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shabububu/20191115_ww2color_full_250epochs_eval2.ipynb

Created November 15, 2019 22:05
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20191115_ww2color_full_250epochs_eval2.ipynb
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20191115_ww2color_full_250epochs_eval2.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "20191115_ww2color_full_250epochs_eval2.ipynb",
"provenance": [],
"collapsed_sections": [],
"machine_shape": "hm",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/shabububu/95fbbed0e6ef4024f1c6d123bd25328f/20191115_ww2color_full_250epochs_eval2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cRcFfSy5XCv0",
"colab_type": "text"
},
"source": [
"\n",
"Attempt to reproduce https://github.com/emilwallner/Coloring-greyscale-images/blob/master/Full-version/full_version.ipynb using photographs from http://ww2db.com"
]
},
{
"cell_type": "code",
"metadata": {
"id": "5kIH_hWlXDW8",
"colab_type": "code",
"outputId": "145e40ee-7167-4069-b58d-e36ea15c7d13",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81
}
},
"source": [
"from keras.applications.inception_resnet_v2 import InceptionResNetV2\n",
"from keras.preprocessing import image\n",
"from keras.engine import Layer\n",
"from keras.applications.inception_resnet_v2 import preprocess_input\n",
"from keras.layers import Conv2D, UpSampling2D, InputLayer, Conv2DTranspose, Input, Reshape, merge, concatenate\n",
"from keras.layers import Activation, Dense, Dropout, Flatten\n",
"from keras.layers.normalization import BatchNormalization\n",
"from keras.callbacks import TensorBoard, EarlyStopping, ModelCheckpoint\n",
"from keras.models import Sequential, Model\n",
"from keras.layers.core import RepeatVector, Permute\n",
"from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
"from skimage.color import rgb2lab, lab2rgb, rgb2gray, gray2rgb\n",
"from skimage.transform import resize\n",
"from skimage.io import imsave\n",
"import numpy as np\n",
"import os\n",
"import random\n",
"import tensorflow as tf"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
"text/html": [
"<p style=\"color: red;\">\n",
"The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
"We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
"or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
"<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "UlV1bC7PbMCC",
"colab_type": "code",
"outputId": "7793ab64-b7ec-453d-d947-97a6d5c32091",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive') "
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "nti15o4xcnSY",
"colab_type": "code",
"outputId": "13666ef5-9f34-4925-ae5a-16ef1c16696a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# Set my drive path for convenience\n",
"my_drive = \"drive/My Drive/Learning/CapstoneData/\"\n",
"\n",
"# Get training image path\n",
"train_path = os.path.join(my_drive, 'ww2db', '256', 'ww2db_20191102_256x256_a_1600_color')\n",
"!ls \"$train_path\" | wc -l"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"1600\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "dorr1X3gXL_K",
"colab_type": "code",
"colab": {}
},
"source": [
"# Get images\n",
"X = []\n",
"for filename in os.listdir(train_path):\n",
" #print(os.path.join(train_path, filename))\n",
" X.append(img_to_array(load_img(os.path.join(train_path, filename))))\n",
"X = np.array(X, dtype=float)\n",
"\n",
"# Set up train and test data\n",
"split = int(0.95*len(X))\n",
"Xtrain = X[:split]\n",
"Xtrain = 1.0/255*Xtrain"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "pYpmUbCNTmxM",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 478
},
"outputId": "8b6cd740-a8ae-4b2a-86c0-99f4109b4faa"
},
"source": [
"#Load weights\n",
"inception = InceptionResNetV2(weights='imagenet', include_top=True)\n",
"inception.graph = tf.get_default_graph()"
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:203: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2041: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4271: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "wImTugAadH-A",
"colab_type": "code",
"outputId": "07c8de8f-2b8d-4e0d-9305-e9af7f6508c7",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52
}
},
"source": [
"embed_input = Input(shape=(1000,))\n",
"\n",
"#Encoder\n",
"encoder_input = Input(shape=(256, 256, 1,))\n",
"encoder_output = Conv2D(64, (3,3), activation='relu', padding='same', strides=2)(encoder_input)\n",
"encoder_output = Conv2D(128, (3,3), activation='relu', padding='same')(encoder_output)\n",
"encoder_output = Conv2D(128, (3,3), activation='relu', padding='same', strides=2)(encoder_output)\n",
"encoder_output = Conv2D(256, (3,3), activation='relu', padding='same')(encoder_output)\n",
"encoder_output = Conv2D(256, (3,3), activation='relu', padding='same', strides=2)(encoder_output)\n",
"encoder_output = Conv2D(512, (3,3), activation='relu', padding='same')(encoder_output)\n",
"encoder_output = Conv2D(512, (3,3), activation='relu', padding='same')(encoder_output)\n",
"encoder_output = Conv2D(256, (3,3), activation='relu', padding='same')(encoder_output)\n",
"\n",
"#Fusion\n",
"fusion_output = RepeatVector(32 * 32)(embed_input) \n",
"fusion_output = Reshape(([32, 32, 1000]))(fusion_output)\n",
"fusion_output = concatenate([encoder_output, fusion_output], axis=3) \n",
"fusion_output = Conv2D(256, (1, 1), activation='relu', padding='same')(fusion_output) \n",
"\n",
"#Decoder\n",
"decoder_output = Conv2D(128, (3,3), activation='relu', padding='same')(fusion_output)\n",
"decoder_output = UpSampling2D((2, 2))(decoder_output)\n",
"decoder_output = Conv2D(64, (3,3), activation='relu', padding='same')(decoder_output)\n",
"decoder_output = UpSampling2D((2, 2))(decoder_output)\n",
"decoder_output = Conv2D(32, (3,3), activation='relu', padding='same')(decoder_output)\n",
"decoder_output = Conv2D(16, (3,3), activation='relu', padding='same')(decoder_output)\n",
"decoder_output = Conv2D(2, (3, 3), activation='tanh', padding='same')(decoder_output)\n",
"decoder_output = UpSampling2D((2, 2))(decoder_output)\n",
"\n",
"model = Model(inputs=[encoder_input, embed_input], outputs=decoder_output)"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2239: The name tf.image.resize_nearest_neighbor is deprecated. Please use tf.compat.v1.image.resize_nearest_neighbor instead.\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "s_R5Tam0R6n8",
"colab_type": "code",
"outputId": "593761c5-e4ba-432b-d777-78161bcd047f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52
}
},
"source": [
"# Create results directory\n",
"import datetime\n",
"\n",
"timestamp='{:%Y%m%d_%H%M%S}'.format(datetime.datetime.now())\n",
"result_path = os.path.join(my_drive, 'ww2db', 'results', \"20191115_ww2color_full_250epochs_eval2_\"+timestamp)\n",
"print(\"Result directory '{}'\".format(result_path))\n",
"if not os.path.exists(result_path):\n",
" print(\"Creating new directory: {}\".format(result_path))\n",
" tf.gfile.MkDir(result_path)\n",
"else:\n",
" print(\"Directory already exists: {}\".format(result_path))\n",
"\n",
"!ls \"$result_path\""
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"text": [
"Result directory 'drive/My Drive/Learning/CapstoneData/ww2db/results/20191115_ww2color_full_250epochs_eval2_20191115_151044'\n",
"Creating new directory: drive/My Drive/Learning/CapstoneData/ww2db/results/20191115_ww2color_full_250epochs_eval2_20191115_151044\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "9UmEAqt-UcGC",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "c94e9e78-671f-4d64-f1cf-e9ebb5a5ad5f"
},
"source": [
"def create_inception_embedding(grayscaled_rgb):\n",
" grayscaled_rgb_resized = []\n",
" for i in grayscaled_rgb:\n",
" i = resize(i, (299, 299, 3), mode='constant')\n",
" grayscaled_rgb_resized.append(i)\n",
" grayscaled_rgb_resized = np.array(grayscaled_rgb_resized)\n",
" grayscaled_rgb_resized = preprocess_input(grayscaled_rgb_resized)\n",
" with inception.graph.as_default():\n",
" embed = inception.predict(grayscaled_rgb_resized)\n",
" return embed\n",
"\n",
"# Image transformer\n",
"datagen = ImageDataGenerator(\n",
" shear_range=0.2,\n",
" zoom_range=0.2,\n",
" rotation_range=20,\n",
" horizontal_flip=True)\n",
"\n",
"#Generate training data\n",
"batch_size = 50\n",
"\n",
"def image_a_b_gen(batch_size):\n",
" for batch in datagen.flow(Xtrain, batch_size=batch_size):\n",
" grayscaled_rgb = gray2rgb(rgb2gray(batch))\n",
" embed = create_inception_embedding(grayscaled_rgb)\n",
" lab_batch = rgb2lab(batch)\n",
" X_batch = lab_batch[:,:,:,0]\n",
" X_batch = X_batch.reshape(X_batch.shape+(1,))\n",
" Y_batch = lab_batch[:,:,:,1:] / 128\n",
" yield ([X_batch, create_inception_embedding(grayscaled_rgb)], Y_batch)\n",
"\n",
"\n",
"# Generate Validation Set\n",
"Xtest_tmp = gray2rgb(rgb2gray(1.0/255*X[split:]))\n",
"Xtest_embed = create_inception_embedding(Xtest_tmp)\n",
"Xtest = rgb2lab(1.0/255*X[split:])[:,:,:,0]\n",
"Xtest = Xtest.reshape(Xtest.shape+(1,))\n",
"\n",
"Ytest = rgb2lab(1.0/255*X[split:])[:,:,:,1:]\n",
"Ytest = Ytest / 128\n",
"\n",
"# Train model \n",
"tensorboard = TensorBoard(log_dir=os.path.join(result_path,'tensorboard_logs'))\n",
"#earlyStopping = EarlyStopping(monitor='val_loss', patience=50, verbose=0, mode='min')\n",
"mcp_save = ModelCheckpoint(os.path.join(result_path,'weights.{epoch:03d}-{val_loss:.4f}.hdf5'), save_best_only=True, monitor='val_loss', mode='min')\n",
"\n",
"model.compile(optimizer='rmsprop', loss='mse')\n",
"model.fit_generator(image_a_b_gen(batch_size), callbacks=[tensorboard, mcp_save], epochs=250, steps_per_epoch=30, validation_data=([Xtest, Xtest_embed], Ytest))"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/callbacks.py:1122: The name tf.summary.merge_all is deprecated. Please use tf.compat.v1.summary.merge_all instead.\n",
"\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/callbacks.py:1125: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.\n",
"\n",
"Epoch 1/250\n",
"30/30 [==============================] - 103s 3s/step - loss: 0.1060 - val_loss: 0.0077\n",
"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/callbacks.py:1265: The name tf.Summary is deprecated. Please use tf.compat.v1.Summary instead.\n",
