bRGLNco2JrtBGRzsv18Ggt/fM2Itd6SyzXELvaK/elvTUvdjRC3RVVQ6JSifUE4N+/uQeAA8eOBWB+mUemq6PayqVf+zSgCLWIiIjIrqJItYiIiIhIB3VupHqZ5z1vLfM85wHDi2Oj5UVtKc8CEXneVj86Kl3kutXkiiFTo50fbY5kx/NiNEEqop71tgh1ru6RK47sU7S9ndUV9yke2Z/3xL0AfP5ar/qxrszrTvcaeTwAV37xrQActxumg4uIiIjsSRSpFhERERHpoM6NVK+I6G/PqJwxIupR57rK26p+5DaPXui5wWx8wNsFj8LYP/jPPQ6LZaLKx7b7gByxXhTtnKJt5ZzsA6PNVUNyRLq4/vTiaHPkOh+acex+PBf9uZWeQ71svUftbbDv28ff/W5AEWoRERGRrqJItYiIiIhIB3VupHqpV9rYYhEirRkcM3JFjhxdziHUnL8cFTl6vc/bgz4HVMW8BdHmSPTyaNdHm3Ojs6hfzYhocy3svO2lRX3oWbTcC95siqohVbkiSc7h3i/aAXQ9zxf/16+vBuArv/BRJKv2PQSAo199DgAfPGafFp4rIiIiIruKItUiIiIiIh3UuZHqOo8aNzRGreb+OW85V954JNochS7Otc4VPwprSOec5pwTnat7FNe2zgZGG1U/qC5aZ952jljPi7ayeVuVo+hLiraXq5CMjjbnbOec7KiPvS1SniPtef25f/nQ5372KlqucJux7mV3AbC8wR9v7uV9LBvikemTXnMW0PH6JXs7MzsT+CGe9H91SunyEstNBB4CLkgp3diFXRQREZHdjCLVIgXMrAz4KfA64FDg7WZ2aInlvgXc0bU9FBERkd1Rp0aq1y7zfOcNWyNaW57zkAcXLZmjxnlExRyp7k1pq6PN0dscsc651b2K2izvYs6ZXhPt49HOiDbnIefoeR6xMVcoyX3LEfOnox0fbVPR8idEWxvtY9Gua76eTbE/jXEM+lQDueJJ3tfno+vR1yU+/YPnew76fZs8v3vMaJX76ATHAs+llOYAmNnvgLOBJ4uW+yjwJ2Bi13ZPREREdkeKVIs0NxJYWPB4EdvzkgAws5HAm4Er21qZmV1kZtPMbFpdXV2ndlRERER2H50aqV6xxCtnvLBqVUzJlTimRFtW1ObocY5C5+cVRrZztDbXj845x7nrOTKdq3jkqHdU8dhWrzrnOHsE+snpPhrhoUdGdNdy1DyvJ8tVPnJudu5H3gcrmp/3ZW60+fpsbLP9WfPLrwDQe5HPr+gd+3PweHjdpbFsjp7HcTHf17d96UcAzJjlwdPJ07zvhxSX3pad0VJKeip6/APgMymlRrPWM9hTSlcBVwFMmDCheD0iIiKyh+jcLyqKvPwtAkYVPN6X7d9WzSYAv4sL6hrgLDPbmlK6uWu6KCIiIrubTrqo9ujr0pUeBR40pLjyRv7YO0eNc5Q3b36QN//+b28PORCG51TVHNzLVTNyXeocQY7nbotQ58h2VlxVYzgAD9zrozceetQHYnqOKOe+59rauY8NRY9zFZG+RdPzel4T3fl1bD5XC/Hc7Gcf9yjz6BUeUR/YcxMAFfuOYntUPedIx3Ece0izPXtxidfufjHtDzS/EpSdNhUYa2b742VX3gZcWLhASnHAATO7DrhNF9QiIiJ7N0WqRQqklLaa2SV4VY8y4NqU0iwzuzjmt5lHLSIiInufTrqo9tEKt/T2KGyvgblSRs5PHhptjlDnqHGu4BGVME77cDzuxfZIcc6zHh5trktdVrBs4bqKpw8r6quv95D98siIuQ72pmjz9FylIx+iHDUuXn/OG49qIVu9Gsn6eZMB6FseEe7a/F03j2SvWub7Nco8Em89PTd3a+9hlFNcxSOi4mtiGxGknz7lHgDq9jkc6Twppb8Bfyua1uLFdErpPV3RJxEREdm9qfqHiIiIiEgHdU6keqWPMrh81YsAjDs8R04rixbM+c7FFTOGFD1uYNtIh2s9b3jJdN/GiFOPimVyveiN0ebIcY6S5xzsXEEj8yjwiWe+saiPedu5Ckh+flNRmyuW5MhzjoTH81Z5bveUOz3Qedob87Eob7ZczyaPYPeNXO3yMo9Gp975WBTKUfTaZlMfnul1wW20xlAUERER6U6KVIuIiIiIdFDnRKqXzgdgzXqvZNG3+qCYMSfaXDkj50nnWs+5pnPOh86jHVbBPM/HvuYHnpt85yyPSH9hnVfNOOQAz13uMSyqgZRHtLZ/cW51cRT3WW9me1S9acMCX8+oiDT3jPrWg3Mud86tztVGcnWQHBnPkfI4lL1rAZh0XOSRNxQfYo9kV1RGRH2zR++3VHqkvKK6OAcctkXJBzQvRL1infd5/+ri5UVERESkKylSLSIiIiLSQZ0TqV74PABbGj1q229gjuIuiDZHpnO+skeZeexpAOpX+PJ9hh0MwNxn72XNco/oDuznkeeRAxcDcOt13i45aDwAY/f1KHjt2R55pt/j3lqu6XxYtDGiYb1Hvv95660AVPbyCPTYsV56+Pd/eASAOYtmA/DJi18PwOi3Xhzr8frWLI19G55HTox96uP1qHscmXOj877Pp9DI0d7/1c/6aJP9enoOd8WAobxUzqke2Gxqj0gHP6Ba90YiIiIi3UlXYyIiIiIiHdQpkepnZ88DoGGrh04HDcxJvsUR6hj1sM4jrw/f6+3ke3y5QdWeYz3m2EbGHXAAAPsf4qMLnn76cwD0XOe5zsuWeK7z6lURxX00tnVm5HOXvyO2mUc4jJGm+3hd6n1H1gIwsMarg8yv9xznQWO9Wscxpx4PwLTZ3qfRS339z2/2USF79/J9Hc7gWL/ng2+Z69ubNfkxAGb29sj6K885AoCDKvyQjx7vUectZf3iCEUUekhxxRRYvXAmAPfP8dzq15/qEf1+Az3KPqLqJU8RERERkS6kSLWIiIiISAd1SqT6hbUeid6YfHW2bUTFiEznSHWKKhY9vbLHcW841pfa7HnKJ5zxap/f19i0zqtilFd4XenyMaf6vAqP7NZyTKw716GOqh5TI+f5KI8oU36vty/293aoR6DrG28GYNVyH8xhzwAAIABJREFUz+deutbDvYMH+XYak+don/UWj3jfPsvzv6et9cj1F845K7Yb1TrW1/mx6HsoAL1qnwLg1C0+v7biP2L5qE7yFo9wl630/Vw2y3PB+5c9D8yKZad715/7NwD7D4287uTP2TrAI9XDqzXavIiIiEh3UqRaRERERKSDOhji9IjpvPleCaOB5JP75Uh11KfeGiMp1vsIgPSO+ZGDfcJ/fS6WjwodC++jfItX4WDDCm+f8qoa6zcPB2DFEo9Ij37T//P5azyHub73iQD0nO+R4Nv+7pHqgYPHAHDqBZ5bPe6oVwJQWeN1p6tG5VraFdF+JNq/A3DygV4j+r5v3QPAM2WeN37IGI+cP7xkhu9xnw/79tf4vh09PnK9myL63CNXI3mdPzzDI+AHDYwRIvtuBmKf8cog40Z5FJxBXrHk79ev9Of29Sj9Sa8YjoiIiIh0H0WqRUREREQ6qJ2R6k1FbR5l0Gszz33a84e3VkeUt3fkTq+PGs49Y9TB+hhRsc5rOM99yPOTF6zxnOEhgz2KPLq2jpXLvU50Y4w4WNXDo9vLBk0AYM16z3kenbvY36O3CxZ5xPeQkR5BrtrXI9GnvuY/fTnzHOp+R721aB9jZEaizjVXNdvnvpVe1aPnfI+2z+/ryy2eG/Wph3lkvGGDj8h4yllv8OkVEb3PIzs2Rc734ojET53q7YQ3x3KDgYfj5zu9Ge3Hl3X++F/3eoT6wUVHAdBSZWsRERER6TqKVIuIiIiIdFA7ItVNbK/isTTasmi9ZvT8VT7/kJFRiaPRR1gk8ozpH9fumz3K/NubPVpcs5+PKlg2xKPQazd6rec+/Q7i/qkeGf7VPzyP+Fdf2A+AqnKPZm+uiDztzdd6u+UtAOw/5Kv+eK7nSFf380g1vfL9Q47rxgiMPB3t4miHNXv8x/f+FYCDT34jAM8+7hHxL18adbCro6pI/6PjefvTXM7Vjij/XK85TY/fe7tPjMy4rbLHFEhek5ut8Zz1Xi2Ffh71nrPJc6hXRpD8pZWtRURERKQrKVItIiIiItJBbUSqE17hI3Kht12DR3S37iYANmzwGs9DBkVOdUNEVus9cr35Ra9TXbnZo8wDa7w+9ZJlHg1etMArZIzr7xHrsq1P07/Gc5RPPjbWOcijtwPGzfPHPkAhz3ogm3HnXghAVe//AuBfj3wNgAee8MoZJ77qn9H3i5rvYv0cbzd4dY6vXPg3AD70358F4O+LPbJ8/vumAfDz4yJPfN+JsYJX0LJcweNBb5oisj7iD96/33gVkDM+8Cmfnk4BYOPM2+nVPyqGVMVxNM8Pz8H18tW+rgEbIrq+2ddJZXGeuIiIiIh0BUWqRUREREQ6qB051WVsv/aOOtN4njFPevS2rF9fAFY1eq70n672iPSSVR7h7lMxCIBeTV4Z46lGj/5OqPVI9rD+Xt3izJM8/3ljj3H0OvUyAI7j7bHN+dH+y5v1Hi0fd/rhADz78B8BeN9/fxeA177VR1Q86aBcVSNGdWSyN/O8esdD99wOwPRFvlzDAZ7jfPVV3wfgmn/8Jp7nIyH2OuTMeFwqQp0NjjaqgPSI7a7y7TzyoEeXz/jAu3x6nR+DO58/hZE9/TjUbPQofc0Az13vNdJrZvec73ndb66NUSMrfxrbynndY9rom4iIiIh0JkWqRUREREQ6qI1IteEjDOa61LlCRlSs8GIdbKzyutR3zvC603UPeCR1VZ1XCxk/1KtVvP44r1d93nEepT3i9BG+gojEEqnDvXq8APwptjUSgLXLPNrdr5dX3di80nOWK/c71afXeHT8vvs88vzX6dcAcG3Uwj76d77tipq7ALj+Rq+NfcRRkwD48PuOBODJJ3z5Q199eWw/Vz6JQ9XjVHZMjIYYVUBm3e7R+c98OkaPjAj4knu8islNDxzN9Mc8D3vt408AMGyAf0Jw/61vAuBXV3ou+9KF+XXJNbavjPa7O9hHEREREekIRapFRERERDqonSMqZlXRDvTmTK/Wce7jHim95jYvT1HZLyp2rPM6yydM8NrQ7/50PL0morcDcoWMHGmNahc0ArObbev5KR697TfQK18cePInfPZGX27YPh7xnTFrCgBX3eEjFq6vPgSA2x/xnOUXotLIC718PQdXRJS82qcf+upcQWN4tMWHKE/Pfe5D6zzSXT/Nc8CnTf0lAIedeigATTN8RMVHn/D+bt46honHngjA4opqAI6s9ePyrW973vhn3u99Gn5g3BOtiT72j0om3BLtm9rom4iIiIh0BkWqRYqY2Zlm9oyZPWdmn21h/jvM7LH494CZje+OfoqIiMjuo50jKuaobIxGuMirfDDXo6+jK31UwV6VPuLfgHKv+jG81qPMjz/nEdQtj3n1j4qzPMLNvIO8rf1MrL90ZPWoN50GQN1j/wfA5mmePzxvtteNXlfj1zUPb/W+bVw3CoCjG71vB48/AIDDT/Qo+sEnHO99GToptuAjOLLGc7JXzvVDM+jI95XoUakIdR51cpk3z/8CgN/9wPOgzzjvDJ/e10eIvO0ajz6vrfCKH2NGDmHFMo++DxrufX16jq9zWFlUFBk2xNvVUdFkdeR9988573+P9qhoR5XoqxQzszLgp8CrgUXAVDO7JaX0ZMFic4FTU0qrzOx1wFXAcV3fWxEREdldKFIt0tyxwHMppTkppc3A74CzCxdIKT2QUoq8IR4C9u3iPoqIiMhupo1IdRNetWJtwWNgS9SrXvU8AI/c5w+XLfCc6+oYDXFIX49Iv7jKy3rUN3i1igFVXtd6+mOe13zUiPj0vLJg0ykivZYn+jqGRH4x/bz+87gJhzXr8THMAODi110AQA8iqs4j0R4abS+aG+BNf+/bIC8GwqKn7gFg30PaqvqxOlqPxrPFR4Bc+4RHoEfUDANgv1GRY/28V0iZ8aQfk3VDvF+V1bMZGOva2ujlVZ6s88j12MNi319oiF3YFNuKTxK2xvzyGPWRXGP7v6OtaGMfBC83s7Dg8SJaj0K/H7i91Ewzu4gYxnO//fbrjP6JiIjIbqgdF9Ub2V5Sz78MuH7zqwDoW+ll6j72ZU+lmPP9uwG4beqzADw01y8gjxhmADSu9gtWXvTpA+b5gC6fONcvqr/3kxioZXQvaDjYf66Kq1v8i4b0y9c7cdHNnQBM/ZN/Ov/vR8sAOObU0wE44zVvieWifN+2i9/ii+r6osd+AbrvIa1/qv/Q/fcD0KfJL3xfMdH3bdYf/wHAY6v8y4Zv/9KH/QmDfX+euuI6AJ5Y5RfCCzf49g49YBM1vf0DhLIK35eR+3oKy3o8vWbFUt/G4HHx8jX668CquGgeEkOikzMW7oj2Da3uiwBeR7JYanFBs1fhF9UnlVpZSukqPD2ECRMmtLgeERERefnbweofInu8RTRPQt8XWFK8kJkdAVwNvC6ltKJ4voiIiOxd2hGprmf7EN8eCe17kJd8W3ivD1M+6jSPAs+N7+jVr10PwAmjPEpbt9XTT2+8yzf3ofM8Wrz/x3z5731sZGxuSmxnKFTFtG3XN7lcXI5Q10R7MQATzx0Qbal98YFU/vJbL8133DlfBGCfXjmtPH/xMB+S/KW/PPhLjHRDRIVjUJd9mqYDUHtiDA3+nF9/3f+YR6Av+sREnz44DyHuEfjVZf4lzg09D4y1+fIz69awYonv48kH+D5WlXmax9xVnvbx4kbvw+B1HslmoH8xlA3xeFvpw7wP/442px8cgZQ0FRhrZvvjB/BtwIWFC5jZfsCfgXemlJ7t+i6KiIjI7kaRapECKaWtZnYJnjNTBlybUpplZhfH/CuBLwODgZ+ZGcDWlNKE7uqziIiIdL925lTnHN0cAfXobc/+tf6w0nOu75vvyx3fz6OuI/p4ZHtenUedb1vrkdQPNeTVR652Dhb3iC9AMpBtX/ijf7TV0eYI9keiHdD6LvBitP5FxYnDfIj1fYpTqrcZVdRmuVTeomb9qD05R4d9X771TS+dN3WBfxHxorXR3+EeoU7zbwNg5rMeeR8zwQtLlL3o+/vi4hmMHey55w898igAjZt9m+PH+HVb9cBcbCL6VBGfJFTEwDT18bjPylguD6STc6sVqW5NSulvwN+Kpl1Z8PMHgA90db9Edle1n/1rd3dButm8y1/f3V0Q6XYqqSciIiIi0kHtSP/YyvZr71zezvOG1zV6uHfoIi9jN66nR1jPOsDzh/sd7dHhG77nucFjD4inPxyDv5wS0eghOSodkfAty6EiV+nwvG3mzQLg+bqTAThw4tBWe/2nP/0YgHPPzfngvr4Rp0UJvvrveNvnM7Tm6psnA3DMEE+dPerEnJccA7HkXOvHPG95QcNYAC58i2/3d1f+FoATP+Yl/hb80yPZddW+H48v9nKFG5Z7O6hnD1453j8JmPekr+PQg32dM+d7HveDT/q2z7/Ip7NxevQlPlFIcTybYt975I8GpkZ7c7TntLrvIiIiItI+ilSLiIiIiHRQG5HqhOdPF197e0R5bX1EQB/xaPJrjvD84ec2eB7z997g0eG7nvLKGg0bPUr8nT95XvPH53medOX4qBE9KaK/aRXbc5c9UvzcA1694xczfTCWDX95AIDvf/3LzXpWP98HpDn3jQ/ElEHR5tznyNvuk6t6XOPN1ig1XO5Dp3/lCh8+fPBAz0s+6sQTYvmI9q691dt+ZwJww5W+3Q9+3OtiH3mcT3/gXccCMGq416deuDgGfVnkueKH7ed50bfM8uj/+nJYcb/novcZ7FVV6pZHgYmNXkJ5xkx/2c6vi0puPeN16Jl3NSLUcyOf/MDjY0auWz0z2vzRQa6kEnXERURERGSHKFItIiIiItJBbUSqDc+jHl7wGMCjrQMG+HDYS5Z4PvC05zwaXNXLI9E8749/+nGPbP/iQc/BvvcWX/5NT3mt5qP+7jnWF9zlodYxI+roe0QMtX3SEJ/2Jh998ePmy37njx5JPu88j+IePd4jv5//Uq4GknctajjHaJAvHVHxLm+aPHo79Qbvw2UfzsUdnon2YQA2/+P/fC33e7WPoZedAcCFP/tELJcHzXsagJrBnoO9+mnf7pz5HlVeMN+PyQG9ve3T5Pnq+/Ttyemv9Mjxtb/2HOrGGB6+odGHar/5QY+Kf2Nd5FTn6h5leRjyiMKXRe76ttctL39btG+Ldn20eeRMEREREdkRilSLiIiIiHRQG5HqKjy6WXTtXe9RX6vySOqWGJXwqEO8IsfwoREhbfK86KrePiLgh4/z6O2yB7xKxZ0veL3l2+b6+qbFdvqvXcJpB3jU9MIZHkEecIZX2Rj6es/P/s5rIw+4OipdrPNtEDWdqczR2Vz7urgwda4KEhVNKv3xxAtjROqtn/P2Qe/TXQ95H2fP9WjvqTUeIR/6zK98uX2PivV47jRzfPlV5X6IR628H4A1yzyvuab/PABmPuOjTa7r4RHyPoNq+M1Nd/s6+nnO+aplvq4+Azzn/PQYvXHLWo9+V4w+3JdfG5HqLZE/Xhb7XB/1qvvkY5Irp8Q+bqsF3lbNbxERERFpiSLVIiIiIiId1I461duvux+f8Q8A6mbfB8BpI6NaRJlX/TjlUI+Uzl7vz5m+xPOFj9riEW1qvb3sJx5JvawmRgRc6dFnVsWGlg5l5UKPZq9f7BMHzI5c5YXR5S2ea81pUfIiF/kg5xFHRYyU28ip7pFHhczVQHIE2/O8edYj4g/87DkArpvl66va32tvH97bo70HHeL54KzzqO/Wp7w/D9Z7P+tWeV552brFACy79X99V6MyypCBHuk+7JBXADBrmfdj3IgKRlR6BH/qMx5l/69LvJ7083M9ij7lkWUA3DLCq3+cWxsR5tQU+xLHJEemK3Kudc73ziMyzog2jxapeywRERGRndGOi2oRERGR3VftZ//a3V2Qbjbv8td3dxdav6iet3g57/ni1Tzy9DwAXlzqVSf26+kR05//h+fqHlnrUVszj4TO2+DVJ2yTR0hH9vJI9NClUZViSUSNy6KOcp+IoFZH9LhPJYMO9qodg0ZElHWDR8X/+GePXD/+lFe8OOZar3F98D4evT3o3GN8+ZMiGlse626K0QZ75Lzi6MvaqHhxr6/3uz/2CPJVkz3Hunyw525PqnzK9/0Qn74xl4J+zms9z+/t0fol8z263Hu+j5y4tcHXO/dZj+6vsmEAPLXOj0F5vUeXl6/xFd73+ByGNfm0ZRs83/urv/Tj3bDO95HKhQDMWb5PHLcY5bE+KpX0iJc18rlpzFH53OZKKPtHW1whRURERER2hD7vFxERERHpoFYj1fX165jywD00NHq+8Yb1HkFdutrzjNcui5zq/hsBeGG1t5URHF630effMdfzjd8y0HOq+/SNih0573lLXNuvjEhpYxOUxbyI2rLVI8tPLPdt37zMo7hXz/BoeU2ZP/ddq/zxWfd7hHjccG/Lj/IazwyLHOpFnnc8/Z8eIb78r94+X/ZaAI56+zjv4RyvT91zddSdbvLI9uJ1XjHjqaiE0jTSo8BzF3mke2SD93cwHsHeWO9R/rr+Y+LYeHT/n1PnAXDgAO/X2P1GsuBFj0zvV+3R9YpyP25zmvz4HjV0FADH7h953fllHBivR2NEnsuiskl5tBv9+fTKo1UOaf58EREREdkpilSLiIiIiHRQqyHKkQOM/zmnkucXetT1mge9ysTiObMAqOk7HoC02XN5V671aG3jKo/mjj/Ic3b/Ndun1yzx6O9+lVH5Yt/Ia658IlrPj6axDDaMip8jj7jKR/2rW+E51v0aow50tPPXed72/97qtZt/c2f0YbDXr/7gmb6tYTUeAf/LNO/THx7z528a4rnRRxzsEeORAz3c3uvk1wFw/z89arx8k9fN3rDWR5lc19urf4we6rnSI7Z4FP7VNb69BUu8HdT/WN+PQ719o3m5krsf8eogA3r5cmcdNZgn5nkfn37a9/28iR7dtpN8W7fe5X24/VHf1qmTIqp/mDdsiEi1ReTa4hOAnrn6R66Qkh+LiIiISEcoUi0iIiIi0kGtRqpTMhqbyrnuX/MBWLHZI9U1ld7u19ejrJa8trNt8REAjz3A2+MP9Ehp3TrP6Z080ytjbGzwqO/mjZ6rffDINwIwfLjXkN60ZRVU+vX++Yf8G4CqqNYxdYHnSD+xMEYJjBERt5b58uvWe270ixt8G/OXe+50eY3nD69d58+bEmWpq/t4xLlng0e25y72SHd1P68e0isqaBz/hnMBOKzcazpPX+uRbXp7hDrXz9iAR9uHj/Xo8X1zvb+P1U/0Y7HA+3fK4V65Yx8/VEx/0utaD7P1jDvYI9JT5/jML90wFYBBNR5ZXr/S93XePI/ef/A8j7ofuH9UMmmM0SV7ROWUXL4651av9teNTf58KmN6z2hFREREZIcoUi0iIiIi0kGtRqrnrNjC+b9YTOXWQwA4dKRHb7dO8+hr30PnAdD0qEc8j93X85qPO8xH+pv8oM9n5CsBKHvRo7hHHH4wABMmvgqAex+6C4CHF3gO8cY1DQyoHQ1AVf/IF97o4dZU4dHbUUM9T7hHhUfNFy3zCHRThUdtt2yN8GxPz12evcaj5svW+HL9B3nkeviAAbG8r2/Laq+0sXC2jzbY+wDv64FjvIrHqDKPyt9b73nJjZVR/SN5RPzG6d6/T77Sa0cPXOBVQ6as8vrYSyLPefV6r9G9ZLnvX0OTr++fL/Tm/jrv48o5vq2GxhN83/Eod++BHi3fvMKP+4YNMUpk5HMTeeZUxciK20LVUae6b+RaV8Q9VWOusJKXExEREZEdoUi1iIiIiEgHtatAcXmZL5a2eAR0v6hskUfgiyAvY4fF9HXzAPjrE15J48gTvJ7yARu8VnRq8oTmf//7zwA8+cQcAFZv8KhtFQ1UDI660ptjJMQNHmXt3dv70rTB84Ybe/j8ww+uBaCs0qOzS170UQcHVnru8uatXqN50CCPTDdF1Lah3vO8e8QohBXm9xkVyfdl7qLnAOifvHY0h3sUeU1U0tjU5M87dYBvp46IDg/xyPStdb7cfKuNQ+b9njnP+7Npg+dwj+3vB3HR8hVUDPVPBLZs8U8E9qn1CP66hR4FHzrKj81rxx0IwKiaqJRSFpVS8suT4uVtiiofKY5leeRal1fnBaNRpFpERERkZyhSLSIiIiLSQW1HqpNRGZHqxk0e6dxvSNS62OwRzhz1LS/z6Wuj3HRFX88TTlu3rcz/j8Doli2+vo2x3q2bPHrbQKJxQ5TnaPJ1b6qPCHIP38bwGh/R0HrXeFvh21q5xp936Fiv7dy0waPlGxo80rx584bYcQ/nlld4RLlPL9/H6urqmO6b7z/Ip9da5Cv39Eh0Pb5cr74e+W6M6PCapV6zmw0eud6wzveppr9HxAfX+POfedH3Y59RgwE4eX+PWG/uOYSpT3m+9Uo8Z/pv//l+AO58cDIAX7vZo+fPVHnE+v6oqvL6w+Pl7JlD1ZFDXR7tthB2VnRPZWWIiIiIyI5TpFqkiJmdaWbPmNlzZvbZFuabmf0o5j9mZkd3Rz9FRERk99FmpNroQXlEOlODh5xHj4rc2wZvGyPCWRn5zcuiXHJltUePmzbntcXzIlK9ebOvL0XOr+VIdo+e1PTe2KwfL8Q6e2DknwCsh7dNTY3x2Of26uXb3hLB2YYcSo4a2b17esWMgX09kt2vv1fEKI/nD+jrfR1oHnbfp8xzqes3+3rXNUSOd4Ovd/Wy2QCcWOvLb5rvtaBPPNrrYJ/xqmMAWBw52g/M9X1/9gWPXPfp6/t+yRtO5I4R0wH4+i2+7k/81HPP3/JqX8cHzqkF4Mo//hOAX//N9+n1nxgfh+Ypb1fGge4ZFVTyodsS9anzPZVZs4d7MzMrA34KvBpYBEw1s1tSSk8WLPY6YGz8Ow64IloRERHZS+kySqS5Y4HnUkpzUkqbgd8BZxctczZwfXIPAQPMbHhXd1RERER2H5ZygnNLM83qgPld1x3ZTYxOKQ3p7k50BzM7DzgzpfSBePxO4LiU0iUFy9wGXJ5Sui8e3wl8JqU0rYX1XQRcFA8PAp7Zxbuwt6oBlnd3J2SvpnNQupvOwV2nXddFbQxTvndeWMlezVqYVnzn2Z5lfGJKVwFXdbRT0jozm5ZSmtDd/ZC9l85B6W46B7uf0j9EmlsEjCp4vC+wZCeWERERkb2ILqpFmpsKjDWz/c2sEngbcEvRMrcA74oqIK8E1qSUlnZ1R0VERGT30a4RFUX2FimlrWZ2CXAHXuj72pTSLDO7OOZfCfwNOAt4DtgAvLe7+ivbKMVGupvOQeluOge7WatfVBQRERERkbYp/UNEREREpIN0US0iIiIi0kG6qBaRLmdml5rZJ7u7HyIiIp1FF9UisseIiiz6uybNmNl3zGyWmX2nu/siex4zuy4GDuvs9U6KwcZ2iVj/Ca3MX7+rtr2n0puPiOxyZvYuM3vMzGaa2a+K5n3QzKbGvD+ZWe+Yfr6ZPRHTJ8e0w8xsipnNiPWNNbNaM3vKzH4GPAqMMrO3m9nj8fxvFWyr1PT1ZvYtM3vEzP5lZsea2d1mNsfM3tQ1R0l2oQ8BR6eUPtWehc2s2ytjmVlZd/dBukY3vtaTgJIX1Z1hbzuPdVEtIruUmR0GfAE4LaU0Hvh40SJ/TilNjHlPAe+P6V8GXhvT84XtxcAPU0pHAhPwgXjAh4C/PqV0FLAF+BZwGnAkMNHMzjGzES1Nj+f3Ae5OKR0DrAO+DrwaeDPw1U46FLITim/IzGy0md0Z0+40s/1iuevM7Edm9kDcDJ0X02/BX9+HzewCMxsSN29T49+JsdylZnaVmf0DuN7MyiLCPTW29aFYblLccN1oZk+b2W/MzGLexNj+zLj5qy61nhL7OsnM7jKzG4DHY9on4ibwCTP7z4JlXzI9bjCfNrOrY/pvzOwMM7vfzGab2bG74jXa25QIEpzSwrnXLNJsZj8xs/fEz/PM7Mtmdh9wvpmNiRv6mWb2qJkdGE/r29K5VqJfp5vZ9AgcXGtmPQu2VRM/T4jztxb/e/pf5kGKk83HZ3gwztWvFazX4hx+ItZ9QRvTX3Ie7y26/W5cRPZ4pwE3ppSWA6SUVha9LxxuZl8HBgB98RrhAPcD15nZH4A/x7QHgS+Y2b74xfjsWNf8lNJDscxE/AK5DsDMfgOcgg8l39L0m4HNwN/j+Y8DDSmlLWb2OFDbaUdCdohtvyE7MaW03MwGAb/Eb6B+aWbvA34E5Juj4cBJwMH4IE03ppTeZGbr40aMeKP/fkrpPvML8juAQ+L5xwAnpZQ2mtlF+MBOE+Pi5P644AY4CjgMH0n1fuBEM5sC/B64IKU01cz6ARvxm8SXrCelNLfEbh8LHJ5Smmtmx+B18I8DDL8xuAcPiLU0fRUwBjgfuAgfzOrCOCZvAj5fcKxkJ5Q4J79HC+deO1a3KaV0Uqz3YeDylNJNZlaFv8ajaOFcA+5roV9VwHXA6SmlZ83seuDDwA9a2nBKaZ6ZXQmsTyl9N9ZxC3BFSul6M/tIweJvwQMR44EaYKr5p4cnlJgOBedxO47DHkORahHZ1Qy/oC3lOuCSlNIrgMuAKoCU0sXAF/E3lhlmNjildAN+cbARuMPMTot11Bdtr1Q/StmSthftbwIaog9NKPjQnV5yQwYcD9wQ83+FX8hkN6eUmlJKTwLDSqzzDOAnZjYDv/jpZ2bVMe+WlNLG+Pk1+MipM4CHgcHA2Jg3JaW0KM6PGfiN10HA0pTS1Ojr2pTS1jbW05IpBRciJwE3pZTqU0rr8ZvLk1uZDjA3pfR49G0WcGec27pB7BwtnZPQvnOv2O8B4vwbmVK6Kda5KaW0IZZp6VxryUH4a/9sPP4lHjTYEScCv42fC9P0TgJ+m1JqTCm9ANyDBy9KTc/93qsuqEFvFiKy690J3GRm308prYjITqFqYKmZVQDvABYDmNmBKaWH8SjcG/FyjXEPAAAgAElEQVRc6f7AnJTSj8zsAOAIYE7R+h4Gfhgfd64C3g78GJhSYrrsvtq6IaNofkPRc1vSAzi+4OLZF/ZPPIpvzj6aUrqjaLlJRdtpxN9LS/W1xfW0oqM3iIV9ayp4rBvEzlHqdW7p3NtK8+BlVdFz8mvd3tczn2ul+lVKYT+K+1Cs1Dm8o9usb2XeHkuRahHZpVJKs4BvAPeY2Uz8o9JCX8IvhP8JPF0w/TuRp/cEMBmYCVwAPBFRv4OB61vY3lLgc8Bd8ZxHU0p/KTW98/ZUdoE7gbea2WCAuCF7AHhbzH8HLXwU3oZ/AJfkB2Z2ZInl7gA+HDd7mNk4M+vTynqfBkaY2cRYvtr8C487up5Ck4FzzKx3POfNwL2tTJddr6VzspT5wKFm1jMCAqe3tFBKaS2wyOI7HrF87x3s19NArZmNicfvxCPHAPPw1CaAcwuesw4PamT30/x3K5sMXGD+/YAheAR8SivT91q6axWRXS6l9Ev848iW5l0BXNHC9Le0sPg341+hlcDhRc+9ge0pAu2Z3rfg50tLzZOulVKaZWb5hqwRmA58DLjWzD4F1OG5xTviY8BPzewx/D1wMv6FrWJX4x+1P2oexq6jlXzklNLm+KLWj82sF56idMaOrqdonY+a2XVsv1C5OqU0HfyLmcXT48tnsguVOCdLLbswvhPyGDC7tWXxi+D/M7Ov4l+2Pn8H+7XJzN4L/DFu5qYCV8bsy4BrzOzzeAAjuxW40czOBj6Kf4n8BjP7OPCnguVuwtOuZuKR7E+nlJaZWanpB+9I3/cktj2NUEREREREdobSP0REREREOkjpHyIiIl3IzF5B8+oK4GUcj+uO/sjLS6Rd7F80+TM78GVY2UWU/iEiIiIi0kFK/xARERER6SBdVIuIiIiIdJAuqkVEREREOkgX1SIiIiIiHaSLahERERGRDtJFtYiIiIhIB+miWkRERESkg3RRLSIiIiLSQbqoFhERERHpIF1Ui4iIiIh0kC6qRUREREQ6SBfVIiIiIiIdpItqEREREZEO0kW1iIiIiEgH6aJaRGQnmNl6Mzugg+u4zsy+3s5la80smVl5PL7dzN7dke0XrPtkM3um4PE8MzujM9Yd65tlZpM6a31dycwmmdmidi57qZn9elf3qZXtJzMbEz9faWZf2sn1dPjc7i7mfmFmq8xsSnf3R/Yu5d3dARHZe5nZhcAngIOBdcAM4Bsppfva8dz/z96bx1l2lfX6z3uGqlNz9ZROeu50JhImsRNAUKOCMqiB3wWZrgiiMf4E5XpRUNGLAwoXB0TQ3Igxgowy3YDBoFEIczpAEkgg0OnO0HNXV3XNdcZ1/3jftc/eu+tUVXclXU36fT6f6n323muv9a519jm9z3e/+7sCcGEIYfcjG+X8hBD6V6LdVPvPXkq5pYxTCOHzwMUPR1wicgOwL4TwxlT9lz0cdS+h7QAcATaGEBq2rQQcANaFEOR0xHEmEEK4ZinlROSzwD+HEN6dOnZFz+1l8nTgmcCmEML0SgfjnF24Uu04zoogIr8JvB34U2A9sAX4W+CqlYxrMaJS/Gjh0dYf4DiQ/sHxHGBshWI5ZUSkuNIxfJ+yFbj/VC6oz/TPwpken/N9dFEtIm+zW4hvW+lYHMdZHiIyBPwR8GshhI+FEKZDCPUQwidDCL9lZa4QkS+LyHEROSgi7xSRLtt3q1V1p92qfpFt/2kRucOO+ZKIPD7V5pNE5BsiMiki/yIiH0qnXojIL4vIbhEZFZEbRWRDal8QkV8Tke8B30tti7fae0TkL0TkAREZF5EviEiP7fsXETlk228VkSWptiJSFJE/F5EREdkDPDe3/7Mi8kv2+gIR+Zy1MSIiH+o0TjGdQUReLyKHgH/skOJwuYjcY7fR/1FEKlbnK0QkcychjoWIXA28DPhta++Ttj9JJxGRbhF5u4gcsL+3i0i37Yux/U8ROWLv+yuXMl4p3gu8PLX+cuA9uXg32Hs8au/5L6f29Yim5YyJyD3A5fMc+1EROSoie0Xk15cSVKpvv2vv0f0i8rLU/htE5O9E5CYRmQZ+zMbqz0XkQRE5LJrS0ZM65rdsjA6IyC/m2sukFonIVfbZmBCR+0TkWSLyZuCHgXfa+/VOK5s+t4dE5D3W3wdE5I0iUrB9r7Bz/c9tvPaKyJLuoNjxm0XkY1b3sVT7BWvnATsP3iP6nZFOg/oFG5cREfk92/cq4N3AU60/f2jbF/peuN8+C3cB0yJSWug9Fk3x+bDFNCl6XbJzsT7Zvl8UkW/bWN0sIluXMEbzfff8kIjsEv287xKRH0qVX+jcfpPo99E/W+zfFJGLROR3bJwfEpGfXOr758xDCOH74g+YALpPonzpDIi5uNIx+J//nYl/wLOAxkKfU+AHgaegaWrbgG8Dr03tD8AFqfUnobf+nwwUgV8A7ge6gS7gAeA3gDLw/wE14E/s2B8HRqyObuBvgFtzbf07sBroybcPvAv4LLDR2v6h+H0F/CIwYPW+HbgjVe8NMYZ5+n8N8B1gs7X7X9ZmyfZ/Fvgle/0B4PdQoaQCPH2BcbrSxv6tFlOPbduXKnM/8K1U219MjdUrgC/kYk2PxQl9svqeYa//CPgKcA6wDvgS8Me52P7I3qfnADPAqiWeVwF4LHAYGLa/w7YtpMp9Dr0rUgGeCBwFfsL2vQX4vPV7s43DPttXAL4G/AF6Tp0P7AF+yva/CU2lmC+22Le/tHH/UWAauDg1buPA01Lv49uBGy2WAeCTwJ+lPkOxb33A+zu9D8AVVvczre6NwCX586jD+/ke4P9a+9uA7wKvSp0LdeCX0fP+V9FUG7H9bwA+1WE8isCdwF9Z/Ml5i35mdtv49gMfA95r+7ZZfH+PnrtPAKrAY+Y7P1ngeyF1bt5h73XPEt/jOfTcLAJ/BnxlCX16nvXpMeh32huBLy3xnE6+e2w5Bvy81fMSW1+zhHM7xv5Tdux7gL3od0fZ3se9K/l/w/f73+lpRJWCu+xkey96e+YW23YLsMXK3QC8A/2S3QO8wLbfCDTtxH8R+kX8UWCX/T0tdcJcB3wG/YIpAm+zMncBv2LlrkS/SD6C/qf1PtpfApdb+3cCt6FfJPPW06GvV6L/+b0fuMe2/Sb6xfwtshcFJ2xHvzC+g/7a/pbF9gz0P7XvAVes9Enjf/633D9UzTx0kse8Fvh4aj1/sfh32MVZatu96MXLjwD74+fc9n2B9kXHPwD/O7WvH71Y2JZq68dzdQfgAvQ/4VngCUvow7AdN2TrN9D5ovo/gWtS6z9J54vq99h336Z56pnvoroGVHLb8hfV6bafA9xnr1/B8i6q7wOek9r3U+jt+hjHLKkfW+gF0VOWeI7E9+TdwK+gP0z+3rYFK7MZ/f9kIHXcnwE32Os9wLNS+66mfVH9ZODBXJu/A/yjvX4Ti19U96W2fRj4/dS4vSe1T9CL7h2pbU/FLnqA64G3pPZd1Ol9AP4P8Fcd4krOo3nGsYhesF6a2vcrwGdT58Lu1L5eO/bcJbxXT0Uv+E74YY1eF/z/qfWL0c9j/IEdSJ3r6P/VL57v/GSB74XUufmLqX1LeY//I7XvUmB2CX36NPZjxNYL6A/GrUs4p388tf7zwG25Ml+2fi92br8J+PfUvp8BpjABEL3eCcDwUj5v/nfi3yOenyN6q/P30AvfERFZDfwT+uXxT3bL6h3orziA89AHDS5BL6Y/EkL4WRGZCiE80ep8P/oF8QUR2QLcjP76A1W3nh5CmBW9FTkeQrhc9PbiF0XkM1buB4DL0F/VXwSeJvqk8IeAF4UQdonIIPoF/6r56gkh7O3Q7SuAx4YQ9orIDwKvRD+oAnxVRD6HfqDm2z6Gfpm9EP0y3wW81MbkZ4HfTY2V43y/cgxYKyKlYA+U5RGRi1BVbyf6n3UJVZA6sRX4BRF5TWpbF7AB/Y9if7D/OYyHUq83AF+PKyGEKRE5hip6989TPs1aVBW6b54+FIE3o5/ndUArdcz4An2JMaXbfGCBsr8N/DFwm4iMAX8RQrh+gfJHQwhzi7Sfb3tDp4InyQayfcnXfSx3TsygP3JOhvegFxMCvH6e9kdDCJO5GHam9nca963ABhE5ntpWRJXtpTAWsrm++b6n212HnvdfE0merxRrL8aZ/jwsdH5sBm5aYoxp1tK+y5NuZ2Nq/VB8EUKYsViX8n5tBh7o8Pmf7xwpoc9enNAuC58jC30vRB7KlV/sPc63XRHNd16oT1uBvxaRv0htE3QsF3rv8vHlxwba78li5zbo3Y3ILDASQmim1kHHMt1/Z4mcjqT3H0cvjEcAQgijIvJU9PYrqHL9v1PlPxFCaAH3iMh65ucZwKWpL5pBERmw1zeGEOKJ8ZPA40XkBbY+BFyIqjS3hRD2AYjIHeiv33HgYAhhl8U6Yfs71dPpovq21AX301F1bdrq+hiawyYdtt+IKhHftO13A7eEEIKIfNPidJzvd76M3oZ8HnrHaD7+DvgG8JIQwqSIvBZ4QYeyoP/xvDmE8Ob8DhH5UWCjiEjqwnoz7QvhA+h/erF8H7AGVbcj6QvyNCPWlx3oHa40L0UfvHwGenE+hP5wXooLxUGLMbKlU8EQwiH01i0i8nTgP0Tk1tDZ8aNTX9Lk2z5gr6fRiz2svXNPsu441nfPU/fDxedRgSagdyR25NpfLSIDqYuPLbTf6zju6fgiD6HfzxeeYlyrRKQvdWG9Bb0jGUmP3Qh6kXNZCCF9HkaWfH6gce/osG+h92sEVYi3Avek2pkvnpPlIWBLhx/Wmc+jtdlALwg3nUI7834vpMj/2D7V93ihPsU43ncK9abjy48N6Pj8G4uf284jzOl4UFFY/Es2vb+aO3Y+CsBTQwhPtL+NqRMorQII8JpUue0hhKhUp9tpoj8wOsW6UD3zkY9hPhb6TzUdWyu13sJtEJ1HASGEcTRn8V0i8jwR6RWRsog8W0Tij+wB9FmKKRG5BM3XTHMYzXeM/D1wjYg8WZQ+EXmu/eD+Mvo5f7U9iHQVekcp8n7glSLyRLsb9afAV0MI9y+hLy30Vvxf2kNCRRF5qtUzgH5+j6EXon+69FHiw8Cvi8gmEVmF5qfOi4i8UETixcYY+j0W1af8OC2VX7O2V6N3yD5k2+8ELrOxqqC3lNMs1t4HgDeKyDoRWYueBw+rt7P9cPoZ4GdzdycIITyEpvj9mYhURB9aexWaagc67r8jIqtsTNMK523AhOiDbT32Xj9WRDIPMy7CH4pIl4j8MPDTwL906EMLPaf/SkTOARCRjSLyU6k4XyEil4pIL/C/FmjzH9Dz+ydEHwLcaJ8pWOD9MgXzw8CbRWRA9MG63+Theb9uQ38YvMU+qxUReZrt+wDwP0Rku4j0o5+bD3W6q7UIC30vdIrrVN/jhfp0LXpeXQbJA6AvPIX+3ARcJCIvte+yF6EpKJ9awrntPMKcjovqW4CfE5E1APYF/SXgxbb/ZaiScDJ8Bnh1XBGRJ3YodzPwqyJStnIXmQLVie+gt30ut/IDdkvnZOtJcysQLxr6gOejKkqn7Y5zVhBC+Ev0P+g3onmID6Gf609YkdehSu8k+h/jh3JVvAn4J9En+n8uhHA7qta+E72w3I3mGRJCqKF3x16F3tb878CnsB+sIYRbgN9Hn9U4iKp6L2bpvA74JpquNYo+BFhA0xAeQJWie9AH9JbK36PfPXeiqSkfW6Ds5WgK2RR6t+s3UnfL3kRqnE6i/fej37V77O9PAEII30UfJPwP9DmP/Pf3P6B3Eo+LyCc4kT8BbkefT/mm9W2pE+D8roh8eillQwh3hxDu7rD7JehdvwPAx4H/FUL4d9v3h+h7thft/3tTdTbRi/Un2v4RNH97aCkxoWkDY9bu+9C89e8sUP716Hn8FRGZQMf8Yovl0+iDjP9pZf6zUyUhhNvQdMO/Qu/Ifo622vnXwAtEHSneMc/hr0GFoj3oe/1+9Efkoiz0fqXG8gLgQWAf+swUVv970f8n96J3gl4zTzWLstD3wiJxnfR7vFCfQggfR78XPmjv5bfIWj8utT/H0B9j/xP9sf7bwE/HbAAWPredRxjJ/Yh/ZBrRWb9+C1VOvoF+yV+P5msdBV4ZQnhQdNKAT4UQPmLHTQUzoc+9Xos+bR+for01hHCNiLwJmAoh/LmVK6Bf1j+DKsNH0dvNPwC8LoTw01buncDtIYQb7IL6b9CnbGfR27Yz89Vjalu+r1em67Ztv4k+zQzw7hDC2zttF5FtNgaPtTLJmOT3OY5z6ojIV4FrQwj/uNKxOI9+7P+Gfw4hnGz6guM43yeclotqx3GclUY0r/peVHl6GXo79vwQwsEVDcw5K/CLasd59OP5uY7jnC1cjOaH9qMPKL7AL6gdx1lJLLe+U4rM9/N08WclrlSfIiLyOFK5dkY1hPDklYjHcRzHcRzHWTn8otpxHMdxHMdxlsmC6R+VSnfo6+tDzA86Wdr+ZtMcm2xDaM1/gR6iS50tsj72HcjvCrntJzSVL7BYEzLv7hObmb9PcSzaXQt2nOSOm78FfYZy/rGI+yqVCgDFYpGHg/j7aXp6ytZj8K1MudGx4yMhhHUPS6NOwtq1a8O2bdtWOgzHcRzHcU6Cr33ta0u6Llrworqvr4/nPOsZlLu6AegqdwFg13yMj+uEO4WCXhDWqjZBV+46tGUXbfGiu1Qo2HGFTPFW6sI07kvqaOkFfLzgJKewh1bL9ttFbe6HQELcHmPI7W9ZvXF7EnuuvWKxlImjafElfWrlLqqlaOXUZrOrq8vqKWbKlUolymWdV+Hiiy8GYHh4mOUQQ6/Xte2vfvmLADTqdYupmin3gQ99dLHZnZxTYNu2bdx+++0rHYbjOI7jOCeBiCzpumjhBxVDoNVsUZ3VCQrjMqqwweYWaNRN6WxFFdYOt2oK8UK4mFWskwvV/IUvJ17EJspue0MSY7qteHErdrHaVtfjxXbu8NiX5GK6YF3Jt5+NsdnQC1QpxIv37I8AKRTT4SXEi+4Tr/XjBqFUKmXKPlzU6zV9kcTkqT+O4ziO4zgPB6dj8hfHcRzHcRzHeVSzoFLdarWYnZ1pq7ExD9kuxaO4WrDt+VSKE7KJE2HaVOOQVa7zx2d2JtkUC83u3W6jmOQsz1/+RJE8m+6RqOu5HOkYc5ImkuuTENstZsKO9RSKZY3PlOyoaK8aXgPA9vO3J3V1d3cv2NeTpV6rZ/pyolLtyrXjOI7jOM6p4Eq14ziO4ziO4yyTBZVqEaFUal93J/pm/uE9U1YLdo3eyaYvrxoXkrzjVqw4qU/yede5GPIUTBkulKzORA3Ptp0XrsMJ+cVRsV5YxV3MkSO2VyjoEFd69OHD7oqqz0NDQwBs37Y9E0ehUEjuDDzcNBoxp3p+1T0+7Ok4jnMybHvDv650CM4Kc/9bnrvSITjOiuNKteM4juM4juMsk8WnKZcTX7YV6vm9mDvlMXdsIuecEUIrcenIu3ycYKcR98fyiRtH3h0k6yOdam3e9dhHyeVmh5yqm6jKppTH5eDQal0ODAKwarWur12btzm0dh7Bnzdi71O1ppaH0davfYdAF62mK9WO4ziO4zingivVjpNDRJ4lIveKyG4RecM8+68UkXERucP+/mAl4nQcx3Ec58xhEaU6wDx5toVE3M1PnJKdgKWTYt1RyU47ceTtOXJ+0ydOnBhdOmLoYeH1ZGbDTjHO/3tD2pYnWosNz7nnnQfAwOAqAFat0mXMnc63k/alTsc33wyQnWZ17GzWkb2DEP2pp6d0JsVmU5XqOKGOT1XfRtS25V3AM4F9wC4RuTGEcE+u6OdDCD992gN0HMdxHOeMxJVqx8lyBbA7hLAnhFADPghctcIxOY7jOI5zhrN4TvVCKuYiM/PlnTfa27MzL7bV2naZTkpxW6he2M2jlWsjydFeRJVtx5pzHYkp3a3s9lJJpxvv7e0HYMOGDQCUy+VF6j9hx4JxZYPRRcMU5+rcnMWi+dzHjh0DYOToUQBaTVWkp6enrA/ZXOr4ovAIuY58n7EReCi1vg948jzlnioidwIHgNeFEO6erzIRuRq4GmDLli0Pc6iO4ziO45wpuFLtOFkWmIEo4evA1hDCE4C/AT7RqbIQwnUhhJ0hhJ3r1uUfUnUcx3Ec59HC4kr1Emgrz1lv6BOdNObPtU6cNKLTBnLCpU1SR8jO6ngCrejyQWaZ200hHp+7XEpcQqyBmHecxJ5Tc1tB1eLj46MArK9pbnV+NsTOMztmx6bVap04TlZmdHQMaCvSUaHev3+f9VXLzc7NAlCr1uzwVmY9FX2mfumcpH02sQ/YnFrfhKrRCSGEidTrm0Tkb0VkbQhh5DTF6DiO4zjOGYYr1Y6TZRdwoYhsF5Eu4MXAjekCInKu2C8eEbkC/RwdO+2ROo7jOI5zxnBKSvUJsxzm1NalHl+v1wHoKpuqu8DhBZl/tsZk5sO8fXXSWFxkfarbFs2tzPaYVxzV3UJHzTvrWz05cRyAAwdUNT7//AuAdm71Yg4b6TGsVqsAHDlyxGLVcTo2okJouUvHa2pyEoA5U6YluTOQ7Xwwtb3tK540mim3gJ3IWUMIoSEirwZuBorA9SGEu0XkGtt/LfAC4FdFpAHMAi8ObqHiOI7jOGc1D0v6h+M8mggh3ATclNt2ber1O4F3nu64HMdxHMc5c1n4ojowr3gZBc9W3iEjyY2OswvaIskNjgt9USxquUQVjsctQDv3OOfrPL9pR1J3SILpoNbGfPBcOye4hbRyqq6Va9RVXR633OoQTB0mKtW6VqtpXnPMi462JN++R22QBwa6OHz4sJWtW8h6cDPvGX6ChXg2Rzp0UqJzntju+eE4juM4jrM8PKfacRzHcRzHcZbJKflUR6W5kMximJ3Bj3yOdbKu1/CFxLoj5wKSy3tOF2n7Vse2s78HWqbihpwSnffKTlRxU8klOX7+PPFFfa1zinmcvXDfgw8AMDA0DEBPpQLA/n37U72Ao0cO2nGqSh8/XmTKZj4smkVJScqZPiQx5sY7eR/i5ryqnvcHt/VW4pjiacGO4ziO4zingivVjuM4juM4jrNMFlWqw0mol1FhThw5mrosFbO50omCKlkFe946475CVLnbrWl8ur3cpV1Zt+YcAA4fVWvhek29nGM1oRVzmePCYih0yKHuRCICZ91HJsfVS3ranDl6Kn2Z+CYnp6w7MaCYe60VVOfqiULdtJkQi8Xc25Qo0dmk6qhAt3IzJtZqmu9dLJUyoccQEuE6n7PtOI7jOI7jLAlXqh3HcRzHcRxnmZyUpd4J/sc5FTsqpVGFzZl+tI03cq4g+Vxq3R7rUGW5YG4ZlW5Vfnt7dbn+3PNsuR6AelVzk4dXrwHggft3AzA1PWmNNq0v2T7kTUHaPe20P9eZuJaMgb6Yq87ocs5yuQumFrca2WqSWltJJXkF/0RP7qzHdpz9MZaTnONJIf9+JHcKcncYHMdxHMdxnJPClWrHcRzHcRzHWSYLKtWBQKuZyrOV+d0moqIanTfaqm5WKc0J3alqT7y2L9ssi0PDqwHoHegHYLUp0GvWrs3VoW2VK+qUsf7cc/W4PlW0D9pMhw/t22vlTbFuzq+6JynTMc9Y4oyLpqDnTZ7jflRRj6pxPL4Yx6hZz3bUfKpjTrcgbQvsQtbx5ETT8GzsyWyQ0c3DYisWLeZ8DnVyh8COL/pvLMdxHMdxnFPBr6Icx3Ecx3EcZ5ksrFQHaDSbyXpBkmTo9CJVPpvL285HtvUktzd/LR9dQroAWL1uPWvXaI70oPk8Dw4PZsqe4I2dqK5KsaxdGxoa0rotJ7taVzeQQwf3pY5Kzz6Ypa0WJwWtL5nVtjqfE5eTsTihfkn9Oz/FxFtb3wNJxj0/jWQ2tmLeyaSY2IVkI0gO91xqx3Ecx3Gc5eBKteM4juM4juMsk0XdP/JqdGbfCTP6KU0xVwrbkCjTOcU6L5A2LX+71YIum4Gw0luxolklutNsjFGcrc2pIj09PQ3AxIT6R1erc5lykld7O/Q3q4ef6OkcVfycGEwnMVg6qP2CnDjmOYU65qDH2EumyktB1fiGzc7YjPnbzU5BxdhsZkV3/3Acx3EcxzklFp/8JX2dJR3SO5KrtGiDl3uYL5ey0WpmH6xLLOGaajM3OnI4me573Tk6mcvqNfrAYk9Pb6aNeLF6fHwcgGPHjgHti+qm1Tk9o5Z6s3NqcRcv9JO0jPhDgOwPhGQckvGYf/rymA4iyYu4iDcDljaxikg65UQrqXTpD4vBQU2BKZX1Ycy6peb09ulDnNFecHJC+7pn97cBqM5OZmJvp85k+1BYYoyPdkTkWcBfA0Xg3SGEt3QodznwFeBFIYSPnMYQHcdxHMc5w/D0D8dJIWqM/i7g2cClwEtE5NIO5d4K3Hx6I3Qcx3Ec50xkEaU6JNZwkJqcpRAfOIwTs2g13TYl98CAKqrRfm52Vqfmnp3RZZx+OyqnzfggXlSs61WOj44AMG7Tfg8fXQXAqtWqWDcbqqrWG1GJrlrdut6ozVoPTB1P9UPbij3Mrp80ix6YnWAlLru69aHMZsP6XlQLwQ0bNzE8rA9nRjvDgqV1RKW60ttrbedSYKwz+/YfBKBarWZjjCK7We7V6rbfxr9wyoPwqOIKYHcIYQ+AiHwQuAq4J1fuNcBHgctPb3iO4ziO45yJuFLtOFk2Ag+l1vfZtgQR2Qg8H7j2NMblOI7jOM4ZzMJKdchawSWucZZ6WzS1dfPW8wHoN/u6TZs2A1C2B+iiolo35fQbt38NgBFTo0PTFNOoJodC++E5U6KPHTsKwNjoqLZd0raLRVVxV61WC76x4yPWVlSq48OUcQrvVrYzkZzqe+KzfPknDTvsz+dWJ956WaX6kksfb3GvA6BhqnSxVKJcyr4t7Tl3tK56Qx9AbNQb1oLdObDG56YmAGg1NC+9mDs+3imID20WLJ+8mLcPPFi7MNsAACAASURBVDuZT6/Pny1vB14fQmjKQk/yAiJyNXA1wJYtWx6WAB3HcRzHOfNY9EFFxznL2AdsTq1vAg7kyuwEPmgX1GuB54hII4TwiXxlIYTrgOsAdu7c6fYqjuM4jvMoZdFpyhuNRmpK7jjvtSqacV6YbsvxPcecOsrmTpGflrzUpXnDT7riyQDce89dADx0/x4A6jG3utWi1dbFtQprqxlNP+qztr9sMekiunwUE2U6G3pbh7RpxWX+Kbyjyh5dSFqtbP73CeSSs/MWf5GWWfFNz6oLySor39PTnQQ6NalK8/ExdTJpmFofVdGYaz09pTnqY+Oq3jdq1cwy6s6NVnRdaVp9qnR3d2kfu8rR6nD+rp1l7AIuFJHtwH7gxcBL0wVCCNvjaxG5AfjUfBfUjuM4zulh2xv+daVDcFaY+9/y3JUOwZVqx0kTQmiIyKtRV48icH0I4W4Rucb2ex614ziO4zgnsOBFdasVqFariUIa85dD0GWzpUrpvffcCcCUTbCycdMmoO1SEZXW737nXgAmzFO6YKpv0/aniQ4V0Wkk8Vi27ZVedRqZmtK84B07LgBg04YNANxx+20AzFVVEY7Kd8nylc9ZvyGz3mxmFeiLLroIgL5+ddy4646vA3DosDprNBKlPInY4rRVSW89cbryQwceAODAPl1G9blS6UnU8qp5bbcnrMn6eydjkuSJm4sHcRKdrLId88mjx/dAf78FZ+9Dq44DIYSbgJty2+a9mA4hvOJ0xOQ4juM4zpmNP5nmOI7jOI7jOMtkEaW6xdT0bDIFd1eXOm7EfOOCxGmwNe/4e/d8C4A93/sO0Fa2i1a+Nqe5vuVizLlWpbVoPsxRTY7HZTB1NTpj7Lz8qQD866c/DUBPt9Z5/x59pmzOpupumAJdtDzwtWvXAnDJYy4DoNfU9GBqbcx5Try0W6qiX3aZzv/R3a3t33ffd60PFl6MM2SV9ah/JzniVnBibDzTnphFx+z0DBQs5pjo3comhMfpxBNXkJaWK0ajaVOcm8n049pGVOVL3drnguW+x1nMQ6jhOI7jOI7jnDyuVDuO4ziO4zjOMllQqa7X6xw8cICCKcfloinU5mfcVVKls7enR5d9qoB2t1TRDnZc01TjYEpq3ZTUQv6aPgqzrQKFgrY1NKwzKPb1D9g+LfPAg/sBqFS0rY9//MOZSqKa22UKsJg9SNNk2cMHD9jxFS1X0b7Mzmmu9H177gOgNhe9nONskjHY3GyGcWvep1rmd1ELidoc64lqfZFidPkwBX/I/L+HhnSmxVrVHEls9sjJaR3P4X4dizlzLImp6nOzNeu76eYF7Ws9UcC1U3W3/3Acx3EcxzklXKl2HMdxHMdxnGWyqKVeIQgtm7lvrmqKs+2bMWVzekodNnpnVNXtM2eOLstzLpUtt7pL12N+c0F0e6FoM/ol1tSFJP932GYcjG4ckeg/feml6voRZ1y89bP/BUC3KdRRVW/ZbI0jR4/o8shhACTOImgSdHTWiEJ009TgqFDH/RJjbydTW1yWU01MVLZl4oudde6IdFfMw/tJT6FeU2W53tCYyzZug4Oq1s9Oz1mfWpk+3nvPtwFomHd3wcaZsvWtGF1BLH+8EWOIyrfPTeI4juM4jnMquFLtOI7jOI7jOMtkQaW6IEJXVznJQ857OUexNSqmU1M6m+HUpC6j60T0UY4pvKtWaW7wmtXqxNHTo3nNLTP9KJeaNM1RZOSY+kJXHoizOGpdB/drTvT0tKrkYqpsl6m68ddCi+jNbAqxrUfnEWllleVWzmkjenIPD2he85YtWwDoNY/nuuWLz81pHGOjOgvi/v0PJhG0a2vXX4hjF9utquf33r33JX3sthkou7o0hsOHHgJg/Lg6hxwfU1/wOENiydT5ytAaAIpNzXGfq6myHf3Am3bnIarwUbmOs0c6juM4juM4J4cr1Y7jOI7jOI6zTBZUqgPQpJDIrAVppfaQbJdkv+UvR79pU3mDaDMNU7QnJqYBmDVXisEBVX3PWa/KdalSSGZCnNynCu7IEc2Fjh7WYr8HogIc6qY4S/SjPgeALVu2ZmKes9kJx48fB+Co5Vg3TKWNinbJnE22bdPjz9+xA4DR0VEADh5Q95GoKhdK2m5I8pUtF1vmn2mxlaQv2wtT8w/tv59mKyr82RkT47L9S8gqM7vw2Vlte7Y+oW00VdFuNLPuH81GVq3vKlnOu/XZcRzHcRzHOTlcqXYcx3Ecx3GcZbKgUi1AUdr5yphvMkkeciynimmxkPWIjspqLBhn/IvHtSwfudXUZZypsaenwpjlDY+Nquoa/aRL3erFXLLZALvMJaQcZ2G02RqPT+jxhQOah9zdHfOT9fioLEcf6ajiRqU6ru+2mRP37X8gNSptZbtu+cwxLzl6TEuiQJMhyS+PMzbGnGsbqlazTtH6VJSo9NuxraxzSFTFJbFN0UpGR0cAmJiYzPSpp1fHcM3qVTomNt6tlnpzDw1VcBzHcRzHcU4eV6odx3Ecx3EcZ5ksnFMdAo1qlXI5ejLHWQXjtXhUnlUpjX7JkijVNjNgzLG2o6JbSLLdVOaoRnf39FA3h4rJCc2pnhg3R5Eki9rygM2DOcYU90eP5sEBdcDoN5W2bAp1OSrelkccPbMjrRDzj80pYyY6bGi5UiHmXscjChZV1j1E2kbWhrmO5H7ORLW/UJB23rjEpWTKtJ1ETGVvaYzV46rq1+c0Z33NsPqFV7q1703RPg0M6PrqYVWsa3PjVl8Dx3Ecx3Ec5+RxpdpxcojIs0TkXhHZLSJvmGf/VSJyl4jcISK3i8jTVyJOx3Ecx3HOHBbOqRahXC4l+cESZwmMmrMp0lF5lii/2vaosLY9maOibSqz5ReXy6oaV7p7NKhSOVGIo/od84jbs/7FSk1RtmXVZiG0kJIZD4ui9XUPqtNIdLwoWV5xnMGxVNRlVLqjOhxjD7GvheyMjUlf7XdK9HxuWr54nEWyy3LB48yMyYSLtl4UabcdFeqcUh2iQh3V9KbGvG6t5o0PD63WGCzfO8ZcNxeQLutzr/mDD/aXM7GezYjeHngX8ExgH7BLRG4MIdyTKnYLcGMIIYjI44EPA5ec/mgdx3EcxzlTcKXacbJcAewOIewJIdSADwJXpQuEEKZCe575Pk54HNVxHMdxnLONBZVqUJVUkmvvmMPbyhXSRd5Bg1y56GLRtg3RDWVTb6NDR0GKSU51vHRpZyZnnS5iG1ExLpfMnaMRVd4Ym6q0fb2aZ9w3MAi087kT9TzmMxeyDicxkHvvvQ+AuTlVdQcHtZ5+89oeHtaZFxsW8djEjIWpca5fq3nM/b29mX4kSneh0Fa/o0FJzpM7uTPQiop2vDNgM17GA03xbpmXNg3LYS+UMmNTSHLks3nlZykbgYdS6/uAJ+cLicjzgT8DzgGee3pCcxzHcRznTMWVasfJkn+yFOZRokMIHw8hXAI8D/jjjpWJXG1517cfPXr0YQzTcRzHcZwziQWV6marxfHpmSTxt5DkTOt6VHVJ8oqxpeUPR/XX9hcL2Wv40Gxktse85kJRqFdthsPE7tkcQxJl2pTokJ19MLYdyKrkw4OaZ9xrSnXF8olL5mvdvpsf29P1UiEq19re9u2bAajG+Ky9OBbd3eY6MqiK9TpTpmv1mN+sy2g2UgrZGSKLhULiC54o/skiqvAxlzq6rMRcde1LkThjoo2Fqe5i7h6t6HdtfU3a8SwGUGV6c2p9E3CgU+EQwq0iskNE1oYQRubZfx1wHcDOnTt9gB3HcRznUYor1Y6TZRdwoYhsF5Eu4MXAjekCInKB2C8REXkS0AUcO+2ROo7jOI5zxrCgUl0Qoa+nQj2qrFENjgqo6W7NujlumELaNHeKWasnyYs2QbRsDhvRCWNySr2ou8w7mgLMVe1oid7XUZE2xTrWafnB0REjOpQEcw+ZmZkDYHpK6zvQUjGxb1C39/ZobnNUvicn1et5bFS9m6empmMvAOjv0/JdXRp7uUvbj/ngDRurclTd7bhqVds7PqZ+28dHx7QfUYW29sulEt3Rr9vqrFRs2dVNmrlaLRPjqNU5OzdnY6EqeHRXKTSrAGx7zKUArFs3rDHEsXWlmhBCQ0ReDdyMJplfH0K4W0Susf3XAv8NeLmI1NHT/EUhf6vDcRzHcZyzikUfVHScs40Qwk3ATblt16ZevxV46+mOy3Ecx3GcM5dFler+UpFGoqbGfOWoVFt+c+646CUdVdgQzaJjfnSrlV6lv1+dM3pMoa02qonPs0hUZ60tyzcutrI51omPtSVhRyeSlqm5R0dGrG69S1+wmRSjat6KecmJJUZ29sLY19mqquptxwxzPLGSZcvRbiTKupaqWRw1y2uOfS9anEl+emgl49x2NNF9jdhXW0af6pDkVmeGimZD27zkkq0APLT7W1rO+ljKvS+O4ziO4zjOqeE51Y7jOI7jOI6zTBaeURGhIEVKJbv2jonMcTZAuyZv5Zw5xGrtNtvkYtsWBIBqnFnR1OKBgQHdbcpso9GgGDXimENtx0RXkOjBLFGhtmWQrCNakoNtns6t2Afzbq7TsNCiCm+hxpkbc8YYLcmq85L7XVJv1bMHxLhigST321aj6t+KDh7ttyQEc0UxV4+GzXgYJHvHII5FoxmdUUylN9ePI6PHtcv2vlV6dObKoingzUYuZsdxHMdxHOekcKXacRzHcRzHcZbJIg8qBkJoJXnGIZnhL+7NIbkc3TijXzw+xBn9zJXC3D66ustWXhe1ajWZ20+sskYUyeOsjkk+d3a2x1bOhCF6X/faDIb1qblMJ6Kbday33QdTmK1AIaeAR2W71crmVEeik0aS6x2yvtl5D+r0cbGupI72zkxseTW8ZHW2zPu6UNTt4+OqVFcKOt69vUOZPjXbUeE4juM4juOcPK5UO47jOI7jOM4yWYKlXotWVmRNVNootsb850Ii9kalNatQS06xjipyucv8qa2+Wm2OqMKKOVy0mjGImJMcZ1Q0N42swNwWy2Ol5rIRY0hqC22tGqAQO9uebpB5iQp0zCPPl7P9TVOyybmLJNXnqs0r3tDOt277hEdnknjrILFn0TYsB77ZiBq0HlcxT+5ioTsdUqrtIo7jOI7jOM7J40q14ziO4ziO4yyTRZXqAm0VmEQhzRIVaWT+vOY4W2HqAN0uccY/y6m2pO1atR5F10RSjnVEt484w6IUsvnHUTGO5VvmmFGdi6ptKd2V1K+K6AFtoYRsAMEsTaQYVWNT0K28FLISubQrysbXYQyl0I6kINnfOq1EPc+6qMRlHEdsmYy+xJg01u5udf2Is0JCdrZIz6l2HMdxHMc5NVypdhzHcRzHcZxlsqBSHYCmkHg6R0U0zpAYZ0wknzOdrgBoNrN5x3H2wIItS6WuzP7pmTlq0SfaGqnFGRJj4Imo2sFb2fZ3d2sbPV3WVuJXnc2lbtgyNpvM+hjDMLW3YHnHbTOP6BKS98eOThzZsAo5UTgRxFNqcdvkIwlG64pqfVSoE5cQG1dzVUlCiXnoNotjj+VU91RUsQ7NqUwf3afacRzHcRzn1HCl2nEcx3Ecx3GWyYJKdSvAVD1QthzfqA7P1W1WQ5vpr2Lby3HGxGh4YfVUCqqYjs9VAWjatfyGfp1JMc7sF1OFZ2Zmqam4StMU6qo5WRQt4jhLY8xRjk4ibXT7mjVDqbW2ctxlqu/0tPpWd5VjXnJ0GzEV2A6oW252nD2yaIEULPZWKy71uIbJv7WGlm9ERTvmnZvxdqGZz2eWRDlO/KqbWU/rltXRlNyh5jTSrtN22x2GSt+gbYj547ZM/K5xHMdxHMdxTgFXqh3HcRzHcRxnmSysVBOYbTShS32NC5bbW62rpNnVq0p13byiZ6uqRNfIunrUGrp9pqZqbyjq9qOjk7q/rsfPzJgbRUGo9PcBMD05a9tM2U1mKjT1vE/LdVm+cL2qKnrdVNs5bZI5i23d2jVa3vp0+PgBAHZsOkdjttzrB/cdAmDjlg0ADA9q/SOHj2m7ZZWHe3u0nqnJOWtf+9JVNCXd8pnrNR27uo3dnOU7V00tbkUJXQrkrbKT9bwCHd1ALE+8ZhJ3o5iVsOMvpy6bVTIEex9yHtp53+qzFRF5FvDX6Byf7w4hvCW3/2XA6211CvjVEMKdpzdKx3Ecx3HOJFypdpwUorky7wKeDVwKvERELs0V2wv8aAjh8cAfA9ed3igdx3EcxznTWNSnOkjbKWOiqmpsqxA9m3U5M6tqclSJe/v7gbZC3bK86JIp17N1XZ+a1foK5tF87/d2A3B0ZCzJA56tVS0QjaFcNqcQy+eeNbF1dkpjKJjc2tVdAWB8Qo+fm5u2/Vq+UNR66pbzPD01o8tp7cNcXesfOTQGwNiR41re1OFVw9rHRtB6Zqpa/7TljW/cYIp4MZu3XLAxK9qY1k1Bb1gczWagZuPTsDzyejPmZ9u65Zs3WnFGRY21bG+npUxTtfejZWPXW1FVP3EsibNChnxe91nNFcDuEMIeABH5IHAVcE8sEEL4Uqr8V4BNpzVCx3Ecx3HOOFypdpwsG4GHUuv7bFsnXgV8utNOEblaRG4XkduPHj36MIXoOI7jOM6ZxoJKdaFQoK+3j7ExVWvrJpGWNY0YMYV6akpV2qJdo4dJVXXLPeqHXLJlT1mb66qp4jo0vBqAgQHN9f3yrn0AHD0yQslcNcpdqsIODaqLx9ysKsqr7ZjB1cMaq814+ODBEUC9rgFKElVarWd8ctLq7bWlKdamDk/OaJ/mzM2jUtH9JbPaqJpyfvjIuB5fUq/n8XFdrzZUHb7wgvN1bMYmAJg1O5ORUR3LZkPH7AcefzEAw4OqfN+350Fq5iASVfT1a9S1o1Iu2HZto1bV3Oh4h6Ba07EJltPe6DYnk4YeNz11RGOa1Peju5xL3nb7D5hfrp93YETkx9CL6qd3qiyEcB2WHrJz504fYMdxHMd5lLJo+ofjnGXsAzan1jcBB/KFROTxwLuBZ4cQjp2m2BzHcRzHOUNZ8KK62WgwPnKEXnONmDOXj3WrVWE+Pq4qbJxpsWaOFiVTgS0FmEJJpe2JKVWJKzaj38QxvRaZntJ6zrF6uwpCMB/q0TFVvY9P6LFRLZ+cUVW2PK5K8ZqBVdaWKsvlspbrtiTqc9aqon3gyKj2zfo4M6eK9rmX7NB6p/fockLrH9dmE6/u7ooO2diEKtMxHbkUfastP7xa1bEYG5+1eKtWn1Y4PaF9bj1Rj5+Y1ngnZmF6rpGJYbU5lmzerE4k5VLZ2g7WlvahaHni+e1VU99b5uVdLsaZFsmUdwDYBVwoItuB/cCLgZemC4jIFuBjwM+HEL57+kN0HMdxHOdMw5Vqx0kRQmiIyKuBm1FLvetDCHeLyDW2/1rgD4A1wN/alPGNEMLOlYrZcRzHcZyVZ8GL6r7eXp688/E0TYF+4MBBAMQ8olumXItENwnVf9euUcX56Igq0WPHzRljWpXX4dWqqEbHjlHbHz2ih4cGGR7W2RaPj2oecNEcR1poHvGm8/TZsQcPqp/00abmKhNnD+zWGHvMiWSV5W/vM5/p3j5V06OyPW6K9/YtWu82U32/cvud1jft66YNur/f/LFjn2IOuFhe+T3fVieTbnMhmZ6NMzeakl3UZbGkcezfr3HNztWpm4OImJ4+Z2p6MsujtRWl5gce1OyEB/bu1xg3b9HlJo21Z6DbxsJyqbu1b3OWE9+y99fNP5QQwk3ATblt16Ze/xLwS6c7LsdxHMdxzlzc/cNxHMdxHMdxlsmCSnW9Xmff/kMM2OyGXQWVMmdmVVnuN0eNquUbNy3fuShx5kV1pYjqcU+vKqXnrl8LwESvKqW796qDWfSSllDgnHWqLG/bqDMdlkzxnbL862JJ29q+9TwA9t6vam301J6raddWb96qMZsbSNHyu6uzpgYXtZ690xrLZRdrfRPj6iJy3lpVuh88cFjLF7QvJjSzZvUq67vWN3Zcc6abNkPi5LQq4DF/+fGXXQRA34D252u3f0/HZiD6Xc8RU57jHYJ16zSnumzuKSHOpGguHxdddAkA03ZnYOKIuqh8b0xV/osue5xutxksy2vNHcTer5bdYRBXqh3HcRzHcU4JV6odx3Ecx3EcZ5ks7P7RCkzPVCmYQl2bUzW3p0dV1ZjnXKuo4jkwqOrrOeeoerv/sKq71abmQffajIpbTX2eNH/lu+6419prz7w4PqX7ZuvmqmGzB65dtx2A8zao2h3sd8EFF6jfc3XO/KLNbaNq+ckxf3hwUHO19+8/bG1q3woWW7lLVflaQ1Xdvn71x141rH2sm990s25OJyZZd9lMj5WKro+ZbUgS344LNY6q5TGben/0qOapV6ZVQW+1Wonf96y5d8T87Zi73kqSn2Muu65d+oM7ra963J13fEP7ZF7b46M6NquD9qlpM13G3HjHcRzHcRzn1HCl2nEcx3Ecx3GWyYJKdbFYYGBwgNExnV65ZgppT1Gl0Z5uPbxoKu/61aoet+qqTJ+/8VwAKt2qtA4MaU51vaa50805mxHQZgiMucLlMkxZDnRXj84mePSY5gcfHFGf6d0PqMJbr6vyu2aVlttmjhd9vRrbvgc1v/iCC9URY2RMc5wH+1RVP2x5yMWG9uFrX7lLYx1QNbfLnDLqNnvheF1dRgqm0s/M6Vh0d1kO9/CQta/1FwtlG5OGHW+zJY6qa8ngkM2WaO0MDFTotdzzGXMWier43j0PaJlBvRPQbTntZo1NaKoq/917VPlv2J2FA/tt7pKkoC7inYGYu+0p1Y7jOI7jOKeGK9WO4ziO4ziOs0wWVKpbITBXr1JvqrQ5a2pto6YKZ3lIFeg161ShFlM+sfJlyxsOpkQ3qnoNX+lWR416TVXj0Io5vbq/r6fMpvNU8R0yb+VDgxrq7Xffp7HNqAtInA3wyIgqyLOmcFfNoaRiKnpvj7Y5t18V7zWrdYbFyUmtp898q7duXKexmbIc5xpcu0bLj9gskDNzqiLHmR8bLY1zqKXK82qrP1je8u69D+qYVAYsLu1r9LU20xIadWg2dOXc9esBmJjQcdp9nyrVxbIqz0Omiq9bq04p3ebCcvyYTQNpHD+qY9M/pG1/97vqoT0zq+UGLM+8VCziOI7jOI7jnDwLXlSLFCiUK3T16kVxqWzWa0Ev+qJN3fSUXmCuNXu56PhWnY4X03oxXuzWC9wNg3rh2W/LgG4fsYu/gcF+MDu+6XFdHhvVtI+qPdxYsovZeFG9epVeYG7apBeiIyOasjI+pheODx6y41vaVm1KUyXWrNMUlU3n6XL9OVrP9LTG0tejF9sPmGXfvof0Yb96y6z1bOrvgtkIxlSYySm9qD90SNNURsf0uL4+HcNZS8Uo2oWsPU8JEuju0Tri1OxbNuj05FfsvByAZlMv+GctvWN8Sus+Wtf3Y/hcteAzpzxqraqV17b6itqn0WNqG1hr2OQy8crecRzHcRzHOSk8/cNxHMdxHMdxlsnClnrNJlNT00xNafrB5vM0zaOrokrngcOqdO6zdINDx1Xx7OnSh/TK9oDidO04ADKj9YyO2nrRUjOs/DpTusvlIsFU0+6KWs0h8SE7s5UL0U7OlOrVqjBvPFdjrM9pWyPHVKG+36bybiYTp+hSrN7dpkTHaccv3L7BSqncO7xW6//hc55iY6PtjpkCXTWVeP1aHZs+mx590CZ1uePOb+txrZodb3E0JB0OXZUihaKOQ3+vTbpT0nHq6tI2JdhU7JZC0lexFJSaxl61B0Anxiasj/o2m4jOkCnhNXuwtBlHpOyPKjqO4ziO45wKrlQ7juM4juM4zjJZ+EHFVouJiQlqNglJJeZEb7AH47ptopPjqgpPTWi5OLV3yZTUWp9JpDZ5SbOhSmqX5RV3l+yBRrPom6vOUK2pUj1oE8p0V3RZsYlManO1TKzxgcDpiYkYPABbt6mV3rFRjfGoWfKJTWgTJ7ZpNDWG2ZrGPjlj9dmkMZNTWl9/b1SHtU9rhvW4aAtYst8pwR50HOjRBxh/8HGP0bhrMW4tV23omMzF7aFFlwnG69eqktzX12W75uxYU9nN1o9Cj22292NS+zhZ1RhiDnzDcrTHJ3VZN7V80nLf40Q2juM4juM4zsnhV1GO4ziO4ziOs0wWVKpDCDSbzUTNHeyPdnCqnG6zyV3O36LVjFiudMVypA8dUvu5o+OqEheLmh89PqNKdamgOcAtU5m7bAKVtavWUCrYtOCWc3y+Kc6bN+vkLrt23QG087NLpnpPTauyHPPAt194AQCzsw1ryyY6aZqVXWKapzEdH1fXj+9ZuRmb8ju6BfZUVBXu7bGJb8y9o2wJy+PWbpwCvGwjvH7dgB2nedLNRsOO07Gq26Qw1bkGzaCxhKAKcqsVc53NfSWq4ZhbhynRLQsy9qhYtFxqu0PQ1aUxhqLW15yNY9y0vkYLkrMbEXkW8NdAEXh3COEtuf2XAP8IPAn4vRDCn5/+KB3HcRzHOZNY8KLacc42RKQIvAt4JrAP2CUiN4YQ7kkVGwV+HXjeCoToOI7jOM4ZyKIX1SGEZPrqUkkV0ulJVYMbidqqCmhftyrRZiGd+CdPTWkO76pV6iE9OqHq8uy0bt+6RdXn6VlVeXds24KYSlu1Kc8PHVO/525ra8eW7QBs32T+0JYPHFX1pinRPd2qBM/OmutGK07JHVVf62d03+i2/O66+kw3LNe53KUKdcvMn6erWk/d/LTjhC2t0B43aCvjhw5rn7dtU3V/i00yU0bHsBBUJa7SoGWT4cSJaUrmKx1z2GPQca6WOBV62XLS4yQ9YzYFe3xDhocHrC9abqhXY1xvzinxnb7/wCHOYq4AdocQ9gCIyAeBq4DkojqEcAQ4IiLPXZkQHcdxHMc50/CcasfJshF4KLW+z7adEiJytYjcLiK3Hz16dNnBOY7jOI5zZrIkpbpgWbp1U23F7CmaqnWUMQAAEnFJREFUpqjWZ1RlLRQ0X7jSoznXl152EQBrBuMshZqfPDapy+OjOmX4DvOEvv3r3wLg0NEjDAyoMnxsTFXx6GVdndU87FJBVfGSJS2L5VRHlXz1sCrBcdbBoQGN7dBh7Uu1ajGb3Fu0KdXjzIhPeJzmYrda9rvD1OCqqcCTU7r87m6dNn12NtZn8cRALO+5ita/Z6+qwEeOqgrd36f9ONdmQdx/6BBrVulMk6VinDXS8sFtNsiCWIJ30oS+qDezOdWr1qjf9YP7D2vfj+udgQ3rVS0PdR2b0DI3lrJnA9Ee1TRhnm1LIoRwHXAdwM6dO0+5HsdxHMdxzmxcqXacLPuAzan1TcCBFYrFcRzHcZzvE5YkTVp6ML02u2HJZvgrFlVNjqqsmN90KVFpLem3qSpujzlmNMzRY2DDOQD098acYc35nZhsMHJc84Hr5mm9focqzVPTVqfNEhhiLrV5NAdTz4dX91vbqoqfv3mD1adK95Ej6vJRraq6Ozury7kZy5W27bHPlR6NsTiogxGdS2LOdcytFsszjwq4pXgnudWhpMfPzun+ak3HZnJK4xwe7KViDiODg7osJonfZEicTOL4xzsKNptkjL1u+d7H7Y5C8bjGOj42YmMwg5OwC7hQRLYD+4EXAy9d2ZAWZtsb/nWlQ3BWmPvf4un9juM4K43f73ecFCGEhoi8GrgZ/VV4fQjhbhG5xvZfKyLnArcDg0BLRF4LXBpCmFixwB3HcRzHWVEWvagWaTtuDAxqru+s5TUPDKoaHF1AsLzmmUl1/TCxl66CqryFks0M2NTylV5VYmdsdsSmOWvMzNVpNFWhFstRvuPuvVqZuWRcvGMbAOtXay5ys6nlWmje8KEDqsKeu0EdR2ZmtI0LL1TXkE2mkhM05i/f9nUARkb2A7Bm9eO0/Zi7bcpzqahq8MGDmhs9Padj0TTbD7GU3KK5i7QsVzt6TMd+tcyFJPpez5gTyvlbdyR+0om7is1kWSgUMsuYR57cSjDWrtEZL+PWtebuUW2aSj6jqnjV6m96FlCGEMJNwE25bdemXh9C00Icx3Ecx3EAz6l2HMdxHMdxnGWzpPSPiuXmls0dol4zL2dTlmPecN1U2O5uVaRLBStvXtM1U6RLRd0fZwaMqu3goCqyPT0F9h3QvOCBIc2lrlpbtboeM3JsFIDtW9YC8I27vg3AYx+3A4A+U9WPjKjDxZyGwPRhrXeoT91EzjtnGIDnPutHAfjKV1WxLpXNsWRK7+j39ZesHlV3Dx89bPHEMTCnDqJibeS8JCwFPBmTmWlVuvstnmIJQkMLxdkaY8509L6OszXmicp09A2XksZ83nmqXEdF+sGHxnL1yLyxOo7jOI7jOEvDlWrHcRzHcRzHWSaLK9UBekx5LhRUC630qHLdbOacLlRwThRSE6CZnVW1uGY5vT0Vy72e0e1RWT13nXpLV3r7qddUyZ2eUWW5r6Jlnn/VU7RScxCZGFWld9bU82Nj2sb0XFzX/O6yzaw4V9OgpqdVgT5wUFXb7Vt1fo8nPGGn9sHU4YEBVbxj2nLT3D4qXToGA+YKMm155km5oO1E3+uY/9xs6diUSlr/5TsfC0CXxVcsCMWYO50o1FpnVMGjsl8saB/j+5Ao2bb/yGGdbGSgX2Nct06Xo6M6W2Tf4HkA1My5JPqQT47rfsdxHMdxHGdpuFLtOI7jOI7jOMtkcfcPoLdHVdRSWZXTguVKxxzeZsOcLcxNomazDjYsZ7dgimqcsa/V0u1Ny8GOxxVLeo3f013myTufoMeY80Wjrq2Va6aSo8fMVNXJomZtPbRPXT+mp1V1rVZVDe/pVfW2fyA6luh69Href0RztPc+qC4jP/a0nVbO3DoapoBPWU52r9ZTPkdV/PEpHaOaqb1VU39nbObG2A6mXJdK2o/BIa2nZC4nxUKgt2I556JthlzOc5y1MfH8iEntUdE2J5K+fo0puaMQtNzwsPqBU+6xWEqZ5aEDh3Acx3Ecx3GWjivVjuM4juM4jrNMFlWqCyKJ+0ectbBFVHnNkcPU2abNZhgnVIwCatkUUAqxuZgbbDMAJrMQmmtFS+iqmHqaOGBom7MNzfeN+b/HzAVkdEQV6lp/K9VCe0bG1lR025jMBBdzp4sW46qhIeuLxWa53eOWZzwzo7nT0QllqKyq8pAdF9XkmC++75CqvlNT2m63eVBvOfdcAOYmtd7ePlWNK709dHfreBclKv1i49Wad/wisS/RxzqOXb2l2+fmVDUvWJD7TJHOTdToOI7jOI7jnCSuVDuO4ziO4zjOMlmST3XZcp1r9TgboG6PubulcjGztJTpJNe6UIw5weYSUojqsCqy09OqoCZ5xcVi4uNcj84WrajOZmckXLNWfaY3bdIZEvftOwZAb9+ALTVnuWa51c2Gqep1y2024+gtm3Rmxic+9lJtL+aD20yIk5OaSx29nVtRko4uH7Yulrfca2rzY3ZcoMUSCw8tXzZVvmJ97u/V/OdCuUTJ3FDMbCVxA2mZvUqsS2y9VWtkxiQqz3E855Ic9+hEEr20c57ablTtOI7jOI5zSrhS7TiO4ziO4zjLZEk+1aWyXns3GtkZ+BI/5YIqq4kYW4gezbZuub6zltPbY+4WPRX1Ta7Xo++yhRMCVStbNcU6zi4YHUSw/O6yxbbjfM1R7unRug8eVv/pmRlVcctFVY5LJVWE+3WVHds3AHD+1k0acys6bpCpf9WqHhsDjWtyYs7iyCrUsdMF86EuJCK95YZTJoM5o8zOaL3lnnROesG6ahuacXjiQIfM2ESiyh6LxQorlewMlwO9OlZR4U4cShzHcRzHcZyTwpVqx3Ecx3Ecx1kmS8ipDoljxQnI/Dm44YRXlhNsCmpUXqNLhSSqsynehMS1Y3ZOFeGoXHd3RbcNnelQWqZ2mx/0RRdeDMCGDZoDfWD/YQAajZiHrG1s27IWgM0bdRlV4HrNcqYt97loOdf9NrPiyIjOxNhts0x2mfvH3JzG24z5zonKbL9bcm4jZVOoe3tUAS9YPnqxXE7yx0nGJx4blWTdHpXo6Psd12PbRctlr3TZzIxNfbsbdW3rnDVxtsiQOX73nvtxHMdxHMdxlo4r1Y6TQ0SeJSL3ishuEXnDPPtFRN5h++8SkSetRJyO4ziO45w5LKxUB/3r7u6gVC9CiO4SJmi3Yg5wVG9tR9NyeqNyHUJKPSWqqLFMVMdNfbVlzRTmoaHVAKxZrb7RfebCMTF3HIC16zR3eqhb3UGKUf3t1uXUnCrcJVN5i0GV6PseVMX7wQOqVJdsSPorqlD3mprfZTnTjRDjszGIfS/FPHRzVDF3ky47vlIqE3/rJCp37HEctqB9bWdSRy9vXYsOJW0HE6xP5cySUjZHvsONh7MKESkC7wKeCewDdonIjSGEe1LFng1caH9PBv7Olo7jOI7jnKW4Uu04Wa4AdocQ9oQQasAHgatyZa4C3hOUrwDDInLe6Q7UcRzHcZwzhwWV6lZojUzMzjzwyX/fdbriWTb/dsutKx3Co4GtKx3ACrIReCi1vo8TVej5ymwEDuYrE5GrgattdUpE7n34QnVSrAVGVjqIlULeutIROPg56Kw8fg4+cizpumjBi+oQwrqHJxbH+b5hviSY/EzuSymjG0O4DrhuuUE5CyMit4cQdq50HM7Zi5+Dzkrj5+DK4+kfjpNlH7A5tb4JOHAKZRzHcRzHOYvwi2rHybILuFBEtotIF/Bi4MZcmRuBl5sLyFOA8RDCCakfjuM4juOcPSzBp9pxzh5CCA0ReTVwM2qrcn0I4W4Rucb2XwvcBDwH2A3MAK9cqXidBE+xcVYaPwedlcbPwRVGkimvHcdxHMdxHMc5JTz9w3Ecx3Ecx3GWiV9UO47jOI7jOM4y8Ytqx3FOOyLyJhF53UrH4ZwdiMjbRORuEXnbSsfiPPoQkRtE5AWPQL1XisinHu56c/X/0AL7px6pth+t+IOKjuM8ahARQZ8Vaa10LM4Zxa8A60II1aUUFpFSCKHxCMe0WAzFEEJzJWNwTg8r+F5fCUwBX3qkGjjbzmNXqh3HecQRkZeLyF0icqeIvDe375dFZJft+6iI9Nr2F4rIt2z7rbbtMhG5TUTusPouFJFtIvJtEflb4OvAZhF5iYh8045/a6qtTtunROStIvI1EfkPEblCRD4rIntE5GdPzyg585E/d0Rkq4jcYttuEZEtVu4GEXmHiHzJ3rcX2PYbgT7gqyLyIhFZZ+fZLvt7mpV7k4hcJyKfAd4jIkVTuHdZW79i5a60c+MjIvIdEXmf/ZhDRC639u+083SgUz0d+nqliPyXiLwf+KZt+007X78lIq9NlT1hu30WviMi77bt7xORZ4jIF0XkeyJyxSPxHp1tdPg++5F5zr2M0iwi7xSRV9jr+0XkD0TkC8ALReQC++65U0S+LiI77LD++c61DnH9hIh8w77jrheR7lRba+31Tjt/twHXAP/Dvk9/WNRK9st2rv5xql6xc/hbVveLFtl+wnl81hBC8D//8z//e8T+gMuAe4G1tr4aeBPwOltfkyr7J8Br7PU3gY32etiWfwO8zF53AT3ANqAFPMW2bwAeBNahd+P+E3hep+12TACeba8/DnwGKANPAO5Y6TE8W/86nDufBH7B1n8R+IS9vgH4F1QsuhTYnapnKvX6/cDT7fUW4Nv2+k3A14AeW78aeKO97gZuB7aj6t44OulTAfgy8HQ7H/cAl9sxg3aezVtPh/5eCUzH/cAP2uegD+gH7gZ+YIHt24AG8DiL7WvA9egssFfFsfK/h/2cnPfcs/fzU6lj3wm8wl7fD/x2at9Xgefb6wrQ2+lc6xBXBXgIuMjW3wO8NtVWjHcn8NnUOf+6VB03Ai+3178WPzfAfwP+HbWZXY9+j563wPbMeXw2/blS7TjOI82PAx8JIYwAhBBGc/sfKyKfF5FvAi9D/9MC+CJwg4j8MvqlDfqfyu+KyOuBrSGEWdv+QAjhK/b6cvQ/jaNBb+G/D/iRBbYD1IB/s9ffBD4XQqjb623LHwLnFJnv3HkqemEM8F70gjbyiRBCK4RwD/qf/Hw8A3iniNyBXkQMisiA7bsxdU79JDrJ0x3oBc8a4ELbd1sIYV/QNKM70HPkYuBgCGGXxTph59lC9czHbSGEvfb66cDHQwjTIYQp4GPADy+wHWBvCOGbFtvdwC1Br4z8XH546PR9tpRzL8+HAOz82xhC+LjVORdCmLEy851r83Ex+t5/19b/ifb321J5GvABe52+o/h04AMhhGYI4TDwOfT7tNP2GPdezjI8p9pxnEcaQZXgTtyAKsZ32q3RKwFCCNeIyJOB5wJ3iMgTQwjvF5Gv2rabReSXUHVwOtdepzg6UbcLD1DVu2oxtETEvydXjsXOHXL70znTnd7vAvDU1MWzFta76vnz6DUhhJtz5a7MtdNE/y/tFOu89SzAcs/ldGyt1HoL/z//4aDT+zzfudcgm2ZbyR0T3+ulvp/xXOsUVyfSceRjyNPpHD7ZNqcX2PeoxZVqx3EeaW4Bfk5E1gCIyOrc/gHgoIiUUaUaK7cjhPDVEMIfACNorvT5wJ4QwjtQlfHx87T3VeBHRWStiBSBl6AKSqftzpnLfOfOl4AX2/6XAV84yTo/A7w6rojIEzuUuxn4VTsvEZGLRKRvgXq/A2wQkcut/ID9IDvZetLcCjxPRHrtmOcDn19gu/PIs9j3WZoHgEtFpFtEhoCfmK9QCGEC2Cciz7M6u8WeLTkJvgNsE5ELbP3naX+/3Y+mDIGmbEQm0e/fyBfJfrYitwIvEn0+YB2qgN+2wPazFv/V6jjOI0rQad7fDHxORJrAN9Av+cjvoxe8D6C3qOOX/NtE5EJUDbkFuBN4A/DfRaQOHAL+CM1dTbd3UER+B/gvO/amEML/Bei03Tkz6XDu/DpwvYj8FnAUeOVJVvvrwLtE5C70/8Bb0Qe28rwbvdX+dXs47Ciam98p1po9qPU3ItIDzKKpJidVT67Or4vIDbQvVN4dQvgG6IOZ+e328JnzCNLhnOxU9iER+TBwF/C9hcqiF8H/R0T+CKgDLzzJuOZE5JXAv9iPuV3Atbb7D4F/EJHfRb9rI58EPiIiVwGvAX4DeL+I/Abw0VS5j6NpV3eiSvZvhxAOyf9r7w5xAARiIAC2T8Fh0DyNt6MOg4EEcWmAQGZkVeWKvWvm1Xzs2f1PnCkHAIAi9Q8AAChS/wCAB2XmFMffFSIi1tba/MY+fMteuxhO46XjMSw3Uf8AAIAi9Q8AACgSqgEAoEioBgCAIqEaAACKhGoAACjaACFzR8BvrdS3AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Run