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[dbf742]:
/
Model_Training.ipynb
1265 lines (1264 with data), 751.9 kB
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import necessary packages"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import os\n",
"import random\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use(\"ggplot\")\n",
"%matplotlib inline\n",
"\n",
"from tqdm import tqdm_notebook, tnrange\n",
"from skimage.io import imshow\n",
"from skimage.transform import resize\n",
"# from skimage.morphology import label\n",
"# from skimage.feature import structure_tensor\n",
"from sklearn.model_selection import train_test_split\n",
"# from PIL import Image, ImageDraw\n",
"# import cv2\n",
"\n",
"import tensorflow as tf\n",
"\n",
"from keras import backend as K\n",
"from keras.models import Model, load_model\n",
"from keras.layers import Input, BatchNormalization, Activation, Dense, Dropout\n",
"from keras.layers.core import Lambda, RepeatVector, Reshape\n",
"from keras.layers.convolutional import Conv2D, Conv2DTranspose\n",
"from keras.layers.pooling import MaxPooling2D, GlobalMaxPool2D\n",
"from keras.layers.merge import concatenate, add\n",
"from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau, CSVLogger\n",
"from keras.optimizers import Adam\n",
"from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define custom functions"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Convolution block\n",
"def conv2d_block(input_tensor, n_filters, kernel_size = 3, batchnorm = True):\n",
" \"\"\"Function to add 2 convolutional layers with the parameters passed to it\"\"\"\n",
" # first layer\n",
" x = Conv2D(filters = n_filters, kernel_size = (kernel_size, kernel_size),\\\n",
" kernel_initializer = 'he_normal', padding = 'same')(input_tensor)\n",
" if batchnorm:\n",
" x = BatchNormalization()(x)\n",
" x = Activation('relu')(x)\n",
" \n",
" # second layer\n",
" x = Conv2D(filters = n_filters, kernel_size = (kernel_size, kernel_size),\\\n",
" kernel_initializer = 'he_normal', padding = 'same')(input_tensor)\n",
" if batchnorm:\n",
" x = BatchNormalization()(x)\n",
" x = Activation('relu')(x)\n",
" \n",
" return x\n",
"\n",
"# Create u-net model\n",
"def get_unet(input_img, n_filters = 64, dropout = 0.1, batchnorm = True):\n",
" \"\"\"Function to define the UNET Model\"\"\"\n",
" \n",
" # Contracting Path\n",
" c1 = conv2d_block(input_img, n_filters * 1, kernel_size = 3, batchnorm = batchnorm)\n",
" p1 = MaxPooling2D((2, 2))(c1)\n",
" p1 = Dropout(dropout)(p1)\n",
" \n",
" c2 = conv2d_block(p1, n_filters * 2, kernel_size = 3, batchnorm = batchnorm)\n",
" p2 = MaxPooling2D((2, 2))(c2)\n",
" p2 = Dropout(dropout)(p2)\n",
" \n",
" c3 = conv2d_block(p2, n_filters * 4, kernel_size = 3, batchnorm = batchnorm)\n",
" p3 = MaxPooling2D((2, 2))(c3)\n",
" p3 = Dropout(dropout)(p3)\n",
" \n",
" c4 = conv2d_block(p3, n_filters * 8, kernel_size = 3, batchnorm = batchnorm)\n",
" p4 = MaxPooling2D((2, 2))(c4)\n",
" p4 = Dropout(dropout)(p4)\n",
" \n",
" c5 = conv2d_block(p4, n_filters = n_filters * 16, kernel_size = 3, batchnorm = batchnorm)\n",
" \n",
" # Expansive Path\n",
" u6 = Conv2DTranspose(n_filters * 8, (3, 3), strides = (2, 2), padding = 'same')(c5)\n",
" u6 = concatenate([u6, c4])\n",
" u6 = Dropout(dropout)(u6)\n",
" c6 = conv2d_block(u6, n_filters * 8, kernel_size = 3, batchnorm = batchnorm)\n",
" \n",
" u7 = Conv2DTranspose(n_filters * 4, (3, 3), strides = (2, 2), padding = 'same')(c6)\n",
" u7 = concatenate([u7, c3])\n",
" u7 = Dropout(dropout)(u7)\n",
" c7 = conv2d_block(u7, n_filters * 4, kernel_size = 3, batchnorm = batchnorm)\n",
" \n",
" u8 = Conv2DTranspose(n_filters * 2, (3, 3), strides = (2, 2), padding = 'same')(c7)\n",
" u8 = concatenate([u8, c2])\n",
" u8 = Dropout(dropout)(u8)\n",
" c8 = conv2d_block(u8, n_filters * 2, kernel_size = 3, batchnorm = batchnorm)\n",
" \n",
" u9 = Conv2DTranspose(n_filters * 1, (3, 3), strides = (2, 2), padding = 'same')(c8)\n",
" u9 = concatenate([u9, c1])\n",
" u9 = Dropout(dropout)(u9)\n",
" c9 = conv2d_block(u9, n_filters * 1, kernel_size = 3, batchnorm = batchnorm)\n",
" \n",
" outputs = Conv2D(1, (1, 1), activation='sigmoid')(c9)\n",
" model = Model(inputs=[input_img], outputs=[outputs])\n",
" return model\n",
"\n",
"# Compute Intersection over union (IoU), a measure of labelling accuracy\n",
"# NOTE: This is sometimes also called Jaccard score\n",
"def IoU(y_true, y_pred, smooth=1):\n",
" intersection = K.sum(K.abs(y_true * y_pred), axis=-1)\n",
" union = K.sum(y_true,-1) + K.sum(y_pred,-1) - intersection\n",
" iou = (intersection + smooth) / ( union + smooth)\n",
" return iou"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## APONEUROSIS TRAINING"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Set image scaling parameters, determine no. of images for training"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total no. of aponeurosis images = 574\n"
]
}
],
"source": [
"# Images will be re-scaled\n",
"im_width = 512\n",
"im_height = 512\n",
"border = 5\n",
"\n",
"# list of all images in the path\n",
"ids = next(os.walk(\"apo_images\"))[2] \n",
"print(\"Total no. of aponeurosis images = \", len(ids))\n",
"X = np.zeros((len(ids), im_height, im_width, 1), dtype=np.float32)\n",
"y = np.zeros((len(ids), im_height, im_width, 1), dtype=np.float32)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load images and corresponding labels (masks)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "799c2c62245e4b85bb9e7b85cdaf7eda",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, max=574), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\neilj\\AppData\\Local\\Continuum\\anaconda3\\envs\\tf_gpu\\lib\\site-packages\\skimage\\transform\\_warps.py:110: UserWarning: Anti-aliasing will be enabled by default in skimage 0.15 to avoid aliasing artifacts when down-sampling images.\n",
" warn(\"Anti-aliasing will be enabled by default in skimage 0.15 to \"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"# tqdm is used to display the progress bar\n",
"for n, id_ in tqdm_notebook(enumerate(ids), total=len(ids)):\n",
" # Load images\n",
" img = load_img(\"apo_images/\"+id_, color_mode='grayscale')\n",
" x_img = img_to_array(img)\n",
" x_img = resize(x_img, (512, 512, 1), mode = 'constant', preserve_range = True)\n",
" # Load masks\n",
" mask = img_to_array(load_img(\"apo_masks/\"+id_, color_mode='grayscale'))\n",
" mask = resize(mask, (512, 512, 1), mode = 'constant', preserve_range = True)\n",
" # Normalise and store images\n",
" X[n] = x_img/255.0\n",
" y[n] = mask/255.0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Set up aponeurosis training"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Split data into training and validation\n",
"# X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.1, random_state=42)\n",
"X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.1) # i.e. 90% training / 10% test split"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Visualize a random image along with the mask (not necessary, just for checking)\n",
"#ix = random.randint(0, len(X_train))\n",
"#has_mask = y_train[ix].max() > 0 # Check whether there's at least 1 aponeurosis\n",
"#fig, (ax1, ax2) = plt.subplots(1, 2, figsize = (15, 12))\n",
"#ax1.imshow(X_train[ix, ..., 0], cmap = 'gray', interpolation = 'bilinear')\n",
"# if has_mask: # if at least 1 aponeurosis is present\n",
" # draw the aponeuroses on the original image\n",
"# ax1.contour(y_train[ix].squeeze(), colors = 'k', linewidths = 5, levels = [0.5])\n",
"# ax1.set_title('Original image')\n",