"\n",
"Epoch 2/250\n",
"30/30 [==============================] - 89s 3s/step - loss: 0.0220 - val_loss: 0.0078\n",
"Epoch 3/250\n",
"30/30 [==============================] - 98s 3s/step - loss: 0.0142 - val_loss: 0.0078\n",
"Epoch 4/250\n",
"30/30 [==============================] - 97s 3s/step - loss: 0.0083 - val_loss: 0.0076\n",
"Epoch 5/250\n",
"30/30 [==============================] - 99s 3s/step - loss: 0.0084 - val_loss: 0.0077\n",
"Epoch 6/250\n",
"30/30 [==============================] - 98s 3s/step - loss: 0.0080 - val_loss: 0.0077\n",
"Epoch 7/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0082 - val_loss: 0.0077\n",
"Epoch 8/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0081 - val_loss: 0.0077\n",
"Epoch 9/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0080 - val_loss: 0.0077\n",
"Epoch 10/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0082 - val_loss: 0.0077\n",
"Epoch 11/250\n",
"30/30 [==============================] - 99s 3s/step - loss: 0.0080 - val_loss: 0.0077\n",
"Epoch 12/250\n",
"30/30 [==============================] - 97s 3s/step - loss: 0.0080 - val_loss: 0.0078\n",
"Epoch 13/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0085 - val_loss: 0.0077\n",
"Epoch 14/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0084 - val_loss: 0.0077\n",
"Epoch 15/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0081 - val_loss: 0.0076\n",
"Epoch 16/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0081 - val_loss: 0.0182\n",
"Epoch 17/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0083 - val_loss: 0.0077\n",
"Epoch 18/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0080 - val_loss: 0.0081\n",
"Epoch 19/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0082 - val_loss: 0.0077\n",
"Epoch 20/250\n",
"30/30 [==============================] - 99s 3s/step - loss: 0.0084 - val_loss: 0.0079\n",
"Epoch 21/250\n",
"30/30 [==============================] - 98s 3s/step - loss: 0.0079 - val_loss: 0.0073\n",
"Epoch 22/250\n",
"30/30 [==============================] - 98s 3s/step - loss: 0.0079 - val_loss: 0.0074\n",
"Epoch 23/250\n",
"30/30 [==============================] - 98s 3s/step - loss: 0.0077 - val_loss: 0.0072\n",
"Epoch 24/250\n",
"30/30 [==============================] - 99s 3s/step - loss: 0.0075 - val_loss: 0.0069\n",
"Epoch 25/250\n",
"30/30 [==============================] - 98s 3s/step - loss: 0.0079 - val_loss: 0.0069\n",
"Epoch 26/250\n",
"30/30 [==============================] - 98s 3s/step - loss: 0.0076 - val_loss: 0.0070\n",
"Epoch 27/250\n",
"30/30 [==============================] - 97s 3s/step - loss: 0.0078 - val_loss: 0.0073\n",
"Epoch 28/250\n",
"30/30 [==============================] - 98s 3s/step - loss: 0.0073 - val_loss: 0.0071\n",
"Epoch 29/250\n",
"30/30 [==============================] - 98s 3s/step - loss: 0.0076 - val_loss: 0.0069\n",
"Epoch 30/250\n",
"30/30 [==============================] - 98s 3s/step - loss: 0.0075 - val_loss: 0.0071\n",
"Epoch 31/250\n",
"30/30 [==============================] - 99s 3s/step - loss: 0.0077 - val_loss: 0.0068\n",
"Epoch 32/250\n",
"30/30 [==============================] - 101s 3s/step - loss: 0.0073 - val_loss: 0.0067\n",
"Epoch 33/250\n",
"30/30 [==============================] - 97s 3s/step - loss: 0.0073 - val_loss: 0.0068\n",
"Epoch 34/250\n",
"30/30 [==============================] - 98s 3s/step - loss: 0.0073 - val_loss: 0.0071\n",
"Epoch 35/250\n",
"30/30 [==============================] - 99s 3s/step - loss: 0.0073 - val_loss: 0.0065\n",
"Epoch 36/250\n",
"30/30 [==============================] - 98s 3s/step - loss: 0.0073 - val_loss: 0.0071\n",
"Epoch 37/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0073 - val_loss: 0.0070\n",
"Epoch 38/250\n",
"30/30 [==============================] - 98s 3s/step - loss: 0.0071 - val_loss: 0.0069\n",
"Epoch 39/250\n",
"30/30 [==============================] - 93s 3s/step - loss: 0.0072 - val_loss: 0.0069\n",
"Epoch 40/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0071 - val_loss: 0.0066\n",
"Epoch 41/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0072 - val_loss: 0.0066\n",
"Epoch 42/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0072 - val_loss: 0.0067\n",
"Epoch 43/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0072 - val_loss: 0.0064\n",
"Epoch 44/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0072 - val_loss: 0.0070\n",
"Epoch 45/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0072 - val_loss: 0.0068\n",
"Epoch 46/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0070 - val_loss: 0.0071\n",
"Epoch 47/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0072 - val_loss: 0.0063\n",
"Epoch 48/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0072 - val_loss: 0.0078\n",
"Epoch 49/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0071 - val_loss: 0.0066\n",
"Epoch 50/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0070 - val_loss: 0.0077\n",
"Epoch 51/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0069 - val_loss: 0.0066\n",
"Epoch 52/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0072 - val_loss: 0.0071\n",
"Epoch 53/250\n",
"30/30 [==============================] - 93s 3s/step - loss: 0.0070 - val_loss: 0.0068\n",
"Epoch 54/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0070 - val_loss: 0.0068\n",
"Epoch 55/250\n",
"30/30 [==============================] - 93s 3s/step - loss: 0.0069 - val_loss: 0.0068\n",
"Epoch 56/250\n",
"30/30 [==============================] - 92s 3s/step - loss: 0.0073 - val_loss: 0.0070\n",
"Epoch 57/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0066 - val_loss: 0.0064\n",
"Epoch 58/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0070 - val_loss: 0.0064\n",
"Epoch 59/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0069 - val_loss: 0.0065\n",
"Epoch 60/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0071 - val_loss: 0.0078\n",
"Epoch 61/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0070 - val_loss: 0.0062\n",
"Epoch 62/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0068 - val_loss: 0.0065\n",
"Epoch 63/250\n",
"30/30 [==============================] - 98s 3s/step - loss: 0.0068 - val_loss: 0.0063\n",
"Epoch 64/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0070 - val_loss: 0.0066\n",
"Epoch 65/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0069 - val_loss: 0.0064\n",
"Epoch 66/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0071 - val_loss: 0.0065\n",
"Epoch 67/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0070 - val_loss: 0.0068\n",
"Epoch 68/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0066 - val_loss: 0.0063\n",
"Epoch 69/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0071 - val_loss: 0.0064\n",
"Epoch 70/250\n",
"30/30 [==============================] - 93s 3s/step - loss: 0.0068 - val_loss: 0.0064\n",
"Epoch 71/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0069 - val_loss: 0.0068\n",
"Epoch 72/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0066 - val_loss: 0.0063\n",
"Epoch 73/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0069 - val_loss: 0.0064\n",
"Epoch 74/250\n",
"30/30 [==============================] - 93s 3s/step - loss: 0.0070 - val_loss: 0.0069\n",
"Epoch 75/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0068 - val_loss: 0.0063\n",
"Epoch 76/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0066 - val_loss: 0.0064\n",
"Epoch 77/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0072 - val_loss: 0.0064\n",
"Epoch 78/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0064 - val_loss: 0.0064\n",
"Epoch 79/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0070 - val_loss: 0.0063\n",
"Epoch 80/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0067 - val_loss: 0.0065\n",
"Epoch 81/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0066 - val_loss: 0.0070\n",
"Epoch 82/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0067 - val_loss: 0.0064\n",
"Epoch 83/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0069 - val_loss: 0.0067\n",
"Epoch 84/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0067 - val_loss: 0.0062\n",
"Epoch 85/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0066 - val_loss: 0.0063\n",
"Epoch 86/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0067 - val_loss: 0.0063\n",
"Epoch 87/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0068 - val_loss: 0.0062\n",
"Epoch 88/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0067 - val_loss: 0.0063\n",
"Epoch 89/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0066 - val_loss: 0.0064\n",
"Epoch 90/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0068 - val_loss: 0.0061\n",
"Epoch 91/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0068 - val_loss: 0.0069\n",
"Epoch 92/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0066 - val_loss: 0.0067\n",
"Epoch 93/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0067 - val_loss: 0.0065\n",
"Epoch 94/250\n",
"30/30 [==============================] - 99s 3s/step - loss: 0.0067 - val_loss: 0.0063\n",
"Epoch 95/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0067 - val_loss: 0.0065\n",
"Epoch 96/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0066 - val_loss: 0.0061\n",
"Epoch 97/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0067 - val_loss: 0.0061\n",
"Epoch 98/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0066 - val_loss: 0.0063\n",
"Epoch 99/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0067 - val_loss: 0.0064\n",
"Epoch 100/250\n",
"30/30 [==============================] - 93s 3s/step - loss: 0.0065 - val_loss: 0.0062\n",
"Epoch 101/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0065 - val_loss: 0.0070\n",
"Epoch 102/250\n",
"30/30 [==============================] - 93s 3s/step - loss: 0.0067 - val_loss: 0.0063\n",
"Epoch 103/250\n",
"30/30 [==============================] - 93s 3s/step - loss: 0.0068 - val_loss: 0.0063\n",
"Epoch 104/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0065 - val_loss: 0.0062\n",