this cell to view randomly selected images and model predictions\n", + "\n", + "# Get images and ground truth labels\n", + "test_generator = get_generator(image_gen_aug, test_dir, seed=seed)\n", + "batches = []\n", + "for i in range(num_batches):\n", + " batches.append(next(test_generator))\n", + " \n", + "batch_images = np.vstack([b[0] for b in batches])\n", + "batch_labels = np.concatenate([b[1].astype(np.int32) for b in batches])\n", + "\n", + "# Randomly select images from the batch\n", + "inx = np.random.choice(predictions.shape[0], 4, replace=False)\n", + "print(inx)\n", + "\n", + "fig, axes = plt.subplots(4, 2, figsize=(16, 12))\n", + "fig.subplots_adjust(hspace=0.4, wspace=-0.2)\n", + "\n", + "for n, i in enumerate(inx):\n", + " axes[n, 0].imshow(batch_images[i])\n", + " axes[n, 0].get_xaxis().set_visible(False)\n", + " axes[n, 0].get_yaxis().set_visible(False)\n", + " axes[n, 0].text(30., -3.5, lsun_classes[np.where(batch_labels[i] == 1.)[0][0]], \n", + " horizontalalignment='center')\n", + " axes[n, 1].bar(np.arange(len(predictions[i])), predictions[i])\n", + " axes[n, 1].set_xticks(np.arange(len(predictions[i])))\n", + " axes[n, 1].set_xticklabels(lsun_classes)\n", + " axes[n, 1].set_title(f\"Categorical distribution. Model prediction: {lsun_classes[np.argmax(predictions[i])]}\")\n", + " \n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congratulations! This completes the first part of the programming assignment using the tf.keras image data processing tools." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Part 2: tf.data\n", + "\n", + "![CIFAR-100 overview image](data/cifar100/cifar100.png)\n", + "\n", + "#### The CIFAR-100 Dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the second part of this assignment, you will use the [CIFAR-100 dataset](https://www.cs.toronto.edu/~kriz/cifar.html). This image dataset has 100 classes with 500 training images and 100 test images per class. \n", + "\n", + "* A. Krizhevsky. \"Learning Multiple Layers of Features from Tiny Images\". April 2009 \n", + "\n", + "Your goal is to use the tf.data module preprocessing tools to construct a data ingestion pipeline including filtering and function mapping over the dataset to train a neural network to classify the images." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Load the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz\n", + "169009152/169001437 [==============================] - 3s 0us/step\n" + ] + } + ], + "source": [ + "# Load the data, along with the labels\n", + "\n", + "(train_data, train_labels), (test_data, test_labels) = cifar100.load_data(label_mode='fine')\n", + "with open('data/cifar100/cifar100_labels.json', 'r') as j:\n", + " cifar_labels = json.load(j)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Display sample images and labels from the training set" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Display a few images and labels\n", + "\n", + "plt.figure(figsize=(15,8))\n", + "inx = np.random.choice(train_data.shape[0], 32, replace=False)\n", + "for n, i in enumerate(inx):\n", + " ax = plt.subplot(4, 8, n+1)\n", + " plt.imshow(train_data[i])\n", + " plt.title(cifar_labels[int(train_labels[i])])\n", + " plt.axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create Dataset objects for the train and test images\n", + "\n", + "You should now write a function to create a `tf.data.Dataset` object for each of the training and test images and labels. This function should take a numpy array of images in the first argument and a numpy array of labels in the second argument, and create a `Dataset` object. \n", + "\n", + "Your function should then return the `Dataset` object. Do not batch or shuffle the `Dataset` (this will be done later)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following function. \n", + "# Make sure to not change the function name or arguments.\n", + "\n", + "def create_dataset(data, labels):\n", + " \"\"\"\n", + " This function takes a numpy array batch of images in the first argument, and\n", + " a corresponding array containing the labels in the second argument.\n", + " The function should then create a tf.data.Dataset object with these inputs\n", + " and outputs, and return it.\n", + " \"\"\"\n", + " \n", + " dataset = tf.data.Dataset.from_tensor_slices((data,labels))\n", + " return dataset\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Run the below cell to convert the training and test data and labels into datasets\n", + "\n", + "train_dataset = create_dataset(train_data, train_labels)\n", + "test_dataset = create_dataset(test_data, test_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(TensorSpec(shape=(32, 32, 3), dtype=tf.uint8, name=None), TensorSpec(shape=(1,), dtype=tf.int64, name=None))\n", + "(TensorSpec(shape=(32, 32, 3), dtype=tf.uint8, name=None), TensorSpec(shape=(1,), dtype=tf.int64, name=None))\n" + ] + } + ], + "source": [ + "# Check the element_spec of your datasets\n", + "\n", + "print(train_dataset.element_spec)\n", + "print(test_dataset.element_spec)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Filter the Dataset\n", + "\n", + "Write a function to filter the train and test datasets so that they only generate images that belong to a specified set of classes. \n", + "\n", + "The function should take a `Dataset` object in the first argument, and a list of integer class indices in the second argument. Inside your function you should define an auxiliary function that you will use with the `filter` method of the `Dataset` object. This auxiliary function should take image and label arguments (as in the `element_spec`) for a single element in the batch, and return a boolean indicating if the label is one of the allowed classes. \n", + "\n", + "Your function should then return the filtered dataset.\n", + "\n", + "**Hint:** you may need to use the [`tf.equal`](https://www.tensorflow.org/api_docs/python/tf/math/equal), [`tf.cast`](https://www.tensorflow.org/api_docs/python/tf/dtypes/cast) and [`tf.math.reduce_any`](https://www.tensorflow.org/api_docs/python/tf/math/reduce_any) functions in your auxiliary function. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following function. \n", + "# Make sure to not change the function name or arguments.\n", + "\n", + "def filter_classes(dataset, classes):\n", + " \"\"\"\n", + " This function should filter the dataset by only retaining dataset elements whose\n", + " label belongs to one of the integers in the classes list.\n", + " The function should then return the filtered Dataset object.\n", + " \"\"\"\n", + " def filterer(image,label):\n", + " \n", + " \n", + " flag = tf.math.reduce_any(tf.equal(label,classes))\n", + " return flag\n", + " \n", + " \n", + " dataset = dataset.filter(filterer)\n", + " return dataset\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Run the below cell to filter the datasets using your function\n", + "\n", + "cifar_classes = [0, 29, 99] # Your datasets should contain only classes in this list\n", + "\n", + "train_dataset = filter_classes(train_dataset, cifar_classes)\n", + "test_dataset = filter_classes(test_dataset, cifar_classes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Apply map functions to the Dataset\n", + "\n", + "You should now write two functions that use the `map` method to process the images and labels in the filtered dataset. \n", + "\n", + "The first function should one-hot encode the remaining labels so that we can train the network using a categorical cross entropy loss. \n", + "\n", + "The function should take a `Dataset` object as an argument. Inside your function you should define an auxiliary function that you will use with the `map` method of the `Dataset` object. This auxiliary function should take image and label arguments (as in the `element_spec`) for a single element in the batch, and return a tuple of two elements, with the unmodified image in the first element, and a one-hot vector in the second element. The labels should be encoded according to the following:\n", + "\n", + "* Class 0 maps to `[1., 0., 0.]`\n", + "* Class 29 maps to `[0., 1., 0.]`\n", + "* Class 99 maps to `[0., 0., 1.]`\n", + "\n", + "Your function should then return the mapped dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following function. \n", + "# Make sure to not change the function name or arguments.\n", + "\n", + "def map_labels(dataset):\n", + " \"\"\"\n", + " This function should map over the dataset to convert the label to a \n", + " one-hot vector. The encoding should be done according to the above specification.\n", + " The function should then return the mapped Dataset object.\n", + " \"\"\"\n", + " def One_hot(image,label):\n", + "\n", + " if label== 0:\n", + "\n", + " label = [1., 0., 0.]\n", + "\n", + " elif label== 29:\n", + "\n", + " label =[0., 1., 0.]\n", + "\n", + " else:\n", + "\n", + " label = [0., 0., 1.]\n", + " \n", + "\n", + " \n", + " return (image,label)\n", + " \n", + " dataset = dataset.map(One_hot)\n", + " return dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Run the below cell to one-hot encode the training and test labels.\n", + "\n", + "train_dataset = map_labels(train_dataset)\n", + "test_dataset = map_labels(test_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(TensorSpec(shape=(32, 32, 3), dtype=tf.uint8, name=None),\n", + " TensorSpec(shape=(3,), dtype=tf.float32, name=None))" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_dataset.element_spec" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The second function should process the images according to the following specification:\n", + "\n", + "* Rescale the image pixel values by a factor of 1/255.\n", + "* Convert the colour images (3 channels) to black and white images (single channel) by computing the average pixel value across all channels. \n", + "\n", + "The function should take a `Dataset` object as an argument. Inside your function you should again define an auxiliary function that you will use with the `map` method of the `Dataset` object. This auxiliary function should take image and label arguments (as in the `element_spec`) for a single element in the batch, and return a tuple of two elements, with the processed image in the first element, and the unmodified label in the second argument.\n", + "\n", + "Your function should then return the mapped dataset.\n", + "\n", + "**Hint:** you may find it useful to use [`tf.reduce_mean`](https://www.tensorflow.org/api_docs/python/tf/math/reduce_mean?version=stable) since the black and white image is the colour-average of the colour images. You can also use the `keepdims` keyword in `tf.reduce_mean` to retain the single colour channel." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following function. \n", + "# Make sure to not change the function name or arguments.\n", + "\n", + "def map_images(dataset):\n", + " \"\"\"\n", + " This function should map over the dataset to process the image according to the \n", + " above specification. The function should then return the mapped Dataset object.\n", + " \"\"\"\n", + "\n", + " \n", + " def rescale(image, label):\n", + "\n", + " image = tf.cast(image, tf.float32) / 255.0\n", + "\n", + " image = tf.reduce_mean(image,axis = 2,keepdims=True)\n", + "\n", + " return (image, label)\n", + " \n", + " \n", + " \n", + " dataset = dataset.map(rescale)\n", + " return dataset\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Run the below cell to apply your mapping function to the datasets\n", + "\n", + "train_dataset_bw = map_images(train_dataset)\n", + "test_dataset_bw = map_images(test_dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Display a batch of processed images" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Run this cell to view a selection of images before and after processing\n", + "\n", + "plt.figure(figsize=(16,5))\n", + "plt.suptitle(\"Unprocessed images\", fontsize=16)\n", + "for n, elem in enumerate(train_dataset.take(10)):\n", + " images, labels = elem\n", + " ax = plt.subplot(2, 5, n+1)\n", + " plt.title(cifar_labels[cifar_classes[np.where(labels == 1.)[0][0]]])\n", + " plt.imshow(np.squeeze(images), cmap='gray')\n", + " plt.axis('off')\n", + " \n", + "plt.figure(figsize=(16,5))\n", + "plt.suptitle(\"Processed images\", fontsize=16)\n", + "for n, elem in enumerate(train_dataset_bw.take(10)):\n", + " images_bw, labels_bw = elem\n", + " ax = plt.subplot(2, 5, n+1)\n", + " plt.title(cifar_labels[cifar_classes[np.where(labels_bw == 1.)[0][0]]])\n", + " plt.imshow(np.squeeze(images_bw), cmap='gray')\n", + " plt.axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now batch and shuffle the Dataset objects." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Run the below cell to batch the training dataset and expand the final dimensinos\n", + "\n", + "train_dataset_bw = train_dataset_bw.batch(10)\n", + "train_dataset_bw = train_dataset_bw.shuffle(100)\n", + "\n", + "test_dataset_bw = test_dataset_bw.batch(10)\n", + "test_dataset_bw = test_dataset_bw.shuffle(100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Train a neural network model\n", + "\n", + "Now we will train a model using the `Dataset` objects. We will use the model specification and function from the first part of this assignment, only modifying the size of the input images." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Build and compile a new model with our original spec, using the new image size\n", + " \n", + "cifar_model = get_model((32, 32, 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_dataset_bw" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/15\n", + "150/150 [==============================] - 19s 127ms/step - loss: 1.0330 - categorical_accuracy: 0.4607 - val_loss: 0.0000e+00 - val_categorical_accuracy: 0.0000e+00\n", + "Epoch 2/15\n", + "150/150 [==============================] - 18s 122ms/step - loss: 0.9124 - categorical_accuracy: 0.5660 - val_loss: 0.8098 - val_categorical_accuracy: 0.6433\n", + "Epoch 3/15\n", + "150/150 [==============================] - 19s 124ms/step - loss: 0.8025 - categorical_accuracy: 0.6567 - val_loss: 0.7377 - val_categorical_accuracy: 0.7267\n", + "Epoch 4/15\n", + "150/150 [==============================] - 19s 126ms/step - loss: 0.7323 - categorical_accuracy: 0.6940 - val_loss: 0.6823 - val_categorical_accuracy: 0.7367\n", + "Epoch 5/15\n", + "150/150 [==============================] - 18s 122ms/step - loss: 0.6936 - categorical_accuracy: 0.7160 - val_loss: 0.6610 - val_categorical_accuracy: 0.7333\n", + "Epoch 6/15\n", + "150/150 [==============================] - 18s 123ms/step - loss: 0.6527 - categorical_accuracy: 0.7307 - val_loss: 0.6424 - val_categorical_accuracy: 0.7533\n", + "Epoch 7/15\n", + "150/150 [==============================] - 18s 121ms/step - loss: 0.6353 - categorical_accuracy: 0.7353 - val_loss: 0.6299 - val_categorical_accuracy: 0.7567\n", + "Epoch 8/15\n", + "150/150 [==============================] - 18s 121ms/step - loss: 0.5887 - categorical_accuracy: 0.7553 - val_loss: 0.6124 - val_categorical_accuracy: 0.7533\n", + "Epoch 9/15\n", + "150/150 [==============================] - 18s 121ms/step - loss: 0.5996 - categorical_accuracy: 0.7613 - val_loss: 0.6354 - val_categorical_accuracy: 0.7500\n", + "Epoch 10/15\n", + "150/150 [==============================] - 18s 121ms/step - loss: 0.5592 - categorical_accuracy: 0.7700 - val_loss: 0.6084 - val_categorical_accuracy: 0.7533\n", + "Epoch 11/15\n", + "150/150 [==============================] - 18s 123ms/step - loss: 0.5527 - categorical_accuracy: 0.7720 - val_loss: 0.6201 - val_categorical_accuracy: 0.7433\n", + "Epoch 12/15\n", + "150/150 [==============================] - 18s 121ms/step - loss: 0.5486 - categorical_accuracy: 0.7787 - val_loss: 0.5873 - val_categorical_accuracy: 0.7633\n", + "Epoch 13/15\n", + "150/150 [==============================] - 18s 122ms/step - loss: 0.5131 - categorical_accuracy: 0.7913 - val_loss: 0.5851 - val_categorical_accuracy: 0.7800\n", + "Epoch 14/15\n", + "150/150 [==============================] - 18s 123ms/step - loss: 0.5064 - categorical_accuracy: 0.7987 - val_loss: 0.5622 - val_categorical_accuracy: 0.7767\n", + "Epoch 15/15\n", + "150/150 [==============================] - 18s 122ms/step - loss: 0.4948 - categorical_accuracy: 0.8007 - val_loss: 0.5729 - val_categorical_accuracy: 0.7900\n" + ] + } + ], + "source": [ + "# Train the model for 15 epochs\n", + "\n", + "history = cifar_model.fit(train_dataset_bw, validation_data=test_dataset_bw, epochs=15)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot the learning