"# ax2.imshow(y_train[ix].squeeze(), cmap = 'gray', interpolation = 'bilinear')\n",
"# ax2.set_title('Mask only')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Compile the aponeurosis model\n",
"input_img = Input((im_height, im_width, 1), name='img')\n",
"model_apo = get_unet(input_img, n_filters=64, dropout=0.25, batchnorm=True)\n",
"model_apo.compile(optimizer=Adam(), loss=\"binary_crossentropy\", metrics=[\"accuracy\", IoU])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"__________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
"img (InputLayer) (None, 512, 512, 1) 0 \n",
"__________________________________________________________________________________________________\n",
"conv2d_2 (Conv2D) (None, 512, 512, 64) 640 img[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_2 (BatchNor (None, 512, 512, 64) 256 conv2d_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_2 (Activation) (None, 512, 512, 64) 0 batch_normalization_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"max_pooling2d_1 (MaxPooling2D) (None, 256, 256, 64) 0 activation_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_1 (Dropout) (None, 256, 256, 64) 0 max_pooling2d_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_4 (Conv2D) (None, 256, 256, 128 73856 dropout_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_4 (BatchNor (None, 256, 256, 128 512 conv2d_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_4 (Activation) (None, 256, 256, 128 0 batch_normalization_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"max_pooling2d_2 (MaxPooling2D) (None, 128, 128, 128 0 activation_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_2 (Dropout) (None, 128, 128, 128 0 max_pooling2d_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_6 (Conv2D) (None, 128, 128, 256 295168 dropout_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_6 (BatchNor (None, 128, 128, 256 1024 conv2d_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_6 (Activation) (None, 128, 128, 256 0 batch_normalization_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"max_pooling2d_3 (MaxPooling2D) (None, 64, 64, 256) 0 activation_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_3 (Dropout) (None, 64, 64, 256) 0 max_pooling2d_3[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_8 (Conv2D) (None, 64, 64, 512) 1180160 dropout_3[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_8 (BatchNor (None, 64, 64, 512) 2048 conv2d_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_8 (Activation) (None, 64, 64, 512) 0 batch_normalization_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"max_pooling2d_4 (MaxPooling2D) (None, 32, 32, 512) 0 activation_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_4 (Dropout) (None, 32, 32, 512) 0 max_pooling2d_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_10 (Conv2D) (None, 32, 32, 1024) 4719616 dropout_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_10 (BatchNo (None, 32, 32, 1024) 4096 conv2d_10[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_10 (Activation) (None, 32, 32, 1024) 0 batch_normalization_10[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_1 (Conv2DTrans (None, 64, 64, 512) 4719104 activation_10[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_1 (Concatenate) (None, 64, 64, 1024) 0 conv2d_transpose_1[0][0] \n",
" activation_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_5 (Dropout) (None, 64, 64, 1024) 0 concatenate_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_12 (Conv2D) (None, 64, 64, 512) 4719104 dropout_5[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_12 (BatchNo (None, 64, 64, 512) 2048 conv2d_12[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_12 (Activation) (None, 64, 64, 512) 0 batch_normalization_12[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_2 (Conv2DTrans (None, 128, 128, 256 1179904 activation_12[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_2 (Concatenate) (None, 128, 128, 512 0 conv2d_transpose_2[0][0] \n",
" activation_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_6 (Dropout) (None, 128, 128, 512 0 concatenate_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_14 (Conv2D) (None, 128, 128, 256 1179904 dropout_6[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_14 (BatchNo (None, 128, 128, 256 1024 conv2d_14[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_14 (Activation) (None, 128, 128, 256 0 batch_normalization_14[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_3 (Conv2DTrans (None, 256, 256, 128 295040 activation_14[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_3 (Concatenate) (None, 256, 256, 256 0 conv2d_transpose_3[0][0] \n",
" activation_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_7 (Dropout) (None, 256, 256, 256 0 concatenate_3[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_16 (Conv2D) (None, 256, 256, 128 295040 dropout_7[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_16 (BatchNo (None, 256, 256, 128 512 conv2d_16[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_16 (Activation) (None, 256, 256, 128 0 batch_normalization_16[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_transpose_4 (Conv2DTrans (None, 512, 512, 64) 73792 activation_16[0][0] \n",
"__________________________________________________________________________________________________\n",
"concatenate_4 (Concatenate) (None, 512, 512, 128 0 conv2d_transpose_4[0][0] \n",
" activation_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"dropout_8 (Dropout) (None, 512, 512, 128 0 concatenate_4[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_18 (Conv2D) (None, 512, 512, 64) 73792 dropout_8[0][0] \n",
"__________________________________________________________________________________________________\n",
"batch_normalization_18 (BatchNo (None, 512, 512, 64) 256 conv2d_18[0][0] \n",
"__________________________________________________________________________________________________\n",
"activation_18 (Activation) (None, 512, 512, 64) 0 batch_normalization_18[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2d_19 (Conv2D) (None, 512, 512, 1) 65 activation_18[0][0] \n",
"==================================================================================================\n",
"Total params: 18,816,961\n",
"Trainable params: 18,811,073\n",
"Non-trainable params: 5,888\n",
"__________________________________________________________________________________________________\n"
]
}
],
"source": [
"# Show a summary of the model structure\n",
"model_apo.summary()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# Set some training parameters\n",
"callbacks = [\n",
" EarlyStopping(patience=8, verbose=1),\n",
" ReduceLROnPlateau(factor=0.1, patience=10, min_lr=0.00001, verbose=1),\n",
" ModelCheckpoint('model-apo2-nc.h5', verbose=1, save_best_only=True, save_weights_only=False), # Give the model a name (the .h5 part)\n",
" CSVLogger('apo2_weights.csv', separator=',', append=False)\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train the aponeurosis model (keep batch size small!)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 516 samples, validate on 58 samples\n",
"Epoch 1/60\n",