"Epoch 105/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0065 - val_loss: 0.0072\n",
"Epoch 106/250\n",
"30/30 [==============================] - 93s 3s/step - loss: 0.0066 - val_loss: 0.0068\n",
"Epoch 107/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0069 - val_loss: 0.0064\n",
"Epoch 108/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0063 - val_loss: 0.0065\n",
"Epoch 109/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0068 - val_loss: 0.0061\n",
"Epoch 110/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0063 - val_loss: 0.0060\n",
"Epoch 111/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0068 - val_loss: 0.0067\n",
"Epoch 112/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0064 - val_loss: 0.0063\n",
"Epoch 113/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0064 - val_loss: 0.0064\n",
"Epoch 114/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0066 - val_loss: 0.0068\n",
"Epoch 115/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0064 - val_loss: 0.0062\n",
"Epoch 116/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0065 - val_loss: 0.0062\n",
"Epoch 117/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0065 - val_loss: 0.0065\n",
"Epoch 118/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0064 - val_loss: 0.0063\n",
"Epoch 119/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0065 - val_loss: 0.0063\n",
"Epoch 120/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0066 - val_loss: 0.0063\n",
"Epoch 121/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0062 - val_loss: 0.0061\n",
"Epoch 122/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0064 - val_loss: 0.0061\n",
"Epoch 123/250\n",
"30/30 [==============================] - 93s 3s/step - loss: 0.0064 - val_loss: 0.0061\n",
"Epoch 124/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0064 - val_loss: 0.0061\n",
"Epoch 125/250\n",
"30/30 [==============================] - 98s 3s/step - loss: 0.0064 - val_loss: 0.0064\n",
"Epoch 126/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0064 - val_loss: 0.0065\n",
"Epoch 127/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0064 - val_loss: 0.0062\n",
"Epoch 128/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0063 - val_loss: 0.0060\n",
"Epoch 129/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0063 - val_loss: 0.0065\n",
"Epoch 130/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0064 - val_loss: 0.0062\n",
"Epoch 131/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0064 - val_loss: 0.0065\n",
"Epoch 132/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0063 - val_loss: 0.0060\n",
"Epoch 133/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0065 - val_loss: 0.0059\n",
"Epoch 134/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0061 - val_loss: 0.0065\n",
"Epoch 135/250\n",
"30/30 [==============================] - 93s 3s/step - loss: 0.0063 - val_loss: 0.0063\n",
"Epoch 136/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0063 - val_loss: 0.0063\n",
"Epoch 137/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0065 - val_loss: 0.0062\n",
"Epoch 138/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0062 - val_loss: 0.0067\n",
"Epoch 139/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0064 - val_loss: 0.0062\n",
"Epoch 140/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0067 - val_loss: 0.0060\n",
"Epoch 141/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0059 - val_loss: 0.0063\n",
"Epoch 142/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0063 - val_loss: 0.0067\n",
"Epoch 143/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0062 - val_loss: 0.0059\n",
"Epoch 144/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0062 - val_loss: 0.0064\n",
"Epoch 145/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0060 - val_loss: 0.0063\n",
"Epoch 146/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0063 - val_loss: 0.0061\n",
"Epoch 147/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0060 - val_loss: 0.0061\n",
"Epoch 148/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0063 - val_loss: 0.0063\n",
"Epoch 149/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0062 - val_loss: 0.0063\n",
"Epoch 150/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0061 - val_loss: 0.0062\n",
"Epoch 151/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0064 - val_loss: 0.0067\n",
"Epoch 152/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0061 - val_loss: 0.0060\n",
"Epoch 153/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0061 - val_loss: 0.0063\n",
"Epoch 154/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0060 - val_loss: 0.0063\n",
"Epoch 155/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0062 - val_loss: 0.0060\n",
"Epoch 156/250\n",
"30/30 [==============================] - 97s 3s/step - loss: 0.0061 - val_loss: 0.0064\n",
"Epoch 157/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0060 - val_loss: 0.0064\n",
"Epoch 158/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0060 - val_loss: 0.0060\n",
"Epoch 159/250\n",
"30/30 [==============================] - 97s 3s/step - loss: 0.0060 - val_loss: 0.0059\n",
"Epoch 160/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0060 - val_loss: 0.0067\n",
"Epoch 161/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0059 - val_loss: 0.0059\n",
"Epoch 162/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0062 - val_loss: 0.0060\n",
"Epoch 163/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0059 - val_loss: 0.0066\n",
"Epoch 164/250\n",
"30/30 [==============================] - 93s 3s/step - loss: 0.0061 - val_loss: 0.0062\n",
"Epoch 165/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0061 - val_loss: 0.0061\n",
"Epoch 166/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0058 - val_loss: 0.0059\n",
"Epoch 167/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0057 - val_loss: 0.0061\n",
"Epoch 168/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0061 - val_loss: 0.0060\n",
"Epoch 169/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0061 - val_loss: 0.0080\n",
"Epoch 170/250\n",
"30/30 [==============================] - 97s 3s/step - loss: 0.0060 - val_loss: 0.0061\n",
"Epoch 171/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0060 - val_loss: 0.0063\n",
"Epoch 172/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0059 - val_loss: 0.0062\n",
"Epoch 173/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0058 - val_loss: 0.0060\n",
"Epoch 174/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0059 - val_loss: 0.0060\n",
"Epoch 175/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0059 - val_loss: 0.0062\n",
"Epoch 176/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0058 - val_loss: 0.0064\n",
"Epoch 177/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0058 - val_loss: 0.0062\n",
"Epoch 178/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0057 - val_loss: 0.0064\n",
"Epoch 179/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0061 - val_loss: 0.0062\n",
"Epoch 180/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0058 - val_loss: 0.0060\n",
"Epoch 181/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0057 - val_loss: 0.0062\n",
"Epoch 182/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0057 - val_loss: 0.0060\n",
"Epoch 183/250\n",
"30/30 [==============================] - 97s 3s/step - loss: 0.0058 - val_loss: 0.0063\n",
"Epoch 184/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0057 - val_loss: 0.0061\n",
"Epoch 185/250\n",
"30/30 [==============================] - 97s 3s/step - loss: 0.0056 - val_loss: 0.0060\n",
"Epoch 186/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0056 - val_loss: 0.0065\n",
"Epoch 187/250\n",
"30/30 [==============================] - 98s 3s/step - loss: 0.0057 - val_loss: 0.0064\n",
"Epoch 188/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0057 - val_loss: 0.0060\n",
"Epoch 189/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0056 - val_loss: 0.0061\n",
"Epoch 190/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0056 - val_loss: 0.0065\n",
"Epoch 191/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0055 - val_loss: 0.0063\n",
"Epoch 192/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0056 - val_loss: 0.0063\n",
"Epoch 193/250\n",
"30/30 [==============================] - 92s 3s/step - loss: 0.0056 - val_loss: 0.0065\n",
"Epoch 194/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0057 - val_loss: 0.0065\n",
"Epoch 195/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0057 - val_loss: 0.0061\n",
"Epoch 196/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0054 - val_loss: 0.0062\n",
"Epoch 197/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0056 - val_loss: 0.0065\n",
"Epoch 198/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0056 - val_loss: 0.0061\n",
"Epoch 199/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0055 - val_loss: 0.0061\n",
"Epoch 200/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0055 - val_loss: 0.0061\n",
"Epoch 201/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0056 - val_loss: 0.0063\n",
"Epoch 202/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0053 - val_loss: 0.0059\n",
"Epoch 203/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0057 - val_loss: 0.0063\n",
"Epoch 204/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0054 - val_loss: 0.0064\n",
"Epoch 205/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0056 - val_loss: 0.0061\n",
"Epoch 206/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0054 - val_loss: 0.0060\n",
"Epoch 207/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0055 - val_loss: 0.0061\n",
"Epoch 208/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0053 - val_loss: 0.0062\n",
"Epoch 209/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0054 - val_loss: 0.0065\n",
"Epoch 210/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0055 - val_loss: 0.0058\n",
"Epoch 211/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0051 - val_loss: 0.0063\n",
"Epoch 212/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0054 - val_loss: 0.0060\n",