curves" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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gSw3PBcA59xLwEkBOTo76rIiIVMM5x6Y9B5jvS/gW5O9hyaYiDpZXApASF0lWRhLDu7cgISq82iTu8M9xkWGEhmj2t8YkIiyEUX1b89xnq8nbsY/MlNhghyQi4hexsUd/n61evZpnn32W2bNnk5SUxE033VTt8gwREUcfaIaGhlJeXl5t2ZGRkV87xkt76oZAJoJzgI5m1hbYBFwP3HDcMRuBYcA0M2sBdAbWAXtqcK6IiJxAycFyFuXvYX7+niPJ346SgwBEhoXQIy2Rm/q34ezWSWRlJJGWFK2pveWkbujXmuenrOH1WRv42SXdTn2CiEg1Trflrjbs3buX+Ph4EhIS2LJlC5MmTWLkyJF+vcbgwYN55513OOecc1i8eDHLlgVvLsyAJYLOuXIzuweYBIQC45xzS83sTt/+F4D/B7xiZovxuoM+5JzbAVDduYGKVUSkPquodKwqLD7SxXN+/m5Wbyvh8EPHdimxnNsxxZf0NaFLy3jCQ7WMrHwzLRKiGNEjlXfm5vPAhZ2JjggNdkgiIn6VnZ1Nt27d6NGjB+3atWPQoEF+v8YPfvADRo8eTc+ePcnOzqZHjx4kJgZn3LzVpebJM5WTk+Pmzp0b7DBERAKqcG/pkVa++Rt3s3hTEfsPVQCQFBNOVkYSZ2c0Iat1Er3SE0lqgGPyzCzXOZcT7DjqC3/Vj7PX7+LaF2fyxFVncX1fjcsXkZpZvnw5Xbt2DXYYdUJ5eTnl5eVERUWxevVqLrzwQlavXk1Y2Om1z1X33da0jgxk11AREfGD/YfKmbR0K5OXFTJ/4x62FHnjFcJDjW4tE/h273TObt2ErIwk2iTHqIunBEyfzCZ0SY3n1ZkbuK5Phv6tiYh8QyUlJQwbNozy8nKcc7z44ounnQSeKSWCIiJ1kHOO2et3MXFeAf9dtIV9hypomRhFTmZTzs5IIqt1Et1aJhAVXk+65zkHBXNh3qvQ7QroeEGwI5LTYGaMHpDJI+8vJnfDbnIymwY7JBGReiUpKYnc3NxghwEoERQRqVPyd+1n4rwCJs4rIH/XAWIjQrn4rJZc3TudvplNCalvs3Xu3wWL3oZ5E2DbMgiPhZa9gh2VnIErzm7F4x8t59WZG5QIiojUY0oERUSCrORgOR8u3sLE3AK+Wr8LMxjYPpkfXdCJkT1SiYmoZ7+qKyshb5rX+rf831BxCFplw6XPQverICoh2BHKGYiJCOPbvTOYMDOPbd/qSvOEqGCHJCIip6Ge/XUhItIwVFY6Zq7bycTcAj5aspUDZRVkJsfw4IWduDI7nbSk6GCH+M0Vb4UFb8C812D3eohKhN63QvbNkHpWsKMTP7p5QBvGfbmeN2fn88MLOgY7HBEROQ1KBEVEatH6HfuYmFvA+/M3sWnPAeIjw7ji7DSu6Z1Gdusm9W/yjYpyWPOp1/Vz1cfgKqDNYBj6CHS9FMLrYUIrp9Q2JZZzOzXjja82cPfQ9lqORESkHtJvbhGRACs6UMbfv9rI1X+ZwdDff86fP19D++ZxPDfqbOb87AIev+oserdpWr+SwN158Nmv4Jmz4M3roGA2DLwH7smFW/8LPa9VEngazGycmW0zsyUn2G9m9pyZrTGzRWaWXdsxHnbLgDZsKz7IJ0sLgxWCiEiNDBkyhEmTJh2z7ZlnnuHuu+8+4TlxcXEAbN68mWuuueaE5Z5qaZ5nnnmG/fv3H/l88cUXs2fPnpqGHlBqERQRCYCKSse01duZOG8TnyzdysHySjo0j2PsRV248uw0WtTHcVXlB2HFf73Wv3VTAIMOF8DFv4NOIyE0PNgRNgSvAH8CJpxg/0VAR9+rH/AX33utG9K5ORlNo3l1Zh7f6tkyGCGIiNTIqFGjeOuttxgxYsSRbW+99RZPPvnkKc9t1aoV77777mlf+5lnnuGmm24iJiYGgA8//PC0y/I3JYIiIn60urCYd+cV8M/5myjce5DE6HCu65PB1dnp9ExPPP1Wv+KtkDcdYpIhPhXiWkB0E6iNVsTtK73kb+GbsH8nJGbAkIch60ZIygj89RsR59xUM8s8ySGXAxOccw6YZWZJZtbSObelVgKsIjTEuKlfGx7/aAUrtu6lS6omARKRuumaa67hZz/7GQcPHiQyMpK8vDw2b95MVlYWw4YNY/fu3ZSVlfGrX/2Kyy+//Jhz8/LyuOSSS1iyZAkHDhzg1ltvZdmyZXTt2pUDBw4cOe6uu+5izpw5HDhwgGuuuYZf/vKXPPfcc2zevJmhQ4eSkpLClClTyMzMZO7cuaSkpPDUU08xbtw4AG6//Xbuu+8+8vLyuOiiixg8eDAzZswgLS2Nf/3rX0RH+7+XjRJBEZEzUFpWwcqtxeRu2M2/FmxiYUERoSHGkE7NePTSdM7v2pzIsDNY669oE3z5LOS+AhUHj90XGuklhPEtfO+pEJfq+5zqfY5PhZgUCPmGIwEO7YOl//QSwPxZEBIGnS+G3rdAu6EQUk/WL2x40oD8Kp8LfNtqPREEuDYng6cmr2LCzA385kpNCCQidVNycjJ9+/bl448/5vLLL+ett97iuuuuIzo6mvfff5+EhAR27NhB//79ueyyy0740PYvf/kLMTExLFq0iEWLFpGdfbR3/q9//WuaNm1KRUUFw4YNY9GiRdx777089dRTTJkyhZSUlGPKys3NZfz48Xz11Vc45+jXrx/nnXceTZo0YfXq1bz55pv89a9/5dprr2XixIncdNNNfv9elAiKiNTQ/kPlLN9SzJJNRd5r815WFxZTXukA6JIaz8++1ZXLs9JoFh95ZhfbsxGmPw3zXwdXCb2uh5zboKwUSrZCceHR9+ItsHON12JYWs24AwuFuOZVksXj31OPJpOFS71lHxa/Cwf3QnIHGP4Y9LoB4pqd2T2JP1T314mr9kCzO4A7AFq3bh2QYJrERnBZr1a8P28TD43sQmK0ugeLyCl8NBa2LvZvmalnwUVPnPSQw91DDyeC48aNwznHI488wtSpUwkJCWHTpk0UFhaSmppabRlTp07l3nvvBaBnz5707NnzyL533nmHl156ifLycrZs2cKyZcuO2X+86dOnc+WVVxIbGwvAVVddxbRp07jsssto27YtWVlZAPTu3Zu8vLxv8m3UmBJBEZFqFJeWsXTzXpZsKjryvnZ7Cb6cj+TYCHqkJXJ+l2b0aJVIj7REMprGnPmFd62DaU953TAxOPsmGPwjaNKmZueXlUJJofcq3nr0vXirlzgWbYJNubBvByfIHyAsCrpdAdmjoc3A2ul+KjVVAFTtj5sObK7uQOfcS8BLADk5OSf4j33mbhmYyT9yC5iYW8Btg9sG6jIiImfkiiuu4P7772fevHkcOHCA7OxsXnnlFbZv305ubi7h4eFkZmZSWlp60nKqay1cv349v//975kzZw5NmjRhzJgxpyzH6+FfvcjIow+TQ0NDj+mC6k9KBEWk0duz/xBLNu1lyeaiI4nf+h37juxvkRDJWWmJXHxWS3qkJdIjLYHUhCj/zvK5YzVM+wMsesfrhplzGwz6ISSmf7NywqO8pPFUiWNFGezbfmyyWFIIsSnQ42pv/KHURR8A95jZW3iTxBQFY3xgVT3SEsluncRrszYwZmAmISF6cCAiJ3GKlrtAiYuLY8iQIdx2222MGjUKgKKiIpo3b054eDhTpkxhw4YNJy3j3HPP5Y033mDo0KEsWbKERYsWAbB3715iY2NJTEyksLCQjz76iCFDhgAQHx9PcXHx17qGnnvuuYwZM4axY8finOP999/ntdde8/+Nn4QSQRFpVHaUHDzatdOX/BXsPvqkLS0pmrPSErk6O43uaYl0b5VA8/gAzvC5bTlM/T0sfc8b89fvThh0r9dlM5BCwyGhlfeSOsPM3gSGAClmVgD8AggHcM69AHwIXAysAfYDtwYn0mONHpDJfW8vYPqaHZzbSV2IRaRuGjVqFFdddRVvvfUWADfeeCOXXnopOTk5ZGVl0aVLl5Oef9ddd3HrrbfSs2dPsrKy6Nu3LwC9evXi7LPPpnv37rRr145BgwYdOeeOO+7goosuomXLlkyZMuXI9uzsbMaMGXOkjNtvv52zzz47YN1Aq2Mna5asb3Jyctyp1vIQkYalvKKS4tJyikvL2Vta5vv5uPeD5azbXsKSTXvZuvdoV43M5BhfC18iPVp5SV+T2IjaCXzrYpj6JCz7AMJjoO/tMOAHGodXQ2aW65zLCXYc9UWg68eD5RUMeuIzsjKS+NstfQJ2HRGpn5YvX07Xrl2DHUaDVN13W9M6Ui2CIhJU5RWVbCkqpejAiZO44tIy9h5O9g6UVTmmnANlFae8RmRYCBlNYxjQPpnurRLokZZIt1YJJEQFYWKLzfPhiydh5X8hMgHOeQD63w2xybUfi4ifRIaFcn2f1jz/+Rryd+33z3hZEREJKCWCIlKrKisdy7bsZda6ncxcu5PZ63dRfLD8hMdHhoUQHxVOQlQY8VFhxEeF0zIx6sjPCVHhvp/Dqhx37LaIsG+4dEIg5M+Bqb+D1Z9AVKK3Dl+/72ksnjQYN/RrzV++WMvrX23g4Yv05F9EpK5TIigiAeWcY1VhCTPX7mDmup3MWreLogNlALRNieWSXq3IykgkMTqi7iZxZ2LDDPjid7BuCkQ3hfN/Dn3vgCgtvi0NS6ukaIZ3bcHbc/L50QWdiArXWpMiInWZEkER8SvnHOt27GPm2p3MXLeTr9btZEfJIQDSm0RzYbcWDOyQTP92ybRMjA5ytAHiHORN8xLAvGkQ28xbiy/nOxAZF+zoRAJm9MA2fLx0K/9euJlv52Sc+gQRaTScc/6dbVtOugRFTSgRFJEzlr9rPzPW7jiS/BXuPQhAakIU53RsxoD2yQxol9zwxw05B2s/8xLA/FneQu0jHofeYyCigd+7CDCgXTIdm8fx6sw8rumdrj/6RASAqKgodu7cSXJysn4v+Ilzjp07dxIVdfozmysRFJFvbPOeA0eSvplrd7Jpj7f8QkpcBP3bJTOwfQoD2ieTmRzTOH7hOwerJ8MXT3iLtSekwcW/h7Nv9tb1E2kkzIzRA9rw838tZX7+HrJbawysiEB6ejoFBQVs37492KE0KFFRUaSnf8P1hqtQIigip7StuJSZa3cemeAlb+d+AJJiwunfNpnvndeOAe2S6dA8rnEkflVtXwkf/QTWfQ6JreGSZyDrBgiLDHZkIkFxZXY6v/14Ja/N3KBEUEQACA8Pp23btsEOQ46jRFBEjnDOsbmolJVb97J8SzErtxazdHMRa7fvAyA+Kox+bZO5eUAmA9ol0yU1npCQRpb4HVZa5HUB/eoFiIiFkb+FPt/xFmoXacTiIsO4OjuNN2fn89NvdSUlTg9FRETqIiWCIo1U0YEyVhUWs2JrMSu27GXl1mJWFhZTXHp0KYe0pGi6pMZzbU6Gbw2+REIba+J3WGUlLHwTPn0U9m2H7Jth2C8gNiXYkYnUGTcPyOTVmRt4e04+3x/aIdjhiIhINZQIijRwh8orWbejhJVbj036NheVHjkmPiqMrqkJXJGVRufUeLqkxtMpNT44C67XZZty4cOfwKa5kN4Hbngb0rKDHZVIndOheRyDOiTz+qwNfO/cdoSF1vNlYEREGiAlgiL+4hwc2A3FW6FkKxQXQmU5xLWA+BYQ3xJiUiAkMH8QVe3W6SV8XtfOdTtKKKvwphcODzXaN4ujb9umdE5NoEtqPJ1T42mZGNX4xvZ9EyXb4X+/hPmve0tBXPEX6Hl9wP5bijQEowdk8r3Xcvl0eSEje7QMdjgiInIcJYIip1JZAft2HE3ujnnfCiWFRz9XHDp5WRYKcc19yWFqNe+pvvfmpxxrVlHp+M+izczJ2+UlfdV06+ycGs/5XZvTJTWeLqkJtE2Jrf8LtNemijKY8zeY8jiU7YOB98C5P9Fi8CI1MKxLc9KSopkwc4MSQRGROiigiaCZjQSeBUKBvznnnjhu/4+BG6vE0hVo5pzbZWZ5QDFQAZQ753ICGas0QhVlxyZxR5K6495LtoGr+Pr5UUle0hafCm0Geq1+calV3lMhJNQ7v2qZh1sMizZ5XQ337QCOXxDUICb5BMliC1bsi+HJGUVM3xpGRFQMXVLjuTyrFZ1TE+iqbp0gT+HkAAAgAElEQVT+se4L+Ogh2L4c2p/vTQbTrFOwoxKpN8JCQ7ihX2uenLSS1YXFdGwRH+yQRESkioAlgmYWCjwPDAcKgDlm9oFzbtnhY5xzTwJP+o6/FPiRc25XlWKGOud2BCpGaaT27YTPHoN5r1WT4Jk36cfhRC61R5VWuioJWVyLmq8P1yTz5PsryrxJR6pLRA8njduWw75tXldToAvwMkAUuKhErDwVilpARSrsbQGbq8bc0ktOI4PwR9jB4moS7S1fb1m1EC/Z6jQSOlwAMU1rP9bD9uTDJz+FZf+CpDZw/d+h88WgrrMi39j1fTJ49tPVvDZrA49d3iPY4YiISBWBbBHsC6xxzq0DMLO3gMuBZSc4fhTwZgDjkcauohzmjoMpv4KDJZA9Glr2OjbJi20OobXcYzo0HBJaea8TOFReyfjpa3n9s3k0qdjNzWdFcln7ECIPbMeqJo/5s7zkquLg1wsJj62m1bKaVszoJidPeg6PhTwmaa0muSsu9LpTfu1+I49er1knaHsuHNoHaybDkoleUpjeBzqNgI4joEX32knCyg7Al8/B9Ke9z0N/CgN/AOHRgb+2SAOVHBfJJT1bMjG3gB+P6Ey8eiqIiNQZgfyLNw3Ir/K5AOhX3YFmFgOMBO6pstkBn5iZA150zr0UqEClEcib7s32uG0ptD0PLvodNO8S7Khq5ItV2/nlv5eybvs+hnVpx88v6UZmSuyJT3AOSvdUn5gdbpnbsghKJsOhkq+fHxp5dIKbuBZe69z+XVW60Z4g0YyIO5pQt8yCTsd3a/W1TEYlVZ/YVVbC5vmwehKsmgT/e8x7JaRDpwu9pLDtuRARc/pf5om+rxX/gUmPwJ6N0O0KuPBXkJTh3+uINFKjB2by3vxNvD9/E6MHZAY7HBER8QlkIljdI/zjB0Iddinw5XHdQgc55zabWXNgspmtcM5N/dpFzO4A7gBo3br1mcYsDU1RAXzyc1j6HiRmwLUToOtl9aKb38ad+3nsP8v4dHkhmckxjB/Th6Fdmp/6RDOvVS+6yamT3YMlVVr2qkkad66B/F1eMhjXAtoMOJrcHTO5TQuIjDuzGw4JgfTe3mvoI15Mqz/xksJF73ituWFRkHmO11rYaQQkneH/89tXeuMA102BZl1h9AfQ7rwzK1NEjpGVkUSv9ERenZHHzf3baIZiEZE6IpCJYAFQ9ZF6OrD5BMdez3HdQp1zm33v28zsfbyupl9LBH0thS8B5OTknCjRlMamrBRm/hGmPQWuEs4bC4N+6P/WpADYf6icv3y+lhenriMsxHhoZBduG5xJZFio/y8WGee9ktv7v+wzFZ/qdd/NHg3lB2HDl7DqE1j1MXw4GT580EveDieF6X1r3q23dC988Vv46gWvy+zI30Kf22u/W7BII3HzgEwe/MdCZqzdyaAOKcEOR0RECGwiOAfoaGZtgU14yd4Nxx9kZonAecBNVbbFAiHOuWLfzxcCjwUwVmkonPMShY/Hwu486HopXPhraNIm2JGdknOO/y7ewm/+u5zNRaVckdWKhy/uSouEGk5K05CFRXqTybQ/H0Y+7rVUrprkdSOd+Sf48hmvy2mHC7yk8EQTzlRWwsI34dNHvQl6sm+GYb/wJggSkYC5pGdLfv3fZUyYmadEUESkjghYIuicKzeze4BJeMtHjHPOLTWzO337X/AdeiXwiXOu6qwSLYD3fd1HwoC/O+c+DlSs0kDsWAMfPwRrPoWUznDzP6H90GBHVSMrtu7l0Q+WMmvdLrq1TODZUWfTJzOIM2fWZWaQ0tF7DbwHSotg7RSvG+nqT2DJu9VPOLN5Pnz0EyiY4+274W1Iyw723Yg0ClHhoVzXpzUvTV3Lpj0HSEvSJEwiIsFmzjWc3pQ5OTlu7ty5wQ5DatvBYpj6JMz8szfD45Cx0PeOUy7IXhcU7S/j6U9X8dqsDcRHhfHghZ0Z1bc1oSEaQ3Najp9wZssCb3tcqjcWMrYZDP8l9LzeG5Mo9ZaZ5Wp92ZqrC/Vj/q79nPfkFO4a0p4fj6gfk3WJiNRHNa0jNSBG6i/nvElEJv+fN7FJ1k1wwS8grgYTqgRZRaXjnbn5PDlpJXv2H+LGfm24f3gnmsRGBDu0+u1EE86s+R80bQuD74eohGBHKdIoZTSN4fwuLXhzdj4/OL8jUeEBGPcsIiI1pkRQ6qfNC7xufvlfQatsuP4NSK8fjQO5G3bz6AdLWbypiL6ZTfnFZd3o3iox2GE1TFUnnBGRoLtlYBs+XV7Ih4u3cFV2erDDERFp1JQISv2ybyd89v8g9xWISYbL/gRZN9aLbn7bikt54qMVvDdvE6kJUTx7fRaX9WqlqdRFpNEY1D6Fds1imTBzgxJBEZEgUyIo9UNFOeSOh89+5Y0J7H8XnPcQRCcFO7JTOlReySsz1vPc/9ZwqLySu4e05/tDOxAbqf/9RKRxCQkxbu7fhl/+exmLCvbQM73u/w4XEWmo9Jeo1IxzsHWRt47b6k+gdM+JFxY/ssB4vH8Wbs/70usGWrgE2p4HF/3u1Aul1xFTV23n0X8vZd32fQzr0pyfX9KNzJTYYIclIhI0V/dO58lJK5kwcwO//7YSQRGRYFEiKCd2sATWf+Fbr+0TKN7ibW+VDc27QnGhNxV/8VYoL/36+eExxyaG8S0hvoUvaWxxNHmMblJ9wli0CSb/HJZMhMQMuHYCdL3MP8mlHx0sryB/1wE27trHhp37fS/v53U79pGZHMP4MX0Y2qXuT2IjIhJoCVHhXHl2Gv/ILeCRi7vSVJNkiYgEhRJBOdau9V7St2oS5E2DikMQEQ8dzvfWY+s4/OuzcjrnreVWUuglhce/F2/1WvPW/A8OFX/9mqERXqJYNWkMDYd5E8BVwnljYdAPISKmdr6DauwtLWPj4SRv1z427txP3k7vfcveUqquwhIbEUrr5Fg6p8ZzY/823NS/NZFhmh1PROSw0QMyeeOrjbw9J5+7hrQPdjgiIo2SEsHGrqIMNs7yrbv2CexY6W1P7uCtxdfxQmg9AMJO8sTWzBurF50EzTqf/HqH9lWTLG7xWhdLtsLOtbDhSziwB7p8C0b8Bpq08d/9noBzju3FB9mwy0v2Nu7cR97O/WzY5f28e3/ZMcenxEXQumkM/dol0yY5hjbJMbRuGkub5BiSYyM0AYyIyEl0To2nX9umvD5rA3ec205rp4qIBIESwcZo3w5YPdlL/tZ8BgeLICQcMgdBzq1e8pccoCe0EbFe2acqv7ICQgLXirYwfw8fLt7C+h1eF86Nu/ZzoKziyP4Qg1ZJ0bRJjmFkj5ZkVkn2WifHEKeJXkSkMXEOti6Glj39VuQtAzO5+415fLZiG8O7tfBbuSIiUjP6a7YxOFyBr5rkJX8FcwHndcHsdil0GgnthniTu9QVAUgCyyoq+WjJVsZ/uZ75G/cQERZCm6ZegjeoQ8qRlr02ybGkJUUTEVb3l6QQEakV81+HD+6B703zWzI4vFsLUhOimDAzT4mgiEgQKBFsqA7tg3VfwKqPvda/4s3e9lbZMORh6HQhpPaqF+vvnamdJQd5c/ZGXpu1gcK9B8lMjuEXl3bjmt7pxEeFBzs8EZG6r9tlMOkR+PIZuGacX4oMDw3hhn6teWryKtZuL6F9szi/lCsiIjWjRLCh2bEGPn4I1k+DioPeRC/th3qtftVN9NKALd1cxPgv8/hg4WYOlVdyTscUnriqJ+d1akaIxqOIiNRcVCLk3AYznoOhP/Xb8IHr+2bwx89W89rMDTx6WXe/lCkiIjWjRLCh+fQXsPEr6HM7dBpx6oleGpjyikomLytk/Jd5zM7bRXR4KNfmpDNmYCYdmtehrq8iIvVN/7tg1l9gxh/h0mf8UmTz+Cgu6tGSibkF/HhEZ2I1/lpEpNboN25DUrQJVn7oLbVwwaPBjqZW7dl/iDdn5/PazDw2F5WS3iSan17clWv7ZJAYre6fIiJnLD4Vsm6ABW/AkLHeZz+4ZWAbPli4mffnb+Km/oGfJVpERDxKBBuSeRO8iWGybwl2JLVm5dZiXpmxnvfnb6K0rJIB7ZJ59LLuDOvaQtORi4j426B7Yd6rXsvg8F/6pcjs1k3o3iqB12Zu4MZ+rbX8johILVEi2FBUlHmVc4dh0LRtsKMJqIpKx2crtjH+y/XMWLuTyLAQrspO45aBmXRJTQh2eCIiDVfTdtD9Spg7Ds653xs7eIbMjNED2vDQxMV8tX4X/dsl+yFQERE5lYY/ZWRjsepjb2H2nO8EO5KAKTpQxt+mrWPI76fw3Qlzyduxj4dGdmHWw8N4/KqeSgJFpEEws5FmttLM1pjZ2Gr2NzGz981skZnNNrMetRrgoPvg4F6Y8ze/FXlZrzQSo8N5dUae38oUEZGTU4tgQzHnZUhI8xaDb2DWbCvh1Rl5TJxXwP5DFfTNbMrDF3Xlwm4tCAvVswwRaTjMLBR4HhgOFABzzOwD59yyKoc9Aixwzl1pZl18xw+rtSBb9oQOF3jdQ/vfDeHRZ1xkdEQoN/RrzQtfrGX5lr10bakHeyIigaa/ohuCnWth3RToPQZCG0ZuX1npmLJyG6PHzeaCp77g7Tn5XHxWS/7zg8G8c+cALj6rpZJAEWmI+gJrnHPrnHOHgLeAy487phvwPwDn3Aog08xqd0X2wT+Cfdu9iWP85HvntiM+MozffbzCb2WKiMiJNYysobHLfQUsFM6+OdiR+MXMtTv5+b+WsGZbCc3jI3lgeCdG9WtNSlxksEMTEQm0NCC/yucCoN9xxywErgKmm1lfoA2QDhTWSoQAbQZBeh/48jnIHuOXh5BJMRHcNaQDv/14BbPW7dRYQRGRAFOTSn1XVgrzX4cu34KElsGO5ozsLS3j4fcWM+qvszhUXsmz12cx/aHz+cGwjkoCRaSxqG7KTHfc5yeAJma2APgBMB8o/1pBZneY2Vwzm7t9+3Y/R2kw+H7YswGW/dNvxd46KJPUhCie+GgFzh1/2yIi4k9KBOu75R/AgV2Qc1uwIzkjk5cVMvypL3h7zkbuOLcdk+47l8uz0ogI0z9REWlUCoCMKp/Tgc1VD3DO7XXO3eqcywJGA82A9ccX5Jx7yTmX45zLadasmf8j7TQSmnWB6U97Sxf5QVR4KD8a3pEF+XuYtHSrX8oUEZHq6a/s+m7Oy9503m3PC3Ykp2VHyUHu+fs8vjthLk1iInj/7kE8cnFXoiNCgx2aiEgwzAE6mllbM4sArgc+qHqAmSX59gHcDkx1zu2t5TghJMSbQbRwCaye7Ldir85Op32zWH43aSXlFZV+K1dERI6lRLA+K1wK+bO81sCQ+vWf0jnHe/MKuOCpL/hkaSEPDO/EB/cMpldGUrBDExEJGudcOXAPMAlYDrzjnFtqZnea2Z2+w7oCS81sBXAR8MPgRAucdQ0kpHutgn4SFhrCT0Z2Yd32ffwjt8Bv5YqIyLE0WUx9Nnc8hEZC1o3BjuQbKdi9n5++v4QvVm0nu3USv726Jx1bxAc7LBGROsE59yHw4XHbXqjy80ygY23HVa3QcBj4A/j4Idg4C1r390uxF3ZrQXbrJJ6evIorstLUS0REJADqVzOSHHWwBBa+Bd2vhJimwY6mRiorHRNm5jHi6anMydvFo5d24x93DlQSKCJSn2XfDNFNYfozfivSzBh7UVe2FR9k/IyvDX8UERE/UCJYXy15Fw4V15tJYtZsK+HaF2fyf/9aSnabJky671zGDGpLaEh1E+SJiEi9EREL/e+CVR9B4bJTH19Dfds2ZViX5vzl87Xs3nfIb+WKiIhHiWB95Jw3SUyLHpDRN9jRnFRZRSXPT1nDxc9OY/W2Ev7w7V5MuK0vGU1jgh2aiIj4S5/bITwWvvRfqyDAT0Z2oeRgOX/+fI1fyxURkQAngmY20sxWmtkaMxtbzf4fm9kC32uJmVWYWdOanNuobZoHWxdBzq3eWk511OKCIi7705c8OWklw7u34NP7z+Pq3ulYHY5ZREROQ0xTr05a/C7s3uC3YjunxnN1djqvztzApj0H/FauiIgEMBE0s1DgebwZzboBo8ysW9VjnHNPOueyfGshPQx84ZzbVZNzG7W5L3tPXs+6NtiRVOvAoQoe/3A5lz8/nZ0lB3nx5t48f0M2zeK1KLyISIPV/26wEJj5J78W+6PhnQB4evIqv5YrItLYBbJFsC+wxjm3zjl3CHgLuPwkx48C3jzNcxuPA7thyUToeS1EJQQ7mq+ZuXYnFz07lRenruO6PhlMvv88RnRPDXZYIiISaIlp0Os6mDcBSrb7rdi0pGhuGdCGifMKWLm12G/liog0doFMBNOA/CqfC3zbvsbMYoCRwMRvem6js/AtKC+tc5PE7C0t4+H3FjPqr7OodPD32/vx+FU9SYwOD3ZoIiJSWwbdB+UHYfaLfi327iEdiIsM48lJK/xarohIYxbIRLC6gWDuBMdeCnzpnNv1Tc81szvMbK6Zzd2+3X9PIOsk52DuOEjvAy17BjuaIz5dVsjwp77g7Tkb+e45bZl037kM7JAS7LBERKS2pXSErpfC7JfgoP9a75rERnDnee35dPk2Zq/fdeoTRETklAKZCBYAGVU+pwObT3Ds9RztFvqNznXOveScy3HO5TRr1uwMwq0H8qbDjlV1pjVwR8lB7vn7PG6fMJcmMRG8f/cgfvqtblr4V0SkMRt8H5QWwdzxfi32tkFtaR4fyRMfLce5Ez1XFhGRmgpkIjgH6Ghmbc0sAi/Z++D4g8wsETgP+Nc3PbfRmfsyRCV5i8gHkXOO9+YVcMFTXzBp6VbuH96JD+4ZTK+MpKDGJSIidUBab2h7Hsx83usm6ifREaH8aHgn5m3cw+RlhX4rV0SksQpYIuicKwfuASYBy4F3nHNLzexOM7uzyqFXAp845/ad6txAxVovlGyD5f+GrBshPDpoYZRXVPKDN+dz/zsLaZcSy4f3nsO9wzoSEaYlKUVExGfwj6Bkqzeu3Y++3Tudds1i+d2klZRXVPq1bBGRxiagf7075z50znVyzrV3zv3at+0F59wLVY55xTl3fU3ObdTmTYDKcm+dpiBxzvGzfy7hP4u28OCFnfjHnQPp2CI+aPGIiEgd1W4ItDobvnwWKiv8VmxYaAg/GdGZNdtKmDivwG/liog0RmrGqQ8qKyD3VWh7rjcQP0j+8Mkq3pqTzz1DO3DP+R0JDdHC8CIiUg0zr1Vw11qvN4sfjeieSlZGEk9PXk1pmf+STBGRxkaJYH2w5n9QtDGok8S88uV6/jRlDaP6ZvDAhZ2CFoeIiNQTXS6B5A4w/Slv1ms/MTPGXtSFrXtLeWVGnt/KFRFpbJQI1gdzX4a4Fl6lGgQfLNzML/+zjAu7teD/Xd4DM7UEiojIKYSEwqAfwpaFsG6KX4vu3y6ZoZ2b8ecpayjaX+bXskVEGgslgnXdno2wahKcfTOE1v7i7FNXbeeBdxbQJ7Mpz406m7BQ/ZMREZEa6nkdxLeE6U/7veifjOxC8cFy/vzFGr+XLSLSGOiv+rou91XvvfcttX7pBfl7uPP1XNo3i+Ovo3OICtf6gCIi8g2ERcKA78P6qVCQ69eiu7ZM4Mqz0xj/ZR6b9xzwa9kiIo2BEsG6rKLMmy200whIal2rl167vYRbx88mOS6CCbf1JTG69lsjRUSkAeg9xlsD90v/twreP7wTOHjm01V+L1tEpKFTIliXrfgv7NtW65PEbC0qZfTLswkNMV67rR/NE6Jq9foiItKARMZD3ztg+X9gu38TtvQmMdw8oA3v5hawurDYr2WLiDR0SgTrsrkvQ2Jr6HBBrV2yaH8Zt4ybTdGBMl65tS+ZKbG1dm0REWmg+n0PwqK8dQX97PtDOxAbEcbvJq30e9kiIg2ZEsG6asdqb0xF71u8mddqwYFDFXzn1Tms37GPl27uTY+0xFq5roiINHCxKZA9Gha9DUX+XQi+aWwEdw5pz+RlhczN2+XXskVEGjIlgnXV3PEQEuZVnLWgvKKSe/4+j9yNu3nm+iwGdkipleuKiEgjMfAewMHMP/u96FsHZdIsPpInPlqB8+OahSIiDZkSwbqo7AAseAO6XgpxzQN+OeccD7+3mP+t2MZjl/fg4rNaBvyaIiLSyCS1hrO+DbmvwH7/ttzFRIRx3wUdmbthN/9bvs2vZYuINFRKBOuipe9D6Z5amyTmtx+v5B+5BfxwWEdu7t+mVq4pIiKN0KAfQtk+mP1Xvxd9bU4G7VJi+e3HK6ioVKugiMipKBGsi+aOg+SOkHlOwC/1t2nreOGLtdzYrzX3XdAx4NcTEZFGrHlX6HwxfPUCHNrn16LDQ0N4cERnVm8rYeI8/45DFBFpiJQI1jVbFkHBHK810Cygl3pvXgG/+u9yLj4rlccu74EF+HoiIiIM/hEc2OWtk+tnF/VIpVdGEk9PXkVpWYXfyxcRaUiUCNY1c8d5U2xnjQroZaas3MZP3l3EwPbJPH1dFqEhSgJFRKQWZPSFNoNgxp+g/JBfizYzxo7swpaiUibMzPNr2SIiDY0SwbrkYDEs/gf0uBqimwTsMvM27ubu1+fRpWU8L97cm8iw2lmeQkREBIDB98PeAljyrt+LHtA+mfM6NeP5KWspOlDm9/JFRBoKJYJ1yaK34VAJ5HwnYJdYs62Y216ZQ/OESMaP6Ut8VHjAriUiIlKtDsOgxVkw/RmorPR78Q+N7MLe0jJe+GKt38sWEWkolAjWFc7BnHGQ2hPSsgNyic17DnDzy7MJDw3htdv60Sw+MiDXEREROSkzGHwf7FgJKz/0e/HdWiVwRVYa46avZ2tRqd/LFxFpCJQI1hX5s2Hb0oBNErN73yFGj5tNSWk5r97al9bJMX6/hoiISI11uwKaZML0p7yHoX52//BOOAfPfLrK72WLiDQESgTrirnjICLeW2zXz/YfKue2V+ewcdd+/npLDt1aJfj9GiIiIt9IaBgMvBc25ULedL8Xn9E0hhv7t+adufms2Vbi9/JFROo7JYJ1wf5d3iLyva6DyDi/Fl1WUcndb8xjYf4enrv+bPq3S/Zr+SIiIqct60aIbQ7Tnw5I8fcM7UBMRBhPTloRkPJFROozJYJ1wYI3oOKg1y3UjyorHT95dxGfr9zOr688i5E9Uv1avoiIyBkJj4IBd8Pa/8GWhX4vPjkuku+d245JSwvJ3bDb7+WLiNRnSgSDrbIS5o6HjP7QorvfinXO8ZsPl/P+/E08MLwTo/q29lvZIiIifpNzG0QmeDOIBsB3zmlLSlwkv/1oBS4AYxFFROorJYLBtv4L2LXW762BL05dx9+mr+eWAW245/wOfi1bRETEb6ISoc93YNk/Yaf/l3uIiQjjhxd0ZHbeLqas3Ob38kVE6qtTJoJmdo+ZBW5188Zu7jiIbgrdLvdbkf+Ym88TH63g0l6t+MWl3bEAzEIqIiLiN/3ugpBw+PLZgBR/fZ8MMpNj+O1HK6moVKugiAjUrEUwFZhjZu+Y2UhTVuE/e7fAiv/C2Td64yT8YMbaHYx9bzHndEzhD9/uRUiI/nOJiEgdF98Ceo+BeRNg/VS/Fx8eGsKDIzqzsrCYifMK/F6+iEh9dMpE0Dn3M6Aj8DIwBlhtZr8xs/YBjq3hm/8auArofavfinxp6jqaxUXyl5t6ExGmnr8iIvWN76HrSjNbY2Zjq9mfaGb/NrOFZrbUzPxXiQTTsP+D5A4w8buwb4ffi7+4R0uyWyfx6/8uZ9teLTIvIlKjTMF5o6u3+l7lQBPgXTP73cnOO1Vl5jtmiJkt8FVmX1TZnmdmi3375tb4juqLinLIfQXaDYVk/+TUhXtLmbpqO9f0TicuMswvZYqISO0xs1DgeeAioBswysy6HXfY94FlzrlewBDgD2YWUauBBkJkHFwzDg7shvfv9CZT86OQEOPJb/eitKyCh99brIljRKTRq8kYwXvNLBf4HfAlcJZz7i6gN3D1Sc47ZWVmZknAn4HLnHPdgeNXUx/qnMtyzuV8g3uqH1Z/Ans3eQPk/eT9+ZuodHB173S/lSkiIrWqL7DGObfOOXcIeAs4fhC5A+J9QzXigF14D2nrv5Y9YcSvYc1kmPW834tv3yyOh0Z24X8rtvGPXHURFZHGrSYtginAVc65Ec65fzjnygCcc5XAJSc5ryaV2Q3Ae865jb4yG890XnPHQXxL6DTSL8U553g3t4CcNk1omxLrlzJFRKTWpQH5VT4X+LZV9SegK7AZWAz80FcnNwx9bocul8Cnj8KmXL8XP2ZgJv3aNuWxfy+jYPd+v5cvIlJf1CQR/BDvaSMAZhZvZv0AnHPLT3JeTSqzTkATM/vczHLNbHSVfQ74xLf9jhrEWX/szoM1n0L2aAgN90uRCwuKWLOthGvUGigiUp9VN8PX8X0YRwALgFZAFv+fvfsOr6rK+jj+3ek9kBAIhISEDgFCCaEjiCAgghQRbCPqMFjHUee1O+qMjqOMvWLBxogKIqgUFUEEFAlICQlNagghgEDopOz3jxMwIiWBe3NTfp/nOU/uPfecfVYYh8O6e5+14CVjTNgfBjJmtDEm1RiTunPnTtdH6i7GwKCXnC9LJ10PR/a5dHgvL8PYy5MotJZ7Jq+gUFVERaSKKkki+CpwoNj7g0X7zqYkNzMfnCWml+Dc2B4yxjQu+qyLtbYtztLSW4wx3U95kYp4o1vyjnOja/snlw05aclWAny96N+qtsvGFBGRMpcJxBZ7Xxdn5q+4UTiraay1dj2wEWh68kDW2nHW2mRrbXJUVJTbAnaLwOow9C3YuxU+vwNc/DxfbEQQD17SnAXrdzNh0WaXji0iUlGUJBE0ttgT1UXLT0pSiaQkN7NMYKa19qC1dhcwD0gquk5W0c8cYArOUtM/qHA3uvxjsPR9aNwPwk+eID03R/IKmLYsi76J0YQFuGaGUUREPGIx0MgYk1BUAGYEMO2kY7YAvQCMMbWAJt2HaRYAACAASURBVMCGMo2yLMR1gAsfgFWfOm0lXGxkSizdG0fxxPTVbNp10OXji4iUdyVJBDcUFYzxLdr+SsluOCW5mU0FuhljfIwxQUAHIMMYE2yMCQUwxgQDfYC0kv5S5VrGNDi0C9pf77Ihv8nYQe6RfIa1iz37wSIiUm5Za/OBW4FZQAbwsbV2lTFmjDFmTNFh/wQ6G2NWArOBe4q+TK18uvwN6veAGfdAzpmeRik9Ywz/GdoSH2/D3Z8sV6N5EalySpIIjgE6A9twZvA6AGd9Zq8kN7OiZwxnAiuAn4A3rbVpQC1gvjFmedH+L621M0v7y5VLqeOhejzUv9BlQ05akkmd8AA6NYh02ZgiIuIZ1trp1trG1toG1trHi/a9Zq19reh1lrW2j7W2pbW2hbX2A89G7EZeXjB4nNNa4pNRcMy1xV1qhwfy6MBEUjfv4e35G106tohIeXfWJZ5FSzNHnMvg1trpOMVmiu977aT3TwNPn7RvA0VLRCuVzT/A5vlw0SPOzc0FjvcOvLlHQ7y9TvVYpoiISAUWWgsGvw4fDIGZ98LAF1w6/OA2McxIy+bpr9bQo0kUjWqFunR8EZHyqiR9BAOMMbcYY14xxrx9fCuL4CqVw3vg0z9D9QRIVu9AEZHKzhjTwBjjX/S6R9FjFtU8HVeF1LAXdP0bLH0X0ia7dGhjDE8Mbkmwnzd3fbKc/ILK04lDRORMSjIt9T4QjVPV8zucoi/73RlUpWMtTLsd9m+HYW9BwB+qfJ/jsOodKCJSjk0GCowxDYG3gATgf54NqQLr+QDUTXGqiP7q2mWcUaH+PD64JSsy9/Hq3F9cOraISHlVkkSwobX2IeCgtfZdnFYPLd0bViWz5B2nSEyvhyGmncuGVe9AEZFyrbDoefnBwHPW2r8B6vFzrrx9YeibTvulSdc7VbhdqH/L2lyaVIfnZ69jVZZrexeKiJRHJUkE84p+7jXGtADCgXi3RVTZ5GQ4zzQ0uBA63ebSodU7UESkXMszxowE/gR8UbRPPX7OR/V6MPAlyFoKsx91+fCPDUykerAfd328nKP5BS4fX0SkPClJIjjOGFMdeBCn/UM68B+3RlVZ5B12vrX0D4XLXnNZgRhQ70ARkQpgFNAJeNxau9EYkwBU3gqfZaX5QGh/I/zwEqz9yqVDVw/248khLVmdvZ8XZq9z6dgiIuXNGTMTY4wXkGut3WOtnWetrW+trWmtfb2M4qvYZj0AOekw+DWn6pkLqXegiEj5Zq1Nt9bebq39sOgL1VBr7ZOejqtS6PM41GoBn42B3CyXDt2rWS0ub1eXV+f+ws9b9rh0bBGR8uSMiaC1thCnF6CUVsbnkPoWdL4NGl7k8uHVO1BEpHwzxsw1xoQZYyKA5cB4Y8wzno6rUvANgGHjnZU3n46GQtcu43zo0uZEhwVw1yfLOZKnJaIiUjmVZK3i18aYu40xscaYiOOb2yOryPZuham3Qp02cOHDLh/+eO/AIW3rqnegiEj5FW6tzQWGAOOtte0A138zWFVFNYb+Y2HT9/D9f106dFiAL08NS2LDzoM8PWuNS8cWESkvSpIIXg/cAswDlhRtqe4MqkIryC/6djIfhr4FPn4uv4R6B4qIVAg+xpjawHB+KxYjrtT6Smg5HOb+GzYtcOnQXRvV4JqO9Xh7wUZ+3LDbpWOLiJQHZ00ErbUJp9jql0VwFdK8p2HLQhjwLEQ2cPnw6h0oIlJhPAbMAn6x1i42xtQHVIHElYyBAc9A9XiYfCMc+tWlw9/brylxEUH8fdJyDh7Nd+nYIiKedtZE0Bhz7am2sgiuwtm0AOY9BUkjodVwt1zieO9AzQaKiJRv1tpPrLWtrLU3Fb3fYK0d6um4Kh3/UOd5wUO74LObwFqXDR3s78PYy5PI3HOYJ6ZnuGxcEZHyoCRLQ9sX27oBjwAD3RhTxXToV/j0z1A9Afo/7bbLTFqyFX8fLy5R70ARkXLNGFPXGDPFGJNjjNlhjJlsjNG3eO5QpzX0/iesnQmLXnPp0O3jI7ixawITFm3hu7U7XTq2iIgnlWRp6G3Ftj8DbQDXP/hWkVkL026DAzkw7C3n20k3ONE7sIV6B4qIVADjcfrv1gFigM+L9ok7dPgLNOkPXz0EWT+7dOi7+jShYc0Q7pm0gn2H81w6toiIp5xLh/NDQCNXB1KhLX4TVn8BvR91KoW6yW+9A/WFsohIBRBlrR1vrc0v2t4BojwdVKVlDAx6GUJqwiej4Eiuy4YO8PXmv5cnsfPAUR77PN1l44qIeFJJnhH83BgzrWj7AlgDTHV/aBXEjlVO4/iGvaHDTW691KQlmdQOD6BzgxpuvY6IiLjELmPM1cYY76LtakDlJ90pKAKGvgl7N8OXd7r0ecGk2Grc3KMBk5dm8tWqbJeNKyLiKT4lOGZssdf5wGZrbaab4qlYjh1yvnUMrAaXvQpe5zLBWjLHewfe1KOBegeKiFQM1wMvAc8CFlgIjPJoRFVBvc7Q436Y8y+o3wPaXO2yoW+7sBHfZORw/5SVJMdHEBGsJ2VEpOIqSeayBVhkrf3OWrsA2G2MiXdrVBXFrPtg11oY/DqEuHe1z4negW21LFREpCKw1m6x1g601kZZa2taay/DaS4v7tbtTojvBtP/Djtd1xDez8eLZ4Ynse9wHg9NTXPZuCIinlCSRPAToLDY+4KifVXbqimw5B3oegc06OnWSx3vHdiuXnXqR4W49VoiIuJWd3o6gCrByxuGvAG+Qc7KnbzDLhu6We0w7rioMV+u2M7ny7NcNq6ISFkrSSLoY609dvxN0euqvRZi7xaY9leISYaeD7j9csd7B6pIjIhIhae1/WUlrDYMfg1yip7ld6G/dK9PUmw1HpqaRs7+Iy4dW0SkrJQkEdxpjDnRN9AYMwjY5b6QyrmCfJh8I2CdVhHe7m/joN6BIiKVhuuql8jZNeoNnW+D1Lcg3XV17ny8vfjv5UkcPlbAfZNXYl1YlEZEpKyUJBEcA9xvjNlijNkC3AP8xb1hlWPfPQlbF8GAZ6F6vNsvp96BIiIVizFmvzEm9xTbfpyeglKWLnwYYtrB1Ntgz2aXDduwZgh/v7gJs1fnMGmJauiJSMVTkobyv1hrOwLNgURrbWdr7Xr3h1YObZwH88Y6FchaDiuTS6p3oIhIxWKtDbXWhp1iC7XWlqRat7iSjx8MfQuwMPkGKHBdQ/jruySQkhDBY5+ns22v655DFBEpCyXpI/iEMaaatfaAtXa/Maa6MeZfZRFcuXJwN3w6GiIbQr+nyuyy6h0oIiJyniISYOALkLkYZtzjsv6CXl6GscOSKLCWeyat0BJREalQSrI0tJ+1du/xN9baPUB/94VUDlkLU2+BQ7th2NvgF1wmlz3eO3BI2xj1DhQRETkfiYOh8+3O84Lf/MNlyWBcZBD392/G/PW7+GDRFpeMKSJSFkqSCHobY/yPvzHGBAL+Zzi+8vlpHKydAb3/CbVbldll1TtQRETEhXo/BsnXw4Ln4TvXre65qkMc3RrV4IkvM9i8+6DLxhURcaeSJIIfALONMTcYY24AvgbedW9Y5cj2FfDVg9C4L3Qouxo56h0oIiLiYsZA//9C0pUw9wlY+KKLhjX8Z2grfLwNd3+ynIJCLREVkfKvJMVingL+BTTDKRgzE6jn5rjKh2MHYdL1EBQJg15xbiBlRL0DRURE3MDLCwa+CM0vc77oXfymS4atUy2QRy5NZPGmPdw8YQkHj+a7ZFwREXcpyYwgQDZQCAwFegEZJTnJGNPXGLPGGLPeGHPvaY7pYYxZZoxZZYz5rjTnut2M/4Pd62HIOAiOLNNLq3egiIiIm3j7wJA3oHE/+PIuWPY/lww7pG0MDw9oztfpOxj22g9k7jnkknFFRNzhtImgMaaxMeZhY0wG8BKwFTDW2p7W2pfONrAxxht4GeiHM5M40hjT/KRjqgGvAAOttYnA5SU91+1WToKfP4Bud0FC9zK9tHoHioiIuJmPH1z+DtTv6RSES5t83kMaY7i+awLjR6WQuecQl728gCWbfz3/WEVE3OBMM4KrcWb/LrXWdrXWvggUlGLsFGC9tXaDtfYYMBEYdNIxVwKfWmu3AFhrc0pxrvvs2QRf/A3qpkCP+8rsssepd6CIiEgZ8A2AERMgtoPTImrNDJcMe0HjKKbc3IUQfx9GjlvEZDWcF5Fy6EyJ4FCcJaFzjDFvGGN6AaV5SC4GZxbxuMyifcU1BqobY+YaY5YYY64txbnuUZAHk24ADAx901k+UsbUO1BERKSM+AXDlR9DdCv4+Fr45VuXDNuwZgif3dKF5Pjq3PXJcv49I0NFZESkXDltImitnWKtvQJoCswF/gbUMsa8aozpU4KxT5U0nvw3oA/QDrgEuBh4yBjTuITnOhcxZrQxJtUYk7pz584ShHUWcx6Hbakw8HmoXvY1cdQ7UEREpIwFhMHVk6FGY/jwSti0wCXDVgvy493rU7i6Yxyvf7eB0e+lckBFZESknChJ1dCD1toJ1toBQF1gGVCS4i2ZQGyx93WBrFMcM7PoGruAeUBSCc89Ht84a22ytTY5KiqqBGGdwS9zYP5z0PZPTuNZD1DvQBEREQ8IioBrPoNqsfC/4ZC5xCXD+np78a/LWvLPQYnMXbuToa8sZOuvKiIjIp5X0qqhAFhrf7XWvm6tvbAEhy8GGhljEowxfsAIYNpJx0wFuhljfIwxQUAHnIqkJTnXtfIOw2c3Od8G9n3SrZc6HfUOFBER8aCQKLh2KgTXgA8GO72EXeSaTvG8OyqF7fsOM+jlBSzasNtlY4uInItSJYKlYa3NB24FZuEkdx9ba1cZY8YYY8YUHZOB05dwBfAT8Ka1Nu1057orVgB8A2HQSzDsbfALcuulTke9A0VERDwsrA5cOw38QuH9y2DnGpcN3bVRDT67pQvVAn25+q1FfLR4i8vGFhEpLWNt5XlwOTk52aampno6jHP24Gcr+SQ1k8UPXqS2ESIiZ2CMWWKtTfZ0HBVFRb8/esTuX2B8P8DA9TMgor7Lht53KI9bP1zK9+t2cUPXBO7v30x1AUTEZUp6j3TbjKCUjnoHioiIlCORDZxlogXH4N1BsHfr2c8pofAgX8Zf157rOsfz1vyNXP/OYnKP5LlsfBGRklAiWE6od6CIiAAYY/oaY9YYY9YbY/5QnM0Y83djzLKiLc0YU2CMifBErJVezWZwzRQ4sg/eGwj7s102tI+3F48MTOSJwS1ZsH4Xg19ewKZdB102vojI2SgRLCfUO1BERIwx3sDLQD+gOTDSGNO8+DHW2qetta2tta2B+4DvrLW/ln20VUSd1nD1JNi/A94bBAddW+Tlyg5xvHdDCrsPHuOyVxaw8JddLh1fROR0lAiWA+odKCIiRVKA9dbaDdbaY8BEYNAZjh8JfFgmkVVlsSlw5UewZ5NTQObwXpcO37lBDabe0oUaIf5c+9ZPTFi02aXji4icihLBcuAz9Q4UERFHDFD8YbTMon1/UNR2qS8w+TSfjzbGpBpjUnfu3OnyQKuchG5wxQTIyYAJw+DofpcOXy8ymE9v7kzXRjV4YEoa/5iaRn5BoUuvISJSnBJBD1PvQBERKeZUy0JOV977UmDB6ZaFWmvHWWuTrbXJUVFRLguwSmt0EVz+DmxbCh+OdHoQu1BYgC9v/ak9N3ZN4N0fNnPd+MXsO6QiMiLiHkoEPWxF5j7WqXegiIg4MoHYYu/rAlmnOXYEWhZa9poNgCHjYNN8+OhqyD/q0uG9vQwPDmjOf4a2ZNHG3Qx+ZQEbdh5w6TVERECJoMdNWpKJv48Xl7Sq7elQRETE8xYDjYwxCcYYP5xkb9rJBxljwoELgKllHJ8AtBwGA1+E9d/ApOuhwPWzdle0j2PCjR3ZeziPy15ewPfrtLxXRFxLiaAHHckrYNpy9Q4UERGHtTYfuBWYBWQAH1trVxljxhhjxhQ7dDDwlbVW/QY8pe010O9pWP0FTBkDhQUuv0RKQgRTb+lC7fBArhu/mHcXbsLa060UFhEpHR9PB1CVzc7IYd/hPC0LFRGRE6y104HpJ+177aT37wDvlF1UckodRkPeQfjmEfANhEtfAC/XfsceGxHE5Js7c8fEn/nHtFWs3bGfRwYm4uut7/JF5PwoEfSgSUu2qnegiIhIRdb1b07RmO/+A75B0O8/YFzbCirE34fXr0nmqVmref27DWRsz+XmHg3p2bSm2k6JyDlTIughOblH+G7tTm7q0UB/iYuIiFRkPe6DYwfhh5ecmcGLHnF5MujtZbivXzOaRofy7+mrufG9VOqEBzAiJY4r2sdSKyzApdcTkcpPiaCHTFHvQBERkcrBGOjzL2dmcMFzsGYGtLsOkkZAUIRLLzW4TV0GtKrD7IwdTFi0hWe+Xsvzs9fRp3ktrupQj84NIvHSF8wiUgJKBD1AvQNFREQqGWOg/1iomwypb8Os+5xnBxMvg3ajIK6jy2YJfb296NuiNn1b1GbTroN8+NMWPk7dyoy0bOIjg7iyQxzD2sUSEeznkuuJSOVkKlP1qeTkZJuamurpMM5q+da9DHp5Af8e0pKRKXGeDkdEpMIxxiyx1iZ7Oo6KoqLcHyuV7JWw5B1Y8TEczYWopk5CmHQFBFZ3+eWO5BUwMy2bCYs2s3jTHvy8vejfMpqrOtYjuV51jIuXqopI+VXSe6RmBD1AvQNFREQqueiWcMl/ofdjkPYpLBkPM++Bb/4BiYOdpDA2xWWzhAG+3lzWJobL2sSwJns//1u0mU+XbuOzZVk0qRXKVR3juKxNjNpVicgJmhEsY0fyCujwxGx6NIni+RFtPB2OiEiFpBnB0qkI98cqYfuK32YJj+2Hms2dZwlbXQGB1Vx+uUPH8vl8eRYf/LiFldv2EejrzaDWdbiqQz1a1g13+fVEpHzQjGA5pd6BIiIiVVTtVjDgmaJZwslOUjjj/+Drf0CLIU5SWLe9y2YJg/x8uKJ9HFe0j2NF5l4m/LiFqcuymLh4K0l1w7mqQz0GJNUmyE//HBSpijQjWMZGjf+J1dn7mX/PhWobISJyjjQjWDoV4f5YZWUtcxLClZ/AsQNQMxGSR0Gr4RDg+lm7fYfz+OznbUxYtJm1Ow4QGuDD0LZ1ubJDHI1rhbr8eiJS9kp6j1QiWIZyco/Q8d+zualHA/5+cVNPhyMiUmEpESyd8n5/FODofmeWMHU8bF8GPoHQYqiTFMa0c3lfQmstqZv3MOHHzUxfmc2xgkJS4iO4qmMcfVtE4+/j7dLriUjZ0dLQcmjWqmwKLQxqHePpUERERKQ88Q91loa2uw6yfnYSwpWTYNkHUKsltPuTS2cJjTG0j4+gfXwED196jElLtjJh0Rb+OnEZEcF+XN0hjhu61Sc8UMVlRCorzQiWoSvf+JHs3CPMvvMClXEWETkPmhEsnfJ+f5TTOLrfWTKaOh6yV4BvkDNL2O46t8wSFhZaFvyyi/d/2MxX6TsIC/BhdPf6XNclgRB/zR2IVBSaESxnfj14jEUbf2XMBfWVBIqIiMjZ+YdC8vVOq4msn50WFCsnw8/vQ0R9aDEMWl4OUY1dcjkvL0O3RlF0axTFqqx9PPv1OsZ+tZa3F2zipgsacE2negT4asmoSGXh5ekAqopv0ndQUGjpm6jegSIiIlIKxkBMWxj4Ity1Gga9DNXi4Pux8HJ7eK0bLHgB9mW67JKJdcJ580/JfHZLFxLrhPH49Ay6PzWH937YxNH8ApddR0Q8R4lgGZmRtp261QNpERPm6VBERESkogoIgzZXw7VT4c4M6PskePvC1w/Bs4kwvj+kvg2HfnXJ5VrHVuP9Gzrw0eiOxEcG8/DUVVw49js+WryFvIJCl1xDRDxDiWAZyD2Sx4L1u+mbGK1loSIiIuIaodHQ8Sb487dw21Lo+SAc3Alf/A3GNoIJw2HFJ3D0wHlfqkP9SD76S0fevyGFGqH+3DN5Jb2f+Y7Pft5GQWHlqTchUpXoGcEyMGd1DscKCunbItrToYiIiEhlFNkALvg7dL8bsldC2iTnecJ1s5wiM036Oc8TNugFPn7ndAljnGcIuzaswbercxj71Vru+GgZL89Zz529G3NxYjRe6pEsUmEoESwDM9OyqRnqT9u46p4ORURERCozY6B2K2fr9Qhs/dFpQ7FqitOnMKAaNB/kJIX1uoBX6ReHGWPo1awWPZvUZEZaNs9+s5abJiwlsU4Yd/VpTM8mNbUCSqQCcOvSUGNMX2PMGmPMemPMvaf4vIcxZp8xZlnR9nCxzzYZY1YW7a+wNa8PHytg7pqd+pZMREREypaXF9TrDAOegbvXwpWfQKM+TmL47gDnmcJZD0DWMjiHdmJeXoZLWtVm1h3defaKJA4czef6d1IZ8upC5q/bRWVqUSZSGbltRtAY4w28DPQGMoHFxphp1tr0kw793lo74DTD9LTW7nJXjGXhu7U5HM4r0LJQERER8RxvX2jcx9mOHYS1M52EcNHr8MNLENnQmSVsMQxqNCzd0F6GwW3qMqBVHSYvyeSF2eu4+q1FdEiI4O6Lm9A+PsJNv5SInA93Lg1NAdZbazcAGGMmAoOAkxPBSm1mWjbVgnzpkKC/BEVERKQc8At2GtO3GOpUF82Y5iSFc5+Euf+G2q2h6SUQ18lpXO8XVKJhfb29GJESx+C2MUz8aSsvzVnP5a/9QPfGUdzVuzFJsdXc/IuJSGm4MxGMAbYWe58JdDjFcZ2MMcuBLOBua+2qov0W+MoYY4HXrbXj3BirWxzNL2B2Rg59W0Tj460CrSIiIlLOBEVAu+ucLTcL0j51Cs3MeQKw4OULdVpDXEeI6+z8DDrzl9v+Pt78qXM8w5Njef/HTbw69xcGvbyA3s1rcWfvxjSrrVZaIuWBOxPBUz0Qd/Ji8aVAPWvtAWNMf+AzoFHRZ12stVnGmJrA18aY1dbaeX+4iDGjgdEAcXFxroveBRb+spv9R/Pp11LLQkVERKScC6sDnW91tsN7YOtPsOUH2PyDs4R04YvOcVFNf58YVotzitScJNDPm9HdG3Blh3qMn7+Rcd9voN/z3zOgVW3uuKgxDWuGlPEvKCLFuTMRzARii72vizPrd4K1NrfY6+nGmFeMMTWstbustVlF+3OMMVNwlpr+IREsmikcB5CcnFyunkqeuTKbEH8fujSs4elQREREREousDo0vtjZAPKOQNbPsGUhbPkR0qbAknecz8JiihLDTs5Ws/nvqpGG+PtwW69GXNspnjfnb+Dt+RuZvnI7V3aI4+99mhIe5Fv2v58r7P4FbCHUaHT2Y0XKIXcmgouBRsaYBGAbMAK4svgBxphoYIe11hpjUnCqmO42xgQDXtba/UWv+wCPuTFWl8svKOSr9GwubFoTfx9vT4cjIiIicu58A6BeJ2cDKCyAnAxnxvD4rGHaZOcz/3CI6/DbrGGdNuAbQHiQL3f1acJ1neN58dv1vPfDJqavzObefk0Z1rZuxaiuXlgIv3wLi16F9d84+5oNhB73Qa3mno1NpJTclghaa/ONMbcCswBv4G1r7SpjzJiiz18DhgE3GWPygcPAiKKksBYwpagHjQ/wP2vtTHfF6g4/bfqVPYfy6KdqoSIiIlLZeHlDdAtnS/mz035i7xZntvD4rOG6r5xjvf2cojNFs4aRsR14ZGAiw5NjeWhqGv83aQUfLd7KY4MSSawT7tnf63SOHoDlHzpLZHevg5Ba0PMBKMyHH1+FjM8hcbCTEEY19nS0IiViKlOPl+TkZJuaWj5aDj48NY2PU7ey9KHeBPm5c+JVRKTqMcYssdYmezqOiqI83R+lCjm4G7Yu+m3WMOtnJ3HCOMtHG19MYbNBTM6K4MmZa9hz6BjXdornzj6NCQsoJ8tF92yCn96Ape/D0X1Qpy10vAmaXwY+fs4xh351WnD8+BrkH3bacFxwD0Q28GjoUnWV9B6pRNANCgstnZ6cTevYarx+jf6dIiLiakoES6e83B+lijt2CLYtcWYLN82DTQvAFkD1BI40vpR39rbiqRWBRAQHcH//pgxuE4M5RREat7MWNs2HRa/BmumAgeaDnASwbvtTFsYBnMR34fNO4ph/FJJGQPe/Q0RCmYYvokTQg5Zs3sPQVxfy3BWtuaxNjKfDERGpdJQIlk55uT+K/M7B3bDmS0ifChvmQmE+x0Ji+bIghXf3JuEX157HBregaXQZtZvIOwwrP3GWf+5Ig8AISB4FyTdAeCn+PXcgBxY8D4vfdGZAW18F3e92qquKlAElgh70+JfpvLNwE6kP9iY8sJwsbRARqUSUCJZOebk/ipzWoV9hzQxI/wz7yxxMYR7biWR6QQe8EwcxdNBgQgP93XPt3CwnaUsdD4d/hZqJ0HGMs8TTN/Dcx92fDd8/A0vGO7OMba+FbneVLqkUOQdKBD3EWkv3p+fQICqEd0aleDQWEZHKSolg6ZSH+6NIiR3eC2tncmzFp3ht+BYfm8cOIjhQvz/1u1+Jiev0u/YU58RayFzsLP9Mn+pUQW16CXQYA/FdT7/881zs2wbf/xeWvueM224UdLsTQstBQcGj+yF3u9MCwxPLcMUtSnqPVBUTF1uVlcvWXw9za8+Gng5FREREpOIJrAZJI/BLGgFHctn8w2SyFn5E218+wmz4gPygWvgkDnSe26vX2algWlL5xyD9M6fSZ9ZSp9VFhzFO5dPq8e75fcJjYMAz0PUOmDcWUt+Cpe86S0673gEhNd1z3ZNZC7vXOwnw1p+cnznpTi/EBr3gkrEQUb9sYpFyQTOCLjZ21hpembuexQ9cRGSIm5YwiIhUcZoRLJ3ycH8UOR8FhZZPFmbw8zcf0bNwIb18luNbeBSCo6DZpUVJYVfwPs0cx4GdzhLNxW/BgWyIbOgkgEkjwT+kbH+ZXzfCvKeddhQ+AU4S2vmvEBzp2uscyXWK82SmQmZR4nd4j/OZfzjUbQd1U8DHMn7/5AAAIABJREFU31nCWpgH3e6GLrc7+6TC0tJQD7nome+ICvHnw9EdPRqHiEhlpkSwdMrD/VHEFXYfOMp/Zq7mi9T1XBaSzq210qidMw+TdwiCIqHpACcpTOgO3r6wfblT/GXlJ1BwzJn56niT8/N8l5eer13rYd5TsOJj8AuGDn+BTrdCUETpxyosLJrtK0r4thbN9mEBA1FNoW4yxKY4yV+Nxr///XO3w6z7YdWnENnImcFM6O6q31TKmBJBD1ifs5+LnpnHowMT+VPneI/FISJS2SkRLB1P3x9FXG3J5j089Fka6dtz6dUghMdbZhOdOQvWzIS8gxBY3VnqmfUz+AZD65GQ8pfy2ex95xqY+ySsmgL+oU6i2vFmZ4ns6RzJhW2pTsKXWbQd2et8FhAOMceTvvYQ0+7MYxW37huYfpfTPzFpJPT+J4REnfevKGVLiaAHvPTtOsZ+tZYf7+tFdHiAx+IQEanslAiWjqfvjyLukF9QyIRFWxj71RqO5BXw5271ubVbDEFbvnMKwOxa41T+bHNNyRMhT9qxykkIM6Y5yVyn25xZQr8Q2L2u6Lm+n5ylnjkZ/G62L7a9M9MXm+LM6J3PbGfeYae4zfznnJnK3o9Cm2s9P4MqJaZE0AMueeF7/H28+PTmLh6LQUSkKlAiWDqevj+KuNPO/Uf594wMPl26jZhqgTw0oDkXJ9byTDN6V9i+wkkI13zpJIQAR/Y5PwPCnVm+uilO8hfT7rdjXG3nGvjiTtg837negGchuoV7riUupaqhZWzL7kOsysrl/v5NPR2KiIhUYMaYvsDzgDfwprX2yVMc0wN4DvAFdllrLyjTIEXKkahQf54Z3poR7eN46LM0xnywhB5NovjHpYkk1Aj2dHilV7sVjPwfbFvqVDf1Dfzt2b7IhmU3MxfVBK77ApZPhK8egNe7Q6ebocd9zkyhVHhKBF1k1qpsAPq1qO3hSEREpKIyxngDLwO9gUxgsTFmmrU2vdgx1YBXgL7W2i3GmDKqPS9SvqUkRPDF7V1574fNPPv1WnqOnUt0WACNo0NpUiuExrVCaRIdSsOaIQT5VYB/Ase0haFveDYGY5znKxtfDN88AgtfhFWfQb+noGl/z8Ym560C/L+gYpiRtp3EOmHERgR5OhQREam4UoD11toNAMaYicAgIL3YMVcCn1prtwBYa3PKPEqRcsrX24sbuiYwoFVtpvy8jTXZ+1mTvZ8fN+zmWH4h4OQ2cRFBNK4VSuNiCWL9GiH4+eg5uFMKioCBL0DrK+GLv8HEkdDkEuj3H6gW6+no5BwpEXSBHblHWLplL3f1LoeVqEREpCKJAbYWe58JdDjpmMaArzFmLhAKPG+tfa9swhOpGGqFBTDmggYn3ucXFLLl10Os3bGfNdkHnJ879vPt6hwKCp16GT5ehoQawUUziKEnEsS4iCC8vSro84auFtcR/jIPfnzFeY7x5Q7Q8z6nJ6O3r6ejk1JSIugCJ5aFtoz2cCQiIlLBnepfmydXdfMB2gG9gEDgB2PMj9batb8byJjRwGiAuLg4N4QqUnH4eHtRPyqE+lEh9C1W7+RofgEbdh5k7Y79J5LElZn7+HLF9hPH+Pt40bBmiJMcHk8So0OpEx5QcQvSnA9vX+jyV0gcDNP/D7560HmOcMCzzrOMUmEoEXSBGSuzaRAVTMOaoZ4ORUREKrZMoPg6q7pA1imO2WWtPQgcNMbMA5KA3yWC1tpxwDhwqoa6LWKRCszfx5tmtcNoVjvsd/sPHs1nfc4B1uzYz9rs/azNOcDCX3bz6c/bThwT4u9D14Y1uKdf04pZlOZ8VYuDkR/C6i9hxv/BW72h3XVw0SNOH0cp95QInqdfDx5j0cbd3NyjoadDERGRim8x0MgYkwBsA0bgPBNY3FTgJWOMD+CHs3T02TKNUqSSC/b3ISm2Gkmxv+8/uO9QHmtznOcOM7bnMnVZFrOf/Y5RXRK49cKGhAVUseWRxkCzAVC/B8z9t1PlNOMLuPgJaDXc+VzKLT0Re56+Ts+m0ELfFloWKiIi58damw/cCswCMoCPrbWrjDFjjDFjio7JAGYCK4CfcFpMpHkqZpGqJDzIl/bxEVzdsR6PD27Jt3dfwOA2Mbzx/QYuHDuXiT9tOfHMYZXiHwIXPw5/+Q4iEmDKaHj3Uti1ztORyRmoofx5GjX+J9blHOD7/+tZNdeJi4h4gBrKl44ayou418rMfTz6+SpSN+8hsU4Y/7g0kZSECE+H5RmFhbD0HafdRN5h6HIHdLvT6YcoZUIN5ctA7pE85q/fxXWd45UEioiIiFRRLeuG88mYTny+Yjv/np7B8Nd/4JJWtbmvX1PqVq9ircW8vCD5emg6AGY9APOegoUvgH8o+AY5m9/xn8FOgugb/Nu+M35+imN9/LUE9RwpETwP32bkkFdgtSxUREREpIozxjAwqQ69m9Xi9Xm/8Np3v/BN+g7+0r0+Y3o0qBhN7F0ppCYMfQPaXgNrZ8Gxg84MYd5BOHYI8g7B/u2/vT7+ecHR0l3HeDnJYWx7p41Fw95OMipnVcX+i3StmWnZ1Arzp02sKiOJiIiICAT6eXPHRY0ZnhzLkzNW88K36/k4NZN7+zVlUOs6VW8VWUJ3ZyupgnwnMTy+HTvF65OTyqO5kPE5/G84RNSHlL9A6yshIOzs16vClAieo0PH8pm7NofhybF4qcmoiIiIiBRTp1ogL4xsw7Wd6vHo5+nc8dEy3vthE/+4NPEP1UilGG8f8A4rfRJ38ROQMQ1+fA1m3gPf/gvaXAUpoyGygXtireA0b3qO5q3dyZG8QvomalmoiIiIiJxacnwEU2/pwlPDWrHl18MMenkBd328nJzcI54OrXLx9oUWQ+HGr+HP30LT/rD4LXixHUwYDutnQyUqkukKSgTP0Yy0bKoH+VbdilAiIiIiUiJeXobhybHMufsCxlzQgM+XZ9Fz7FxembueI3kFng6v8olpB0PGwd9WwQX3QNbP8MEQeLkDLH7TWVoqSgTPxdH8Ar7NyKFP82h8vPVHKCIiIiJnFxrgy739mvL1nd3p0rAGT81cQ59n5zEzLZvK1NKt3AitBT3vg7+lweBxTrXRL++CZ5o5FU33bPJ0hA5rYe9WWPdNmV5Wzwieg4Xrd7P/aL6qhYqIiIhIqdWLDGbctcksWL+Lxz5PZ8wHS+jcIJKHL21O02gVOHE5H39IugJaDYfMxfDjq0XbK9CkP3T4C8R3K5s2FNZCbhZsX+bMVGb9DFnL4NAu5/N7t5ZZkRu3JoLGmL7A84A38Ka19smTPu8BTAU2Fu361Fr7WEnO9aQZadsJ9fehc8NIT4ciIiIiIhVUl4Y1+PL2rnz40xb++/Va+j//PVd2iOPO3k2ICPbzdHiVjzEQm+Js+7ZB6tuwZDys/gJqJjoJYavhTr9CV9mf/fuEL+tnOJhTFI831GwGTfpC7dZQp63TG7GMuC0RNMZ4Ay8DvYFMYLExZpq1Nv2kQ7+31g44x3PLXH5BIV+n7+DCZjXx9/H2dDgiIiIiUoH5eHtxTad4Lk2qw3PfrOP9HzczbVkWd1zUmGs61cNXjyG5R3gM9HoIut8NaZOdaqOf3w7f/APaXQftb4TwuqUb80DOb8le1s/OrN/+7c5nxguimkLDi6BOG2eLbuHapLOU3DkjmAKst9ZuADDGTAQGASVJ5s7nXLf6aeOv7DmURz8tCxURERERF6kW5McjAxO5qkMcj32RzmNfpDNh0Wbax0dQUGgpsJbCQkuBhYLCQmdfIRRaS0GhpdBa8guKH+f8zC/87XPnJ+QXFlJYCAWFFl8fQ5/m0VzRPpbGtUI9/cdQ9nwDoc3V0Poq2LwQFr0GC56HBS9As0udJvVxHf+4bPTgrt+SvuPLPHO3FX1ooEZjSLigKOlrDdEtwS+4zH+9M3FnIhgDbC32PhPocIrjOhljlgNZwN3W2lWlOLfMzUjLJsDXiwsa1/R0KCIiIiJSyTSqFcp716fw7eoc/vvVWr5dnYOPl8HLy+DtZfA2Ra+P//QCby8vvA14exm8jMHPx+vE6xPnGud85zUn9u05dIz3ftjEW/M30jq2GiPaxzIgqQ4h/lWslIgxEN/F2fZucaqLLnkX0j+D2knQ9lo4su+3JZ77iqUqkY2gXudiM30twb/8J9Xu/F/4VE9bnlwOaSlQz1p7wBjTH/gMaFTCc52LGDMaGA0QFxd37tGWQGGhZdaqbHo0rkmgn5aFioiIiIjrGWPo1awWvZrVKpPr7TpwlClLt/FR6lbu/XQlj32RziUta3NF+1ja1auOKYsiKuVJtTjo/ZjTemLFx7DodafaKEBEfecZw5TRTtJXuxUEhHs23nPkzkQwE4gt9r4uzqzfCdba3GKvpxtjXjHG1CjJucXOGweMA0hOTnZr3d2ft+4hZ/9R+rXUslARERERqRxqhPjz5+71ubFbAku37OXjxVv5fEUWnyzJpEFUMFe0j2VI27rUCPH3dKhlyy8Ykkc5zwzuXA2htSGwmqejchl3JoKLgUbGmARgGzACuLL4AcaYaGCHtdYaY1Jw+hruBvae7VxPmLEyGz9vLy5sqmWhIiIiIlK5GGNoV6867epV5+FLm/Pliu18lLqVJ6av5qmZa+jVrCZXtI+le6OoqtVL2xinumcl47ZE0Fqbb4y5FZiF0wLibWvtKmPMmKLPXwOGATcZY/KBw8AI63TTPOW57oq1JKy1zFyVTZeGkYQG+HoyFBEpp/Ly8sjMzOTIkSOeDqXSCAgIoG7duvj66u9dEZGyFOzvw/D2sQxvH8v6nP18tHgrny7dxqxVO6gV5s+wdnUZnhxLvcjyVQBFSs44eVflkJycbFNTU90ydtq2fQx4cT5PDW3F8PaxZz9BRKqcjRs3EhoaSmRkZNV7nsINrLXs3r2b/fv3k5CQ8LvPjDFLrLXJHgqtwnHn/VFEqo5j+YV8u3oHHy3eyndrd1JooVP9SK5oH0vfFtEE+KqGRnlQ0ntkFSsHdO5mpmXj7WW4qHnZPLQrIhXPkSNHiI+PVxLoIsYYIiMj2blzp6dDERERwM/Hi74tatO3RW227zvMpNRMPl6ylTs+WkbYVB8GtY7hivaxtIipmMVTqholgiU0I207HRIiiAj283QoIlKOKQl0Lf15ioiUT7XDA7mtVyNu6dmQHzfs5qPUrXyUupX3f9xM89phjEiJZVBSDOFBWtpfXlWhpzzP3bod+/ll50E1kReRcm337t20bt2a1q1bEx0dTUxMzIn3x44dK9EYo0aNYs2aNWc85uWXX2bChAmuCFlERCo4Ly9D54Y1eH5EGxbffxGPDkwE4OGpq2j/xDf8deLPzFu7k2P5hR6OVE6mGcESmJmWDUCfRCWCIlJ+RUZGsmzZMgAeeeQRQkJCuPvuu393jLUWay1eXqf+HnD8+PFnvc4tt9xy/sGKiEilEx7ky586x/OnzvGkbdvHR4u38tmybUxdlkWIvw/dG9egV9Na9GgSRWRVa0VRDmlGsARmpGXTrl51aoUFeDoUEZFSW79+PS1atGDMmDG0bduW7du3M3r0aJKTk0lMTOSxxx47cWzXrl1ZtmwZ+fn5VKtWjXvvvZekpCQ6depETk4OAA8++CDPPffciePvvfdeUlJSaNKkCQsXLgTg4MGDDB06lKSkJEaOHElycvKJJFVERCq/FjHh/POyFix+4CLeuDaZAa1qs3jTHu76ZDnJj3/D0FcX8src9azJ3k9lKl5ZkWhG8Cy27D5E+vZcHuhf+XqHiIj7PPr5KtKzcl06ZvM6Yfzj0sRzOjc9PZ3x48fz2muvAfDkk08SERFBfn4+PXv2ZNiwYTRv3vx35+zbt48LLriAJ598kjvvvJO3336be++99w9jW2v56aefmDZtGo899hgzZ87kxRdfJDo6msmTJ7N8+XLatm17TnGLiEjFFuDrTe/mtejdvBaFhZa0rH3Mzshh9uodPDVzDU/NXEPd6oH0alqTXs1q0aF+BP4+qj5aFpQInsXMVdsB6KvnA0WkAmvQoAHt27c/8f7DDz/krbfeIj8/n6ysLNLT0/+QCAYGBtKvXz8A2rVrx/fff3/KsYcMGXLimE2bNgEwf/587rnnHgCSkpJITDy3BFZERCoPLy9Dq7rVaFW3Gn/r3ZjsfUf4dnUOszN2MHHxVt79YTPBft50axRFr2Y16dm0JjW0hNRtlAiexYy0bFrEhBEbEeTpUESkAjnXmTt3CQ7+reHvunXreP755/npp5+oVq0aV199NUeOHPnDOX5+v1VJ9vb2Jj8//5Rj+/v7/+EYLfMREZGziQ4P4MoOcVzZIY7DxwpY+MsuZq/O4duMHGauysYYaB1b7cRsYdPoUFWTdiElgmeQve8IP2/Zy919Gns6FBERl8nNzSU0NJSwsDC2b9/OrFmz6Nu3r0uv0bVrVz7++GO6devGypUrSU9Pd+n4IiJSuQT6edOrWS16NauFvcyyKiv3xBLSsV+tZexXa4mpFsiFTWvSq1lNOtaPVAP786RE8AxmrXKqhfZtUdvDkYiIuE7btm1p3rw5LVq0oH79+nTp0sXl17jtttu49tpradWqFW3btqVFixaEh6vBsIiInJ0xhhYx4bSICeevFzUiJ7doCenqHCYtyeT9HzcT5OdN14Y1TiwhrRmqoo6lZSrT8p3k5GSbmprqsvFGjvuRXQeO8vWdF7hsTBGpvDIyMmjWTIWlAPLz88nPzycgIIB169bRp08f1q1bh49P6b9/PNWfqzFmibU22VXxVnauvj+KiHjKkbwCftiwm9kZO/g2I4esfc6jDW3iqnFxYjR9E6OJrxF8llEqt5LeIzUjeBq7Dxxl0cbd3NKzoadDERGpcA4cOECvXr3Iz8/HWsvrr79+TkmgiIhIcQG+3vRsUpOeTWpiB1kytu9ndsYOvkrfwZMzVvPkjNU0jQ51ksIW0Xqu8Ax0Vz6Nr9N3UGhVLVRE5FxUq1aNJUuWeDoMERGpxIwxNK8TRvM6YdzWqxFbfz3EV+k7mJWWzQvfruP52euoFxnExYnRXJwYTZvYanh5KSk8TongacxclU1sRCDNa4d5OhQRERERETmL2IggbuiawA1dE9i5/yjfZOxgZlo24xdsZNy8DdQM9T8xU5iSEIGvt5enQ/YoJYKnsO9wHgvW72JUlwRNJYuIiIiIVDBRof6MTIljZEoc+w7nMWd1DrNWZZ8oNhMe6MtFzWrRt0U03RrVqJIVSJUInsKc1TnkFVgtCxURERERqeDCA325rE0Ml7WJ4fCxAuat28mstGy+Ts9m8tJMgvyc5w4vbhFNzyZRhAb4ejrkMqFE8BRmpG2nVpg/retW83QoIiIiIiLiIoF+3ieeGcwrKOTHDbuZmZbNrFU7+HLldvy8vejSMJK+LaK5qFktIkP8PR2y21TthbGncOhYPt+t3UnfxGg9TCoiFUqPHj2YNWvW7/Y999xz3Hzzzac9JyQkBICsrCyGDRt22nHP1nrgueee49ChQyfe9+/fn71795Y0dBERkTLn6+1Ft0ZRPD64JYvu78WkMZ24tlM91uUc4J7JK2n/+DeMGPcD4xdsJGvvYU+H63KaETzJd2t2ciSvUE3kRaTCGTlyJBMnTuTiiy8+sW/ixIk8/fTTZz23Tp06TJo06Zyv/dxzz3H11VcTFBQEwPTp0895LBERkbLm7WVIjo8gOT6CBy5pRvr2XGalZTNzVTaPfp7Oo5+nUzs8gMQ64STWCXO2mHDqhAdU2JoiSgRPMiMtm4hgP9rHV/d0KCIipTJs2DAefPBBjh49ir+/P5s2bSIrK4vWrVvTq1cv9uzZQ15eHv/6178YNGjQ787dtGkTAwYMIC0tjcOHDzNq1CjS09Np1qwZhw//9i3oTTfdxOLFizl8+DDDhg3j0Ucf5YUXXiArK4uePXtSo0YN5syZQ3x8PKmpqdSoUYNnnnmGt99+G4Abb7yRO+64g02bNtGvXz+6du3KwoULiYmJYerUqQQGBpbpn5mIiMjJjDFFCV84d/ZpwoadB/h2dQ4rt+1jVVYus1fvwFrn2GpBvkWJYfiJnwk1gvGuACsLlQgWczS/gG9X5zCgVW18qng5WRE5TzPuheyVrh0zuiX0e/K0H0dGRpKSksLMmTMZNGgQEydO5IorriAwMJApU6YQFhbGrl276NixIwMHDjztN5ivvvoqQUFBrFixghUrVtC2bdsTnz3++ONERERQUFBAr169WLFiBbfffjvPPPMMc+bMoUaNGr8ba8mSJYwfP55FixZhraVDhw5ccMEFVK9enXXr1vHhhx/yxhtvMHz4cCZPnszVV1/tmj8rERERF6kfFUL9qJAT7w8dyydj+37Ss5zEcFVWLu8s2MSxgkIAAn29aVY79HfJYePoEPx9yldlUiWCxSxYv4sDR/O5WNVCRaSCOr489Hgi+Pbbb2Ot5f7772fevHl4eXmxbds2duzYQXT0qf+umzdvHrfffjsArVq1olWrVic++/jjjxk3bhz5+fls376d9PT0331+svnz5zN48GCCg4MBGDJkCN9//z0DBw4kISGB1q1bA9CuXTs2bdrkoj+Fis0Y0xd4HvAG3rTWPnnS5z2AqcDGol2fWmsfK9MgRUSqsCA/H9rVq067er+tIMwrKGTdjgOsKkoO07NymfLzNt7/cTMAPl6GRrVCf1tWWiecZrVDPVqhVIlgMTNWZhMa4EOXBjXOfrCIyJmcYebOnS677DLuvPNOli5dyuHDh2nbti3vvPMOO3fuZMmSJfj6+hIfH8+RI0fOOM6pZgs3btzI2LFjWbx4MdWrV+e666476zj2+NqZU/D3/60Sm7e39++WoFZVxhhv4GWgN5AJLDbGTLPWpp906PfW2gFlHqCIiJySr7cXzeuE0bxOGJcX7SsstGz59VDRrOE+0rJymbsmh0lLMk+cFx8Z5MwcxjjJYcf6EWU2c6hEsEhBoeWbjB1c1KwWfj5aFioiFVNISAg9evTg+uuvZ+TIkQDs27ePmjVr4uvry5w5c9i8efMZx+jevTsTJkygZ8+epKWlsWLFCgByc3MJDg4mPDycHTt2MGPGDHr06AFAaGgo+/fv/8PS0O7du3Pddddx7733Yq1lypQpvP/++67/xSuPFGC9tXYDgDFmIjAIODkRFBGRcs7LyxBfI5j4GsFc0sopRGmtJWf/UWfmcJuzrHTFtr18uXI7xkDaIxfjX0YZmhLBIt5ehs9v60pewem/vRYRqQhGjhzJkCFDmDhxIgBXXXUVl156KcnJybRu3ZqmTZue8fybbrqJUaNG0apVK1q3bk1KSgoASUlJtGnThsTEROrXr0+XLl1OnDN69Gj69etH7dq1mTNnzon9bdu25brrrjsxxo033kibNm20DPT0YoCtxd5nAh1OcVwnY8xyIAu421q76uQDjDGjgdEAcXFxbghVRERKyxhDrbAAaoUFcGHTWif27zuUx/qdBwguqywQMGdatlPRJCcn27P1uhIRcZeMjAyaNWvm6TAqnVP9uRpjllhrkz0UktsYYy4HLrbW3lj0/hogxVp7W7FjwoBCa+0BY0x/4HlrbaMzjav7o4hI1VHSe6TWQIqIiJQfmUBssfd1cWb9TrDW5lprDxS9ng74GmP0cLuIiJSKWxNBY0xfY8waY8x6Y8y9ZziuvTGmwBgzrNi+TcaYlcaYZcYYfY0pIiJVwWKgkTEmwRjjB4wAphU/wBgTbYqq+RhjUnDu5bvLPFIREanQ3LYItaSVz4qO+w8w6xTD9LTW7nJXjCIiIuWJtTbfGHMrzj3RG3jbWrvKGDOm6PPXgGHATcaYfOAwMMJWpuc8RESkTLjzacSSVj67DZgMtHdjLCIiZcJae9pG7VJ6VTG/KVruOf2kfa8Ve/0S8FJZxyUiIpWLO5eGnqryWUzxA4z5//buNcTyuo7j+PuDuzVesdo0c8zdSiwTbyxiCj3QDDPRogcqFlY+sosWXVSEHkUYRZkoyWa2RpsRm5oEmcsWSWRWmte0FDMdXdt1w+yGt749OH9l2gsz58yc+Z/zP+8XDPM/v1nOfr6cOfOd7/822R94H3Al2yvg5iS3N3c+k6SRNjU1xdatWydyeBmGqmLr1q1MTU21HUWSpM4Z5hHBHe0S3/a3o0uBC6rqxR3sQT+uqp5Isg+wIckDVXXLdv+Jt8eWNCKmp6eZmZlhy5YtbUfpjKmpKaanp9uOIUlS5wxzEJzzzmfAauD7zRC4Ajg5yQtVdUNVPQFQVZuTXE/vVNPtBsGqWgOsgd7tsRe9Ckmap+XLl7Nq1aq2Y0iSJM1pmKeGznnns6paVVUrq2olsB74aFXdkGT3JHsCJNkdeBdw7xCzSpIkSdLEGNoRwXne+Wxn9gWub44ULgO+V1U3DSurJEmSJE2SYZ4aOuedz7ZZ/9Cs7YeBw4eZTZIkSZImVbp0d7skW4C/LPBpVgDj/rcLu1ADdKOOLtQA3ajDGkbHYtRxYFW9djHCTAL74//pQh1dqAG6UYc1jI4u1LFYNcyrR3ZqEFwMSX5XVavbzrEQXagBulFHF2qAbtRhDaOjK3VMmq68bl2oows1QDfqsIbR0YU6lrqGYd4sRpIkSZI0ghwEJUmSJGnCOAhub03bARZBF2qAbtTRhRqgG3VYw+joSh2TpiuvWxfq6EIN0I06rGF0dKGOJa3BawQlSZIkacJ4RFCSJEmSJoyDYCPJSUn+mOShJBe2nWcQSQ5I8vMk9ye5L8n5bWcaVJJdkvw+yY/bzjKoJHsnWZ/kgeY1eXvbmfqV5FPN99K9Sa5NMtV2pvlIcnWSzUnunbX26iQbkjzYfH5VmxnnspMavtx8P92d5Poke7eZcS47qmHW1z6TpJKsaCOb+jPuPdL+OFrsj+3pQn8Ee+RicRCk90MVuAJ4N3AIcGaSQ9pNNZAXgE9X1VuBY4CPjWkdAOcD97cdYoG+DtwTzdCkAAAFYklEQVRUVW8BDmfM6kmyP3AesLqqDgV2Ac5oN9W8rQVO2mbtQmBjVR0EbGwej7K1bF/DBuDQqjoM+BNw0VKH6tNatq+BJAcAJwKPLnUg9a8jPdL+OFrsj+1Zy/j3R7BHLgoHwZ6jgYeq6uGqeg74PnBay5n6VlWbquqOZvsf9H6w7t9uqv4lmQbeA1zVdpZBJdkLeAfwLYCqeq6qnm431UCWAbsmWQbsBjzRcp55qapbgL9ts3wacE2zfQ3w3iUN1acd1VBVN1fVC83DXwPTSx6sDzt5HQC+BnwO8CL18TD2PdL+ODrsj+3qQn8Ee+RicRDs2R94bNbjGcawQcyWZCVwJHBbu0kGcim9N8B/2w6yAG8EtgDfbk7huSrJ7m2H6kdVPQ58hd4eqU3A36vq5nZTLci+VbUJer8UAvu0nGehPgL8pO0Q/UpyKvB4Vd3VdhbNW6d6pP2xdfbH0dO1/gj2yHlxEOzJDtbGdk91kj2AHwKfrKpn2s7TjySnAJur6va2syzQMuAo4BtVdSTwL8bjVIuXNdcInAasAl4P7J7kA+2mEkCSi+md6rau7Sz9SLIbcDHw+bazqC+d6ZH2x5Fgf9RQ2SPnz0GwZwY4YNbjacbkEP+2kiyn1+TWVdV1becZwHHAqUkeoXf60fFJvttupIHMADNV9dIe5/X0Gt84eSfw56raUlXPA9cBx7acaSH+mmQ/gObz5pbzDCTJ2cApwFk1fn//5030fnG6q3mPTwN3JHldq6k0l070SPvjyLA/jp5O9EewR/bLQbDnt8BBSVYleQW9C35vbDlT35KE3jn391fVV9vOM4iquqiqpqtqJb3X4WdVNXZ72arqSeCxJAc3SycAf2gx0iAeBY5JslvzvXUCY3ZB/zZuBM5uts8GftRiloEkOQm4ADi1qv7ddp5+VdU9VbVPVa1s3uMzwFHN+0Wja+x7pP1xdNgfR9LY90ewRw7CQRBoLiz9OPBTem/kH1TVfe2mGshxwAfp7SW8s/k4ue1QE+wTwLokdwNHAF9sOU9fmr2164E7gHvo/bxY02qoeUpyLXArcHCSmSTnAJcAJyZ5kN7duC5pM+NcdlLD5cCewIbm/X1lqyHnsJMaNGY60iPtj6PF/tiSLvRHsEcuWobxO2oqSZIkSVoIjwhKkiRJ0oRxEJQkSZKkCeMgKEmSJEkTxkFQkiRJkiaMg6AkSZIkTRgHQWlEJHlx1m3N70xy4SI+98ok9y7W80mStFTsj9JwLGs7gKSX/aeqjmg7hCRJI8b+KA2BRwSlEZfkkSRfSvKb5uPNzfqBSTYmubv5/IZmfd8k1ye5q/k4tnmqXZJ8M8l9SW5OsmtrRUmStED2R2lhHASl0bHrNqe+nD7ra89U1dHA5cClzdrlwHeq6jBgHXBZs34Z8IuqOhw4CrivWT8IuKKq3gY8Dbx/yPVIkrQY7I/SEKSq2s4gCUjyz6raYwfrjwDHV9XDSZYDT1bVa5I8BexXVc8365uqakWSLcB0VT076zlWAhuq6qDm8QXA8qr6wvArkyRpcPZHaTg8IiiNh9rJ9s7+zY48O2v7RbxGWJI0/uyP0oAcBKXxcPqsz7c2278Czmi2zwJ+2WxvBM4FSLJLkr2WKqQkSUvM/igNyD0e0ujYNcmdsx7fVFUv3SL7lUluo7fz5sxm7Tzg6iSfBbYAH27WzwfWJDmH3p7Nc4FNQ08vSdJw2B+lIfAaQWnENddArK6qp9rOIknSqLA/SgvjqaGSJEmSNGE8IihJkiRJE8YjgpIkSZI0YRwEJUmSJGnCOAhKkiRJ0oRxEJQkSZKkCeMgKEmSJEkTxkFQkiRJkibM/wB/vgnvlrsNhQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Run this cell to plot accuracy vs epoch and loss vs epoch\n", + "\n", + "plt.figure(figsize=(15,5))\n", + "plt.subplot(121)\n", + "try:\n", + " plt.plot(history.history['accuracy'])\n", + " plt.plot(history.history['val_accuracy'])\n", + "except KeyError:\n", + " try:\n", + " plt.plot(history.history['acc'])\n", + " plt.plot(history.history['val_acc'])\n", + " except KeyError:\n", + " plt.plot(history.history['categorical_accuracy'])\n", + " plt.plot(history.history['val_categorical_accuracy'])\n", + "plt.title('Accuracy vs. epochs')\n", + "plt.ylabel('Accuracy')\n", + "plt.xlabel('Epoch')\n", + "plt.legend(['Training', 'Validation'], loc='lower right')\n", + "\n", + "plt.subplot(122)\n", + "plt.plot(history.history['loss'])\n", + "plt.plot(history.history['val_loss'])\n", + "plt.title('Loss vs. epochs')\n", + "plt.ylabel('Loss')\n", + "plt.xlabel('Epoch')\n", + "plt.legend(['Training', 'Validation'], loc='upper right')\n", + "plt.show() " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an iterable from the batched test dataset\n", + "\n", + "test_dataset = test_dataset.batch(10)\n", + "iter_test_dataset = iter(test_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Display model predictions for a sample of test images\n", + "\n", + "plt.figure(figsize=(15,8))\n", + "inx = np.random.choice(test_data.shape[0], 18, replace=False)\n", + "images, labels = next(iter_test_dataset)\n", + "probs = cifar_model(tf.reduce_mean(tf.cast(images, tf.float32), axis=-1, keepdims=True) / 255.)