"304/516 [================>.............] - ETA: 47:22 - loss: 0.5634 - acc: 0.7641 - IoU: 0.72 - ETA: 24:13 - loss: 0.5395 - acc: 0.7862 - IoU: 0.74 - ETA: 16:31 - loss: 0.5115 - acc: 0.8076 - IoU: 0.74 - ETA: 12:39 - loss: 0.4852 - acc: 0.8313 - IoU: 0.75 - ETA: 10:20 - loss: 0.4663 - acc: 0.8477 - IoU: 0.76 - ETA: 8:47 - loss: 0.4472 - acc: 0.8612 - IoU: 0.7672 - ETA: 7:41 - loss: 0.4353 - acc: 0.8736 - IoU: 0.769 - ETA: 6:51 - loss: 0.4198 - acc: 0.8838 - IoU: 0.774 - ETA: 6:12 - loss: 0.4061 - acc: 0.8912 - IoU: 0.779 - ETA: 5:41 - loss: 0.3956 - acc: 0.8963 - IoU: 0.784 - ETA: 5:15 - loss: 0.3819 - acc: 0.9029 - IoU: 0.788 - ETA: 4:54 - loss: 0.3687 - acc: 0.9086 - IoU: 0.794 - ETA: 4:36 - loss: 0.3569 - acc: 0.9124 - IoU: 0.799 - ETA: 4:20 - loss: 0.3460 - acc: 0.9162 - IoU: 0.804 - ETA: 4:07 - loss: 0.3357 - acc: 0.9193 - IoU: 0.809 - ETA: 3:55 - loss: 0.3266 - acc: 0.9220 - IoU: 0.814 - ETA: 3:45 - loss: 0.3170 - acc: 0.9249 - IoU: 0.818 - ETA: 3:35 - loss: 0.3082 - acc: 0.9275 - IoU: 0.823 - ETA: 3:27 - loss: 0.2993 - acc: 0.9301 - IoU: 0.827 - ETA: 3:19 - loss: 0.2943 - acc: 0.9312 - IoU: 0.830 - ETA: 3:12 - loss: 0.2887 - acc: 0.9324 - IoU: 0.833 - ETA: 3:06 - loss: 0.2833 - acc: 0.9335 - IoU: 0.836 - ETA: 3:00 - loss: 0.2768 - acc: 0.9353 - IoU: 0.839 - ETA: 2:55 - loss: 0.2713 - acc: 0.9368 - IoU: 0.842 - ETA: 2:50 - loss: 0.2661 - acc: 0.9382 - IoU: 0.845 - ETA: 2:45 - loss: 0.2612 - acc: 0.9393 - IoU: 0.848 - ETA: 2:41 - loss: 0.2562 - acc: 0.9406 - IoU: 0.851 - ETA: 2:37 - loss: 0.2525 - acc: 0.9415 - IoU: 0.853 - ETA: 2:33 - loss: 0.2474 - acc: 0.9428 - IoU: 0.856 - ETA: 2:30 - loss: 0.2464 - acc: 0.9429 - IoU: 0.858 - ETA: 2:27 - loss: 0.2432 - acc: 0.9436 - IoU: 0.860 - ETA: 2:24 - loss: 0.2390 - acc: 0.9447 - IoU: 0.862 - ETA: 2:21 - loss: 0.2358 - acc: 0.9453 - IoU: 0.863 - ETA: 2:18 - loss: 0.2320 - acc: 0.9463 - IoU: 0.865 - ETA: 2:15 - loss: 0.2287 - acc: 0.9469 - IoU: 0.867 - ETA: 2:13 - loss: 0.2279 - acc: 0.9467 - IoU: 0.868 - ETA: 2:10 - loss: 0.2264 - 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IoU: 0.950 - ETA: 3s - loss: 0.0943 - acc: 0.9721 - IoU: 0.950 - ETA: 3s - loss: 0.0942 - acc: 0.9721 - IoU: 0.950 - ETA: 3s - loss: 0.0940 - acc: 0.9722 - IoU: 0.950 - ETA: 2s - loss: 0.0942 - acc: 0.9721 - IoU: 0.950 - ETA: 2s - loss: 0.0943 - acc: 0.9721 - IoU: 0.950 - ETA: 2s - loss: 0.0944 - acc: 0.9720 - IoU: 0.950 - ETA: 1s - loss: 0.0943 - acc: 0.9720 - IoU: 0.950 - ETA: 1s - loss: 0.0941 - acc: 0.9720 - IoU: 0.951 - ETA: 1s - loss: 0.0940 - acc: 0.9720 - IoU: 0.951 - ETA: 0s - loss: 0.0938 - acc: 0.9721 - IoU: 0.951 - ETA: 0s - loss: 0.0939 - acc: 0.9720 - IoU: 0.951 - 94s 182ms/step - loss: 0.0937 - acc: 0.9721 - IoU: 0.9514 - val_loss: 0.0625 - val_acc: 0.9764 - val_IoU: 0.9797\n",
"\n",
"Epoch 00001: val_loss improved from inf to 0.06251, saving model to model-apo2-nc.h5\n",
"Epoch 2/60\n"
]
},
{
"name": "stdout",
"output_type": "stream",
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"\n",
"Epoch 00002: val_loss improved from 0.06251 to 0.04607, saving model to model-apo2-nc.h5\n",
"Epoch 3/60\n",
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acc: 0.9804 - IoU: 0.982 - ETA: 3s - loss: 0.0481 - acc: 0.9804 - IoU: 0.982 - ETA: 3s - loss: 0.0480 - acc: 0.9804 - IoU: 0.982 - ETA: 2s - loss: 0.0479 - acc: 0.9804 - IoU: 0.982 - ETA: 2s - loss: 0.0479 - acc: 0.9805 - IoU: 0.982 - ETA: 2s - loss: 0.0478 - acc: 0.9805 - IoU: 0.982 - ETA: 1s - loss: 0.0478 - acc: 0.9805 - IoU: 0.982 - ETA: 1s - loss: 0.0478 - acc: 0.9805 - IoU: 0.982 - ETA: 1s - loss: 0.0477 - acc: 0.9805 - IoU: 0.982 - ETA: 0s - loss: 0.0477 - acc: 0.9805 - IoU: 0.982 - ETA: 0s - loss: 0.0477 - acc: 0.9805 - IoU: 0.982 - 90s 175ms/step - loss: 0.0476 - acc: 0.9805 - IoU: 0.9828 - val_loss: 0.0458 - val_acc: 0.9810 - val_IoU: 0.9865\n",
"\n",
"Epoch 00003: val_loss improved from 0.04607 to 0.04581, saving model to model-apo2-nc.h5\n",
"Epoch 4/60\n"
]
},
{
"name": "stdout",
"output_type": "stream",
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"\n",
"Epoch 00004: val_loss improved from 0.04581 to 0.04446, saving model to model-apo2-nc.h5\n",
"Epoch 5/60\n",
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acc: 0.9812 - IoU: 0.984 - ETA: 3s - loss: 0.0441 - acc: 0.9812 - IoU: 0.984 - ETA: 3s - loss: 0.0440 - acc: 0.9813 - IoU: 0.984 - ETA: 2s - loss: 0.0439 - acc: 0.9813 - IoU: 0.984 - ETA: 2s - loss: 0.0439 - acc: 0.9813 - IoU: 0.984 - ETA: 2s - loss: 0.0439 - acc: 0.9813 - IoU: 0.984 - ETA: 1s - loss: 0.0439 - acc: 0.9813 - IoU: 0.984 - ETA: 1s - loss: 0.0438 - acc: 0.9813 - IoU: 0.984 - ETA: 1s - loss: 0.0438 - acc: 0.9813 - IoU: 0.984 - ETA: 0s - loss: 0.0437 - acc: 0.9813 - IoU: 0.984 - ETA: 0s - loss: 0.0441 - acc: 0.9812 - IoU: 0.984 - 90s 175ms/step - loss: 0.0440 - acc: 0.9813 - IoU: 0.9845 - val_loss: 0.0392 - val_acc: 0.9824 - val_IoU: 0.9879\n",
"\n",
"Epoch 00005: val_loss improved from 0.04446 to 0.03921, saving model to model-apo2-nc.h5\n",
"Epoch 6/60\n"
]
},
{
"name": "stdout",
"output_type": "stream",
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"\n",
"Epoch 00006: val_loss did not improve from 0.03921\n",
"Epoch 7/60\n",
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acc: 0.9824 - IoU: 0.986 - ETA: 3s - loss: 0.0391 - acc: 0.9824 - IoU: 0.986 - ETA: 3s - loss: 0.0391 - acc: 0.9824 - IoU: 0.986 - ETA: 2s - loss: 0.0391 - acc: 0.9824 - IoU: 0.986 - ETA: 2s - loss: 0.0391 - acc: 0.9824 - IoU: 0.986 - ETA: 2s - loss: 0.0390 - acc: 0.9824 - IoU: 0.986 - ETA: 1s - loss: 0.0390 - acc: 0.9825 - IoU: 0.986 - ETA: 1s - loss: 0.0390 - acc: 0.9824 - IoU: 0.986 - ETA: 1s - loss: 0.0391 - acc: 0.9824 - IoU: 0.986 - ETA: 0s - loss: 0.0391 - acc: 0.9824 - IoU: 0.986 - ETA: 0s - loss: 0.0394 - acc: 0.9823 - IoU: 0.986 - 90s 175ms/step - loss: 0.0394 - acc: 0.9823 - IoU: 0.9860 - val_loss: 0.0569 - val_acc: 0.9729 - val_IoU: 0.9828\n",
"\n",
"Epoch 00007: val_loss did not improve from 0.03921\n",
"Epoch 8/60\n"
]
},
{
"name": "stdout",
"output_type": "stream",
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"\n",
"Epoch 00008: val_loss did not improve from 0.03921\n",
"Epoch 9/60\n",
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acc: 0.9828 - IoU: 0.986 - ETA: 3s - loss: 0.0381 - acc: 0.9827 - IoU: 0.986 - ETA: 3s - loss: 0.0380 - acc: 0.9827 - IoU: 0.986 - ETA: 2s - loss: 0.0380 - acc: 0.9827 - IoU: 0.986 - ETA: 2s - loss: 0.0379 - acc: 0.9827 - IoU: 0.986 - ETA: 2s - loss: 0.0379 - acc: 0.9827 - IoU: 0.986 - ETA: 1s - loss: 0.0378 - acc: 0.9827 - IoU: 0.986 - ETA: 1s - loss: 0.0378 - acc: 0.9827 - IoU: 0.986 - ETA: 1s - loss: 0.0378 - acc: 0.9827 - IoU: 0.986 - ETA: 0s - loss: 0.0378 - acc: 0.9828 - IoU: 0.986 - ETA: 0s - loss: 0.0377 - acc: 0.9828 - IoU: 0.986 - 90s 175ms/step - loss: 0.0377 - acc: 0.9828 - IoU: 0.9867 - val_loss: 0.0373 - val_acc: 0.9829 - val_IoU: 0.9880\n",
"\n",
"Epoch 00009: val_loss improved from 0.03921 to 0.03734, saving model to model-apo2-nc.h5\n",
"Epoch 10/60\n"
]
},
{
"name": "stdout",
"output_type": "stream",
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"\n",
"Epoch 00010: val_loss improved from 0.03734 to 0.03641, saving model to model-apo2-nc.h5\n",
"Epoch 11/60\n",
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acc: 0.9837 - IoU: 0.987 - ETA: 3s - loss: 0.0347 - acc: 0.9837 - IoU: 0.987 - ETA: 3s - loss: 0.0347 - acc: 0.9837 - IoU: 0.987 - ETA: 2s - loss: 0.0349 - acc: 0.9836 - IoU: 0.987 - ETA: 2s - loss: 0.0348 - acc: 0.9836 - IoU: 0.987 - ETA: 2s - loss: 0.0348 - acc: 0.9837 - IoU: 0.987 - ETA: 1s - loss: 0.0347 - acc: 0.9837 - IoU: 0.987 - ETA: 1s - loss: 0.0348 - acc: 0.9837 - IoU: 0.987 - ETA: 1s - loss: 0.0347 - acc: 0.9837 - IoU: 0.987 - ETA: 0s - loss: 0.0347 - acc: 0.9837 - IoU: 0.987 - ETA: 0s - loss: 0.0348 - acc: 0.9837 - IoU: 0.987 - 90s 175ms/step - loss: 0.0348 - acc: 0.9837 - IoU: 0.9876 - val_loss: 0.0420 - val_acc: 0.9811 - val_IoU: 0.9882\n",
"\n",