"Epoch 213/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0052 - val_loss: 0.0063\n",
"Epoch 214/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0054 - val_loss: 0.0069\n",
"Epoch 215/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0053 - val_loss: 0.0063\n",
"Epoch 216/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0052 - val_loss: 0.0062\n",
"Epoch 217/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0054 - val_loss: 0.0064\n",
"Epoch 218/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0052 - val_loss: 0.0066\n",
"Epoch 219/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0052 - val_loss: 0.0065\n",
"Epoch 220/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0050 - val_loss: 0.0063\n",
"Epoch 221/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0053 - val_loss: 0.0062\n",
"Epoch 222/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0051 - val_loss: 0.0061\n",
"Epoch 223/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0052 - val_loss: 0.0064\n",
"Epoch 224/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0052 - val_loss: 0.0068\n",
"Epoch 225/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0050 - val_loss: 0.0061\n",
"Epoch 226/250\n",
"30/30 [==============================] - 93s 3s/step - loss: 0.0052 - val_loss: 0.0064\n",
"Epoch 227/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0051 - val_loss: 0.0061\n",
"Epoch 228/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0052 - val_loss: 0.0062\n",
"Epoch 229/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0049 - val_loss: 0.0061\n",
"Epoch 230/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0053 - val_loss: 0.0065\n",
"Epoch 231/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0051 - val_loss: 0.0063\n",
"Epoch 232/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0050 - val_loss: 0.0064\n",
"Epoch 233/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0052 - val_loss: 0.0060\n",
"Epoch 234/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0051 - val_loss: 0.0072\n",
"Epoch 235/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0049 - val_loss: 0.0066\n",
"Epoch 236/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0050 - val_loss: 0.0068\n",
"Epoch 237/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0052 - val_loss: 0.0067\n",
"Epoch 238/250\n",
"30/30 [==============================] - 94s 3s/step - loss: 0.0047 - val_loss: 0.0067\n",
"Epoch 239/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0050 - val_loss: 0.0062\n",
"Epoch 240/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0049 - val_loss: 0.0063\n",
"Epoch 241/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0048 - val_loss: 0.0069\n",
"Epoch 242/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0048 - val_loss: 0.0061\n",
"Epoch 243/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0049 - val_loss: 0.0066\n",
"Epoch 244/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0048 - val_loss: 0.0066\n",
"Epoch 245/250\n",
"30/30 [==============================] - 97s 3s/step - loss: 0.0048 - val_loss: 0.0063\n",
"Epoch 246/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0048 - val_loss: 0.0064\n",
"Epoch 247/250\n",
"30/30 [==============================] - 95s 3s/step - loss: 0.0048 - val_loss: 0.0063\n",
"Epoch 248/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0047 - val_loss: 0.0071\n",
"Epoch 249/250\n",
"30/30 [==============================] - 98s 3s/step - loss: 0.0049 - val_loss: 0.0064\n",
"Epoch 250/250\n",
"30/30 [==============================] - 96s 3s/step - loss: 0.0046 - val_loss: 0.0064\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7fa12fde37b8>"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "3WxWjWorlLsB",
"colab_type": "code",
"colab": {}
},
"source": [
"# Save last model (just in case)\n",
"model_json = model.to_json()\n",
"with open(os.path.join(result_path,'250-model.json'), \"w\") as json_file:\n",
" json_file.write(model_json)\n",
"model.save_weights(os.path.join(result_path,'250-model.h5'))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"outputId": "4a561d9e-1026-464f-f896-2f527d6a3dcc",
"id": "Di-kaVZeUl4_",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 158
}
},
"source": [
"!ls \"$result_path\""
],
"execution_count": 10,
"outputs": [
{
"output_type": "stream",
"text": [
"250-model.h5\t\t weights.024-0.0069.hdf5 weights.084-0.0062.hdf5\n",
"250-model.json\t\t weights.029-0.0069.hdf5 weights.090-0.0061.hdf5\n",
"tensorboard_logs\t weights.031-0.0068.hdf5 weights.096-0.0061.hdf5\n",
"weights.001-0.0077.hdf5 weights.032-0.0067.hdf5 weights.110-0.0060.hdf5\n",
"weights.004-0.0076.hdf5 weights.035-0.0065.hdf5 weights.128-0.0060.hdf5\n",
"weights.015-0.0076.hdf5 weights.043-0.0064.hdf5 weights.133-0.0059.hdf5\n",
"weights.021-0.0073.hdf5 weights.047-0.0063.hdf5 weights.210-0.0058.hdf5\n",
"weights.023-0.0072.hdf5 weights.061-0.0062.hdf5\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "a2xV-1j2LhKn",
"colab_type": "code",
"colab": {}
},
"source": [
"# Load best model from training\n",
"# TODO: Figure out how to load it manually, instead of having to look up the best model by hand.\n",
"best_model_path=os.path.join(result_path,'weights.210-0.0058.hdf5')\n",
"model.load_weights(best_model_path)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "H86NX0QalXbi",
"colab_type": "code",
"outputId": "4d9397b8-68ab-41ab-b99e-ae4326e74970",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52
}
},
"source": [
"# Validation Set images\n",
"print(model.evaluate([Xtest, Xtest_embed], Ytest))"
],
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"text": [
"80/80 [==============================] - 1s 15ms/step\n",
"0.005837143771350384\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "xFhZHuC-lpz2",
"colab_type": "code",
"outputId": "a70b6a5b-180a-4102-8b80-8ed4c9723ddb",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# Now testing on black and white images without color ground truth\n",
"test_path = os.path.join(my_drive, 'ww2db', '256', 'ww2db_test_bw_256x256')\n",
"\n",
"!ls \"$test_path\" | wc -l"
],
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"text": [
"26\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "MX7FdxZro3N6",
"colab_type": "code",
"outputId": "2d57f715-d2d4-4719-ca88-dc3a33e0b049",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"from IPython.display import Image, display\n",
"\n",
"for filename in os.listdir(test_path):\n",
" #print(os.path.join(test_path, filename))\n",
" display(Image(os.path.join(test_path, filename)))"
],
"execution_count": 16,
"outputs": [
{
"output_type": "display_data",
"data": {
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KkZ2jk4QFr91wGX2wbTzUMSgGteUg2IMXev6jfDiArJCNi6cgSbH32xno5ni7\nyzOWG1w1sD1Bp60FKiKOdOvEUMPzwyyvsHlclLLLFNU0gqYmimjpJiqsp2II3G3hbmMGjsJl/wDw\n1HkUk8slOs4meTSqu5DXsSB4bemMPnnZzP6StqpTls3AqJ9fFoXMyIoYsBpFmFzpvcdMYqmr6iLN\nlmjkWnqbsdRBHDJ+YW63G1jhlJUQ1LlppHeQcl0EBfc4oq1o6jLtInkZ+KFZVjIVEuLtfkeothln\nVRl0+d1Yy+kEcSsFs+yqbb2GFMkSzzFgWeNRbWBtf9sXwRRBgI0WW3JS9gPpzwbRwz1WaVdJADxj\no7kQ5WXxPLnbFGdJXZfFFGKaJ55pOEV0h+ZtYW2vseXLlscab7Pq6ioM5qkqwUrG/wCXp3lAUahc\nso6k2HhyHmMHVvZ3K5O1pzitzGeonaYSrTiIaAv8vO9r487QdoFr5Kylkhp6cUMKy/Eyd2yl9JFx\nuB1t12xhpEllb4qMSvTu3cqafZTbrbB9NQNWvGnBqZWl2VliJLH15Yap2Hz8qXihmVOiT1Ed/wD2\nm4GAZ+y+exMeNlzFR0ljuPZgf3x8uWzpC0tfJBRU8e7a49bN5f0GM7mmaS5jKqgkU8IK08OwCg8z\nYbXPU/sMWQ1VDQ5ynBiSqpD3F+IS5uQLsALWIN7fvhy9DK8plglVEbcARBtP1549jQ013SpjDD5h\nGrIR63wXHmRkQrPXRItrAAam+p/XEZc0jUhagT1EZO51rb12uMXUtRSRTKaanLNK2kIt2B8hiOZd\noqemCzUoRYlbS84VS0pU96KLoR0aQ3A3AucajKs3EmVUU9VSQQzSR65IggAS9yLA72tbDGOvQHaN\ne8bo8abfU4Mp80E2oywTIRt/q8xbGYVDLKAd+lhsBhJk+fTyivqKqkC/DyCGnRBvJNv3d+dtifAe\nuHHYXPqjJaw9ns2V14sktQlRKwUAEAk7nkWO3mcdKlqI4YDKzd3y3vgZ6pqijkeli450myh7HVbY\nEdN8c77NPl/b3LaiHNaeBM4pnOv7rZ1J2NvI909dh44W5n2GhpGKh56UMbK8T6o2Pobj2wlrezmZ\nR00sFNJT1gcaQFRkkJPgASCcLKyoiWrqUih1PCqxT6W4oWQXuQQAdPS5HS2/PEsvf4yZIqcwSuzr\nGIeKAd+tj0HXBNbNHJWTCljMEIjVOCW3vtqPIWw+7JiPJqXNc1ml4DU7qsZ4mzMYwbHxA1Xt428M\nQyXNaypds6NOk3w8c5hMcaqeMLWNr2PecHUbfljGT0k+XCOr4cgdXDKGNyshueY8x9cb1K2rnejk\naucBmKh1RV2Iv0GE+b1lXk3apq2OeaYpCdbSRcQNsLa7giw3sSOdsfVXbCnhMWX0mTLDThgUlidV\nYMxPE1KLixbUQBa2N/kVbLVUdPNQ5lDPTSL3A8TWI+lwcaeGdtI16A3gCbYhVVFUsR+DihkfoXk0\nj8ueOW9puzfbHNag1NTEk6g92OndbKPJf7OMXLl9XSS8Oop5YpB+F0IODMozGny2aaKqo+MKqLgq\nxt92SQdW/kDyw8pMyiDMtNIURNzdLhfU8sEyVmXVNlqyzseUgUMv5c8fJkCSNxIFZVYXXUEsT9ds\nfDs3WRhpFqYoUALMASALcySdgMJq/MqeGlUgs0MibFWZHrR5dY4fFtmfkLDfBmSZKKlBnmeOioFA\np6cR3AUfLZByUdF98aSGZKutjedW+HICtKrguvPcLyta22Jx1E0VSY6aWWSJdoyy2uOZ5cuuNHRN\nT1KRpLwuXysulvYjnjPfESa9UcYVQblnHMDwHhhR2YzGPO8yzfMs6qo4YqWECFraUiDE30gW529S\nbYy1BSK+ZVVVXshpIkDGWWTWsQJOnUBuWvbuDcnbYXI1OU/aRTx1UmWzxzDLEjjSleW2tCoC3a21\njz/y+eOoU80FEilEkkcgHiAWB9PLHIWzI9kPtUnqI9UVFNNqkU73hksT9Dc/+OOsSy0oeSITqyaj\nqinW6H0PTGb7R9nauoyyofJXj0SRtxIT/iNYXCo4NgCdjfmOuOa9gspr/j8xRqabUqrG6FCCGuTY\n43x7F09eAa6OEjwMYY/XHk/2f5OYDGhrG6gNUuyqbWvpHh62xmJuz9JIMzy6GvhosvpK3Qq1EhZi\nwjQFiBu3X3ODMikpssq6Ohosyp8wtI5Ilp+HGoK97djuTYfTCLtCGoHr8vlfQjVDRI5HNLllP0sL\n4Ny6vC5BSVDOokieNtBO5K7H9MW9oMxetqpTRVqL8bEtPISGKqAwYEgAkjY7AHC3P4VzHLsoy2iS\nSXMKKMq0z05hV0PM3Y3Y6t+nM4aMzZH2YyygWUiQJ98YzyYEk2PUbj6YcZhnE2VLl+bU0rfB5hEJ\nGi6Bx84Hhvv74b/8TRPSrLDKJI3FwTsQcCt2wih2NXCZCLmJWuyjzxOn7c9nKpfh85r6YOx7qTo2\nkDx1WsPrhymRdnc3pxLR5bR1UMgsZ4ZAVG+4BB+bf8saCjy6gyyiNPSQRQ06jvAAWPmfH3xz/tlF\nSUS5fNkuX01S9ZM8chRCR06Lstr87bYeSdhlhkL08qTWNgsvdP1AN8YnteuaUUTLVZTJFRBrRQaN\nUcpB/wASdluNN91ivvzbwxn8qy6eql/idfFJVPIdah2trN7Any22H7YfSIZTxKxiGBuqrIu3rg+O\nB5YQ6VBeNkAKaNY/rfDHL4opQEjyyVJFPNtgfbGgpo0p1VJYIViPzKWFva+MrVTmmp5JpCqhFLaW\n5GwPPyxzulreF2WqlEmhqisHEVVBuqJcDyF2/LGtrKeLIuxWV1VTlkVVS5gzTVEZuoSVh90D1sql\nreZPljOpS0uYZhT5NkzsFqjxquedd40UkhCRyVQLk9SbnkMbnIu1rUOahKicz0FQyRQzN3bAlzxD\nfoSPl6KFxX9rGXLVUFBntOA3CY08xA/C2639GuPfGk+z/MlzXsfSvKEaemvBISASdPyk/wDiRhwm\nc0tZVVmX0M8UlfStolgN10bXuduXpfwxmc6bNWqGposvmrip3aaQU9KD6A3f3xTQUPaqSp49bnOX\n00AFhT01NrA22G9retzhRV/xp3aJquaRwSGmkbSoPXSi7D3ucZCjoIZpqxK2qmWqFSRo0XLeBv54\nbQZVDTyRzwzPxIWDgyxo63H+U7H3x5VfAVs/EramrnkSQlS8ttN/Cwvbn1wVS5T2e4JLmcgcozO2\nlR5WsfqcTDZJRpHLFQwU1QAbst2ZTy2Y74W1OeU/GDBtbKCAT/flgZa+hzevhpsxrJIEIPDeJA12\n27pvy9cM5M9y742m7K1NOajLqX/DlZysnEsWJJFtu8RbCKilPaLtI+W5