\n", + "preds = np.argmax(probs, axis=1)\n", + "for n in range(10):\n", + " ax = plt.subplot(2, 5, n+1)\n", + " plt.imshow(images[n])\n", + " plt.title(cifar_labels[cifar_classes[np.where(labels[n].numpy() == 1.0)[0][0]]])\n", + " plt.text(0, 35, \"Model prediction: {}\".format(cifar_labels[cifar_classes[preds[n]]]))\n", + " plt.axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congratulations for completing this programming assignment! In the next week of the course we will learn to develop models for sequential data." + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "tensor-flow-2-2", + "graded_item_id": "3hWzU", + "launcher_item_id": "AStQh" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Week 3 Programming Assignment.ipynb b/Week 3 Programming Assignment.ipynb new file mode 100644 index 0000000..61bda69 --- /dev/null +++ b/Week 3 Programming Assignment.ipynb @@ -0,0 +1,1081 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Programming Assignment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Language model for the Shakespeare dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instructions\n", + "\n", + "In this notebook, you will use the text preprocessing tools and RNN models to build a character-level language model. You will then train your model on the works of Shakespeare, and use the network to generate your own text..\n", + "\n", + "Some code cells are provided you in the notebook. You should avoid editing provided code, and make sure to execute the cells in order to avoid unexpected errors. Some cells begin with the line: \n", + "\n", + "`#### GRADED CELL ####`\n", + "\n", + "Don't move or edit this first line - this is what the automatic grader looks for to recognise graded cells. These cells require you to write your own code to complete them, and are automatically graded when you submit the notebook. Don't edit the function name or signature provided in these cells, otherwise the automatic grader might not function properly. Inside these graded cells, you can use any functions or classes that are imported below, but make sure you don't use any variables that are outside the scope of the function.\n", + "\n", + "### How to submit\n", + "\n", + "Complete all the tasks you are asked for in the worksheet. When you have finished and are happy with your code, press the **Submit Assignment** button at the top of this notebook.\n", + "\n", + "### Let's get started!\n", + "\n", + "We'll start running some imports, and loading the dataset. Do not edit the existing imports in the following cell. If you would like to make further Tensorflow imports, you should add them here." + ] + }, + { + "cell_type": "code", + "execution_count": 210, + "metadata": {}, + "outputs": [], + "source": [ + "#### PACKAGE IMPORTS ####\n", + "\n", + "# Run this cell first to import all required packages. Do not make any imports elsewhere in the notebook\n", + "\n", + "import tensorflow as tf\n", + "import numpy as np\n", + "import json\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "# If you would like to make further imports from tensorflow, add them here\n", + "\n", + "from tensorflow.keras.preprocessing.text import Tokenizer\n", + "from tensorflow.keras.preprocessing.sequence import pad_sequences\n", + "from tensorflow.keras import Sequential\n", + "from tensorflow.keras.layers import Embedding,GRU,Dense" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Shakespeare image](data/shakespeare.png)\n", + "\n", + "#### The Shakespeare dataset\n", + "\n", + "In this assignment, you will use a subset of the [Shakespeare dataset](http://shakespeare.mit.edu). It consists of a single text file with several excerpts concatenated together. The data is in raw text form, and so far has not yet had any preprocessing. \n", + "\n", + "Your goal is to construct an unsupervised character-level sequence model that can generate text according to a distribution learned from the dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Load and inspect the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the text file into a string\n", + "\n", + "with open('data/Shakespeare.txt', 'r', encoding='utf-8') as file:\n", + " text = file.read()" + ] + }, + { + "cell_type": "code", + "execution_count": 212, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a list of chunks of text\n", + "\n", + "text_chunks = text.split('.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To give you a feel for what the text looks like, we will print a few chunks from the list." + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "BIANCA:\n", + "And may you prove, sir, master of your art!\n", + "\n", + "LUCENTIO:\n", + "While you, sweet dear, prove mistress of my heart!\n", + "\n", + "HORTENSIO:\n", + "Quick proceeders, marry! Now, tell me, I pray,\n", + "You that durst swear at your mistress Bianca\n", + "Loved none in the world so well as Lucentio\n", + "\n", + "\n", + "LEONTES:\n", + "Good queen!\n", + "\n", + "PAULINA:\n", + "Good queen, my lord,\n", + "Good queen; I say good queen;\n", + "And would by combat make her good, so were I\n", + "A man, the worst about you\n", + "\n", + "\n", + "FLORIZEL:\n", + "I not purpose it\n", + "\n", + "\n", + "FLORIZEL:\n", + "O, that must be\n", + "I' the virtue of your daughter: one being dead,\n", + "I shall have more than you can dream of yet;\n", + "Enough then for your wonder\n", + "\n", + "\n", + "PAULINA:\n", + "Nay, rather, good my lords, be second to me:\n", + "Fear you his tyrannous passion more, alas,\n", + "Than the queen's life? a gracious innocent soul,\n", + "More free than he is jealous\n" + ] + } + ], + "source": [ + "# Display some randomly selected text samples\n", + "\n", + "num_samples = 5\n", + "inx = np.random.choice(len(text_chunks), num_samples, replace=False)\n", + "for chunk in np.array(text_chunks)[inx]:\n", + " print(chunk)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create a character-level tokenizer" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should now write a function that returns a `Tokenizer` object. The function takes a list of strings as an argument, and should create a `Tokenizer` according to the following specification:\n", + "\n", + "* The number of tokens should be unlimited (there should be as many as required by the dataset).\n", + "* Tokens should be created at the character level (not at the word level, which is the default behaviour).\n", + "* No characters should be filtered out or ignored.\n", + "* The original capitalization should be retained (do not convert the text to lower case)\n", + "\n", + "The `Tokenizer` should be fit to the `list_of_strings` argument and returned by the function. \n", + "\n", + "**Hint:** you may need to refer to the [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text/Tokenizer) for the `Tokenizer`." + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following function.\n", + "# Make sure not to change the function name or arguments.\n", + "\n", + "def create_character_tokenizer(list_of_strings):\n", + " \"\"\"\n", + " This function takes a list of strings as its argument. It should create \n", + " and return a Tokenizer according to the above specifications. \n", + " \"\"\"\n", + " tokenizer = Tokenizer(num_words=None,\n", + " filters = None,\n", + " lower=False,\n", + " char_level=True\n", + " )\n", + " tokenizer.fit_on_texts(list_of_strings)\n", + " \n", + " return tokenizer \n", + " \n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "metadata": {}, + "outputs": [], + "source": [ + "# Get the tokenizer\n", + "\n", + "tokenizer = create_character_tokenizer(text_chunks)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Tokenize the text\n", + "\n", + "You should now write a function to use the tokenizer to map each string in `text_chunks` to its corresponding encoded sequence. The following function takes a fitted `Tokenizer` object in the first argument (as returned by `create_character_tokenizer`) and a list of strings in the second argument. The function should return a list of lists, where each sublist is a sequence of integer tokens encoding the text sequences according to the mapping stored in the tokenizer.\n", + "\n", + "**Hint:** you may need to refer to the [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text/Tokenizer) for the `Tokenizer`." + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following function.\n", + "# Make sure not to change the function name or arguments.\n", + "\n", + "def strings_to_sequences(tokenizer, list_of_strings):\n", + " \"\"\"\n", + " This function takes a tokenizer object and a list of strings as its arguments.\n", + " It should use the tokenizer to map the text chunks to sequences of tokens and\n", + " then return this list of encoded sequences.\n", + " \"\"\"\n", + " sequences = tokenizer.texts_to_sequences(list_of_strings)\n", + " \n", + " return sequences" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": {}, + "outputs": [], + "source": [ + "# Encode the text chunks into tokens\n", + "\n", + "seq_chunks = strings_to_sequences(tokenizer, text_chunks)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Pad the encoded sequences and store them in a numpy array" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since not all of the text chunks are the same length, you will need to pad them in order to train on batches. You should now complete the following function, which takes the list of lists of tokens, and creates a single numpy array with the token sequences in the rows, according to the following specification:\n", + "\n", + "* The longest allowed sequence should be 500 tokens. Any sequence that is longer should be shortened by truncating the beginning of the sequence.\n", + "* Use zeros for padding the sequences. The zero padding should be placed before the sequences as required.\n", + "\n", + "The function should then return the resulting numpy array.\n", + "\n", + "**Hint:** you may want to refer to the [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/sequence/pad_sequences) for the `pad_sequences` function." + ] + }, + { + "cell_type": "code", + "execution_count": 218, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following function.\n", + "# Make sure not to change the function name or arguments.\n", + "\n", + "def make_padded_dataset(sequence_chunks):\n", + " \"\"\"\n", + " This function takes a list of lists of tokenized sequences, and transforms\n", + " them into a 2D numpy array, padding the sequences as necessary according to\n", + " the above specification. The function should then return the numpy array.\n", + " \"\"\"\n", + " \n", + " padded_sequences = pad_sequences(sequence_chunks,maxlen = 500,truncating = 'pre',padding = 'pre',value = 0)\n", + " \n", + " return padded_sequences" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": {}, + "outputs": [], + "source": [ + "# Pad the token sequence chunks and get the numpy array\n", + "\n", + "padded_sequences = make_padded_dataset(seq_chunks)" + ] + }, + { + "cell_type": "code", + "execution_count": 220, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7886" + ] + }, + "execution_count": 220, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(padded_sequences)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create model inputs and targets\n", + "\n", + "Now you are ready to build your RNN model. The model will receive a sequence of characters and predict the next character in the sequence. At training time, the model can be passed an input sequence, with the target sequence is shifted by one.\n", + "\n", + "For example, the expression `To be or not to be` appears in Shakespeare's play 'Hamlet'. Given input `To be or not to b`, the correct prediction is `o be or not to be`. Notice that the prediction is the same length as the input!\n", + "\n", + "![sequence_prediction_example](data/rnn_example.png)\n", + "\n", + "You should now write the following function to create an input and target array from the current `padded_sequences` array. The function has a single argument that is a 2D numpy array of shape `(num_examples, max_seq_len)`. It should fulfil the following specification:\n", + "\n", + "* The function should return an input array and an output array, both of size `(num_examples, max_seq_len - 1)`.\n", + "* The input array should contain the first `max_seq_len - 1` tokens of each sequence. \n", + "* The output array should contain the last `max_seq_len - 1` tokens of each sequence. \n", + "\n", + "The function should then return the tuple `(input_array, output_array)`. Note that it is possible to complete this function using numpy indexing alone!" + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following function.\n", + "# Make sure not to change the function name or arguments.\n", + "\n", + "def create_inputs_and_targets(array_of_sequences):\n", + " \"\"\"\n", + " This function takes a 2D numpy array of token sequences, and returns a tuple of two\n", + " elements: the first element is the input array and the second element is the output\n", + " array, which are defined according to the above specification.\n", + " \"\"\" \n", + " max_seq_len = array_of_sequences.shape[1]\n", + " input_array = array_of_sequences[:,0:max_seq_len - 1]\n", + " output_array = array_of_sequences[:,1:max_seq_len]\n", + " \n", + " return (input_array,output_array)\n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 222, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the input and output arrays\n", + "\n", + "input_seq, target_seq = create_inputs_and_targets(padded_sequences)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Preprocess sequence array for stateful RNN\n", + "\n", + "We will build our RNN language model to be stateful, so that the internal state of the RNN will be maintained across batches. For this to be effective, we need to make sure that each element of every batch follows on from the corresponding element of the preceding batch (you may want to look back at the \"Stateful RNNs\" reading notebook earlier in the week).\n", + "\n", + "The following code processes the input and output sequence arrays so that they are ready to be split into batches for training a stateful RNN, by re-ordering the sequence examples (the rows) according to a specified batch size. " + ] + }, + { + "cell_type": "code", + "execution_count": 223, + "metadata": {}, + "outputs": [], + "source": [ + "# Fix the batch size for training\n", + "\n", + "batch_size = 32" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": {}, + "outputs": [], + "source": [ + "# Prepare input and output arrays for training the stateful RNN\n", + "\n", + "num_examples = input_seq.shape[0]\n", + "\n", + "num_processed_examples = num_examples - (num_examples % batch_size)\n", + "\n", + "input_seq = input_seq[:num_processed_examples]\n", + "target_seq = target_seq[:num_processed_examples]\n", + "\n", + "steps = int(num_processed_examples / 32) # steps per epoch\n", + "\n", + "inx = np.empty((0,), dtype=np.int32)\n", + "for i in range(steps):\n", + " inx = np.concatenate((inx, i + np.arange(0, num_processed_examples, steps)))\n", + "\n", + "input_seq_stateful = input_seq[inx]\n", + "target_seq_stateful = target_seq[inx]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Split the data into training and validation sets\n", + "\n", + "We will set aside approximately 20% of the data for validation." + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the training and validation splits\n", + "\n", + "num_train_examples = int(batch_size * ((0.8 * num_processed_examples) // batch_size))\n", + "\n", + "input_train = input_seq_stateful[:num_train_examples]\n", + "target_train = target_seq_stateful[:num_train_examples]\n", + "\n", + "input_valid = input_seq_stateful[num_train_examples:]\n", + "target_valid = target_seq_stateful[num_train_examples:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create training and validation Dataset objects\n", + "\n", + "You should now write a function to take the training and validation input and target arrays, and create training and validation `tf.data.Dataset` objects. The function takes an input array and target array in the first two arguments, and the batch size in the third argument. Your function should do the following:\n", + "\n", + "* Create a `Dataset` using the `from_tensor_slices` static method, passing in a tuple of the input and output numpy arrays.\n", + "* Batch the `Dataset` using the `batch_size` argument, setting `drop_remainder` to `True`. \n", + "\n", + "The function should then return the `Dataset` object." + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following function.\n", + "# Make sure not to change the function name or arguments.\n", + "\n", + "def make_Dataset(input_array, target_array, batch_size):\n", + " \"\"\"\n", + " This function takes two 2D numpy arrays in the first two arguments, and an integer\n", + " batch_size in the third argument. It should create and return a Dataset object \n", + " using the two numpy arrays and batch size according to the above specification.\n", + " \"\"\"\n", + " \n", + " dataset = tf.data.Dataset.from_tensor_slices((input_array,target_array))\n", + " dataset = dataset.batch(batch_size,drop_remainder = True)\n", + " return dataset\n" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the training and validation Datasets\n", + "\n", + "train_data = make_Dataset(input_train, target_train, batch_size)\n", + "valid_data = make_Dataset(input_valid, target_valid, batch_size)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Build the recurrent neural network model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You are now ready to build your RNN character-level language model. You should write the following function to build the model; the function takes arguments for the batch size and vocabulary size (number of tokens). Using the Sequential API, your function should build your model according to the following specifications:\n", + "\n", + "* The first layer should be an Embedding layer with an embedding dimension of 256 and set the vocabulary size to `vocab_size` from the function argument.\n", + "* The Embedding layer should also mask the zero padding in the input sequences.\n", + "* The Embedding layer should also set the `batch_input_shape` to `(batch_size, None)` (a fixed batch size is required for stateful RNNs).\n", + "* The next layer should be a (uni-directional) GRU layer with 1024 units, set to be a stateful RNN layer.\n", + "* The GRU layer should return the full sequence, instead of just the output state at the final time step.\n", + "* The final layer should be a Dense layer with `vocab_size` units and no activation function.