"Epoch 00011: val_loss did not improve from 0.03641\n",
"Epoch 12/60\n"
]
},
{
"name": "stdout",
"output_type": "stream",
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"\n",
"Epoch 00012: val_loss did not improve from 0.03641\n",
"Epoch 13/60\n",
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acc: 0.9841 - IoU: 0.988 - ETA: 3s - loss: 0.0331 - acc: 0.9841 - IoU: 0.988 - ETA: 3s - loss: 0.0331 - acc: 0.9840 - IoU: 0.988 - ETA: 2s - loss: 0.0333 - acc: 0.9840 - IoU: 0.988 - ETA: 2s - loss: 0.0333 - acc: 0.9839 - IoU: 0.988 - ETA: 2s - loss: 0.0336 - acc: 0.9838 - IoU: 0.988 - ETA: 1s - loss: 0.0336 - acc: 0.9838 - IoU: 0.988 - ETA: 1s - loss: 0.0336 - acc: 0.9838 - IoU: 0.988 - ETA: 1s - loss: 0.0336 - acc: 0.9839 - IoU: 0.988 - ETA: 0s - loss: 0.0337 - acc: 0.9838 - IoU: 0.988 - ETA: 0s - loss: 0.0337 - acc: 0.9838 - IoU: 0.988 - 91s 175ms/step - loss: 0.0337 - acc: 0.9838 - IoU: 0.9880 - val_loss: 0.0449 - val_acc: 0.9800 - val_IoU: 0.9868\n",
"\n",
"Epoch 00013: val_loss did not improve from 0.03641\n",
"Epoch 14/60\n"
]
},
{
"name": "stdout",
"output_type": "stream",
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"\n",
"Epoch 00014: val_loss did not improve from 0.03641\n",
"Epoch 15/60\n",
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acc: 0.9840 - IoU: 0.988 - ETA: 3s - loss: 0.0328 - acc: 0.9839 - IoU: 0.988 - ETA: 3s - loss: 0.0328 - acc: 0.9839 - IoU: 0.988 - ETA: 2s - loss: 0.0328 - acc: 0.9840 - IoU: 0.988 - ETA: 2s - loss: 0.0327 - acc: 0.9840 - IoU: 0.988 - ETA: 2s - loss: 0.0327 - acc: 0.9840 - IoU: 0.988 - ETA: 1s - loss: 0.0327 - acc: 0.9840 - IoU: 0.988 - ETA: 1s - loss: 0.0327 - acc: 0.9840 - IoU: 0.988 - ETA: 1s - loss: 0.0326 - acc: 0.9840 - IoU: 0.988 - ETA: 0s - loss: 0.0326 - acc: 0.9840 - IoU: 0.988 - ETA: 0s - loss: 0.0328 - acc: 0.9839 - IoU: 0.988 - 90s 175ms/step - loss: 0.0328 - acc: 0.9840 - IoU: 0.9882 - val_loss: 0.0409 - val_acc: 0.9822 - val_IoU: 0.9891\n",
"\n",
"Epoch 00015: val_loss did not improve from 0.03641\n",
"Epoch 16/60\n"
]
},
{
"name": "stdout",
"output_type": "stream",
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"\n",
"Epoch 00016: val_loss improved from 0.03641 to 0.03614, saving model to model-apo2-nc.h5\n",
"Epoch 17/60\n",
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"\n",
"Epoch 00017: val_loss improved from 0.03614 to 0.03587, saving model to model-apo2-nc.h5\n",
"Epoch 18/60\n"
]
},
{
"name": "stdout",
"output_type": "stream",
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"\n",
"Epoch 00018: val_loss improved from 0.03587 to 0.03430, saving model to model-apo2-nc.h5\n",
"Epoch 19/60\n",
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acc: 0.9845 - IoU: 0.988 - ETA: 3s - loss: 0.0307 - acc: 0.9845 - IoU: 0.988 - ETA: 3s - loss: 0.0307 - acc: 0.9845 - IoU: 0.988 - ETA: 2s - loss: 0.0306 - acc: 0.9845 - IoU: 0.988 - ETA: 2s - loss: 0.0306 - acc: 0.9845 - IoU: 0.988 - ETA: 2s - loss: 0.0306 - acc: 0.9845 - IoU: 0.988 - ETA: 1s - loss: 0.0305 - acc: 0.9845 - IoU: 0.988 - ETA: 1s - loss: 0.0305 - acc: 0.9846 - IoU: 0.988 - ETA: 1s - loss: 0.0304 - acc: 0.9846 - IoU: 0.989 - ETA: 0s - loss: 0.0305 - acc: 0.9846 - IoU: 0.989 - ETA: 0s - loss: 0.0304 - acc: 0.9846 - IoU: 0.989 - 90s 175ms/step - loss: 0.0304 - acc: 0.9846 - IoU: 0.9890 - val_loss: 0.0334 - val_acc: 0.9839 - val_IoU: 0.9888\n",
"\n",
"Epoch 00019: val_loss improved from 0.03430 to 0.03340, saving model to model-apo2-nc.h5\n",
"Epoch 20/60\n"
]
},
{
"name": "stdout",
"output_type": "stream",
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"\n",
"Epoch 00020: val_loss did not improve from 0.03340\n",
"Epoch 21/60\n",
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acc: 0.9849 - IoU: 0.989 - ETA: 3s - loss: 0.0289 - acc: 0.9849 - IoU: 0.989 - ETA: 3s - loss: 0.0289 - acc: 0.9850 - IoU: 0.989 - ETA: 2s - loss: 0.0290 - acc: 0.9849 - IoU: 0.989 - ETA: 2s - loss: 0.0290 - acc: 0.9849 - IoU: 0.989 - ETA: 2s - loss: 0.0290 - acc: 0.9849 - IoU: 0.989 - ETA: 1s - loss: 0.0289 - acc: 0.9849 - IoU: 0.989 - ETA: 1s - loss: 0.0289 - acc: 0.9849 - IoU: 0.989 - ETA: 1s - loss: 0.0289 - acc: 0.9849 - IoU: 0.989 - ETA: 0s - loss: 0.0290 - acc: 0.9849 - IoU: 0.989 - ETA: 0s - loss: 0.0289 - acc: 0.9849 - IoU: 0.989 - 90s 175ms/step - loss: 0.0289 - acc: 0.9849 - IoU: 0.9894 - val_loss: 0.0371 - val_acc: 0.9821 - val_IoU: 0.9882\n",
"\n",
"Epoch 00021: val_loss did not improve from 0.03340\n",
"Epoch 22/60\n"
]
},
{
"name": "stdout",
"output_type": "stream",
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"\n",
"Epoch 00022: val_loss did not improve from 0.03340\n",
"Epoch 23/60\n",
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acc: 0.9853 - IoU: 0.989 - ETA: 3s - loss: 0.0281 - acc: 0.9853 - IoU: 0.989 - ETA: 3s - loss: 0.0281 - acc: 0.9853 - IoU: 0.989 - ETA: 2s - loss: 0.0280 - acc: 0.9853 - IoU: 0.989 - ETA: 2s - loss: 0.0280 - acc: 0.9853 - IoU: 0.989 - ETA: 2s - loss: 0.0280 - acc: 0.9853 - IoU: 0.989 - ETA: 1s - loss: 0.0280 - acc: 0.9853 - IoU: 0.989 - ETA: 1s - loss: 0.0279 - acc: 0.9853 - IoU: 0.989 - ETA: 1s - loss: 0.0279 - acc: 0.9854 - IoU: 0.989 - ETA: 0s - loss: 0.0279 - acc: 0.9854 - IoU: 0.989 - ETA: 0s - loss: 0.0279 - acc: 0.9854 - IoU: 0.989 - 90s 175ms/step - loss: 0.0278 - acc: 0.9854 - IoU: 0.9897 - val_loss: 0.0357 - val_acc: 0.9834 - val_IoU: 0.9898\n",
"\n",
"Epoch 00023: val_loss did not improve from 0.03340\n",
"Epoch 24/60\n"
]
},
{
"name": "stdout",
"output_type": "stream",
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"\n",
"Epoch 00024: val_loss did not improve from 0.03340\n",
"Epoch 25/60\n",
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acc: 0.9856 - IoU: 0.990 - ETA: 3s - loss: 0.0271 - acc: 0.9856 - IoU: 0.990 - ETA: 3s - loss: 0.0272 - acc: 0.9856 - IoU: 0.990 - ETA: 2s - loss: 0.0272 - acc: 0.9856 - IoU: 0.990 - ETA: 2s - loss: 0.0272 - acc: 0.9856 - IoU: 0.990 - ETA: 2s - loss: 0.0272 - acc: 0.9856 - IoU: 0.990 - ETA: 1s - loss: 0.0274 - acc: 0.9855 - IoU: 0.989 - ETA: 1s - loss: 0.0274 - acc: 0.9855 - IoU: 0.989 - ETA: 1s - loss: 0.0274 - acc: 0.9855 - IoU: 0.989 - ETA: 0s - loss: 0.0274 - acc: 0.9855 - IoU: 0.989 - ETA: 0s - loss: 0.0274 - acc: 0.9855 - IoU: 0.989 - 90s 175ms/step - loss: 0.0274 - acc: 0.9855 - IoU: 0.9899 - val_loss: 0.0358 - val_acc: 0.9834 - val_IoU: 0.9877\n",
"\n",
"Epoch 00025: val_loss did not improve from 0.03340\n",
"Epoch 26/60\n"
]
},
{
"name": "stdout",
"output_type": "stream",
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"\n",
"Epoch 00026: val_loss did not improve from 0.03340\n",
"Epoch 27/60\n",
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acc: 0.9860 - IoU: 0.990 - ETA: 3s - loss: 0.0255 - acc: 0.9860 - IoU: 0.990 - ETA: 3s - loss: 0.0255 - acc: 0.9860 - IoU: 0.990 - ETA: 2s - loss: 0.0254 - acc: 0.9861 - IoU: 0.990 - ETA: 2s - loss: 0.0254 - acc: 0.9861 - IoU: 0.990 - ETA: 2s - loss: 0.0254 - acc: 0.9861 - IoU: 0.990 - ETA: 1s - loss: 0.0254 - acc: 0.9861 - IoU: 0.990 - ETA: 1s - loss: 0.0253 - acc: 0.9861 - IoU: 0.990 - ETA: 1s - loss: 0.0253 - acc: 0.9861 - IoU: 0.990 - ETA: 0s - loss: 0.0252 - acc: 0.9862 - IoU: 0.990 - ETA: 0s - loss: 0.0253 - acc: 0.9861 - IoU: 0.990 - 90s 175ms/step - loss: 0.0253 - acc: 0.9861 - IoU: 0.9906 - val_loss: 0.0375 - val_acc: 0.9841 - val_IoU: 0.9905\n",
"\n",
"Epoch 00027: val_loss did not improve from 0.03340\n",
"Epoch 00027: early stopping\n"
]
}
],
"source": [
"results = model_apo.fit(X_train, y_train, batch_size=2, epochs=60, callbacks=callbacks, validation_data=(X_valid, y_valid))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualise the results of training"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Variables stored in results.history: val_loss, val_acc, val_IoU, loss, acc, IoU, lr\n",