SYqKkZm4Zd20Iii5Ykkn\ne1/cYh2t7L1nZxkropnqKSpGk1KbDV4Hwva4vjKSSarMSbjlc47p9nmecPJ/4S0SRLRwRurKps6s\nuosTbmSTjT0zvBRSyvVE/EyNUESnaOMgEgDwCi/ucJOwPaCGuqM4y+mpRTQxT8eMd48RX5tv4kCw\n6DDKt7cUGUZrmtLmMnDWjgiljspJfUDcbedh74tyHtrS9oMnFdAnBGto5Ec3ta35G+JVUOWV9Iaq\nnEUUq3uUUWbex2whko44E4kzwkE7a1/pbEDVmCRI45YIg3PYj6dBi6arzOmAZYHnjbcEbj6jF9Jm\nGYySqP4aiJfdmuQPrjPVdE1bl1VCzmPixMoUG5Oxtc+F+gxytgFy8G9i13I/L9sdH7U5vVZTSZTl\nDslVlogVahCo3I20kjlsBY8zzwlqu01OuSPS0FDS5fQ1Uh4lPHMXkdBYHU3zAEi3Ppt4lR/H55s1\np6qjo4qeqppBwIgPu7Abawedt9z441VPmso7O1eSvTrVwVUNtUcpd2lYai1yehUHYczgPIu0VV2V\n7GTV1II5vjZFUNf/AAJLNfUPGwPrYHFPYfts1B2smqs0KuMxYLPUEWZWJ2J8r8/9sdfzypECK2lW\n1YzLZqQrohUMdu8dvXbCDMc6NG+gS62cXLWsPbGWXMZGzRalo2QOpQ3sCeqnFP8AFah5ZTI7EH2w\nBNXMCQLi/PEVr5BGQG29cVyV0kvzPc2tgJ6kgkk4omkJAsTfpg6sSdaierkl7/CBvexLFbb+HXC6\njqHpmLRsUc90Ebc+eNE+e5pmmXS5O9XGtKwTuyuAp0m/M9dsHZVlEFLHxZIZZ+ayMoKix6arEi48\nLeuFsmZVBgqFesqeCZTaOOfSNKAIO6OdgAL+WDaXtBW19ZSRRzV9SohCSxNUli/jpHS4AFuWHmTd\nv6rJpKyFKeGqEp1cOKTTHARfuod9fmTzI22xRTQHtVnFVn9cOBEzBeALvxCvS38vlgXPYHy+eCiy\nVnoqercmTXdAHvtbwFjjR9iczrMhq4aLMYJaiRo9MSx9/UGdtx4+XvjVVAdgTU0jbbHiWUp7YCNX\nl0SiNniXfukgkr+WLqbM6qGdV0rNEeTGLTt7YfxSVlQhRouEpHdlR+R8x4YzGriFhTbq22phsPTH\nG5v8bgvsqNpuPAG2Om5z2jppMtpaeWnWPOokirxURrsZLAgW/wBFufhjmeZyxy1lXVQRJFDPKzJE\nnJLm5FvDDjNs4hrQ+XU1DQoJHLGrdbSXJv8AP0XpblizsjW/CdqstdpVcRMylYfltY3JvboB+WNR\nDkGXZ/NnWVZQ7U9HVQw1dIHFx8QNXcueQIax64w+Z9ncwyaiqWzGH4cxziJbrfitvfS3gAOY2ONt\nN27y5+x+Wx1FTJLmawhJIolJIttdidhewPPGTftWOICsUxAPVxiOYVnGVZA5YHlcWP64DkmvGhJJ\nIt1wK9QxJ3xQpIDDxN749JYLexxEMeuPdGo8sexRqJ4WkH3auC/oN/2xOkp6rN8wliQAvOdTk8lA\nPM+Q5YeZh2dpMuy2SoppGnlp1DS6wNI3t05c+WM4pWaVkEZKsu6nffBVG01AxemqZoAylWVWIVgR\nYgjGjApKXsJDl0NNCKyrAnmqHHf0B20KD/LYHbxN8D13aJQsVJVxRtwqBKZWWIXKg3Fz/fO2MtLK\nqRqYmBufnBtYDG8yPKpKXKYPis0WCVxxDEG3Grff2wZJS0jaTUyiSOI8RH4gLAjqN9vTFnZfNape\n0uS1um7Sxy6dY/CS/wCdiPrjp2Z0T5vlQzSmllSS19DKAGUEi9va+MnDHShr1clOr31XJs2GlM1O\nVJgQaekmrSv64KiqhT1CFp0kuOes2/3wmeGRktJYoOiHT1xx/N4xDm9ZELFVndQL7EajgnNMwjnz\nGWrj1RmQD7pmLFduh2uLdOnLCpzGYJXv3mOy4tqFPHlsDtGLi1iOWC8hqFgkIleThOHR1jFybqbf\nQ2Ptgym7Y1+V5McqorQgG7yj5yw8/C+J9sO00vaGpppGgSIKgaRlveVyBdj6WtthbT5FPUZScxFT\nTJDqZdMjkNceVvpgJadAfvGv5Dli5pCQqjkMRYmwUc/HHyQm+++LFiZmIAsLdMFQ5e83JHfyUXwW\nvZyuYXSkqD5cJj+2KZMrmivriceqkWxUKUhTqGHeS0bRU2lFESSd6SS3fbwUX5DzxHOs4o0yqpy6\nnXiFoyp0fKm/O/U4x1P3SSCRth3lOS1GZh3DCKFf+o3InwAwfngekoMtk1gU7UxpG0re0kTd4eV7\nhvGxxn0V5Z5p3QmMAAlgbdeZ6YFroUWNpEAVLgWBvufTDOkmzKjRZYKvWAB3Jl1j033wzbtbmnw8\nlPU0cKRMjKTBGo5gjr/XGw7KUUNbnuSwS1cVOlNATEjg3mkYt3R9Ofl546TUV7ZbW5VlzygvwyZU\nQfMOQG/TnjGTUympq1o41lZHIItdgNRGw/fFGsFdJglv+IAWH1wzpTTvpuoh2uEL3GBpNUjKtQ2k\nHlEtz9SOf6Y5p2wp0j7RS8Md10VzYctt/wBMJah0m0aARZLEHpgIggkdL4IWfiCSWY6iAFAOL8qq\nhQVsFW8RlSKQOUDadY5EX6bXx8IqWspa6qOqOWJ1dUBB1IxsR6g23wE8hnbcWt18saOINFkVPCdQ\nLIZNP+o3H5WwnmfSTtz/ACxVxNQ23HjiSyEsABc+XXGm7OdmarP5mEfchjNpJiCQp8AB8zeX1x0e\nn7E5RS0iRpSFpRznmYu59vlHsMHU2TUULKkiyqAP+4QPythomUUukmKWpXbbTUN/U4j/AA+TkZ5S\nP/yBXH7HAlRls2kiTLqerjOx0Wvb0YfvjkXaSXNKWtkpKinkooVJADKRxB435EeQxm2VjGV0WBNr\n4P7P5SMylkZzaGMjVbmfIY6BR0qxrHFGmlFsAANgMYqgzCiqaPN8rzaR0inc1FLKBfg1AuNx/KwN\nj6DAFFWTJl1RBGQFYhpLn5tgAvngLNFV6ucwXeOym/lpHP3xsjRZJWJSjLkqmq2ZeJCi6jptuVJ2\nuMBdqMqbJhIsSVs0fJppIdKLytuDvfGhjAo8niz6mqDT1MScGJCoJbWTcjfbY+HXGayzMM6zDNI4\nYKqpesnPw6SGU6rcgNXgBjfr2ez7K5JXgNDmaOgXSzNTybEEeIPLywtNXJlymOvpa3LRf/qxEx38\nnFx+mDaWrWdNVNKJITuXjYOCfbDVotW8mwHS9reuMZneUZvW9p0qspp+KQiJHa3eNiCADzHjhtQ/\nY7mFW8k2bZhT0bMSRHTJxTfz5ADyF8A552TyPsp2Uq6XNFWpzqVnanqYXI0C/wB33SeRsbix589s\ncxIsbC+4vti6NSVsOS7E4+pkJqODcASMqG5sPmHPyw9raeCtzSWatg+EnRylTTxLbW6kgm5PdJ62\n9euKK6vMsxfYE9BsFHQDCuduJ1HniFxoAuL4mj/iBFwfHHcuxWaZFR5PT0dNPHxdN5GeXcsd225D\nfw/PGr40TMCsim/K5/fEXjHzWBIxOJiTYgYLRVC9L4mBjySCGeIxTRxyRnmjqGB9jhRU9juzdWDx\nskornrHHwz/8bYUQ9h+zNBUyij+JpmbZlE5Zf/kDgtuyMUlPKtJXEMyEKWUMFJGxNrYySfZLUU2R\n1FOZKWrqn1FXUlDe3dALDbl44y8P2Y9qQ+iXLWS5tqEiMnrcMf0wfT/ZpU0av8dNVkv8yQRHSRfq\nxG/0xospyalybv08Th2FjJIe8R4f/WDqpTVwtE9ihHykXH0OCe2FFllT2Zp55qmCmip41DmOMar7\nbcxsCfzxzbs1m1RHmVDDBUNUGlaQwQ21DvAgkdbY65RSSy08Uk6aJmUa18D4c8MAqldJuAcLKrsl\nktdMZpKGJJefGp7wv9VthM0YlF5SCP8Atjce/ifyxWc8oOz5FdWCbiLIqxCIAnxN79CBb3w2ou3d\nNJ2fnzqqcR0ySGOIbBpmHQDoQLX6Y4l2s7RTdo8/qK9wUR7LHHe+lQLAfv74R3sTtvbFlMdTEGwA\n7xHiOuL6CSA5tFPUqvA4yvIum403va3hi7NZ4o8xdqOV3jKjUW3Bbra5vbwwA8ztEWstr25YgveU\n6jv0t1xJIAVZi6+QJ54gSOQIuOdjiQkYHukjGn7NUnaXMOJPlMNRLFFdHlD2VTpJtcnnb9R44b5N\n2qqMsy+f4zOdVROlqeFVMrROzd55L2AIAO1zueWPaz7Qsxo6kpTViVMBUFZJItBv1Fr4KH2mZ1Qr\nH8XRIOIuuMsCNS+OHMn2nS02XZfVy0YC1aSEFm27raeXP36+2DMu+1KkrK6mpWoKlTO6xhxYqCSB\nf03wZV/aTltFmVNTzpLHFLGZC2m+xYqm3mBqv4Ebb4Lk7YdnKh3Z64wSJUmmJeJgNdyADt1te+C3\n7WZNQTvQPm1HT1ERtKszaWB58reFsGR5xlckml89pddhtx1B336nDaGppXSMpVrMsh0oeIGDG17f\nQYKSNE+VAPbE2Ct86ggfzC+BZYstYMZUpCEF3JCjSB1J6YTZhJ2TjpZJKmppRFp73Dk13G+1lvf0\nxi4aDs6cxWp7PVTuEb/mEdNLKeg3ANufS3LfGmia1iBa2Dopbc8XcZ3fhxEavxH+XGddUjHdsBzJ\nPPGS7UwrmNC8dNG0ksDFyw5Cw3HmbYyGX082YwS08c4jEa7gi91Ym/7Y9Ts4iOut2mJ5rpIH9+eB\n83yBKGmE8T978UJbUSPEemESmyO2wIFhvzvtti6GK08SXDl7E2BFr9MD3JvcGxvbwOPiTwSALBjz\nx4oKb73vhhksKVufZbTSJqEtTGrDxBYXGO/Z9kWVZnk02XGip0VrlBFEqMjcwVsNj+uPzxXU3wdS\nY1lWaMjUkgFtQ8x0PQjocdX+y7N6fLeytSZg4RJJJ5G03DcgFUdT3eXnjDdtUytc/afKCfhqhBMU\n1atDknUP3t0vjd9g6qhXs1S5bJTiSaoEszcSIOHubWAseQC4yFV2D7Tvmi0slOJFA/8AVavulXxP\nUW8LX5Y1uedl8vzbsjDFk2maqy5fh4WLn7wKx16Ry1M1z58sJeyXZ3PMvzmOozWgeny6LVNOzuoK\nhVNhsb3Jt0w6zOhXMK5c0obVVHLIikgL3WFlCjyAsMU1YeqzZ6ScBFlrGk0X+YlgiH33t64jnVFT\nPnNcZ4YNJnfREVHVjuepJ/LCbtLk7J2fy3MKaGNBEDSSroDagGLI248CR7DAGW0NVmmY5Zkqw0kI\npoHmn+IcBNROpmNrAHTpAHlhxPQTxSU5oNFDSyS6KmtMpSOFPO5Ht1uNsAZtkr/xpXpJB8LIQIAW\nJD2t3mBP4ieRwwq0pFy+jrIqaKGWKpVZIU7oP8wIHMcjj51jHalaczq1RPCO753NgB4WH5YWDMEj\n7chnk4T0Uwpwqg/fLch7n3va3LHWKWXWgO3jgszBfu0txWHdFvl8z5YMpYliSwa5O7HxOMfUGepV\nuKGSPf7oHvN4XPT0GPFjMW2kFNgEFgFGMBlfDpO001K5IVmeO69CDcfph+WjdjFSLqa41yvcKvv1\nPlj1oBDchdcrLZpGG58rdB5YQVuT0a0zqscMAYd5zFqYDy32N8ImpXjqpneWZvhkQ3kG9ugPh5DC\ntRKqDUraNV1Yqbexx7w5NHFseHfSGv1xBwQoF8W0k7Us8dQhKyRuHUjmCDjpQz6t7PQxSzVsM9JV\n0yyxDdmYiw0qQOm3M7AjqLHnVZJ8VV1EjlYg7NKiqCQCxvpH9eW2HtM6/wDA0aJM6stS3GjDcxe6\nPbyNx7jywqzjTw6F006TTILr/MBvfz3x0XsVnC0HZWhgkL0yzrO7zqLEpGRyI3HM+lvTAHa3tPXU\nbs9GUtUwrIGZLvESdyPMMOt+nK2MXBnFUuTrRJI9lckDVYb+ONH2EzWGaszKkzWWeZqmAJEgYElr\nm5FyBqAO1/1thPFJnOT1Ro4p56cR8QxxvsSwFyNPiR5W/LHSewEMuY5N8VmsSNU/FB6SWWPTIV03\n7psNrg+XPGf7cZZXHtQlZTn/AJV5VD3cAodXMjnYjkRttjVPTwyvldHmVQ1HEIJGmRmW4ZjZBY7X\nO53HS2EnZnJY6PPcxpWnjqlHHcSyobOmiwOkEb26XwR8HkOcUk9Lm9WXzZkBpYgbcKwLLYAWsbG/\nlgLsvmAl7PwsymSeMmDh25sDYD6WOFOdZpl+aPFQU9BEtek33lVGtmci+1/W30wT22oajL2yrPKS\nMhqQiN2UfKAbqT5XuPfCunyWtzTPv49lmmSKZzOQXQPEwuXNibBRvuTyx0TLaoyZTT1AtMzqLGJS\nAx5Xt0GG1IjxIWkfXMw7zdPQeWDopSBcbAbk35eeAa3KaqiBcpqQ/wDVU6hhLUukEZZ2tGOp6nwH\n+2OU5tOHziqmhkPemZlYHz8cNsq7Q8GOOnlp2kIFldN2J6C2HEOYVFQra6V4JRuFdhy8Sfwj1xSU\nllkDylrN8rBRd/8A9Y6D/McTqcmpHglZQFkqIQXXVdVkAI1eZ5G/jc4yiZpBL2fky+d2HDIEZjvd\n7G/XYYoynQ+tZERYAe87MFY7fLe2+JV+XwSlpMuPFUfNHGC2j35e2FAHTe/hgxqt2ykU7ktwZdSA\nnZQRv9dvpgB3eSxPIcvTExp4QuTrub49J107FpLFCNKkX1eO/ljX5bnUtbldBQfDukFNAISV2Vhx\nNbsSfHSo5+Pjhd2iqiwV6eQmnqVNrpYkA72JHInw2whhmCoQQSL7HFtCqyVEjMO4LarHexYY1mSV\nlPm/anK6FoY1pTNa1jfSAS253ubHfzxu+0XaLLoHgnol0y0UUioq91EK91duuk3thDRdrictNdUz\nVBnjUQhopo+KWNi0g1qdKmwBVdj1GEqV0uf8Wurs2hp5JGLSSTKfuwhsg1Cx335Dx25Ysy+pWqZK\nPLIpDCD99V7opAN+6PE+B333xHtDXzZLmtPW0jOk708kTGxsEO1wfHe2+NF2A7T5bNW0GWNlNEHm\nDK8ywgEPa9ySTe4A99rY0EyZbT9qKysgy2nhFPEUikemVA7m4Zed3BF7EAHqb2wLAjQTfwupQy0s\n4IpywuGT8UTeJA5eK+YOON1FS8FfVwQSyRUqzSBY45CAFuRbzFgOeNhmOa532XosqWNozTy0iNHY\n8jbcH3OOnUiVjZLRVs0RU1ECOf8AUVBxCWtp7iKeQCIEBluNUzeAHPSOuPUzuoy2Qw0zGQ3sYiLo\nPXw9sLpIfiJXqavSZHuCqiyi/QDkMZl+wMDsJ56iWjopFIjqZU1KGHIWHMc9/I4UUuUwUtRqikMx\nVrLMEPeP+RfrucNo6c6eGAjODuhJZFPi5/E3ly9MGrAFuxYvIT3nYb/35YgWs5SO2sjcnmMJa/Ja\nKaUO8AaU79zuk+tumFU+W5ZR2eeKNVva7ktc+mFtdnUsi8GkJhhGwIAB/LlhQOZN9zvfEqYxGSVZ\nz3ZI2UN/K34T7HFJU32sQPDHl7jn1xOwVLX88MMpzWqyuYTU8jGMIRJFxLBlJ3Fjcc/I4FzHMqrM\n6p6isnaR2NyWN/IfQWGAw5ChQbje2CMvUceRy2kRxs589uWHHY8vDmz5iBZaSJnBI2LlSFH6/TH1\nRLJJl0j3JJJuT5nfFEFTKaBqcaGivfdFJB8jzHpe2AiumEb82OG+RZvUZbdGTi0mu5Q9CfA/t1wR\n2mzNawpIg2lisVPJBqJsPE8rn6YqyWNlydZ0JAWtZjZhcFYxbbn1O+NFleb12Z5jQ0M/CsJAWdFY\nM2nffe17C1wBjfzNlvwzJVVsSVYYNDDqF9Q3Ujre/LHCamlSWH+I05Pw8rFZF5mFzzU+R5g9R5jH\nbayjoMyyykyyqjhljd4kDy7CPkCwPQ2vjY5rafJJqWhqFhYKESUWPDAsCQPEDp44UQxUWU0rTcIa\nrBb2u8h5AE8yThcsMVOCVAW+5NuZOAKirNFOkq94g3RXswY+FuuEua1FbmdTLrKQ0+rW0dzwYz6X\n3PkMfQ0xdSylwCAGdtncf/5XyH5YKWJY1CKukDoBYD+mKpY2dwWLKo222J/piEkkdPBKxRpCouTG\nO9bywors6o4qITxSLMziygHe/n4DGKq55a2peeQrqb+UWGBnFrWB5Y8ZbLqtipV23wfl9Ik2YR0t\nSHRZPYja4xHM8vholD09WlRGzFdhupHj064CYADntbfESV0gD3xS9+VwcQFrX63xdFqWOcL8pQA/\nUYamZ6Xs/TwK6D4iV5m07tYWRQfoxt54okrpZKbhEIq3FwoPe+uICEmLWtyd9Q8Mexqz0y7EDWen\nXGh7K9n6rPs3SkpFIl5yM1tCRi3ebxF9rdbjD/7R6bs3k+VU2S0N585hkDTzr+EWN1f3sQo5dcIe\nzmdUeW5YsFXl5qY5DKZJI376KQBcDraw2/fDjsV8LN2lBhq46hzC7WRWGm9gOY57+2OiyfZ1HVdo\nos7mrXSaPQRAqgqNItuf2xxnNMmzHsn2rkyipEcxlKoefDqI3OxtzH6gjHQcxzOFkko2ymtiMLKV\nq5ZFRSVNhZbXa9iPTfAvZXNKhc5qUlndoWmawZ9rncn9cbamEtdUpVsGVdJNKOqLuDL5k8l+vhgW\npmuhWPSXX5i3yp6+fkN8KDTB6jXK8rMWBUDZ5Bbf/QvLzxJ6ZQoL6LJukaiyJ6DqfPFLWWxYgW32\n2xNnCLq0k2F9he+Bi4qLkghQduYJwHXu9NTmWKESBdymvTYeW25xi82jvO0j0M1GzEXRxsx8b7W9\nMK2tyGKWHhbzx9wybKoLMdgB1OLoAMvqtVXBJrTdY2W1z536emK5aqeuzIT6DxnYALEDc9NvO2H3\naShipqCgZUUQrKVZQOQsP6HFWTVVPFDPBFTGqmlmJSLSNkA2JJ2AwkzWFoMyqFeJIzrvoT5QDvt5\nYXuL9Bj5e6Qf2x6lu/t+H98WpKGBBBsEsN+WPNQLb4mWI2B54MpaoGiem0AkPqRutzzwZldfW5bV\npU0NVLSzqGUSRmxCkWI+mFs4bjPxWBcG5N73vvz8cfUtWtPC7Nub90Y0f2chn7b5fp7qvMFIA5qd\nyPoMd7ybM6ieGV6wxmQyScKKLdiiuV1EG1txz5cscs+1eSOm7d5NWupIVELrbfuve354Y5v/ABiu\nybOOJWcCjp04zxcIHS6i+jV7bjoQPfn3ZuprqzPqGnu06S1CM8Ciwbe5B8rX9sfoOjp3RDrYPI5u\n7AWBPSw6ADbGb03CqqKJEuo0C6Ib7gfzNyueW2JLTpCLn525sTck+ZxROrMSOZ9cCvHpuTYjFZjX\n5m3tyHQYHnmWJlARpHblGm5P9B64nFSd8T1DI8ykldIsqX8PPz574T5/lNXnzQClmRII76g5NyT1\ntbCun7IQzIVNSz/zTqLKvkoPzevL3wDP2YeiY8ZJ5qcb8alALAf5kP7HDbJqHLYoviKICRwbGRzd\nl9f5fTF2a5VTZrAqzOyNEx0yIfqN8U0GS0mXBvhxdzzkfdiP6Y+zzLTmGQPFFI7SxsHRQfnI6fQn\nGAV6qinJQyQyrfxUjBudUsrV1IgdpJKiCI6321MdvpfFWSZSuZ5lJTzSGMQi7KBu29iL9MaDtVl0\nNNkNJwgiJFLpQDqCDtfrjIx04NM8wmQOCFMZ+YjxHS2Ixx3bbnYbY9EffsT64kVsRY4miaY79NWN\nB2UbJ4czlqc7kPw1NHxVpxsahhsEB8yRfyvhLnFecyzatr+CsPxMzS8NSSFub2F8LlAMRPPGp7K5\nnH2ezCDM2QyCmVnsDa7FSB+uNzlnbWtyjLYhJk1U9UwYuRMFUqHJPd/8jbzv54zPaPtAva7Mcuq6\niljooqaURgI5aRwSOd7gAeP643PbOpiyrsRmFBCJHeWLSSx1MqlwCzHzJ9ycZz7JcuSU5jmbC8kb\nLDGTyFxqP7Y7FTtqW3I2+mEChYgQqgW2vbFbqJL2F8DSuLWUEnxAwFwbhnclidgOg/vxwLLJIzcC\nmS7hd3Iuiev+2PqekWnHdBeV/wDEkPNv6DyG2LWAUNJMypCnIkj9cByF6sd28dMpBVQSrP69QOW3\nPbFplIOkrY/hA8cewGF6pFrJHjp7990XUVHp44vzHsaKhPj8qlWqT/v0m0g8nXr6EHGdalqEcxVJ\nu+q2oAgD1U8ji9BpcRpdhbe2INPLM7w07BWQXd2FxGD4+J8Bj6npIVhaNAWRz3y5u0p8/LCTtdlt\naXpK6lpxItMhMjL+GzXG3gMYv4ic1M86uY3m1aymwNzcj0xrMo7JzVlHG+avOkGk8CBWswJ/Fv8A\nL6dcCVvYvMqKOWaExzRKCwUNaTT/AKfH0xm1UmQEHF9S1O1PTcNNMqq3GO/eOrY/TFLWDADcDqcX\n7cOEf+X54mSmq4GB6w/ekm26jYYEQjg29d8MoX1osKgBtNiL8/7GJ1maVEsqvJKXKoI7k8wL/wBT\niqCZauRjIO6rBrfzG/LG+z6t+K+zaQGcPWPND8Sb7vYkBR/lXb3ODPsdlfXmsPOLTHJbwa7D9B+W\nOsrsdsIzqa9tl/PFLvY7KQNrW5nFSfdqQwUemFclV8bJogbTAGId2XeQeC+XMX6WwVDTR00CxxJp\nQdP1J8fXEJXjiTVKwUMQFvzJ6AYEMbTPHLOpQKO7AW7vqfE/UeHjj4FZBpAGo9QP0xGXuIQWGoeW\nFdU0xlB0Na3yWFzi2jqanLalKmOoeKbYaoWsT5EciPLfGqTOaHNQEzqiDSKNqqn2df8AUOv97YQZ\nnTRNmT0mWVK1EKW4tQUKlL/h82/TqMURUiQw/Dwr93vsTcuf6eOCI4VUWUWPW2CAgC3O46YDajo2\nn+IFJAst7h+Et/W9sEhSp3uxbY4+qNMSOTa6xkMb/hPPHGqmmenrpI9CoFa6gNcaelieYt1xCRgY\nwqhbg3vbf09MV6hbfww7yeKklmqFq3it8KwjD3vqAuCPA7dcDJBTvQCXjv8AGNLoSnVNWvz8t9ut\n8La4PHKY5AVlW6shFipB5YFS/DG/I74c04XSNPzuAQf5F8fUnFMUMb/FORqWJDpv1548ySmmqalI\n6eN5JmNgEUm2/P8Avxxru1eUVuSdmYDURpBxpwiJcFrBSx5ct7euGX2OVIGcZlSm15KdHB/0tv8A\n/wBY7KtiBa4A6HCN1ZuX5fvgeYxUkbTTyhQObMeWFrpLXMwnUJT6iBASDrXxb33Fji/hKikkEnxO\nPJJuDGLI0kp+WJTv6nwHnyGBuC3F40hLvfukgWj8h7c/HHzqxRiNPLe+KkQSRKy3VSLkbXb/AGwP\nM5jYk2Itf2/fAgdS7OACrbEk7k+WPDEYV4hJZjuoJ5eeK4J2rDwafVEoBDzgjunqB5+N+WG8UKiJ\nUF2W1gH31+ZP7nFnDZQeRYndj+2PGAUEAbjFayJLV/Dglp7bRgHfyxdJTvA7LJGVblYixHtisqsa\nnfSzdfPCmpqFnkMSMTENnkDfOwPyr+d/DzxTUZPS5pBLTVca67XiMagGEdP/AKxj6rsrmUDLElIr\nsDbiRSqVYdDY7jDOg7FwNGVrp24zWAEJACH1PzH8sTl7C0sdLNIK+RXX5TKgC36A2/vfGX/hlbE4\naSnqAW3U6Dv74uzvLaVeA2X09eAsZ+IkqkK6nv08sBQ5LU6oS66IpAXJBDMEAuTYeXK/XB6y8eUz\nFFUNay9AALAD0Fhi3splAzmvnppDJoWK+mMgFmNwgudgL7nyFuuNR9l1MyUteVF5TMI3YMCCAvK1\ntwSTgn7SMlz2rSCc03HoqKEgSQsCRc7syncchuLjbCHsC9Rlme/GU40sYyjhuRBtt+Qx3fJ6mHMK\nRKqPdWuLeDDmMKauaOjiL6Wkc7Ki7lj+3qdhgAU8k0xqKpg1mvGmkWj9PPfc9fLF4hSI90b8723O\nBaibvNBTaXqBYnV8qC9rnz/y3BOPUp9AYks7E/O+5FzyB8L9MR4DW538dOIyrpQ2W5/l/fCghkJ0\nVBGr8J3AxBy0moDQ7j2OBmik1d6Ii45eOKlnNY3Bi1wwodLyadW/gPDwJPthtSUsMVKqRKBEvdVB\nz9T4D88HBFDC0jAnodse6Ra5UH1wM5eRGEYS6mwU7C2G2WZeuW0SVTkOGA4ciTajTNYkADqT13O2\nBa2qlqpUaZlJC6QALAD0wjrGeWd6SC4AI4zEbgfyjz5dOR53xZHAsahQqx6RZFHQeGIVFoHilZWL\nX06hve/9/niqVCEbYFvxHSe75DF9LCzLxHYpp635X/Ef788QgT4+VYo3SWhiN7Dvaj4keHhzBuDj\nRoTDEqxizdEG18ZjN+ylNV5hNmOY11VNTgakhZ7gN/Ip3NvIemPey+Vx0U0lXLHHT1UqBaahDAGG\nMtc38GIFyDvbBma0cVdNJlNDR0KTyoXqZkh/wEG97gbscc37LZo/Z3PJpqmjnLcB4zGRoNzYi/gP\nz3wf2GzCfJc7RVuaepkCyRattzYHfqL47NXSJFSO72I+XRz1DkfbGKoOxs8ddI1CU+EkOxZrGMHp\n5+ox0fLoIspyxIQSUiXdgLkn0wlhodM7Ty2eoYAM+9gPBfAX3ti5obDbc88AGSaaSSCJmU2Bao03\nVb9F/mNr7jliaJCgKxtyN2J+ZjbmT1Pnj1gApdyFUDFbuBH3SvLYE2F8ByycQ2sbWwDLCZb6ORa3\nhc4gkVPSQqNRUk2+QnligQnNbsWC0Nv8VXtr8QPLoeXlhh8OVIRV0BF0ohA7g8T4ny5e/L5aZIl0\noTzuScWcPWneN/XEgpKDnpGyj+mPljDW0jug2xaAzAomwF7nwwDNNeZqaJmLAfeSoReLba3PfcEb\nWtfFUcZCxxqCoA7pU729/PBJMQADONZ35Wt1tvgOrikm1RxjSCurUzAabYnTQRtGkRYWtsL3JOIS\nRNWztTUoZKdbCSVW+c35X9tz7Yc0iJTRCPRZiLnSOeJtYIWlOlyD8xtpGFOW1FRmuaOyAfw+EFIm\n3GtuTEW8tr+Zt1w1r56ahpoYxTRyVLMBSwKg3fpbwtjzJMtFPE4Z0mZ5BJUSgXE0nMaT0VTaxHPf\nxxVnXYbLM1qJ6tHlgrJjcuDdGbqSvn64RZd9m9VHVq9ZWQxxowY8G7Mbb9QAMb5ouLKP5eQXpbzw\ndCiwoERQFHgMRQfFVCuyaoYW2APzODb8v1wO+lbklRYXJJsAML5C2YpsxShZf8VWKs5v05FRtz63\nxBoBwBHTqscSjSFQWAHlgKU/DMQCA1rA38/DFsTOynjjSwBJDYHkZHVSU3GwB8cR+HmY95QQxsFJ\n5f7YqkDwqWKqEIN9Rte36DzxTHAK5BcAUX4UttKLEFSCLgDbfrg+50BV0lhysuyj+uJJDcd0kE8w\n3X3xW8WlrMDfxI5Y8SxYqNwNtunliSxBhYmw8PDEtAKd3288DzTs5+FpHHHsDIx5Rg+Pg3UA87HF\nYpuGWSDSqA8l8Sbk7/XEgwpydSltV9vDA7DioocaBa6ahuPP0/8AvFUVNLFoaERMLmwPU+nTEClR\nM60cI0z3AkYbBE5gAj+twDfDmipIIo+AhsBuxI+fzwWAFZnJsQLb9fPCGq4ubVMmVwtJEkX/AKqo\nDX7hPygAfMf6+G7hI4MppbiMRQQJ3iBsFHTC6gilzKuWvkGiomW8CbjgU/8AMOXebxG4Bv1xq6Wl\nSmgSGJNKKNrf3zxeF3vztsAMQkAQWuSTucQjI16Qbk8zbEpzOwWCnvqkOnWFuEHUnw8r7Yb00WhE\nQW0qoVbC1gBbGcVHq5UaovHAU2pXUXY8jq5gjlYefXBEqs58Ix4YElYKpCNpUdAMUCkUsJXuD0Xx\nOIzUszsCEDKt7XFrHA8NO6BQwLFiW1MTq89umCXCpFqlFkA5/wA1vLCoI2ZOKlmdaTfTGw/xR0PP\n5SOlr/pgvhGa+kMpUb7bEcrf7YIhjjINgABtbE2UE4pm+8KBlJKG4Ph6Yi0LcQEPy5job4uMI0bq\nFHhgeeQQssMNzNLcINNwoHMnoQPC9zimGA0+inkbUzm7SE7nqefn087YsJRFtHa45k9MD7zNyso+\nXrq/v88WmGQrYEMwN7+BxVMVWSKGNQ08g2FrgAdSNtve+DaGjWnHBUapGF5Gbck+fj54vqEZaiFV\nS6i7MT/XC/NswkaRaGksayXui/KNerMfL++mC8uoI8noVhV9Tk6ncjvOep/T0wrnmbNqs2vJlcUg\njCW3qZeekDmVHNvIW6nGky6ianVnlcPUSnVLIABqPToLgchfphkq27v1NsSFxc23PLEHW79b+WKJ\n54qSF3f5FG+4F/Ab9Sdhjyhp3RuPIn/MSDmVsUXog6gbDbxvh9Tauu/nj//Z\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
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qi7y6f1mnUpDsVpiIhcowUttpUHHmWmUuO5JKFAEYAHI55sjanWe1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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
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VicaDHdlpe71dpW4UzXGnjR5KUSjzF6yQuR8yfb6jU6jrUkp4WJUBkU9QYFSf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\nYcgkDIGAeNRN2+FF8objS0e5t+3pq9D5pt9tmqK5oY+MyMk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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
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zdGg+fnLupqBHTWyTT1TNPFK+xKKKcROUD/AGj2ED9uHQ5lcteTXXjoOmtOutP2TXNppy70s8jO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\n5J4t/LC0y1eh6+0JCzzFPXplMhCscncAf6DsO/fHb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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
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Tp3UAWTSA/ufX/fDaOekQFB35W22Cb6x072vc+n2wRBKGUhyoUXA7e2/9sP02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\nHMc5ip6aIrEFSIoZQJBpvfUBayghW8wIJIGKrxDXST18dHM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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
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ZzAMnOTmno1rHEqkPKrb7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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
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VEG7BIDZ4B+vI0Dm6SuMVR+IkRKkZyED53H2505obHURu260SQq4yZMB9p8HH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\n2iotdPTzYdSI8qxx451UfxU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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
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Y4VdNCZBJDH+EecxKxPEBVY5GeQOME9mPfQeffFrmnp6a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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
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Cvn0ubIj4WDEJ1cektTXzT4g8e1PjmqD5gqpisY1C8gd7PeVlyYHV+G9Lptb4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\ndFV2i/U2v3uAb4i5Vg2OugA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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
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K/b2qMuo3JQr8xMA36hu/xUi21DwkI8rN67kBz+aPFqcYt/5i75DwFCAKo/zT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\npEe6MKzAEnOeavnVUi8rbvz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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
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1aKzsL1uekllAOP8AgJOT05xWDf6Frl1blhoFzNcMd5YR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\nQlYSCFDsV55d+06bPFGvf0Zdt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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
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ccMjD2IOQadxyTQyeCKiFwKz08UJo++TQGUdgKgY881BkXPNBePk4xQ2iFBMf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\nEi4sfxUtrORpthMOlJnUZ81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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
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2/mOWIyRwN9qWuenG/ePCvNGsReXxwQr5xnBRgd+cmkby/wCm2HTTbW3Sba7w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\nSYsDHc53/mhm3uFY6YneUjC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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
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pzg6c7PjI5b71RNxMigBtKY2AANE/FOszl9y8ejUQN/SP9qRlHlxBlBLqeg2O\ndulQE7pH6UjAYYyEwfir7RRJ5Mialgkz5cjxkAkHGM/zFavBZrrhPFDKJ9IVG2A1B843HtnGa6G6\n41MON8JlKp/w+ydmBQ7n70154gkuPFHD78qhNsfSApwRpJO/TnW1B4kksFMqQqYg5Dpv6VO/6c/v\nWvB4iP4gSqq6dB1KuSDywf771fg8VpIcFFjAYAFpMZ+n2onE+MwzW6h5oohqHOX5r50Y5Y5O5qHz\nTZzTEnrTx5JbB6Ve8oQkq6kDpuB+tJlVhhJSew61B4tDHaT5YYz+tDOjP9aQwW2/hUwWAG1JQwG6\nD4xS2AOx+KkqZH5D74qakEYGsRnmVO5HYVc4fa3lz5i2GQiLqkywVQPfP9mgMs0EsgnAKsd2UErn\nt2qskLSQxxs8aIrFtTE9fbry6URAsRLo8qIpx6sEnbsdt/4VXuXCTaUVMR7ZC41HvigF/TyX22q1\nHBJdXHlxjbmPScZ5YHydqhKCqQAoPyZ2+TVqPhl5NaKY40dmkZBGGAf0rqJx2x1qK2d5LaqPV+Hj\nbAzJlVLHB27nH1q3LHMlqwuEYuCVEjDGjAO3zt9q1EKlLEtqURrhgxA04U757birUa6uLxqi5LIW\nUKMluXLvWhaTKJVhfJMx0c9iQMjb42zy3qwPIEGi4RmjjfUmscug9+427VpcMh0G4kiWWYpIAqtL\nnIPPc9BnNbitDGXEyvEqsFR9JYSEgY5ctzjJ6ivBSPaoYO9NjApYokUcuxQDJO303qxFJ5hIZ2Rv\n9OnP0FGB9Oh2UKP3miwfuKH+67eaBpAwCW9W/QYx96GJnzqRQG55GRUSZDku2cnJY9aYqGXccuuM\nUaSaVo9MksrKd8Ek70PLDUWLBcbEJzqaxSyJnUNJ5rkZomGC4LLywP7zXd+Dz+A4NIv4WeSW4IkB\njh0gDBAy7YHLfasri3DZ5y7yCONCd1U6z9Wxj7CsN+FOrrE5Cq5AWQnlvyb37V2x8LQf4bG1zGGY\nAaFYYKY/2rguLWqwcTuUCYCynb2zUUnHDLqaNYIy6syrIw1EAnYgHbYVN+KENCYIF88MrSSM5YSE\nHIGOQGcfUUrTF3LMGWTQQMxpkq+5IB9hn+80ZLO4uDBD+FHlHJj1hhqyQcBhuSduhpRcTu7cb2wZ\nACr6NShlx+UnljvUH4vGY3jNkgIA0sshzn323FWOFXBvLiGOQvHGr7sHLmMdWCgZ2FaFvxP8FPDc\nyxapGjdUiZNRGogZ6YP9ane8S4hFxEyRwm3hT9k3khQpYqcjfrjp7V0Vt5lxbLcsjo8iqTb6DlWO\ndWwHLOD/ADresZoIBIpzH+0wFKY/THKr73EMqgBo2wfbnXgZOCahypiDTYJIGeZxVu1EkjCNNIKZ\nPPnnHWlciRnImVm0/vDcgUJJWQgudY5YY1agZLphFo9XQFgoNWW4RIRiKaPzGOAhYKPuxFNdcEks\n2jFzd2frJGVuVfTjuFzVGSMRPh3bVjIGM1ATP+Ufl9663gPhD8VAsvENSSTnTCpHt1HP3zQr7wRx\nOxuZUTypIY4xJ55bC4PTfr7VmR2N5JpxbllZtOvBCk9BnlXc8FsOMxoIL+SNIUUKqtKGZfbIO3xW\n1c8GhmMZ8sGNdj6/zds9/pRODeEkt/MkvBFKzDTpxqGD3zRLzhVxY25HDCktugP/AAszEaB/9t9y\nP+VsjsRXnfiHh4ueJSTAPAXGXgnUocgb6W/K2cdDzNYsto9zYJIFKyRkJIHGM4GxB67Yz161Ti4X\ncTSoiKrFiBgODjNaEPC8Pi4uYIImPNmJdl6EJz5d8Y9qK3FYbaM29hEVX1K9zKcSsORC6dkHTYkk\ncz0qhc3EqWcTtO2SWBAJ2IwRz/vlVWeLTJKEGVQ/m7j+tK1jaRiUmijK/wD1GK/Y/wB8627a6VPw\ncro6t5bnWkm65LAEasnb5+2K07DxLdK7Rm7smRmBEN5bMQAccmXIGefTnV9ONG8JW+tdf4eBnh/D\n3OpTpBGMD8p9YOOpUCqwh49w/i8kMdxepCP8seezqcr6QWO3MqDy3oy8Q8T2TBr8ytbqhctLFG6k