\n", + "\n", + "In total, the network should have 3 layers." + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following function.\n", + "# Make sure not to change the function name or arguments.\n", + "\n", + "def get_model(vocab_size, batch_size):\n", + " \"\"\"\n", + " This function takes a vocabulary size and batch size, and builds and returns a \n", + " Sequential model according to the above specification.\n", + " \"\"\"\n", + " \n", + " model = Sequential([\n", + " Embedding(input_dim = vocab_size ,output_dim = 256,mask_zero = True,batch_input_shape = (batch_size,None) ),\n", + " GRU(units = 1024,stateful = True,return_sequences = True),\n", + " Dense(vocab_size)\n", + " ])\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_3\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "embedding_4 (Embedding) (32, None, 256) 16640 \n", + "_________________________________________________________________\n", + "gru_3 (GRU) (32, None, 1024) 3938304 \n", + "_________________________________________________________________\n", + "dense_3 (Dense) (32, None, 65) 66625 \n", + "=================================================================\n", + "Total params: 4,021,569\n", + "Trainable params: 4,021,569\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "# Build the model and print the model summary\n", + "\n", + "model = get_model(len(tokenizer.word_index) + 1, batch_size)\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Compile and train the model\n", + "\n", + "You are now ready to compile and train the model. For this model and dataset, the training time is very long. Therefore for this assignment it is not a requirement to train the model. We have pre-trained a model for you (using the code below) and saved the model weights, which can be loaded to get the model predictions. \n", + "\n", + "It is recommended to use accelerator hardware (e.g. using Colab) when training this model. It would also be beneficial to increase the size of the model, e.g. by stacking extra recurrent layers." + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": {}, + "outputs": [], + "source": [ + "# Choose whether to train a new model or load the pre-trained model\n", + "\n", + "skip_training = True" + ] + }, + { + "cell_type": "code", + "execution_count": 231, + "metadata": {}, + "outputs": [], + "source": [ + "# Compile and train the model, or load pre-trained weights\n", + "\n", + "if not skip_training:\n", + " checkpoint_callback=tf.keras.callbacks.ModelCheckpoint(filepath='./models/ckpt',\n", + " save_weights_only=True,\n", + " save_best_only=True)\n", + " model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", + " metrics=['sparse_categorical_accuracy'])\n", + " history = model.fit(train_data, epochs=15, validation_data=valid_data, \n", + " validation_steps=50, callbacks=[checkpoint_callback])" + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "metadata": {}, + "outputs": [], + "source": [ + "# Save model history as a json file, or load it if using pre-trained weights\n", + "\n", + "if not skip_training:\n", + " history_dict = dict()\n", + " for k, v in history.history.items():\n", + " history_dict[k] = [float(val) for val in history.history[k]]\n", + " with open('models/history.json', 'w+') as json_file:\n", + " json.dump(history_dict, json_file, sort_keys=True, indent=4)\n", + "else:\n", + " with open('models/history.json', 'r') as json_file:\n", + " history_dict = json.load(json_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Plot the learning curves" + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Run this cell to plot accuracy vs epoch and loss vs epoch\n", + "\n", + "plt.figure(figsize=(15,5))\n", + "plt.subplot(121)\n", + "plt.plot(history_dict['sparse_categorical_accuracy'])\n", + "plt.plot(history_dict['val_sparse_categorical_accuracy'])\n", + "plt.title('Accuracy vs. epochs')\n", + "plt.ylabel('Accuracy')\n", + "plt.xlabel('Epoch')\n", + "plt.xticks(np.arange(len(history_dict['sparse_categorical_accuracy'])))\n", + "ax = plt.gca()\n", + "ax.set_xticklabels(1 + np.arange(len(history_dict['sparse_categorical_accuracy'])))\n", + "plt.legend(['Training', 'Validation'], loc='lower right')\n", + "\n", + "plt.subplot(122)\n", + "plt.plot(history_dict['loss'])\n", + "plt.plot(history_dict['val_loss'])\n", + "plt.title('Loss vs. epochs')\n", + "plt.ylabel('Loss')\n", + "plt.xlabel('Epoch')\n", + "plt.xticks(np.arange(len(history_dict['sparse_categorical_accuracy'])))\n", + "ax = plt.gca()\n", + "ax.set_xticklabels(1 + np.arange(len(history_dict['sparse_categorical_accuracy'])))\n", + "plt.legend(['Training', 'Validation'], loc='upper right')\n", + "plt.show() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Write a text generation algorithm\n", + "\n", + "You can now use the model to generate text! In order to generate a single text sequence, the model needs to be rebuilt with a batch size of 1." + ] + }, + { + "cell_type": "code", + "execution_count": 234, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 234, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Re-build the model and load the saved weights\n", + "\n", + "model = get_model(len(tokenizer.word_index) + 1, batch_size=1)\n", + "model.load_weights(tf.train.latest_checkpoint('./models/'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An algorithm to generate text is as follows:\n", + "\n", + "1. Specify a seed string (e.g. `'ROMEO:'`) to get the network started, and a define number of characters for the model to generate, `num_generation_steps`.\n", + "2. Tokenize this sentence to obtain a list containing one list of the integer tokens.\n", + "3. Reset the initial state of the network. \n", + "4. Convert the token list into a Tensor (or numpy array) and pass it to your model as a batch of size one.\n", + "5. Get the model prediction (logits) for the last time step and extract the state of the recurrent layer.\n", + "6. Use the logits to construct a categorical distribution and sample a token from it.\n", + "7. Repeat the following for `num_generation_steps - 1` steps:\n", + "\n", + " 1. Use the saved state of the recurrent layer and the last sampled token to get new logit predictions\n", + " 2. Use the logits to construct a new categorical distribution and sample a token from it.\n", + " 3. Save the updated state of the recurrent layer. \n", + "\n", + "8. Take the final list of tokens and convert to text using the Tokenizer.\n", + "\n", + "Note that the internal state of the recurrent layer can be accessed using the `states` property. For the GRU layer, it is a list of one variable:" + ] + }, + { + "cell_type": "code", + "execution_count": 235, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 235, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Inspect the model's current recurrent state\n", + "\n", + "model.layers[1].states" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will break the algorithm down into two steps. First, you should now complete the following function that takes a sequence of tokens of any length and returns the model's prediction (the logits) for the last time step. The specification is as follows:\n", + "\n", + "* The token sequence will be a python list, containing one list of integer tokens, e.g. `[[1, 2, 3, 4]]`\n", + "* The function should convert the list into a 2D Tensor or numpy array\n", + "* If the function argument `initial_state` is `None`, then the function should reset the state of the recurrent layer to zeros.\n", + "* Otherwise, if the function argument `initial_state` is a 2D Tensor or numpy array, assign the value of the internal state of the GRU layer to this argument.\n", + "* Get the model's prediction (logits) for the last time step only.\n", + "\n", + "The function should then return the logits as a 2D numpy array, where the first dimension is equal to 1 (batch size).\n", + "\n", + "**Hint:** the internal state of the recurrent can be reset to zeros using the `reset_states` method." + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following function.\n", + "# Make sure not to change the function name or arguments.\n", + "\n", + "def get_logits(model, token_sequence, initial_state=None):\n", + " \"\"\"\n", + " This function takes a model object, a token sequence and an optional initial\n", + " state for the recurrent layer. The function should return the logits prediction\n", + " for the final time step as a 2D numpy array.\n", + " \"\"\"\n", + " # I couldn't get this one right. Let me know if you figured it out.\n", + " if initial_state is None:\n", + " model.layers[1].reset_states()\n", + " else:\n", + " initial_state = model.layers[1].states\n", + " \n", + " prediction = model.predict(token_sequence)\n", + " \n", + " return prediction[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 252, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-5.115009 , -8.0475 , 1.8503139 , 4.975737 , 2.8871026 ,\n", + " 4.1724453 , 4.437543 , 2.98841 , 0.52165407, 2.4829183 ,\n", + " 3.7190795 , -1.3724718 , 0.30877763, 3.4526918 , 1.047548 ,\n", + " 5.362768 , 3.5962613 , -7.138241 , 3.7604315 , 0.7027515 ,\n", + " 3.0481653 , 1.8022449 , 2.4562337 , 1.38671 , 1.5200446 ,\n", + " -8.977055 , 3.335435 , 1.4620303 , -0.6112106 , 5.011204 ,\n", + " 0.26996726, -1.633431 , 3.2971196 , -1.6511749 , -0.36638367,\n", + " 2.0932577 , 0.33700356, 1.7744293 , -8.395738 , 3.5642414 ,\n", + " -1.4412229 , 2.672013 , 1.1012034 , 3.8206997 , -7.389186 ,\n", + " 1.4560288 , -6.889679 , 0.6923045 , -6.5362697 , 0.43075308,\n", + " 1.169462 , 1.5707369 , -1.5701991 , 0.43702215, 0.89825916,\n", + " 0.96894693, -4.3608193 , -4.027752 , 1.015482 , -3.7264423 ,\n", + " -3.200475 , -2.9556887 , -3.034881 , -5.616536 , -4.1949883 ]],\n", + " dtype=float32)" + ] + }, + "execution_count": 252, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Test the get_logits function by passing a dummy token sequence\n", + "\n", + "dummy_initial_state = tf.random.normal(model.layers[1].states[0].shape)\n", + "get_logits(model, [[1, 2, 3, 4]], initial_state=dummy_initial_state)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should now write a function that takes a logits prediction similar to the above, uses it to create a categorical distribution, and samples a token from this distribution. The following function takes a 2D numpy array `logits` as an argument, and should return a single integer prediction that is sampled from the categorical distribution. \n", + "\n", + "**Hint:** you might find the `tf.random.categorical` function useful for this; see the documentation [here](https://www.tensorflow.org/api_docs/python/tf/random/categorical)." + ] + }, + { + "cell_type": "code", + "execution_count": 238, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following function.\n", + "# Make sure not to change the function name or arguments.\n", + "\n", + "def sample_token(logits):\n", + " \"\"\"\n", + " This function takes a 2D numpy array as an input, and constructs a \n", + " categorical distribution using it. It should then sample from this\n", + " distribution and return the sample as a single integer.\n", + " \"\"\"\n", + " # I couldn't get this one right. Let me know if you figured it out.\n", + " sample = tf.random.categorical(logits,1)\n", + " sampe = tf.squeeze(sample,axis = -1)\n", + " \n", + " return sample[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 239, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Test the sample_token function by passing dummy logits\n", + "\n", + "dummy_initial_state = tf.random.normal(model.layers[1].states[0].shape)\n", + "dummy_logits = get_logits(model, [[1, 2, 3, 4]], initial_state=dummy_initial_state)\n", + "sample_token(dummy_logits)" + ] + }, + { + "cell_type": "code", + "execution_count": 246, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "41 0\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 246, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logits_size = dummy_logits.shape[1]\n", + "dummy_logits = -np.inf*np.ones((1, logits_size))\n", + "dummy_logits[0, 20] = 0\n", + "sample_token(dummy_logits)\n", + "random_inx = np.random.choice(logits_size, 2, replace=False)\n", + "random_inx1, random_inx2 = random_inx[0], random_inx[1]\n", + "print(random_inx1, random_inx2)\n", + "dummy_logits = -np.inf*np.ones((1, logits_size))\n", + "dummy_logits[0, random_inx1] = 0\n", + "dummy_logits[0, random_inx2] = 0\n", + "sampled_token = []\n", + "for _ in range(100):\n", + " sampled_token.append(sample_token(dummy_logits))\n", + " \n", + "l_tokens, l_counts = np.unique(np.array(sampled_token), return_counts=True)\n", + "len(l_tokens) == 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Generate text from the model\n", + "\n", + "You are now ready to generate text from the model!" + ] + }, + { + "cell_type": "code", + "execution_count": 243, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a seed string and number of generation steps\n", + "\n", + "init_string = 'ROMEO:'\n", + "num_generation_steps = 1000" + ] + }, + { + "cell_type": "code", + "execution_count": 244, + "metadata": {}, + "outputs": [ + { + "ename": "IndexError", + "evalue": "index 1 is out of bounds for axis 0 with size 1", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_generation_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mlogits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_logits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0msampled_token\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msample_token\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogits\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mtoken_sequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msampled_token\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mget_logits\u001b[0;34m(model, token_sequence, initial_state)\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0mprediction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtoken_sequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mprediction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprediction\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 19\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mprediction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mIndexError\u001b[0m: index 1 is out of bounds for axis 0 with size 1" + ] + } + ], + "source": [ + "# Use the model to generate a token sequence\n", + "\n", + "token_sequence = tokenizer.texts_to_sequences([init_string])\n", + "initial_state = None\n", + "input_sequence = token_sequence\n", + "\n", + "for _ in range(num_generation_steps):\n", + " logits = get_logits(model, input_sequence, initial_state=initial_state)\n", + " sampled_token = sample_token(logits)\n", + " token_sequence[0].append(sampled_token)\n", + " input_sequence = [[sampled_token]]\n", + " initial_state = model.layers[1].states[0].numpy()\n", + " \n", + "print(tokenizer.sequences_to_texts(token_sequence)[0][::2])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congratulations for completing this programming assignment! In the next week of the course we will see how to build customised models and layers, and make custom training loops." + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "tensor-flow-2-2", + "graded_item_id": "4eYSM", + "launcher_item_id": "HEV6h" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Week 4 Programming Assignment.ipynb b/Week 4 Programming Assignment.ipynb new file mode 100644 index 0000000..f3d8ed7 --- /dev/null +++ b/Week 4 Programming Assignment.ipynb @@ -0,0 +1,1186 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Programming Assignment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Residual network" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instructions\n", + "\n", + "In this notebook, you will use the model subclassing API together with custom layers to create a residual network architecture. You will then train your custom model on the Fashion-MNIST dataset by using a custom training loop and implementing the automatic differentiation tools in Tensorflow to calculate the gradients for backpropagation.\n", + "\n", + "Some code cells are provided you in the notebook. You should avoid editing provided code, and make sure to execute the cells in order to avoid unexpected errors. Some cells begin with the line: \n", + "\n", + "`#### GRADED CELL ####`\n", + "\n", + "Don't move or edit this first line - this is what the automatic grader looks for to recognise graded cells. These cells require you to write your own code to complete them, and are automatically graded when you submit the notebook. Don't edit the function name or signature provided in these cells, otherwise the automatic grader might not function properly. Inside these graded cells, you can use any functions or classes that are imported below, but make sure you don't use any variables that are outside the scope of the function.\n", + "\n", + "### How to submit\n", + "\n", + "Complete all the tasks you are asked for in the worksheet. When you have finished and are happy with your code, press the **Submit Assignment** button at the top of this notebook.\n", + "\n", + "### Let's get started!\n", + "\n", + "We'll start running some imports, and loading the dataset. Do not edit the existing imports in the following cell. If you would like to make further Tensorflow imports, you should add them here." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#### PACKAGE IMPORTS ####\n", + "\n", + "# Run this cell first to import all required packages. Do not make any imports elsewhere in the notebook\n", + "\n", + "import tensorflow as tf\n", + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.layers import Layer, BatchNormalization, Conv2D, Dense, Flatten, Add\n", + "import numpy as np\n", + "from tensorflow.keras.datasets import fashion_mnist\n", + "from tensorflow.keras.utils import to_categorical\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# If you would like to make further imports from tensorflow, add them here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Fashion-MNIST overview image](data/fashion_mnist.png)\n", + "\n", + "#### The Fashion-MNIST dataset\n", + "\n", + "In this assignment, you will use the [Fashion-MNIST dataset](https://github.com/zalandoresearch/fashion-mnist). It consists of a training set of 60,000 images of fashion items with corresponding labels, and a test set of 10,000 images. The images have been normalised and centred. The dataset is frequently used in machine learning research, especially as a drop-in replacement for the MNIST dataset. \n", + "\n", + "- H. Xiao, K. Rasul, and R. Vollgraf. \"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.\" arXiv:1708.07747, August 2017.\n", + "\n", + "Your goal is to construct a ResNet model that classifies images of fashion items into one of 10 classes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Load the dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this programming assignment, we will take a smaller sample of the dataset to reduce the training time." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n", + "32768/29515 [=================================] - 0s 0us/step\n", + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n", + "26427392/26421880 [==============================] - 0s 0us/step\n", + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n", + "8192/5148 [===============================================] - 0s 0us/step\n", + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n", + "4423680/4422102 [==============================] - 0s 0us/step\n" + ] + } + ], + "source": [ + "# Load and preprocess the Fashion-MNIST dataset\n", + "\n", + "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\n", + "\n", + "train_images = train_images.astype(np.float32)\n", + "test_images = test_images.astype(np.float32)\n", + "\n", + "train_images = train_images[:5000] / 255.\n", + "train_labels = train_labels[:5000]\n", + "\n", + "test_images = test_images / 255.