"fig, ax = plt.subplots(1, 2, figsize=(20, 8))\n",
"ax[0].plot(results.history[\"loss\"], label=\"Training loss\")\n",
"ax[0].plot(results.history[\"val_loss\"], label=\"Validation loss\")\n",
"ax[0].set_title('Learning curve')\n",
"ax[0].plot( np.argmin(results.history[\"val_loss\"]), np.min(results.history[\"val_loss\"]), marker=\"x\", color=\"r\", label=\"best model\")\n",
"ax[0].set_xlabel(\"Epochs\")\n",
"ax[0].set_ylabel(\"log_loss\")\n",
"ax[0].legend();\n",
"\n",
"ax[1].plot(results.history[\"val_IoU\"], label=\"Training IoU\")\n",
"ax[1].plot(results.history[\"IoU\"], label=\"Validation IoU\")\n",
"ax[1].set_title(\"IoU curve\")\n",
"ax[1].set_xlabel(\"Epochs\")\n",
"ax[1].set_ylabel(\"IoU score\")\n",
"ax[1].legend();"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# results.history # Show the loss values (these are saved to a .csv file using 'CSVLogger' callback defined above)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Predict on training and validations sets\n",
"preds_train = model_apo.predict(X_train, verbose=1)\n",
"preds_val = model_apo.predict(X_valid, verbose=1)\n",
"\n",
"# Threshold predictions (only keep predictions with a minimum level of confidence)\n",
"preds_train_t = (preds_train > 0.5).astype(np.uint8)\n",
"preds_val_t = (preds_val > 0.5).astype(np.uint8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## END OF APONEUROSIS MODEL TRAINING - NOW USE THE TRAINED MODEL BY RUNNING ONE OF THE INFERENCE NOTEBOOKS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Start of Fascicle Training"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total no. of fascicle images = 309\n"
]
}
],
"source": [
"# list of names of all images in the given path\n",
"im_width = 512\n",
"im_height = 512\n",
"idsF = next(os.walk(\"fasc_images_S\"))[2] \n",
"print(\"Total no. of fascicle images = \", len(idsF))\n",
"XF = np.zeros((len(idsF), im_height, im_width, 1), dtype=np.float32)\n",
"yF = np.zeros((len(idsF), im_height, im_width, 1), dtype=np.float32)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load images and corresponding fascicle masks"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "823c81235e76444dbb512a0c9fc01403",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, max=309), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\neilj\\AppData\\Local\\Continuum\\anaconda3\\envs\\tf_gpu\\lib\\site-packages\\skimage\\transform\\_warps.py:110: UserWarning: Anti-aliasing will be enabled by default in skimage 0.15 to avoid aliasing artifacts when down-sampling images.\n",
" warn(\"Anti-aliasing will be enabled by default in skimage 0.15 to \"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"# tqdm is used to display the progress bar\n",
"for n, id_ in tqdm_notebook(enumerate(idsF), total=len(idsF)):\n",
" # Load images\n",
" imgF = load_img(\"fasc_images_S/\"+id_, color_mode = 'grayscale')\n",
" x_imgF = img_to_array(imgF)\n",
" x_imgF = resize(x_imgF, (512, 512, 1), mode = 'constant', preserve_range = True)\n",
" # Load masks\n",
" maskF = img_to_array(load_img(\"fasc_masks_S/\"+id_, color_mode = 'grayscale'))\n",
" maskF = resize(maskF, (512, 512, 1), mode = 'constant', preserve_range = True)\n",
" # Normalise and store images\n",
" XF[n] = x_imgF/255.0\n",
" yF[n] = maskF/255.0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Set up fascicle training"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"# Split data into training and validation\n",
"# X_trainF, X_validF, y_trainF, y_validF = train_test_split(XF, yF, test_size=0.1, random_state=42)\n",
"X_trainF, X_validF, y_trainF, y_validF = train_test_split(XF, yF, test_size=0.1)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"# Compile the model\n",
"input_imgF = Input((im_height, im_width, 1), name='img')\n",
"modelF = get_unet(input_imgF, n_filters=32, dropout=0.25, batchnorm=True)\n",
"modelF.compile(optimizer=Adam(), loss=\"binary_crossentropy\", metrics=[\"accuracy\", IoU])"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# Set some training parameters (e.g. the name you want to give to your trained model)\n",
"callbacksF = [\n",
" EarlyStopping(patience=7, verbose=1),\n",
" ReduceLROnPlateau(factor=0.1, patience=7, min_lr=0.00001, verbose=1),\n",
" ModelCheckpoint('model-fascSnippets2-nc.h5', verbose=1, save_best_only=True, save_weights_only=False), # Name your model (the .h5 part)\n",
" CSVLogger('fasc2_training_losses.csv', separator=',', append=False)\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train the fascicle model"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 278 samples, validate on 31 samples\n",
"Epoch 1/50\n",
"278/278 [==============================] - ETA: 13:18 - loss: 0.3128 - acc: 0.9766 - IoU: 0.80 - ETA: 6:44 - loss: 0.2973 - acc: 0.9777 - IoU: 0.8097 - ETA: 4:33 - loss: 0.2871 - acc: 0.9731 - IoU: 0.815 - ETA: 3:28 - loss: 0.2788 - acc: 0.9697 - IoU: 0.820 - ETA: 2:48 - loss: 0.2705 - acc: 0.9704 - IoU: 0.824 - ETA: 2:22 - loss: 0.2635 - acc: 0.9729 - IoU: 0.826 - ETA: 2:03 - loss: 0.2565 - acc: 0.9751 - IoU: 0.829 - ETA: 1:49 - loss: 0.2500 - acc: 0.9765 - IoU: 0.832 - ETA: 1:38 - loss: 0.2439 - acc: 0.9780 - IoU: 0.835 - ETA: 1:29 - loss: 0.2379 - acc: 0.9795 - IoU: 0.837 - ETA: 1:22 - loss: 0.2326 - acc: 0.9805 - IoU: 0.840 - ETA: 1:15 - loss: 0.2276 - acc: 0.9814 - IoU: 0.842 - ETA: 1:10 - loss: 0.2225 - acc: 0.9825 - IoU: 0.845 - ETA: 1:06 - loss: 0.2179 - acc: 0.9832 - IoU: 0.847 - ETA: 1:02 - loss: 0.2136 - acc: 0.9839 - IoU: 0.849 - ETA: 59s - loss: 0.2097 - acc: 0.9845 - IoU: 0.851 - ETA: 56s - loss: 0.2058 - acc: 0.9851 - IoU: 0.85 - ETA: 53s - loss: 0.2023 - acc: 0.9854 - IoU: 0.85 - ETA: 50s - loss: 0.1990 - acc: 0.9857 - IoU: 0.85 - ETA: 48s - loss: 0.1954 - acc: 0.9861 - IoU: 0.85 - ETA: 46s - loss: 0.1922 - acc: 0.9866 - IoU: 0.86 - ETA: 44s - loss: 0.1892 - acc: 0.9869 - IoU: 0.86 - ETA: 43s - loss: 0.1862 - acc: 0.9870 - IoU: 0.86 - ETA: 41s - loss: 0.1835 - acc: 0.9870 - IoU: 0.86 - ETA: 40s - loss: 0.1807 - acc: 0.9874 - IoU: 0.86 - ETA: 38s - loss: 0.1780 - acc: 0.9876 - IoU: 0.86 - ETA: 37s - loss: 0.1754 - acc: 0.9879 - IoU: 0.87 - ETA: 36s - loss: 0.1729 - acc: 0.9880 - IoU: 0.87 - ETA: 35s - loss: 0.1706 - acc: 0.9882 - IoU: 0.87 - ETA: 34s - loss: 0.1682 - acc: 0.9883 - IoU: 0.87 - ETA: 33s - loss: 0.1660 - acc: 0.9884 - IoU: 0.87 - ETA: 32s - loss: 0.1637 - acc: 0.9887 - IoU: 0.87 - ETA: 31s - loss: 0.1615 - acc: 0.9889 - IoU: 0.87 - ETA: 30s - loss: 0.1594 - acc: 0.9891 - IoU: 0.88 - ETA: 30s - loss: 0.1577 - acc: 0.9891 - IoU: 0.88 - ETA: 29s - loss: 0.1557 - acc: 0.9892 - IoU: 0.88 - ETA: 28s - loss: 0.1538 - acc: 0.9894 - IoU: 0.88 - ETA: 27s - loss: 0.1520 - acc: 0.9895 - IoU: 0.88 - ETA: 27s - loss: 0.1501 - acc: 0.9897 - IoU: 0.88 - ETA: 26s - loss: 0.1484 - acc: 0.9898 - IoU: 0.88 - ETA: 25s - loss: 0.1468 - acc: 0.9898 - IoU: 0.88 - ETA: 25s - loss: 0.1452 - acc: 0.9898 - IoU: 0.89 - ETA: 24s - loss: 0.1435 - acc: 0.9900 - IoU: 0.89 - ETA: 24s - loss: 0.1420 - acc: 0.9901 - IoU: 0.89 - ETA: 23s - loss: 0.1404 - acc: 0.9902 - IoU: 0.89 - ETA: 23s - loss: 0.1390 - acc: 0.9903 - IoU: 0.89 - ETA: 22s - loss: 0.1377 - acc: 0.9902 - IoU: 0.89 - ETA: 22s - loss: 0.1363 - acc: 0.9903 - IoU: 0.89 - ETA: 21s - loss: 0.1350 - acc: 0.9903 - IoU: 0.89 - ETA: 21s - loss: 0.1336 - acc: 0.9904 - IoU: 0.89 - ETA: 20s - loss: 0.1322 - acc: 0.9905 - IoU: 0.89 - ETA: 20s - loss: 0.1310 - acc: 0.9906 - IoU: 0.90 - ETA: 20s - loss: 0.1296 - acc: 0.9908 - IoU: 0.90 - ETA: 19s - loss: 0.1284 - acc: 0.9908 - IoU: 0.90 - ETA: 19s - loss: 0.1272 - acc: 0.9909 - IoU: 0.90 - ETA: 18s - loss: 0.1261 - acc: 0.9910 - IoU: 0.90 - ETA: 18s - loss: 0.1249 - 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IoU: 0.91 - ETA: 12s - loss: 0.1068 - acc: 0.9915 - IoU: 0.91 - ETA: 12s - loss: 0.1060 - acc: 0.9915 - IoU: 0.91 - ETA: 11s - loss: 0.1052 - acc: 0.9916 - IoU: 0.91 - ETA: 11s - loss: 0.1045 - acc: 0.9916 - IoU: 0.91 - ETA: 11s - loss: 0.1038 - acc: 0.9916 - IoU: 0.91 - ETA: 11s - loss: 0.1031 - acc: 0.9916 - IoU: 0.92 - ETA: 10s - loss: 0.1024 - acc: 0.9917 - IoU: 0.92 - ETA: 10s - loss: 0.1017 - acc: 0.9917 - IoU: 0.92 - ETA: 10s - loss: 0.1010 - acc: 0.9917 - IoU: 0.92 - ETA: 10s - loss: 0.1003 - acc: 0.9918 - IoU: 0.92 - ETA: 9s - loss: 0.0997 - acc: 0.9918 - IoU: 0.9228 - ETA: 9s - loss: 0.0990 - acc: 0.9918 - IoU: 0.923 - ETA: 9s - loss: 0.0984 - acc: 0.9918 - IoU: 0.923 - ETA: 9s - loss: 0.0978 - acc: 0.9919 - IoU: 0.924 - ETA: 9s - loss: 0.0971 - acc: 0.9919 - IoU: 0.924 - ETA: 8s - loss: 0.0965 - acc: 0.9920 - IoU: 0.925 - ETA: 8s - loss: 0.0959 - acc: 0.9919 - IoU: 0.925 - ETA: 8s - loss: 0.0953 - acc: 0.9919 - IoU: 0.926 - ETA: 8s - loss: 0.0949 - acc: 0.9919 - IoU: 0.926 - ETA: 7s - loss: 0.0942 - acc: 0.9919 - IoU: 0.926 - ETA: 7s - loss: 0.0937 - acc: 0.9919 - IoU: 0.927 - ETA: 7s - loss: 0.0931 - acc: 0.9920 - IoU: 0.927 - ETA: 7s - loss: 0.0925 - acc: 0.9920 - IoU: 0.928 - ETA: 7s - loss: 0.0920 - acc: 0.9921 - IoU: 0.928 - ETA: 6s - loss: 0.0915 - acc: 0.9920 - IoU: 0.929 - ETA: 6s - loss: 0.0911 - acc: 0.9920 - IoU: 0.929 - ETA: 6s - loss: 0.0905 - acc: 0.9920 - IoU: 0.929 - ETA: 6s - loss: 0.0900 - acc: 0.9920 - IoU: 0.930 - ETA: 6s - loss: 0.0895 - acc: 0.9921 - IoU: 0.930 - ETA: 5s - loss: 0.0890 - acc: 0.9921 - IoU: 0.931 - ETA: 5s - loss: 0.0885 - acc: 0.9921 - IoU: 0.931 - ETA: 5s - loss: 0.0879 - acc: 0.9922 - IoU: 0.931 - ETA: 5s - loss: 0.0875 - acc: 0.9922 - IoU: 0.932 - ETA: 5s - loss: 0.0870 - acc: 0.9922 - IoU: 0.932 - ETA: 4s - loss: 0.0866 - acc: 0.9922 - IoU: 0.932 - ETA: 4s - loss: 0.0861 - acc: 0.9922 - IoU: 0.933 - ETA: 4s - loss: 0.0857 - acc: 0.9922 - IoU: 0.933 - ETA: 4s - loss: 0.0852 - acc: 0.9922 - IoU: 0.933 - ETA: 4s - loss: 0.0847 - acc: 0.9922 - IoU: 0.934 - ETA: 4s - loss: 0.0843 - acc: 0.9923 - IoU: 0.934 - ETA: 3s - loss: 0.0838 - acc: 0.9923 - IoU: 0.934 - ETA: 3s - loss: 0.0834 - acc: 0.9923 - IoU: 0.935 - ETA: 3s - loss: 0.0830 - acc: 0.9923 - IoU: 0.935 - ETA: 3s - loss: 0.0826 - acc: 0.9923 - IoU: 0.935 - ETA: 3s - loss: 0.0822 - acc: 0.9923 - IoU: 0.936 - ETA: 2s - loss: 0.0818 - acc: 0.9924 - IoU: 0.936 - ETA: 2s - loss: 0.0814 - acc: 0.9924 - IoU: 0.936 - ETA: 2s - loss: 0.0810 - acc: 0.9924 - IoU: 0.937 - ETA: 2s - loss: 0.0806 - acc: 0.9924 - IoU: 0.937 - ETA: 2s - loss: 0.0802 - acc: 0.9924 - IoU: 0.937 - ETA: 2s - loss: 0.0798 - acc: 0.9924 - IoU: 0.938 - ETA: 1s - loss: 0.0795 - acc: 0.9924 - IoU: 0.938 - ETA: 1s - loss: 0.0791 - acc: 0.9924 - IoU: 0.938 - ETA: 1s - loss: 0.0787 - acc: 0.9924 - IoU: 0.939 - ETA: 1s - loss: 0.0783 - acc: 0.9925 - IoU: 0.939 - ETA: 1s - loss: 0.0780 - acc: 0.9925 - IoU: 0.939 - ETA: 1s - loss: 0.0776 - acc: 0.9925 - IoU: 0.939 - ETA: 0s - loss: 0.0773 - acc: 0.9924 - IoU: 0.940 - ETA: 0s - loss: 0.0770 - acc: 0.9924 - IoU: 0.940 - ETA: 0s - loss: 0.0766 - acc: 0.9924 - IoU: 0.940 - ETA: 0s - loss: 0.0763 - acc: 0.9924 - IoU: 0.940 - ETA: 0s - loss: 0.0760 - acc: 0.9924 - IoU: 0.941 - 25s 91ms/step - loss: 0.0756 - acc: 0.9924 - IoU: 0.9414 - val_loss: 0.0219 - val_acc: 0.9930 - val_IoU: 0.9862\n",
"\n",
"Epoch 00001: val_loss improved from inf to 0.02189, saving model to model-fascSnippets2-nc.h5\n",
"Epoch 2/50\n",
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acc: 0.9948 - IoU: 0.98 - ETA: 15s - loss: 0.0263 - acc: 0.9946 - IoU: 0.98 - ETA: 15s - loss: 0.0260 - acc: 0.9948 - IoU: 0.98 - ETA: 15s - loss: 0.0264 - acc: 0.9944 - IoU: 0.98 - ETA: 14s - loss: 0.0264 - acc: 0.9943 - IoU: 0.98 - ETA: 14s - loss: 0.0262 - acc: 0.9944 - IoU: 0.98 - ETA: 14s - loss: 0.0262 - acc: 0.9944 - IoU: 0.98 - ETA: 14s - loss: 0.0262 - acc: 0.9943 - IoU: 0.98 - ETA: 14s - loss: 0.0261 - acc: 0.9943 - IoU: 0.98 - ETA: 14s - loss: 0.0260 - acc: 0.9944 - IoU: 0.98 - ETA: 14s - loss: 0.0258 - acc: 0.9944 - IoU: 0.98 - ETA: 14s - loss: 0.0259 - acc: 0.9944 - IoU: 0.98 - ETA: 13s - loss: 0.0260 - acc: 0.9943 - IoU: 0.98 - ETA: 13s - loss: 0.0260 - acc: 0.9943 - IoU: 0.98 - ETA: 13s - loss: 0.0259 - acc: 0.9943 - IoU: 0.98 - ETA: 13s - loss: 0.0260 - acc: 0.9941 - IoU: 0.98 - ETA: 13s - loss: 0.0260 - acc: 0.9942 - IoU: 0.98 - ETA: 13s - loss: 0.0259 - acc: 0.9942 - IoU: 0.98 - ETA: 13s - loss: 0.0259 - acc: 0.9942 - IoU: 0.98 - ETA: 12s - loss: 0.0259 - acc: 0.9941 - IoU: 0.98 - ETA: 12s - loss: 0.0258 - acc: 0.9941 - IoU: 0.98 - ETA: 12s - loss: 0.0257 - acc: 0.9941 - IoU: 0.98 - ETA: 12s - loss: 0.0258 - acc: 0.9940 - IoU: 0.98 - ETA: 12s - loss: 0.0258 - acc: 0.9940 - IoU: 0.98 - ETA: 12s - loss: 0.0257 - acc: 0.9940 - IoU: 0.98 - ETA: 12s - loss: 0.0258 - acc: 0.9939 - IoU: 0.98 - ETA: 12s - loss: 0.0258 - acc: 0.9938 - IoU: 0.98 - ETA: 11s - loss: 0.0257 - acc: 0.9938 - IoU: 0.98 - ETA: 11s - loss: 0.0256 - acc: 0.9939 - IoU: 0.98 - ETA: 11s - loss: 0.0257 - acc: 0.9938 - IoU: 0.98 - ETA: 11s - loss: 0.0257 - acc: 0.9937 - IoU: 0.98 - ETA: 11s - loss: 0.0256 - acc: 0.9937 - IoU: 0.98 - ETA: 11s - loss: 0.0255 - acc: 0.9938 - IoU: 0.98 - ETA: 11s - loss: 0.0254 - acc: 0.9938 - IoU: 0.98 - ETA: 11s - loss: 0.0253 - acc: 0.9938 - IoU: 0.98 - ETA: 10s - loss: 0.0253 - acc: 0.9938 - IoU: 0.98 - ETA: 10s - loss: 0.0253 - acc: 0.9938 - IoU: 0.98 - ETA: 10s - loss: 0.0253 - acc: 0.9937 - IoU: 0.98 - ETA: 10s - loss: 0.0253 - acc: 0.9937 - IoU: 0.98 - ETA: 10s - loss: 0.0252 - acc: 0.9937 - IoU: 0.98 - ETA: 10s - loss: 0.0251 - acc: 0.9937 - IoU: 0.98 - ETA: 10s - loss: 0.0250 - acc: 0.9938 - IoU: 0.98 - ETA: 9s - loss: 0.0250 - acc: 0.9937 - IoU: 0.9821 - ETA: 9s - loss: 0.0249 - acc: 0.9938 - IoU: 0.982 - ETA: 9s - loss: 0.0248 - acc: 0.9938 - IoU: 0.982 - ETA: 9s - loss: 0.0248 - acc: 0.9937 - IoU: 0.982 - ETA: 9s - loss: 0.0247 - acc: 0.9938 - IoU: 0.982 - ETA: 9s - loss: 0.0247 - acc: 0.9937 - IoU: 0.982 - ETA: 9s - loss: 0.0246 - acc: 0.9938 - IoU: 0.982 - ETA: 9s - loss: 0.0246 - acc: 0.9937 - IoU: 0.982 - ETA: 8s - loss: 0.0246 - acc: 0.9937 - IoU: 0.982 - ETA: 8s - loss: 0.0245 - acc: 0.9937 - IoU: 0.982 - ETA: 8s - loss: 0.0245 - acc: 0.9937 - IoU: 0.982 - ETA: 8s - loss: 0.0244 - acc: 0.9938 - IoU: 0.982 - ETA: 8s - loss: 0.0243 - acc: 0.9938 - IoU: 0.982 - ETA: 8s - loss: 0.0243 - acc: 0.9938 - IoU: 0.982 - ETA: 8s - loss: 0.0242 - acc: 0.9938 - IoU: 0.982 - ETA: 8s - loss: 0.0241 - acc: 0.9938 - IoU: 0.982 - ETA: 7s - loss: 0.0240 - acc: 0.9938 - IoU: 0.982 - ETA: 7s - loss: 0.0239 - acc: 0.9939 - IoU: 0.982 - ETA: 7s - loss: 0.0239 - acc: 0.9939 - IoU: 0.983 - ETA: 7s - loss: 0.0238 - acc: 0.9939 - IoU: 0.983 - ETA: 7s - loss: 0.0238 - acc: 0.9939 - IoU: 0.983 - ETA: 7s - loss: 0.0238 - acc: 0.9938 - IoU: 0.983 - ETA: 7s - loss: 0.0238 - acc: 0.9938 - IoU: 0.983 - ETA: 7s - loss: 0.0238 - acc: 0.9938 - IoU: 0.983 - ETA: 6s - loss: 0.0238 - acc: 0.9938 - IoU: 0.983 - ETA: 6s - loss: 0.0238 - acc: 0.9937 - IoU: 0.983 - ETA: 6s - loss: 0.0237 - acc: 0.9937 - IoU: 0.983 - ETA: 6s - loss: 0.0237 - acc: 0.9937 - IoU: 0.983 - ETA: 6s - loss: 0.0237 - acc: 0.9937 - IoU: 0.983 - ETA: 6s - loss: 0.0236 - acc: 0.9937 - IoU: 0.983 - ETA: 