\naSRuB1OK4JjvUD8b03fNRDYOc+9aXD5YVDiRghwNOeu5NBuFCz/s3UAjOc/3ihiFWXJdM9PVTtdL\npCKi6Qc8qnFPbOGW4i2YYDZ1FfvWzwbh1lcRu1xcWaRAAeogHOOeDvXTt/5amsjY2MMMcirpFwVw\nZD12HP61mW/gi8NpJdwvYugOIzJNjUe4H6bkV0FgtxZLJccTuReXTP8Ah4kh6HG68gAffsKnxvil\no3CUC4S8xiaNcuoXOAHBzz6devKqAkmWKxukuphEEzEGTVGo7DfP3G1btpLFcWwICxId9McOkt78\ngKhPxj8MFigDvjYu4Hp7nbYn2rcS8jh4XHcuyZSMudJONvf5rJ4D4uj4xO8LBYpA2VGrO1XOL2LO\nVjRFCTDruB3yK5ay4dZS38dvJbW07iQhiq+nZcnlj2q23gK3cRtFNLGdi2sl1Y9c9hjasSfw9w0X\nMsK/iLePby20mZTg4Yk4zj6fxqlxbw0ZHeewuI7mEEeuJW9OdyCMdDtVC0kW1XyzEkk5nWT1ISAF\nzyJyPmnbyXggGAEkuJGYDByAEXGeuRn71WuYo2kYomFDEKqbaP03o9o1vdrGhhZY0QuV3/KDyzvz\n5e9adjZwiK4iWHzJYidC6hsTjHUHYHPI8t8Uf/y3dm481EiRW9Ma+f60XbngduozXQXXE1nuGzDc\nw+a6D9tG2ATkLgjIG4H6GiT3EKi3guH8iWPy1kjZgCMHlzx+7+teRtvmo43xjJqOcb0SNA+c5G2c\ngUxO2SRqog8tsAtg/WnFuztpjBIJ59qdrQrnDBu+OnyaInC5nRnwq6f3Tux+g5VZSyVF2aRHxnSE\nzj5yasWsdun5fPlm05BK+jPv8Vo2y3axyJLDK8Q9ZyMKMDoe/wAUa1mt+I3ayI7RONyPysf67V0j\nQ3AgLwLYFiCxaSLddsZzyFZY4Dfyhr6dS1wrDy0zkuD1XG1CuL+7ju2jnl/aI2CzZOfg9aX4ksBg\nM5zn0ip8ZvLq5sYbRIRDEF32JLnPvWVw2GS0vFkB0Sq4y3IrXqnD+LWvE4RDcywpOeoO2fbtVFPD\nMXD+O219APNgy6uU2KFlxkjqNudb91GIrKXTnKRnGNzyrDi4eJOL28I/y4oRqB2zt/XNdRFGsUeh\nVAXGMAbCuY8U+FLK7t3uIh5EwOS0YxkdfrXBX3B57eVibWAwQjCIZWUDYe+dz0zVa2e/nKq7SAsp\nGhkHlqDy9J2AxnlvVdoxLIyrrhkACgAFowe+nOpT9/iuj8OcElsrR/xMgYu2pAq4K5G5JIzn57V0\ncMJVVAkLYO5YZJHartukojfMaOAPSEb825239sfrRX4faXgkW4sI2yQW8yMHXjIG/Xb9CK8CIBJ7\n1DvjFMR0pwxBI2pipxlRlc4yBR4rSaQEqurr7fU0ip1gYViOgORR0mkjXDkIw5Y6fanHEXdgWRZC\noA1EFTj5Bq3Gkc6FoMRyAD0nkx9mz+hquZby2nHmyhiea51AgdDWpZcffQYJFZQTv6SdvpWg09n5\nYlMRbVvqVAeXv0rpfDx4Xcw/sXb8RnVIG079sCizQ2XCneVrePIHpd5R6fZRms+S6gubX/i40Gk/\nk1hlI/k3uMfWqcFwYQ0kFrH5AOCSCce2a0bTithcBouJokKgHDjOR0xjnnf9KypYIJLpobedHQHa\nRDnUO/Ouks04ZbRpHclVLYHpBYgn45V0EF9b2sxtwCulc4LA/wBnlVwcRgnGkAjuTyomuPR+yVS7\nZ351CyvvPRy3/pnBI/n2q1cos9q68yVIrh+N2P4x3RQCqzB3yM4xuAR71Rm4Q0tmkbAl0C6SG0kc\nuv32q7awfhLVFmaNp1XDuF5nvU2ukjuI4xG7B85ZTsuBnf5q9CVcAqM+9Xrc4AHvVpXOCK+dycE1\nEcz8UqKqAaXIGNDfcCtXh989vwqOMkCHWSVGxY98/wB8qaGKGVgJI58ycmJ1bfz+1RPDUZnEbMwU\n80GdX8xQJLDyOecZ/eUiqgzHIHAC4Oa6K0MdymtirSOQArhdJOP0qtcsRMUkSNlB3250reytZrVw\nZ2jIbkW2xV+y4b5LGWNZQoHLl9assXWdXzGs+cKC+5HuB0962uE8Dv8AjM3mXJgQLsDK+sgeyjnW\n+3gyQquniEAI2ybYDA++Kt2nhB86uIcTuJ2BzGIyVCn37/FEuPCYuLlppLpZnwFDzW6uRj6gH5Iq\nvN4Rkecab0CFR6VKAH9KwuJ8MbhlxGJbgMrcxEp2361W4XBLd8Y8uJgxdiQ7HYf2K7S24SkTPJcT\nHyk6k4zVG54q7zubGGURRkBcjGo98HptWpYTW0PCdBuY2mOXlKsBqJrAt+Pyx3ksc07xomdIB1Nj\n7V0tva23GoNcqeaHwRKjBXH2xVHivDDY28s8RlutOCIljBfGd8b7n2rnP8Ttb0zR2vqmRSdLDSQQ\nDsQdwfpVi306Ecruyg4+d6sxn9oQFIAUY5Y68vp3qzBIqjGd/nlVjzTkkN9K8BYDcZqC8zj4pzsT\nvzpDLYUbnPKtJYpre0jIKen8wIIKnsaPFf8A4RldIo2KElvR+XYYIJP8q0rdbXiVybjz1jwMsXYK\neW4xjl9aMq29ykiW+WkG4B5OvcA1iXVmkb6pcD5Q0GJYRICHQLjfatBmhnRVSNtsHWUJo1tw7zHE\ntoXZs9FGK0RYXk6+W6Ej3Ut9qsQ8Cv42LQqwDbHCkZ9htXScE4ZxPhMgmm8zBXePA1D6k5/Suite\nMo6aZYHB6qVLH+FWFvLaDLq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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
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xxSGcRky2rgggCxz2+Y759DqPanV9V63H1L8Fp9OyRGEgyM4ZS2481fnnqdP1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\nJwPMVu5+ff4YnUBljA/Fwm/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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
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DuY2we2S0Nkl/i/6PtVdv80P67vyKxf8AnfAfkakpsUM21Mse2qCNfVf+k/d3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\nP0UlNUBviiN28bgrLMemSHE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2SiVJIo3Vs47XBnQ8AjHg85PN+4fj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ttupqr4h623CopfmEFVvaKOOMFuQsKgCIAtxHgEZ1WG5dxdF6Komsm6eoG5N8SxPGzVvryVEeFX\n9KuCpYA4xzge2pD08vfw7lZZ7ds6srK5YxLDMtC89Wr5xhRHI7cA+WI0huTcG4p5P4fB0zT5SaJH\nRJIaupkXJIVpO9MoxxkDyAdTG3bK3sbPQUViuVPYahZjIIqe3pHE/eAWV4znuIGT5B586C3GO6bf\nrJJN+7toLLBSkyLcqSugh+YXJKhaVlaRz4BHIH30hufqT0SvlTbrbDbKbeU1coWdI41pngkBx3rL\nIERFIHPPk+MaEyb+6Q7alqqCr2FT0tRCoehSW+PcHSQ+GjMZaFTx7kEcal1v699I47dS3yaOOa+J\nAIEjqaEwMijIwKgI4IGTyPP40xpvieu97rI7LR32ybatrFpHkrqee4ZIX6FC9qgHk8jnRWv3dvOn\nFDuS1dQrruJzVpEaCmtwoKGSNSuC8nps6ggngBs4OWGpZbNz7uu9Y1P1D6E3VrO8fc9XZ2WoHpYY\nqCQY3QgkHIYEe+n8W7d40e2121srat5udOwQxwbnro1jhiOR24ZnkduAVJB5JOdNaGo3RQWusodz\nnb9pMkpCytWST+nnkAGVVyD4HPGnN6t1ziaKO3XO3tBUFVmBp2kkLAc9sinn28gjRC2Weio8SXS5\nRxdg9eSnnAgeVRkAp3cYyD750S2jcIfn2rauqpUhMZWKsQLF6aYOCwZsMTxyDr6TqTZ7XKF3bf7Q\ntpqP5U7VFJIvpRnBykyZ9Qk5/UuORzpCq639MJ7hbZrfumGuFsiMVFDb7W00saEsQxPZjILtznOM\nDPGo51T3vY7tTpuawVFQ9dWR+jUUV3eWLuZMiGQNgFe0sWwjnIY5GNcy1UHUKSs+Vr9w0Cjub6pJ\nDVdmTz2lhk/ccnHGidF03stbSwSVV5qrhLHGJZI2kVI0A4BXt5xnGc4I1PtgXKwbGins1cRSyTSx\ntSz0yDvYgEAHJweSDkjPBH21Er/vnd9xrY7rWVlPCtuWRf8ArX6QncVwoJx3HuPjxnjxoTc6b+J7\nXnoKK5zfMTziSStjdh3AEMyeoAQOMge2T+NCeolguPTey11Tsnd9ZPZai5vFQVlbTds8kXJChkBT\nu7ABIPGc499Ct37evFko7r/E9wxVxqVgnrqe2VPrQUzzxq6hgyjE6vlX7MgYwGIXTLaHSe/br21f\ndy0dkuNfZ9twrU3O4U6sYqRWVQqkgFWfvdcr5xnPHOpbtTfNk6cSzU3Trbptt4E4ih3TVtHVV5iY\n9kiplfTpAYywDQr6mWIMnbxr1xvFxjtVnra5qqakooaiKeonoYykkcafUe/Bzgn/AE9jnVPbx3Fv\nW9bHa/1tPRbv9Wp+at1vpaeOppo42J9F2/mq0jGMgEMCFLEjga4y+I20Wu9bhiX5C3ba3S9FA1Rt\nqnQVgj9Re4Rq0A7YzwP0qPPJznVIWzb+7bPdJqebpbXV9Y3Ihlo3KoPc4Cn8atCzbl61USrIehe2\nZ4BE1PHDWU0cUQDNnJjEihmGcZYHjW9oqviNq9xXG/ncMGyDDEZIKKl9FaZjwvpRx/UqrjnLcf1O\nl6ql617roHoLz1Su0MjMFqIwsYjljJwfTaID2+5GkX6DbcqO6OPem5qjOCXiqu9vbIK499TraPQr\np/bJLbVvZZFkUMrSVqrO8r+zP6nj9hjTm89JeiayyVlw29RxtLIzSTzERRMSeQArYHOdBqG2/Dzt\nofLLX26Kdm7VkpqF6ll9+Tgj/Ual9BvrpxQf9cs+3r9ueoijEUPpWwCPJ9vrAUD8c6HSb23ct1z/\nAOj9UGjQEwoamFJvq8kqBj/y1YEW5uo4prLPZds2ayVUJYzQXepkb0ohjtwsQA+/k84xxqc1W/Ii\n1Zb6qaaOtkg/kOGVVqHRctgH9Kn27j9s6o2Tq/U01Ybrcem+6FlhCsZBHHUo58eFbgc6bP1/tV4j\na413TfckSQR+irm1K0IGeXPOcjHjJ0tU783CTJetp2m33CnpwjRo008FUrOOVZQhUkD2U51kXDqz\nvW2SrueN6SlDdsdKlqhqJJMAglTUFhgA4JOOPbTfaPR6ouN0hqrhT1NVRxQYWC4wrAikY5Ag+nAH\nsQNTWu6OdOHoa6ooqezU1UkYcPJQeuoHaPDAk5Ple1dQ3aX/AEh0VsuFksEM01GsgjYrCYagnwoW\nTgr+3A+41D99UF228fTvb1RqSxytXPHI6YxkeT/bOcaF0Ff8zRMZLdGsQYyLOEPcBjwOPvn+/njU\ncm3Q3z8k1iuElLWOGy7YBYKvIZRwfp4PHOgnzVbcKerqZKumSSFPUjdRwSDyR4IP7asXZ2ya+ipd\nwXu4xD+HPtaoq3pKiSD5moieEo7JESRgFycnDKoDAE40DsVu2RVbcSx22y3YXQoRNUrVSemy9ww6\nx+Bj38ePzpz1O3LVt04orVUfzY6OjmmpP+rRtMYwwVsOAPcK+CO4cn3Oqfp6iqk2vQJfLTcESgqJ\nyphqI45JkdFeMOcF+DllGO0gnHJzqa2PfW75ts1Owo993CosVTUetU0EVQ6wTMEI7vTQBeVd8/73\nhuQNMobM9S1OzCkSnkn7ElZWzlSPICnA4PJJ162XVXjSavtVnvFYk8gmc0EQpVLBQMl+Gbx5YHx+\n2qvvx3td6GaOz7QvJjpwzxz1F0T0Y2PJ+gLlmHPuTqtaOfeFprnortty3TyVzFTJhY5GJ+7DltRP\nduw62m+Zvdw3JbLHToQp9aUxRrn2zwDqNU+2trzT0sg3wtRPM4CfKUbujHjOJCO0AZ8nUuWyWugr\n4JpKmingSMxPJJKGJbOe448/toduCGgWp+dtVyooFiikX05VISRyvGSDkDu7ePPHGo1Z7V1juAeu\ni3NtahpowGkFLbTI5B9iXYf6amTbH3Je5aWe87nkFFHT9pho4VikdgeWYglgORgcefOtL3t+GeFo\njGz0lCqgQylR6nPI/fQCqr9s2mFrq1phhFHECwaTthD5wqsRgD25/GkKPqVeZl+YgodvTSqT6TVd\n4jUfSPGFX2HtqSQ9R6Ktt60t1uFha6oVMNpoK5ZmkJXgoxC/UW47ScAeTqK3zrdTw3Ca17h2ruGK\nqVY4zTw0qTiYE5yGQkeTn+mndJvC73EGM7Lu9seSVeyqrQhPp584LDP7Yzox8w4kioaaupfWlnPr\niobtDqP09gJwOfIz76My0lEtJSU0twWORsCeMAsCPPaMHBP7a3EtpuHry1tRUBg4Smi9Rl7iBgM0\nYBZl/Yf11DKPf9s2ruA2q71tdVQ+tl3oLb3tGSfI9U/V7HAAzqdrv6xRWwUlJTb4q6OeUu3pW6Cm\nKufpUlmIyP8AweNVdetz7ztl7VrBNe1gSQI0NTLTdqOvK8Q5AHPjUupbr1RudDPV7k2sIrdBKJ55\nKaCJSjkhS3pqwLsSPBHkZ41DN9WmKtmfc090ikp5D6k0VV2RA9vAHpAlsngcZ1U815pAY/R3VKFY\nu0lIrOnp85AHcOV5I/GNBqq4H5uJKf5aomqASrgYZT4GfYk6uVPh76inp5X723hLRw7TslHUSR1q\n1CPFSz/y+5XEXcwbukTCHBJPsAdCLT18r7Jsy87C2PDTTUF+p1p5blXoHrIY3hWKpp4lLMiRsSw7\n8d5HaeMDAX0LtbLDS3KNVqKdYzTxFatUkpeSvqO68oobDEt/kBOCNRXqSKLcNitIot1vW3hqGI3i\nKliBhjnhknilZJB9Bj9MQEOOG7mzqNTUttpai3rBX1kttq7eVWapeEypUKzpGr+mSFUEAgMASPGB\ng61qaOOHb824obtQwvBUmjaiWdhUMR9RkVccoQ2O7PlSPbUuiutygWSK033+FxClWI08FR3jtkAd\ngoGR9WQxx7nXqdU9ZLElEUtvz0c0isZPXZEUH3y5/PjGqul673C0vN8xbILw0jlFneQpEi54AB8/\nk+/OhV16yirneVNu21qoRlkMAeRhx5OFGAP31X96vlfumGWsutZDTGBg6rVusUKD2KqT/roApnuk\ngq45DVxjKvUwyI8S49sk40lcqSxWWMtuDctptsP68VNbErf1AJOfxjUNufU7pVBA9LQ7me6yI+Xh\npgVTsHn63CjP/nqP2z4lNoWe4JTw7WnkWXCNNLXARxc8sexSSAPYedTKk682W/1s6bQ2juS9tGo9\nlpolHj9THONPEvO5Ls0Utx2RBaULGSRDdFnlEQHkxgcH+utbtM11pY6Sot1PPCzkiL0xhmP3B86W\nsez6aiVqY7WoFjEgkanNInJx75B50V2/Zah69JUqqCnjjJMUZt8UYRSf0Ky4JPPJPjRCsprfbahr\nnGGyzM0YgcEMynnBP59tV/1Bv1JW08PzNjluFRGzMPKiMffvXBH7DQTa27LdbVaa17ChqJol/nOW\nMvGfILZYHPuDqb3DcO87zQS1NDtm6WyjplGJIDEUizye4kZ5Gcc50Fo9x3mxyfOUlwvdUwwZ46tk\nhgCewJOWOf8AdXTjc+8tobitUNptdplo62JQ0lQkwjhaQnlu0AE8aO7Oum0rBUxiHcV4FDOAtQap\nC9PNIDnI7shcEfk/nU0k6o7I/hNXU2KxxVs7O0M9DQUIVY4Sc97ySfqLH3xpL+I7auqJdaTal4ip\noTGxlpwnfTuFCqGC4H1HIH7ng8ai3VSsSGup7/R7arlq44+wUN4oyKSYYIM8b+Q2SMr44zga5kno\n77VX6nnuVsoKiCmM0WUjCOwlnMpPcuO9lz2r3ZwvHjS91rrBTXZq2phiikpalZUoKmNRDPGXJZXZ\nSO0gdowBzz4xrs3pF1X6LdUNiN8PXTQ2/pdet4UUqXORacVhusyR9qUjCX/NKC3ay5JAUD6sa5G3\nBsWs6f7tt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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
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aNFdrMejMO1pSLvONNlxxwXPkAHF7i5Fvc4CJE2u1aMj8Mo0ViVLWnU1JWGwy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\npeQv5CPTy/4wpbCwo9NCrfS54xwR3nHAp0O77lRSVE4+ZgTXdXSivrC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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
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NIgMYG4sx7AVtWGnJDYgXIYKyksehAJ6D64FZ2r3VvMqpbxsvOWz044GKyqVL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\nTxFjgiR2Tb9j/SlcixiG+KX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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
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2H4Tz7aAxTy1rxxJEw3uoVQOSOSf76NQksmyQphggQ5XsAAoGPy1y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\nquFwoJG7GB76d0kdTNOk7TSSTGbasiL5TGRQVwi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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
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gqJSgc/HUJpdoXZoB3iad0eqtRFz2INMl3JcUJpbEB51xC2Xy6cbW0LcZcUkf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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
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jv6jPfRkFSYqMTidYCV/d9I63P0X0799dRvKFjhRvKWTPUOvLMD9Pt20sZkpF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\njBJ54BBbL3tyrh3PVWSjp2aqjrHpEhiOQZFIBPBwM9zjgfb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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