\n", + "\n", + "train_images = train_images[..., np.newaxis]\n", + "test_images = test_images[..., np.newaxis]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Create Dataset objects for the training and test sets\n", + "\n", + "train_dataset = tf.data.Dataset.from_tensor_slices((train_images, train_labels))\n", + "train_dataset = train_dataset.batch(32)\n", + "\n", + "test_dataset = tf.data.Dataset.from_tensor_slices((test_images, test_labels))\n", + "test_dataset = test_dataset.batch(32)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Get dataset labels\n", + "\n", + "image_labels = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create custom layers for the residual blocks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should now create a first custom layer for a residual block of your network. Using layer subclassing, build your custom layer according to the following spec:\n", + "\n", + "* The custom layer class should have `__init__`, `build` and `call` methods. The `__init__` method has been completed for you. It calls the base `Layer` class initializer, passing on any keyword arguments\n", + "* The `build` method should create the layers. It will take an `input_shape` argument, and should extract the number of filters from this argument. It should create:\n", + " * A BatchNormalization layer: this will be the first layer in the block, so should use its `input shape` keyword argument\n", + " * A Conv2D layer with the same number of filters as the layer input, a 3x3 kernel size, `'SAME'` padding, and no activation function\n", + " * Another BatchNormalization layer\n", + " * Another Conv2D layer, again with the same number of filters as the layer input, a 3x3 kernel size, `'SAME'` padding, and no activation function\n", + "* The `call` method should then process the input through the layers:\n", + " * The first BatchNormalization layer: ensure to set the `training` keyword argument\n", + " * A `tf.nn.relu` activation function\n", + " * The first Conv2D layer\n", + " * The second BatchNormalization layer: ensure to set the `training` keyword argument\n", + " * Another `tf.nn.relu` activation function\n", + " * The second Conv2D layer\n", + " * It should then add the layer inputs to the output of the second Conv2D layer. This is the final layer output" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following class. \n", + "# Make sure to not change the class or method names or arguments.\n", + "\n", + "class ResidualBlock(Layer):\n", + "\n", + " def __init__(self, **kwargs):\n", + " super(ResidualBlock, self).__init__(**kwargs)\n", + " \n", + " def build(self, input_shape):\n", + " \"\"\"\n", + " This method should build the layers according to the above specification. Make sure \n", + " to use the input_shape argument to get the correct number of filters, and to set the\n", + " input_shape of the first layer in the block.\n", + " \"\"\"\n", + " self.batchnorm_1 = BatchNormalization(input_shape = input_shape)\n", + " self.conv2d_1 = Conv2D(input_shape[-1],(3,3),padding = \"SAME\")\n", + " self.batchnorm_2 = BatchNormalization()\n", + " self.conv2d_2 = Conv2D(input_shape[-1],(3,3),padding = \"SAME\")\n", + " \n", + " \n", + " def call(self, inputs, training=False):\n", + " \"\"\"\n", + " This method should contain the code for calling the layer according to the above\n", + " specification, using the layer objects set up in the build method.\n", + " \"\"\"\n", + " x = self.batchnorm_1(inputs,training = training)\n", + " x = tf.nn.relu(x)\n", + " x = self.conv2d_1(x)\n", + " x = self.batchnorm_2(inputs,training = training)\n", + " x = tf.nn.relu(x)\n", + " x = self.conv2d_2(x)\n", + " return Add()([inputs,x])\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "residual_block (ResidualBloc (None, 28, 28, 1) 28 \n", + "=================================================================\n", + "Total params: 28\n", + "Trainable params: 24\n", + "Non-trainable params: 4\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "# Test your custom layer - the following should create a model using your layer\n", + "\n", + "test_model = tf.keras.Sequential([ResidualBlock(input_shape=(28, 28, 1), name=\"residual_block\")])\n", + "test_model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should now create a second custom layer for a residual block of your network. This layer will be used to change the number of filters within the block. Using layer subclassing, build your custom layer according to the following spec:\n", + "\n", + "* The custom layer class should have `__init__`, `build` and `call` methods \n", + "* The class initialiser should call the base `Layer` class initializer, passing on any keyword arguments. It should also accept a `out_filters` argument, and save it as a class attribute\n", + "* The `build` method should create the layers. It will take an `input_shape` argument, and should extract the number of input filters from this argument. It should create:\n", + " * A BatchNormalization layer: this will be the first layer in the block, so should use its `input shape` keyword argument\n", + " * A Conv2D layer with the same number of filters as the layer input, a 3x3 kernel size, `\"SAME\"` padding, and no activation function\n", + " * Another BatchNormalization layer\n", + " * Another Conv2D layer with `out_filters` number of filters, a 3x3 kernel size, `\"SAME\"` padding, and no activation function\n", + " * A final Conv2D layer with `out_filters` number of filters, a 1x1 kernel size, and no activation function\n", + "* The `call` method should then process the input through the layers:\n", + " * The first BatchNormalization layer: ensure to set the `training` keyword argument\n", + " * A `tf.nn.relu` activation function\n", + " * The first Conv2D layer\n", + " * The second BatchNormalization layer: ensure to set the `training` keyword argument\n", + " * Another `tf.nn.relu` activation function\n", + " * The second Conv2D layer\n", + " * It should then take the layer inputs, pass it through the final 1x1 Conv2D layer, and add to the output of the second Conv2D layer. This is the final layer output" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following class. \n", + "# Make sure to not change the class or method names or arguments.\n", + "\n", + "class FiltersChangeResidualBlock(Layer):\n", + "\n", + " def __init__(self, out_filters, **kwargs):\n", + " \"\"\"\n", + " The class initialiser should call the base class initialiser, passing any keyword\n", + " arguments along. It should also set the number of filters as a class attribute.\n", + " \"\"\"\n", + " super(FiltersChangeResidualBlock,self).__init__(**kwargs)\n", + " self.out_filters = out_filters\n", + "\n", + " \n", + " def build(self, input_shape):\n", + " \"\"\"\n", + " This method should build the layers according to the above specification. Make sure \n", + " to use the input_shape argument to get the correct number of filters, and to set the\n", + " input_shape of the first layer in the block.\n", + " \"\"\"\n", + " self.batchnorm_1 = BatchNormalization(input_shape = input_shape)\n", + " self.conv2d_1 = Conv2D(input_shape[-1],(3,3),padding = \"SAME\")\n", + " self.batchnorm_2 = BatchNormalization()\n", + " self.conv2d_2 = Conv2D(self.out_filters,(3,3),padding = \"SAME\")\n", + " self.conv2d_3 = Conv2D(self.out_filters,(1,1))\n", + " \n", + " \n", + " \n", + " def call(self, inputs, training=False):\n", + " \"\"\"\n", + " This method should contain the code for calling the layer according to the above\n", + " specification, using the layer objects set up in the build method.\n", + " \"\"\"\n", + " x = self.batchnorm_1(inputs,training = training)\n", + " x = tf.nn.relu(x)\n", + " x = self.conv2d_1(x)\n", + " x = self.batchnorm_2(inputs,training = training)\n", + " x = tf.nn.relu(x)\n", + " x = self.conv2d_2(x)\n", + " x1 = self.conv2d_3(inputs)\n", + " return Add()([x,x1])\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model: \"sequential_1\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "fc_resnet_block (FiltersChan (None, 32, 32, 16) 620 \n", + "=================================================================\n", + "Total params: 620\n", + "Trainable params: 608\n", + "Non-trainable params: 12\n", + "_________________________________________________________________\n" + ] + } + ], + "source": [ + "# Test your custom layer - the following should create a model using your layer\n", + "\n", + "test_model = tf.keras.Sequential([FiltersChangeResidualBlock(16, input_shape=(32, 32, 3), name=\"fc_resnet_block\")])\n", + "test_model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Create a custom model that integrates the residual blocks\n", + "\n", + "You are now ready to build your ResNet model. Using model subclassing, build your model according to the following spec:\n", + "\n", + "* The custom model class should have `__init__` and `call` methods. \n", + "* The class initialiser should call the base `Model` class initializer, passing on any keyword arguments. It should create the model layers:\n", + " * The first Conv2D layer, with 32 filters, a 7x7 kernel and stride of 2.\n", + " * A `ResidualBlock` layer.\n", + " * The second Conv2D layer, with 32 filters, a 3x3 kernel and stride of 2.\n", + " * A `FiltersChangeResidualBlock` layer, with 64 output filters.\n", + " * A Flatten layer\n", + " * A final Dense layer, with a 10-way softmax output\n", + "* The `call` method should then process the input through the layers in the order given above. Ensure to pass the `training` keyword argument to the residual blocks, to ensure the correct mode of operation for the batch norm layers.\n", + "\n", + "In total, your neural network should have six layers (counting each residual block as one layer)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following class. \n", + "# Make sure to not change the class or method names or arguments.\n", + "\n", + "class ResNetModel(Model):\n", + "\n", + " def __init__(self, **kwargs):\n", + " \"\"\"\n", + " The class initialiser should call the base class initialiser, passing any keyword\n", + " arguments along. It should also create the layers of the network according to the\n", + " above specification.\n", + " \"\"\"\n", + " super(ResNetModel, self).__init__(**kwargs)\n", + " self.conv_m_1 = Conv2D(32, (7,7), strides=(2,2))\n", + " self.residualblock = ResidualBlock( )\n", + " self.conv_m_2 = Conv2D(32, (3,3), strides=(2,2))\n", + " self.FiltersChangeResidualBlock = FiltersChangeResidualBlock( out_filters=64)\n", + " self.Flatten_1 = Flatten()\n", + " self.output_l = Dense(10, activation='softmax') \n", + " \n", + " def call(self,inputs, training=False):\n", + " \"\"\"\n", + " This method should contain the code for calling the layer according to the above\n", + " specification, using the layer objects set up in the initialiser.\n", + " \"\"\"\n", + " h = self.conv_m_1(inputs)\n", + " h = self.residualblock(h)\n", + " h = self.conv_m_2(h)\n", + " h = self.FiltersChangeResidualBlock(h)\n", + " h = self.Flatten_1(h)\n", + " h = self.output_l(h)\n", + " return h" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the modelg\n", + "\n", + "resnet_model = ResNetModel()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Define the optimizer and loss function" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use the Adam optimizer with a learning rate of 0.001, and the sparse categorical cross entropy function." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the optimizer and loss\n", + "\n", + "optimizer_obj = tf.keras.optimizers.Adam(learning_rate=0.001)\n", + "loss_obj = tf.keras.losses.SparseCategoricalCrossentropy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Define the grad function" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should now create the `grad` function that will compute the forward and backward pass, and return the loss value and gradients that will be used in your custom training loop:\n", + "\n", + "* The `grad` function takes a model instance, inputs, targets and the loss object above as arguments\n", + "* The function should use a `tf.GradientTape` context to compute the forward pass and calculate the loss\n", + "* The function should compute the gradient of the loss with respect to the model's trainable variables\n", + "* The function should return a tuple of two elements: the loss value, and a list of gradients" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following function. \n", + "# Make sure to not change the function name or arguments.\n", + "\n", + "@tf.function\n", + "def grad(model, inputs, targets, loss):\n", + " \"\"\"\n", + " This function should compute the loss and gradients of your model, corresponding to\n", + " the inputs and targets provided. It should return the loss and gradients.\n", + " \"\"\"\n", + " # Not sure if this is right\n", + " with tf.GradientTape() as tape:\n", + " y_pred = model(inputs)\n", + " y_true = targets\n", + " loss_1 = loss_obj(y_true,y_pred)\n", + " return (loss_1, tape.gradient(loss_1, model.trainable_variables))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Define the custom training loop" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should now write a custom training loop. Complete the following function, according to the spec:\n", + "\n", + "* The function takes the following arguments:\n", + " * `model`: an instance of your custom model\n", + " * `num_epochs`: integer number of epochs to train the model\n", + " * `dataset`: a `tf.data.Dataset` object for the training data\n", + " * `optimizer`: an optimizer object, as created above\n", + " * `loss`: a sparse categorical cross entropy object, as created above\n", + " * `grad_fn`: your `grad` function above, that returns the loss and gradients for given model, inputs and targets\n", + "* Your function should train the model for the given number of epochs, using the `grad_fn` to compute gradients for each training batch, and updating the model parameters using `optimizer.apply_gradients`. \n", + "* Your function should collect the mean loss and accuracy values over the epoch, and return a tuple of two lists; the first for the list of loss values per epoch, the second for the list of accuracy values per epoch.\n", + "\n", + "You may also want to print out the loss and accuracy at each epoch during the training." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "#### GRADED CELL ####\n", + "\n", + "# Complete the following function. \n", + "# Make sure to not change the function name or arguments.\n", + "\n", + "def train_resnet(model, num_epochs, dataset, optimizer, loss, grad_fn):\n", + " \"\"\"\n", + " This function should implement the custom training loop, as described above. It should \n", + " return a tuple of two elements: the first element is a list of loss values per epoch, the\n", + " second is a list of accuracy values per epoch\n", + " \"\"\"\n", + " train_loss_results = []\n", + " train_loss_accuracy = []\n", + " \n", + " for epochs in range(num_epochs):\n", + " \n", + " epoch_loss_avg = tf.keras.metrics.Mean()\n", + " epoch_accuracy = tf.keras.metrics.CategoricalAccuracy()\n", + "\n", + " for x,y in dataset:\n", + " \n", + "\n", + " loss_value, grads = grad_fn(model, inputs = x , targets= y,loss= loss)\n", + " optimizer.apply_gradients(zip(grads, model.trainable_variables))\n", + "\n", + " epoch_loss_avg(loss_value)\n", + "\n", + " epoch_accuracy(y, model(x))\n", + "\n", + " train_loss_results.append(epoch_loss_avg.result())\n", + " train_loss_accuracy.append(epoch_accuracy.result())\n", + " \n", + " return(train_loss_results,train_loss_accuracy)\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n", + "WARNING:tensorflow:Gradients do not exist for variables ['res_net_model/residual_block/batch_normalization/gamma:0', 'res_net_model/residual_block/batch_normalization/beta:0', 'res_net_model/residual_block/conv2d/kernel:0', 'res_net_model/residual_block/conv2d/bias:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/gamma:0', 'res_net_model/filters_change_residual_block/batch_normalization_2/beta:0', 'res_net_model/filters_change_residual_block/conv2d_2/kernel:0', 'res_net_model/filters_change_residual_block/conv2d_2/bias:0'] when minimizing the loss.\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback 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"\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow_core/python/ops/gen_math_ops.py\u001b[0m in \u001b[0;36madd_v2\u001b[0;34m(x, y, name)\u001b[0m\n\u001b[1;32m 531\u001b[0m _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(\n\u001b[1;32m 532\u001b[0m \u001b[0m_ctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_context_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_ctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_thread_local_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"AddV2\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 533\u001b[0;31m name, _ctx._post_execution_callbacks, x, y)\n\u001b[0m\u001b[1;32m 534\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0m_result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 535\u001b[0m \u001b[0;32mexcept\u001b[0m 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epochs')\n", + "axes[1].set_ylabel(\"Accuracy\", fontsize=14)\n", + "axes[1].set_xlabel(\"Epochs\", fontsize=14)\n", + "axes[1].plot(train_accuracy_results)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Evaluate the model performance on the test dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the test loss and accuracy\n", + "\n", + "epoch_loss_avg = tf.keras.metrics.Mean()\n", + "epoch_accuracy = tf.keras.metrics.CategoricalAccuracy()\n", + "\n", + "for x, y in test_dataset:\n", + " model_output = resnet_model(x)\n", + " epoch_loss_avg(loss_obj(y, model_output)) \n", + " epoch_accuracy(to_categorical(y), model_output)\n", + "\n", + "print(\"Test loss: {:.3f}\".format(epoch_loss_avg.result().numpy()))\n", + "print(\"Test accuracy: {:.3%}\".format(epoch_accuracy.result().numpy()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Model predictions\n", + "\n", + "Let's see some model predictions! We will randomly select four images from the test data, and display the image and label for each. \n", + "\n", + "For each test image, model's prediction (the label with maximum probability) is shown, together with a plot showing the model's categorical distribution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run this cell to get model predictions on randomly selected test images\n", + "\n", + "num_test_images = test_images.shape[0]\n", + "\n", + "random_inx = np.random.choice(test_images.shape[0], 4)\n", + "random_test_images = test_images[random_inx, ...]\n", + "random_test_labels = test_labels[random_inx, ...]\n", + "\n", + "predictions = resnet_model(random_test_images)\n", + "\n", + "fig, axes = plt.subplots(4, 2, figsize=(16, 12))\n", + "fig.subplots_adjust(hspace=0.5, wspace=-0.2)\n", + "\n", + "for i, (prediction, image, label) in enumerate(zip(predictions, random_test_images, random_test_labels)):\n", + " axes[i, 0].imshow(np.squeeze(image))\n", + " axes[i, 0].get_xaxis().set_visible(False)\n", + " axes[i, 0].get_yaxis().set_visible(False)\n", + " axes[i, 0].text(5., -2., f'Class {label} ({image_labels[label]})')\n", + " axes[i, 1].bar(np.arange(len(prediction)), prediction)\n", + " axes[i, 1].set_xticks(np.arange(len(prediction)))\n", + " axes[i, 1].set_xticklabels(image_labels, rotation=0)\n", + " pred_inx = np.argmax(prediction)\n", + " axes[i, 1].set_title(f\"Categorical distribution. Model prediction: {image_labels[pred_inx]}\")\n", + " \n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congratulations for completing this programming assignment! You're now ready to move on to the capstone project for this course." + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "tensor-flow-2-2", + "graded_item_id": "2x3vn", + "launcher_item_id": "QKXZc" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}