6s - loss: 0.0236 - acc: 0.9936 - IoU: 0.983 - ETA: 6s - loss: 0.0236 - acc: 0.9937 - IoU: 0.983 - ETA: 5s - loss: 0.0235 - acc: 0.9937 - IoU: 0.983 - ETA: 5s - loss: 0.0235 - acc: 0.9936 - IoU: 0.983 - ETA: 5s - loss: 0.0235 - acc: 0.9936 - IoU: 0.983 - ETA: 5s - loss: 0.0235 - acc: 0.9936 - IoU: 0.983 - ETA: 5s - loss: 0.0234 - acc: 0.9936 - IoU: 0.983 - ETA: 5s - loss: 0.0234 - acc: 0.9936 - IoU: 0.983 - ETA: 5s - loss: 0.0234 - acc: 0.9936 - IoU: 0.983 - ETA: 5s - loss: 0.0233 - acc: 0.9936 - IoU: 0.983 - ETA: 4s - loss: 0.0233 - acc: 0.9936 - IoU: 0.983 - ETA: 4s - loss: 0.0232 - acc: 0.9936 - IoU: 0.983 - ETA: 4s - loss: 0.0231 - acc: 0.9936 - IoU: 0.983 - ETA: 4s - loss: 0.0231 - acc: 0.9937 - IoU: 0.983 - ETA: 4s - loss: 0.0230 - acc: 0.9937 - IoU: 0.983 - ETA: 4s - loss: 0.0230 - acc: 0.9936 - IoU: 0.984 - ETA: 4s - loss: 0.0230 - acc: 0.9936 - IoU: 0.984 - ETA: 3s - loss: 0.0229 - acc: 0.9936 - IoU: 0.984 - ETA: 3s - loss: 0.0229 - acc: 0.9936 - IoU: 0.984 - ETA: 3s - loss: 0.0229 - acc: 0.9936 - IoU: 0.984 - ETA: 3s - loss: 0.0229 - acc: 0.9936 - IoU: 0.984 - ETA: 3s - loss: 0.0229 - acc: 0.9936 - IoU: 0.984 - ETA: 3s - loss: 0.0228 - acc: 0.9936 - IoU: 0.984 - ETA: 3s - loss: 0.0227 - acc: 0.9936 - IoU: 0.984 - ETA: 3s - loss: 0.0226 - acc: 0.9936 - IoU: 0.984 - ETA: 2s - loss: 0.0226 - acc: 0.9936 - IoU: 0.984 - ETA: 2s - loss: 0.0226 - acc: 0.9936 - IoU: 0.984 - ETA: 2s - loss: 0.0225 - acc: 0.9936 - IoU: 0.984 - ETA: 2s - loss: 0.0225 - acc: 0.9936 - IoU: 0.984 - ETA: 2s - loss: 0.0224 - acc: 0.9936 - IoU: 0.984 - ETA: 2s - loss: 0.0224 - acc: 0.9936 - IoU: 0.984 - ETA: 2s - loss: 0.0224 - acc: 0.9937 - IoU: 0.984 - ETA: 2s - loss: 0.0223 - acc: 0.9936 - IoU: 0.984 - ETA: 1s - loss: 0.0223 - acc: 0.9936 - IoU: 0.984 - ETA: 1s - loss: 0.0223 - acc: 0.9936 - IoU: 0.984 - ETA: 1s - loss: 0.0222 - acc: 0.9936 - IoU: 0.984 - ETA: 1s - loss: 0.0223 - acc: 0.9936 - IoU: 0.984 - ETA: 1s - loss: 0.0222 - acc: 0.9936 - IoU: 0.984 - ETA: 1s - loss: 0.0222 - acc: 0.9936 - IoU: 0.984 - ETA: 1s - loss: 0.0222 - acc: 0.9936 - IoU: 0.984 - ETA: 1s - loss: 0.0222 - acc: 0.9936 - IoU: 0.984 - ETA: 0s - loss: 0.0221 - acc: 0.9936 - IoU: 0.984 - ETA: 0s - loss: 0.0221 - acc: 0.9936 - IoU: 0.984 - ETA: 0s - loss: 0.0220 - acc: 0.9936 - IoU: 0.984 - ETA: 0s - loss: 0.0220 - acc: 0.9936 - IoU: 0.984 - ETA: 0s - loss: 0.0219 - acc: 0.9936 - IoU: 0.985 - ETA: 0s - loss: 0.0219 - acc: 0.9936 - IoU: 0.985 - ETA: 0s - loss: 0.0218 - acc: 0.9936 - IoU: 0.985 - 18s 66ms/step - loss: 0.0218 - acc: 0.9936 - IoU: 0.9851 - val_loss: 0.0177 - val_acc: 0.9930 - val_IoU: 0.9886\n",
"\n",
"Epoch 00002: val_loss improved from 0.02189 to 0.01771, saving model to model-fascSnippets2-nc.h5\n",
"Epoch 3/50\n"
]
},
{
"name": "stdout",
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"\n",
"Epoch 00003: val_loss improved from 0.01771 to 0.01255, saving model to model-fascSnippets2-nc.h5\n",
"Epoch 4/50\n",
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acc: 0.9937 - IoU: 0.993 - ETA: 0s - loss: 0.0127 - acc: 0.9937 - IoU: 0.993 - ETA: 0s - loss: 0.0127 - acc: 0.9936 - IoU: 0.993 - ETA: 0s - loss: 0.0127 - acc: 0.9936 - IoU: 0.993 - 19s 67ms/step - loss: 0.0127 - acc: 0.9936 - IoU: 0.9935 - val_loss: 0.0145 - val_acc: 0.9930 - val_IoU: 0.9911\n",
"\n",
"Epoch 00004: val_loss did not improve from 0.01255\n",
"Epoch 5/50\n"
]
},
{
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"\n",
"Epoch 00005: val_loss improved from 0.01255 to 0.01234, saving model to model-fascSnippets2-nc.h5\n",
"Epoch 6/50\n",
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"\n",
"Epoch 00006: val_loss improved from 0.01234 to 0.01130, saving model to model-fascSnippets2-nc.h5\n",
"Epoch 7/50\n"
]
},
{
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"\n",
"Epoch 00007: val_loss did not improve from 0.01130\n",
"Epoch 8/50\n",
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"\n",
"Epoch 00008: val_loss improved from 0.01130 to 0.01055, saving model to model-fascSnippets2-nc.h5\n",
"Epoch 9/50\n"
]
},
{
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"\n",
"Epoch 00009: val_loss improved from 0.01055 to 0.01017, saving model to model-fascSnippets2-nc.h5\n",
"Epoch 10/50\n",
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"\n",
"Epoch 00010: val_loss did not improve from 0.01017\n",
"Epoch 11/50\n"
]
},
{
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"\n",
"Epoch 00011: val_loss did not improve from 0.01017\n",
"Epoch 12/50\n",
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"\n",
"Epoch 00012: val_loss did not improve from 0.01017\n",
"Epoch 13/50\n"
]
},
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"\n",
"Epoch 00013: val_loss improved from 0.01017 to 0.00987, saving model to model-fascSnippets2-nc.h5\n",
"Epoch 14/50\n",
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"\n",
"Epoch 00014: val_loss did not improve from 0.00987\n",
"Epoch 15/50\n"
]
},
{
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"\n",
"Epoch 00015: val_loss did not improve from 0.00987\n",
"Epoch 16/50\n",
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"\n",
"Epoch 00016: val_loss improved from 0.00987 to 0.00982, saving model to model-fascSnippets2-nc.h5\n",
"Epoch 17/50\n"
]
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"\n",
"Epoch 00017: val_loss did not improve from 0.00982\n",
"Epoch 18/50\n",
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"\n",
"Epoch 00018: val_loss did not improve from 0.00982\n",
"Epoch 19/50\n"
]
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{
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"\n",
"Epoch 00019: val_loss did not improve from 0.00982\n",
"Epoch 20/50\n",
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acc: 0.9936 - IoU: 0.996 - ETA: 0s - loss: 0.0087 - acc: 0.9936 - IoU: 0.996 - ETA: 0s - loss: 0.0087 - acc: 0.9936 - IoU: 0.996 - ETA: 0s - loss: 0.0087 - acc: 0.9936 - IoU: 0.996 - 21s 75ms/step - loss: 0.0087 - acc: 0.9936 - IoU: 0.9966 - val_loss: 0.0107 - val_acc: 0.9930 - val_IoU: 0.9970\n",
"\n",
"Epoch 00020: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
"\n",
"Epoch 00020: val_loss did not improve from 0.00982\n",
"Epoch 21/50\n"
]
},
{
"name": "stdout",
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"\n",
"Epoch 00021: val_loss improved from 0.00982 to 0.00962, saving model to model-fascSnippets2-nc.h5\n",
"Epoch 22/50\n",
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"\n",
"Epoch 00022: val_loss improved from 0.00962 to 0.00961, saving model to model-fascSnippets2-nc.h5\n",
"Epoch 23/50\n"
]
},
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"\n",
"Epoch 00023: val_loss did not improve from 0.00961\n",
"Epoch 24/50\n",
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acc: 0.9936 - IoU: 0.996 - ETA: 0s - loss: 0.0082 - acc: 0.9936 - IoU: 0.996 - ETA: 0s - loss: 0.0082 - acc: 0.9936 - IoU: 0.996 - ETA: 0s - loss: 0.0082 - acc: 0.9936 - IoU: 0.996 - 21s 75ms/step - loss: 0.0082 - acc: 0.9936 - IoU: 0.9967 - val_loss: 0.0097 - val_acc: 0.9930 - val_IoU: 0.9965\n",
"\n",
"Epoch 00024: val_loss did not improve from 0.00961\n",
"Epoch 25/50\n"
]
},
{
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"\n",
"Epoch 00025: val_loss did not improve from 0.00961\n",
"Epoch 26/50\n",
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"\n",
"Epoch 00026: val_loss did not improve from 0.00961\n",
"Epoch 27/50\n"
]
},
{
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"\n",
"Epoch 00027: val_loss did not improve from 0.00961\n",