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y/wBQGRQ02BzQ5oI2kz4tcsH7bnPqyK621WWWuh9ZcXsgD6L2fQZ7f3Fr5LbX\n1vuw3AtBc60En2uaHbmtaPoWbvptXO9N6n0wNsORfde/VxqZUdzg8gjbuP8Ag3JIcTKeyq5zWu+z\n3vB9Shpe1oEt7uc71Gbva73qpm9QuxGdLqDy8Gw5bm1NY4s2lzWenfHqWNtd+sPo/R0/11p9V6l0\nq7KcRgnLraTXTusLCSDNnqtpPva38xrFzzrf01j/ALO33FoawRFbWn2MZ7Wbm7W+n+k/waKn/9Xj\nhYS0trYDuhsPO0anQ/1f6/0EUOsdtbXYDWwQwgQNxj1Y3F/85a36X+FQNo7n4xH8Sr2BXU57GlzZ\ndoN5DQ0k7Zrfu9zmt9/0U1K7rC2twdbc58RLXBn+Z6bPzf66vZfWcTP6tZnOx6aH5G31LnNdc+Qy\nukxq1rW/o/U/fWfl+mywsbBiZIgzr+8dyBFzvazntuLRp5bvb/0klOzkdaiz9CPtAAAbY/dUAI2k\nNZLX9/p71Addz7DtDKq2uBbtY1xkR9Fm9z1mMre/b6QYx0e7c9urtfcPo7W7PzEduDeHw/JaHiDF\nTgeeNr6dzd39pJTJvUuoN+heaZ0jHrrpbHh7Gblq9L6/iNxrsLruTb9hqb6tVGMD6+ReXbmsty6o\ntb6Lf5qv9Ey3/C2f6XKyThjbUfTcamkF9YIdYSdbLbLP+g30lA5OOARiUy8wGucCdunuc+dm5zWt\n9vt96SnT6+Ojt6hSemWfa6TDHVWA3jGDI9RrK7WUWMrd/wBp2ZDPWuvrss/m1uHNx3Y+H9gpstvY\nfSdVcGtaWW+2yi1rt+6m7Gd6Gyuz/hP5ytcnXY9pfWyv3NcfVcSXPL49xscQN1iv13dQN+PIteGF\nvpbGbCW67GtbDv8ACfQ9yKnraPq7cMk+jYG4bQ11RcC6xzHEPYXPd+i31+6uz2/zi1s6/FpdTVl2\ntrs5rc47WuDfpzs+i/afb++qODlZzsf07KrJZNwc9rhLJ/S0bnNbvu9XY6v/ANCq1l/Wy77Q3HFr\nH0s9Ta572uaI2nbta1rvpfvJIa2fh052VmZ2HV65x9bKGA7niNH1M/Ofpb6v/BrAOTaK2mhjBW+S\nJcXe3ne4sbt/6f8A57Wn9X7QzqtGR06ppyKSS6qXO3th1drdrfc+x1Tnub/g2fTVPqzaB1TM+xWM\nsx3WOfSawWs2PPq11t/R/o/Ta70f9GklvV0ftNmHS7KOPlv30nY0Orsaxr34zamlv6Kzez7Nb77P\n0llV1f8AOLKxi91tbn7rA8hux9hA1G3/AAe3+b3eotjpeP0q4ZDGWek9wJw3ZZiwPDmxW/03ehvv\n9npvqu302/8AW1mZ7T9ovFr9zvUducBEkGPo/m7klPU/Vf6z5jOos6fc1l2Pc4srcD7mgH02Bttp\n9TIY3/A7/wBN6aJ1puV0vqh6n6It6Ze4DILDuFtZmXWNP6Nln859D37/APhLlyWE5vr1CkAXte3Y\nXauLgT6J9s3uqc53p3tYz/R3/mLtcjPbjtf1J2Ibun5EftDBvh1tVoe9t7a6rm7PR9TZu+hRYkp5\n7qlGVgZFjCS5jgS1zSHNfS73VWVv/SPbU5n0LGO/4NV8fo+TffR6tRxarnS22yG/o2g3X2sba71H\n+lSx9iON9OIzFf8ArODZ6tuHmNlhDHHbkN9P/BVtyW/rGN+Zd/M/o7ffnOraHEYlzBcSZ9NrSdu3\n3753ezY5/wBJFD//2QA4QklNBCEAAAAAAFUAAAABAQAAAA8AQQBkAG8AYgBlACAAUABoAG8AdABv\nAHMAaABvAHAAAAATAEEAZABvAGIAZQAgAFAAaABvAHQAbwBzAGgAbwBwACAAQwBTADUAAAABADhC\nSU0EBgAAAAAABwAEAAAAAQEA/+EbIkV4aWYAAElJKgAIAAAADAAAAQMAAQAAAN8EAAABAQMAAQAA\nAGMDAAACAQMAAwAAAJ4AAAAGAQMAAQAAAAIAAAASAQMAAQAAAAEAAAAVAQMAAQAAAAMAAAAaAQUA\nAQAAAKQAAAAbAQUAAQAAAKwAAAAoAQMAAQAAAAIAAAAxAQIAHAAAALQAAAAyAQIAFAAAANAAAABp\nhwQAAQAAAOQAAAAaAQAACAAIAAgASAAAAAEAAABIAAAAAQAAAEFkb2JlIFBob3Rvc2hvcCBDUzUg\nV2luZG93cwAyMDExOjA4OjI1IDEzOjIxOjUzAAQAAJAHAAQAAAAwMjIxAaADAAEAAAD//wAAAqAE\nAAEAAADfAwAAA6AEAAEAAACxAgAAAAAAAAYAAwEDAAEAAAAGAAAAGgEFAAEAAABoAQAAGwEFAAEA\nAABwAQAAKAEDAAEAAAACAAAAAQIEAAEAAAB4AQAAAgIEAAEAAAChGQAAAAAAAEgAAAABAAAASAAA\nAAEAAAD/2P/tAAxBZG9iZV9DTQAC/+4ADkFkb2JlAGSAAAAAAf/bAIQADAgICAkIDAkJDBELCgsR\nFQ8MDA8VGBMTFRMTGBEMDAwMDAwRDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAENCwsNDg0Q\nDg4QFA4ODhQUDg4ODhQRDAwMDAwREQwMDAwMDBEMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwMDAwM\n/8AAEQgAbwCgAwEiAAIRAQMRAf/dAAQACv/EAT8AAAEFAQEBAQEBAAAAAAAAAAMAAQIEBQYHCAkK\nCwEAAQUBAQEBAQEAAAAAAAAAAQACAwQFBgcICQoLEAABBAEDAgQCBQcGCAUDDDMBAAIRAwQhEjEF\nQVFhEyJxgTIGFJGhsUIjJBVSwWIzNHKC0UMHJZJT8OHxY3M1FqKygyZEk1RkRcKjdDYX0lXiZfKz\nhMPTdePzRieUpIW0lcTU5PSltcXV5fVWZnaGlqa2xtbm9jdHV2d3h5ent8fX5/cRAAICAQIEBAME\nBQYHBwYFNQEAAhEDITESBEFRYXEiEwUygZEUobFCI8FS0fAzJGLhcoKSQ1MVY3M08SUGFqKygwcm\nNcLSRJNUoxdkRVU2dGXi8rOEw9N14/NGlKSFtJXE1OT0pbXF1eX1VmZ2hpamtsbW5vYnN0dXZ3eH\nl6e3x//aAAwDAQACEQMRAD8AE4tiNwny7oRidzUduM2wAjR0n4HunbiEPAJmePgglpGz3TJjuq99\npL9O2oK2LsMNAJbAI7LHysVzX6GB+SElIH2kNAHxKHaXMb7pDkT0fTILtR4BPYaydp1PJSU1w+Gw\n48pV2EOGwQO/ZFmhnDQI1k6qFl9b4LjoPBJTpYWYRZVU98Nc4RJjaSfFdURUX7WjTb7gPEfBefXb\nXFhDgADqD3BXT9K6nbYBWwSY2+0GSR4uP0UlNt4se57WOYHN+i06EH+3trcp1My3FrbSDEgjbLXf\n5perpp2s9RzN0wXiRHy3Ki3LoOTtLfRLuHbdw+WzZ7tySGLun2uaX0B7tujmAlo/zTvbtZ/KYqlN\nOVXcBd6jHNMi3bXI1/fYWLaa3He+bWFpOjbCCZ/qvYfb/noNVeyyyuguI3EBpNxHP8vd9L+2klq1\nvzGEkPrsDezmNLyB/wAI61zHItGTkWbtzGuYTO5hBLfBkbSrlePTYJc0aagPeZ/s67VJ2NRG7YwE\naO3CPh7m7UkNZ2VgizSKrDA/ShjS4Rs0Ptc7+T+jVjHrZYfV3HUcMcx/m1zmtHq/y1Sva/VrKmuZ\nWfe1sO0P0hLLfU9yp1X47DDWvawu9rdRr+631Hbt/wDKYkp1XTqA25tbXTXZ6b3s+TbNtiap+fU5\nhqNb/wC0WNPH5rR9L+WhUZVLwJDqyXEs9SGuHwta1m3+wiOvs+m012bTDrJaXFo592O7fW3+ukp/\n/9CPQMTqOR06rIMZAFYNzq3b3Mcfd+sVfztVm36Xs9/+DWu3CtMEjTy8fKFzvTK7cbZZS59eRXo1\n9ZLXAcakLtOm9ZscwMz623nQG5gDXjzcG+16CXNy8V7K2t+kD4hZGXQCYj5LursTEza3OxbA4/ud\n/wDMXNdW6bbWSXMIjggJIeWvpbW6HS4A8FZ91255a3QDVy1uoMc2PCNPisgUgtLo5OrvgklrXOc6\nWt0aDE+ai2u2t4LjLQP9fajXH04dHjt8P6yq7riZ7Hx7pKbbHNeyHaN7ytDCyPRf6lTi0GA7WJ1W\nM/QAOlxnUBFqyHAaS1vg6e6SnqsLrN3qO9wiIA7fymkTtctuyqh2y3GLXN2kkNnR0N3Aj81vu9j1\nxOBaW2OeGbmj6QOohdRXkUtqZZVp7Z8Dro7c4jbsSU61IazWYIjSRCk5u5syXB2kNJ/745ZTOpVm\nWWHa8RudMx/Jc4K9TkM2knVp0MHmfHZ7XJIX9QssDBc9s/SFrN4n+S6farVeRa47Q1jwAZLTB/zX\nbkP7RUWgakCRIP8AtY5AGWDaWwxwI0iQ4D+tDv8AqklLusptfD221kHQyQJ+LS1WacSu5pYfTvrA\ngQN0ePLXbUDe4ABmnmRuHz52qzhvc2Q4a9nVgH/O2JKar8SmlgZWH0iYOkN/rcPr/wCgs2/Hc47m\nWMtYDzUGEnWNsNeP+pWzmZDHMNYc7nTQtg/nfSa3duWS6x4saANoIJEMAdzEO/OSU//RnhOLWbzX\nMjVwn8dqt3Znon0wANwlp1P9ZvKfAYXYYLYc4mAZiSFXuoYWPNcAyD7j3/qIJXxeoZQvNcObtMjS\nI8Bulbreq3XM9HKHq0vj3EAP4WNgNY10vcJBLS1pBOmi2agxx9nYcx/35JDhdTrDXhkzUfoyNT/5\nksPLw2tdABAd9H4+S6HquI4W+p6n0foz5jwWds9Wn3fTmA1w4P7ySXAtodWYJ9zuJ140QbKAWgjU\njUnsPJbdmHcDGY01hw3B0RIHtHKfCxOltqvz8v31YrWuqxZaDde5wZTjPY/6WP8A9yP5v9H+ekp5\n97GtbDiAGidxPdIMdsDiBJE8Hv8AvNRnYtYyqW3H7Riv2OOTW11Zq3jd6vp7rGWs/P8A/SSPiYdx\ns2lv6IP9pcJEae1zvbvSU6XSsF75O0n2iQ3j8Vt1Opa0gsB2NAcCNACdm4/2nLX6T0yoFvpVOrrL\nCWscd3Ht27/ou/eXP9dbdiEWEOY0kTpuBAJ9zv8AO+gkpe3Eqa55sZDXSY5JPmq+99FrbK37HQZE\ne4j910e1y0KbHW22Y9hLjU4AS0fRI3OrZ9D+jub6T/d73oeXis/n6iIkbiWkH/N/NSUwHUXXtAd7\nXDURP9r2BGov9R29jQA3kAFzgf6u5YuZW9mS6rcQwztlrwQQC7buFe3d7dv8tVXZ1bbKaGuBvsaS\ny6XVvZAHpsDJFVrbP0jW+1JT2jn7gCGkuA4cNPw9zU7TWCCawJ1Jkka+crm6+ruraNwsttBiWw0R\nPm76W3936a6Hp7vtNO+t7LqxzHbyf+45JDPIe2yxjWnaRruPH3+5J+Cy0fpJIMQNCAf+qVr7JRpa\n9gO3kgSQPF2381qK/Hl26tzg0jc1zTLSD+c1zfa7ckp//9KHTOoUU5VLH7/SDiLHGAGjjcGsP9r2\nKOQ919pe4B1b/oVmR/K3bmfnKjdS+vKY+5provBsqsaWmWge9zfo7tks3rRw2tMssAsDRAMkgyNz\nLBt9Pb/a/SMQS1hk5eAD6NbGOPvD3DXa76LtN25rv+MRaOs2B+yx7tDubq1oBI+iNrX/AEfzfei5\nmB9oFNFtm2veA20na1hJ9z7G/ut3f4RYd4ZTbZVaxxtb7awHhrd+7+dt379zfT/cf9P9J/ISU9BR\nbbm0WeoLCGE7LH1uLHfu10PYG2uyNnu/Sf8AkFkvsezJdX1K7Lx8NjN3p4ldRc4gbX12OvHq1+o7\n2b/09f8Awaji5+W7HGIy+yiljy9rQ8srP5z99NLHW5T/AHf8JZ/NfzauDqVVzq6zsLKzvcWMIJcD\nO9zfc1j7G+xlX5n/AF16SnKtr9TqVVXRqMiyt7SX4tTrg10CTtfkMZd9Fvqeq+qmnZ/NY/pqLG5W\nLllmVjudcX0nGxp9STu9d9e0/wA56tdbKrva/wBKm9dz9XrnDNBDmh/uZi2NBfWSTvbQdpZXR6v/\nABf/AFxcy2y236zVXXNOPa/JsN2K4T6djW2F7GWS5uxj27P30lONT1rLrtyKxY2zGut9YtrYwV7y\n0NGwMb+g2M9vo1/za776u4dPUqmWQDw4MMAiBGrB+b+6uF6DjW5H2XEx3trdbsAfY/a0aQ/d6Ie9\n38heq/Vrp2NgWPoZaLHjRw37iCD9Db+ZtSUXTw8E41BrdqRoCPArP6x0xuU0HbvLfzfEfNbj3hsA\n9zCr2bN5BM+KSHgcXpjqcm3Ep2c+oA5215keo9rNd1rGT+arNnqV1g2M9J7fc7TeQS0tcxzN3s37\nkN+RkP8Ar1VhY+HTfk02uDrwSbKsOxrHX2veTVU19jLmfmepR6no17/VXSZvQ7nl2RU0C0wSNxkk\nfRf+cze3+okl4HqdjyKyxvpEg7y0nUcQSw7Xf2lzxoff1G3Ia4j02MdWRpyZZG76LfZ/4Iu0+sPR\n84UttoxQ71HEuDGkbQPot9Pdt937jP7CycTp9WTjvuNbRZuJscOWxLv0Wz2/nfQ2bElOaG2tYyzf\nLbDDSQ0gu/ckfo22f8H/AN8VvEyHUO9YFzXMj9K07HADmeWu/wDPaDkYd9T37S1m6WkMaNp/ODXN\n+h/Vf/O1f4NRpfZTY1zmGtzfcCxosaXN43b5btd/wlexJT1HTfrO9xackepVx6lIG8f9b3bHu/e2\nen/xS0sfJF/URjYNh9GyLLfTIDTZYdzQ2t8ejta39K+v+kWep+jXI/b77HerdZ65a0AvezY5wH6J\nulTanexuxjUfHzcRltT2MNV1bg9tjXb3CHB01C5uN72/m/ziNIf/08XH6lsw6sWzKvtppeHVtFY9\nsAgsx7LLRaxr9/6Vja/0n010WA7Hsxm5Ne9u4e6px3Okx+6fpe1y4epjqrYLyxrhuDmvHuJiLB9B\nrWfmen/I/SLuvqjltrcGHa+2lpG50EvHOtY3bXIJYZoxmtEPL6soNLayTt3NiHOn6W38+ty57Kwn\nV5Arrew3WXNr9Ak1aO/4ba3Hbs/m3Pssq/6+uw+sGHi2NLcV7BXAsa0y0tns/wCjt9y5RztzHFwb\nsa817HEFjnfT2O/OdtYN9v8AI/41JTWyLGVVV4+1uRe4/aSWuJqra9oFNZe8Mfa77Myu61mPWz/B\nfpUzHZHqVlj3lxkM2bWR+dt21fR/65+k/wCEUcgbnuc95se47nE8lx1cf5KvdPGAOl9Rty2Wetj0\nn7I9l/pPN136Op+NSz3vcz2U/Ts3+p/M+n+lSU63Rep1Z+K7peSRiWloc3Kc6AGsbuvc9rXM/SP2\nMs9nvVG59lOfj9TygXVm9r7rHfSe2WsvcHFrXP8A1d35yx8djd7catu5w2sqYG7txPsra2Wv3fSW\n51jqmZ1TJ+zXsbjU4rn1sqZqxjidmQ6Wt32brmP/AOL/AJqtJTX6Rj3YGZbgkG3LwHvYW7JZY2om\nu0bQd/0Wer7f5HprawfrM+nqP7TbUPtDwW5lVj2taWHb6Hpuf+ldkO2/ms/P/wAFT+jWTm5N1PUG\n5uLYCc2mq4XthxksbRa2gPa/9P8AaqMiv1f8GoNprLG+i1jD7i5pdLh47w737nbv5z6b0lPoTL8i\nzKFhucar2jIOM4Bpq3DYK91Tf0n0N3ud6is9Rblm1oqJa9rQ8BsQCNHbt30mrmuh5eY7qLRk2aeo\nw22QDuI+jOv0vZsXW51Lr31upJEOHqObBBaPzXe4fne1JDyFvS+rYH1gzuvY5AflTU1/tmHOYfov\na73PrprXTdP6tk2UhuVXNusOYRqP3nMH539VMGYx03C33FpdzBBQHV+jaLGjaGEuaGkQJ5Lf5Ovv\nYkpuW5ZdULY3tk6azpxp/aVet+HZUHV01lmr9jGhuo03N2bfpIuTl1247vSIl4I2jlvhwqePYabq\n6nkOJA0P3QI/NSU1s3pPTM2p7cWxuPlgA7XTG6I22Og7P7K5LP6L1LB32W436MtMXCH1EEe3a+TX\nvZ+Z716U7DrLXPZoXgguHMELFx8LLwmPdW8+i/8AMc0ek8T+fX/N++UlPnzbr2F7a3Oo3t2WNBkP\naRtfvYdzHs/kv97EhYZDQXMM+5vA3DTd+c5q6rO+reNl1W5WIG4ller6Xu/QugdrLd32R7vos9V1\nmP8A8SqXT+gY9teRZlX/AGbOogY2PkBjmPLmPe31Njt7v9JXZV6fo/z36ZFT/9TkWOxa5sfY31Zh\nrGAucdJ3O2tc3auj+rXUH47MzqFjN1VFfrOpHsLyCxjWNn1HU+q53psfsXM+1oIraNOQBGsfR7K6\nXXUVWY3qFtDbALwzWt7wPYzb7fVsZ7tjUEvfDDt6j0oZWNYHDJ91rKh6j5mfs9E7G7v5b/69i5TL\nqyRlMraRjuocRQ2sAbA87zX6v8473f4V/wClsV76oZuZSRi1F9lmUfTqrBhrOX7d3tYz1I2ut/8A\nRSL1nErpdl323Cl1D2NvbbtG02h/ptpb7mu2bHfpPZ/xKSnlrXEWWVuvOjiOBuLgf0gaNu53v9n0\nVWyHA5OOBcW5Fbt7HuaA0BsOb9HY930PY1bWNjUXYdt+LvtdQ+tgrra4iz1Dtb+mcGNb/hPzn+o9\nEv6dblPNlGKcXHqaRttItt5/Sfm0U1f8Xtt/41JTl09VZZfYxnqPsc4uqcDWxoYJfa/I3Opb7WN9\n72PYz8+yxaNNv2zDqc2n1LmH0zWyWtdSwGzfk+4WWvc7d+krdVV6LPTsZ6iDR0xuZa1ldgseOz4n\naPzq6Wta32f1Fr9W6VjYApYHm0XbX5DC7hx/wbrTuay7+T/o0lNfp7W52KMR0eti+o/He1suNLt1\nuTj11V7XWWY9n65VX+fS/O9L9JVX6l3pZw6G7slu51jYYys8fy99ofu/kVv/AO3FmYVVZyBbTqK3\nbhfU4sALff6ld5b+idXs3f8AW/Yt7JoosxftVYHqEh2S2vSQ47W31hsNbVdZ7Lq2f0fJ/wCCvrSU\n1LWZNTmMofY8tM1entBebD7Xu3H91vtYtvA6v1J2KfSqcchzTiYeRYWt9+u9uxr/AN/b77Wfo7P5\nb1j9KuoFjm31udUCHBh01n2/S2u/19NbHWsJ56ZlY1Nf2ZmUDa+ue4ja0entc72/4P1f+DSQ1+k/\nW3GYBiWgh9Q22GCHc/pHHcPpNfv+n9NPl9QdYK5uBpkuaBqCB/g9o/e/PXH4H2nFsbsM+kTpsAYD\n/pHae537y0quoAO35JEbiSxo2mfJlA9u5JLqUdXtGW+2iuNoIjUtDv3fd9Jv9VbvTb2ZlT3OGy2t\nhs3V6gxzWW/T+ksPLqxWVU2Y22W7t4Zqwj8x+8/RssbZ+k3/AOi96t/V/qAqyGVEtrAeYYXSHSNf\noj+cb+Y1JT1zsv8AUBkUNNgc0OaCNpM+LXLB+25z6siuttVllrofWXF7IA+i9n0Ge39xa+S219b7\nsNwLQXOtBJ9rmh25rWj6Fm76bVzvTep9MDbDkX3Xv1camVHc4PII27j/AINySHEynsquc1rvs97w\nfUoaXtaBLe7nO9Rm72u96qZvULsRnS6g8vBsOW5tTWOLNpc1np3x6ljbXfrD6P0dP9dafVepdKuy\nnEYJy62k107rCwkgzZ6raT72t/Maxc8639NY/wCzt9xaGsERW1p9jGe1m5u1vp/pP8Gip//V44WE\ntLa2A7obDztGp0P9X+v9BFDrHbW12A1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