"Epoch 28/50\n",
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acc: 0.9937 - IoU: 0.996 - ETA: 0s - loss: 0.0080 - acc: 0.9937 - IoU: 0.996 - ETA: 0s - loss: 0.0081 - acc: 0.9936 - IoU: 0.996 - ETA: 0s - loss: 0.0080 - acc: 0.9936 - IoU: 0.996 - 21s 75ms/step - loss: 0.0080 - acc: 0.9936 - IoU: 0.9968 - val_loss: 0.0099 - val_acc: 0.9930 - val_IoU: 0.9966\n",
"\n",
"Epoch 00028: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
"\n",
"Epoch 00028: val_loss did not improve from 0.00961\n",
"Epoch 29/50\n"
]
},
{
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"output_type": "stream",
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acc: 0.9936 - IoU: 0.996 - ETA: 0s - loss: 0.0080 - acc: 0.9936 - IoU: 0.996 - ETA: 0s - loss: 0.0080 - acc: 0.9936 - IoU: 0.996 - ETA: 0s - loss: 0.0080 - acc: 0.9936 - IoU: 0.996 - 21s 76ms/step - loss: 0.0080 - acc: 0.9936 - IoU: 0.9968 - val_loss: 0.0098 - val_acc: 0.9930 - val_IoU: 0.9966\n",
"\n",
"Epoch 00029: val_loss did not improve from 0.00961\n",
"Epoch 00029: early stopping\n"
]
}
],
"source": [
"resultsF = modelF.fit(X_trainF, y_trainF, batch_size=2, epochs=50, callbacks=callbacksF,\\\n",
" validation_data=(X_validF, y_validF))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
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wMOjRSwGoKGstRMIQLoOy6Hv5q6x8Obo9OQV69cPoia7SiSjg1AgmMQkLUKwJT0VERERk/2E//QD36Xmwfau3/ta/cM681As0SLtl3QjszoddubArF7trh7ecl4vdlQt5O/cGj0r3fQoqAMZ4wZCUVO+V2QPTf5C3nJAImzZi16yCFW9735XSMvYGoA4dBj37dNoAlC0uxP77Tez7S6Fwz97gUeWAUmP4/NC7L6bfQOg7wHvv0w8TiKv/WJF2SAGnxohP9N6LC9u2HSIiIiIircBu34r7zKOwaiX07INz7S3YggLsc4/h3vNLzKjjMKdfiMk4oK2but+xJcWQsw127fCCR9Fgko0GlMoDS7hu1QONgdSu0LUbpKVj+vTfG0xK6YLpEl1OLn9PrjebzVoL2ZuxX62Gr1djv14NH7zjBaC6pMHgwzHlGVC9+mKcjv2wdJu7Hbv4ZeyyN6CoAA4ejDnkUPAHIBDw3v0B8PsrLQcgsHfdVN+XvxP77Trst2uxH70H77zh9Z/P5/VZv4HQrzwI1b/FglCutYTd8hdE3L3rkUrby9fLKm2PRB8c4gCOMRgDjvGWHUN0PbrfMRj27vP2G6y1uBZstC2uBdf13iPl6zW8WwuR6LJ3HdG/lbVYwEbrJLpcXm7vPq8OgOTkEgoKCgCvzSY6XZG3vFf5vvJ4qqm0vXK7KrfVYnFdcKNtcy3RttuKdtlo+ytvq3q9+26zNZS5eWIfnDYO9irg1BgJ5QEnZTiJiIiISOdlS0uwrz6Pfe158Pm8oNLkkzH+AAawI0ZhX3se+/oL2FUrMTNOx0ybhYmLb+umd3q2rAz75gLsy8/sm5WUlAJdM6BrN8yBB+1d7ppRsUxqV4wvtsMhjTFeJlPPPnDCdC8Atf37aADqc+zXq7Efved94U/pAgMPxxzqBaFsenpM29KS3I3fEFn0L8o+Xk7E+IiMHId7/HQiBw2oGlxgbyChetDBEg2gUG1fSj8iPUcSHh0N4OTtws3JJpyznfCOHCKbdxDZuJqIWUPE8RFJzSDStRuRtAzcLumEU1IJJOxid2ER4YgXCCoPFJVFou81rVfbVx6okdZXEXjD7A3UOVQE8KoG76quV1323q2lzR+tooBTYyRGn46jgJOIiIiIdELWWvjPCi+racc2zOjjMaf/Fya9W5VyJj4ec+rZ2PGTcZ/9C3bBU9h/L8IJXgRHjuu0Q6jaml39sTe0MXszjBzj/X3SyoNJGS0W8LPWC0iUhi0lEZfSiKUk7L2XZ22UZ5DszRZJww4ajx04HnsSuLt24m7eCJu+xd3yHfbN97BvLsfGP0k4syeRzAOJdOuBm9GdcJc0Itbg2r2ZNBG7N+MmYsuzavbNeqkp08OtninC3qwZiyUSradqJs/e7J5IJEI44hI2PkiaCcfO3Ns5HwEfrWuRfocu3iv1EEituYQ/P4wvrxSfzcbvRvDjErAufiL4sQRw8QMBY4k3Fr+BgGPwO9F3nyHgOPjjHPyOwW/Ab8DngN945fwO+Ex0n2OiL2/Z5zMEjMHneC+LiWbvgGsMLmZvxlJ0n7ceXS7fb7x3Ey7DKS3GlBRhSopwiovwlRThFBdiigtxigtxigpxigu8ZetGXxYHi2NdDBYTTVcqXzbRZQDHWrwWVN1nHF80PckFN+JtNeUlqbJeXput9G9dRTljKtpkiLbNWhxcjN3bxqpt9o6DaBpVrf+G1rK9hs3OlBA4bRvyUcCpMaIZTraosK0DhSIi0oGdfvrpzJkzhwkTJlRsmzdvHuvXr+eOO+6o9bhBgwbxzTffsHXrVm666SbmzZtXY9033XQTRxxxRK31zJs3j3PPPZfERO9z7bzzzuOPf/wjaWlpTb8o4N577yU5OZnLL7+8WfWISNuwWzfjPjMPVn8MvfriXP97zKHD6zzGZPbA95NfYL/8FPfpebgP3QmHjcA58xJM736t1HKPtRZKiqEgOtH17nxsQeVJr71lW7AbIhHMgMMwQ46AgUPaxRw5rrWURiylYZeSSDSwE/a2lezMpXjp65RuXEdp10Mpm3I5ZT0P8oItZeBuAzd7N67d7WXXuDUPO6o+HCfiWkoiltJqQaTSiBvd7m0ri9iK4UjNkwmBTOh3dA0dAGwHtpdGF/blRAMeTjQQ4nMMvjoyPerMAokGVxzj4DN76wo4Bp8DPuvi27YF35Z1+Ap3E4iPx9d/AP5+A/DHx+0NwkQDLb5q5zMQzU6pNpysUjmDV0f5Pl+lOp1owMdXqf7yNvqdvXWRmwPfrsVu+ZZEx1C0axeUlUJpKbasJLpc4j1RsMpy9L2x80y1Np8fkpIhuYs3l1hSCiYjDZJ6exl9ycmQ1AUT3UdikneM41R6+aquG8cbqli+7DhVhnlmZmayPTs7OhdX6d45uWpZtuXlyipNCl8xhi+a6lY+Xi8a7KqIzlK9XHS5Jra2/wtr2e60fdRCAafGSIhmOJUow0lERJru1FNPZcGCBVUCTgsWLOCmm25q0PE9e/asMdjUUI8++iinnXZaRcDpySefbHJdItLx2ZJi7Csh7KIXIRCHOeNizISZ3hwzDWQOG4Fz0/3YZa9hX3wK99arMRNOwpxytvdFMBbtLCqEjd9gN34DudtrDijV9uW58sTXyV3Adb3hgK8+B4E4L+h02AgvANVvAMbxYaMBoMIyN/qKUFSx7BL3fRk783ZTFh2+VBodmlQacSu2VdkXcSuWK7ZHAzwl0WPrlHwMHH6Mt7wZ2Lyt1qK1DbGpHoDxO4Y4nyHO5xDvMyQFHNITnYptcT5DvD/67nOI80fffaZiubze8qSMve+mQevp6V3Zk5eHY138O7JxtmzEt3kjvk0bcDatx19ciM9G8Pn9mD79MX0PgX4DMX0HeBNs+2P7VDe7awd2yULs2695wcuDB2OmzsIcNS7mQxFjotsB0O0AzFHj6JKZSUlOTqMOt67rBUpKS7wn6lkL1vWyfCqWq71bG91fbdmNBlCgahAFqgZLqgRaqq3HxUeDSCne/6tx8W2SMWl8Pi8oFZ9Qf9lWaE9HpYBTY5TP4VSkgJOIiDTdzJkzufvuuykpKSE+Pp6srCyys7MZPXo0BQUF/Nd//Rf5+fmUlZVxww03cOKJJ1Y5PisriwsuuIDFixdTVFTEddddxzfffMPAgQMpLi6uKPeLX/yCVatWUVxczMyZM7n++uuZP38+2dnZzJ49m/T0dJ577jnGjBnDq6++SkZGBn/+85955plnADjrrLO45JJLyMrK4txzz2X06NF8+OGH9OzZk8cee6wiYFWT1atX84tf/ILi4mL69evHvffeS9euXZk/fz5PPvkkfr+fQYMG8dBDD7F8+XJuvvlmwPvS8sILL5CSEpsvqCJSO2stfPQubugx2JmDGTcRc9qFmLSmzaljfD7MxJnYUcd5Q+yWLMSufBsz6zzMcVPrnXi6StsiEdi8Ebv+a9j4tfe+ddPeL63RCa4rnprWbyCkpBJJ7kJJchrFiakUJ6RQFJ9MUVwSxb44iiOG4rBLUZn3KiwuoTBnB0W78ijcU0jhRpeirM0UBnIpCiRR6ARwG/FV0okOVYrzGfw+p2I54POyZgI+Q6LfITXeIeAzxDle0KYisFMpkBPnc4jbvIHAe4uI27WduAGHkjD5JOIzMioCQAGfUyXTxamUNdORhjRmZqaQY6KfXen9YWD/in3WjcC277HfroPv1nmTaq9cBm+/Fp1Q23uqG+mZmPL/HpKrTX5e/kpKqXOycpu1AbvoRezKd7zhVEeOxZk6CwYc1qH6s7GM40B8vPcSiTEFnBrB+HwQF6c5nEREOpFHP8xmw87i+gs2wsHpCfz3D3rUuj8jI4ORI0eydOlSTjzxRBYsWMApp5yCMYb4+Hjmz59Peno627Zt4+STT2batGm13uw+8cQTJCYm8uabb/LFF18wffr0in0///nPSU9PJxKJcMYZZ/DFF19w8cUX88gjj/Dss8+SkZFRpa5PP/2UUCjEyy+/jLWWH/7wh4wbN460tDQ2bNjAgw8+yD333MNll13GwoULOe2002q9xmuuuYbbbruNcePGcc899zB37lxuvfVWHnzwQZYvX058fDx5eXkAPPzww9x+++2MGjWKgoIC4nXTK9Li7PdZuP94BNasgoMOxrn0eszAoTGp26SkYs75Cfb46bhPz8P+7U/Yt1/FOfNSzODD922LtZC7nbJ1X1OwYR17Nm2iYNs2CqyfPf4kCpLTKehzAgUjelCQnEFBYipFrkNR2N0bQAq7FJe5lORayK1ce1H0tS8voyeRxJQUktIdEh2XtKLdJO7eQeLODSTtySUxXEJSnI+kHt1J6nMQSf0PIblbBokBh54HdGNP3q6KgJIvRsNXqj4ZsDfOBZdihh4Zk7o7GuP49k5GPuYEIJqRk5O9NwiVtR5yt2O/Ww+78yqy3PbJFzPO3iy38ifyRZftt2u9/xfiEzATZmAm/RDT/cDWvViRTkgBp0ZyEpOxCjiJiEgzzZo1iwULFlQEnObOnQt4X7zuvPNOVqxYgTGGrVu3sn37drp3715jPStWrOCiiy4CYOjQoQwZMqRi30svvcRTTz1FJBIhOzubb775hqFDa/9CuXLlSqZPn05SkjeEfMaMGaxYsYJp06Zx0EEHMWzYMABGjBhBVlZWrfXk5+eTl5fHuHHjAJg9ezaXXXYZAEOGDGHOnDlMnz69Ijg2atQobrnlFn70ox8xY8YMevXq1aA+FJHGc4sKvEm+3/qX9+X67MswJ0xvVPZRQ9k+/cm78hZyPvqInGWL2fH3l8jtt56dfYewp7CEgoIi9pRGKIgYCnzxFPsygUzoOQZ67lufvwySC30kl0VIjrMk+B0OSA6Q4HdI9DskBhwS/Cb67m1LCFTe51TZV1+AyG7fiv3yU1izCvvZQnjPC5LT/UDMYUeQcuQoSO8BPXvXmTnT4P4qLfGe/Pfqvk8GlL2M43h/g+4Hwqhjq+yz1npDwyrP2VUx5HLv0Eu7J98LWm38xtvWpSvmtAswx50YsyGgIqKAU6OZxCRscWFbN0NERGKkrkykljR9+nRuueUWPvvsM4qLixk+3JuY94UXXmDHjh0sWrQIYwxjxoyhpKSkzrpqyn767rvv+POf/8wrr7xC165dueaaa6oMt6uJrXUySqpkHfl8vnrrqs0TTzzB+++/zxtvvMH999/PkiVLmDNnDpMnT2bx4sWcfPLJPPPMMwwcOLBJ9Yt0ZrZgD2z4ypuktrRk74S25ZMBV16PLttq+3K2fY/N34U5dirmx+djujTtYQFh17KzKExOYRm5hWFyCsPsKCyLvnvLuUVhIhagK/T/MQB+N0zXjdtJCReRUlZETz+kJMWTnJpCSrdEkrtl0CUxQHLAR0qcQ3Kcj+Q4h5Q4H3E+06pDm8wBPTEH9ITjpnlZNVu+xa75FLtmFXbF2+Qve80rGBcHfQ725hTqN8B773VQgwNF1lpYtdLLasrJxow6DjP7on2eDCj1M8Z4c+7EJ0A374ea+v6LKf/s68zD5kTaigJOjWSSlOEkIiLNl5yczLhx47juuuuYNWtWxfbdu3eTmZlJIBDg7bffZtOmTXXWM2bMGP75z38yfvx4vvzyS9asWVNRT2JiIqmpqWzfvp0lS5ZUZBylpKSwZ8+efYbUjR07lmuvvZY5c+ZgreW1117j//7v/xp9bampqaSlpbFixQrGjBnD888/z9ixY3Fdly1btjB+/HhGjx7Niy++SEFBATt37mTIkCEMGTKEjz76iLVr1yrgJFKNjURw77zBm8eoNsZAIAD+OC8IEogDf8CbhDcQgEAccYcfSdmEkzCHHFrvOUsjLt/vLmNzfgmb80vZsruUzfmlbCsIs6sovM+QpTifITPJT7ekAIf3SCIzKUBGor9iW2aSny4FOzCrNmAO7AP9R2OSOkY2iXEcL6jU52CYeio2EiG9pIDcVR95w7q+W4d9fwksXej1i98PvatNcN2n3z5Pw7PbtuD+Yx6s/ggOPAjnf36HOWxEm1zj/kqBJpGWo4BTI5mEJFCGk4iIxMCsWbP47//+bx566KGKbT/+8Y+54IILmDZtGkOHDq038HL++edz3XXXMWXKFIYOHcrIkSMBOPzwwxl+msHDAAAgAElEQVQ2bBgTJ06kb9++jBo1quKYc845h3PPPZfu3bvz3HPPVWwfPnw4s2fPZubMmYA3afiwYcPqHD5Xm/vvv79i0vC+ffsyd+5cIpEIV111Fbt378ZayyWXXEJaWhr33HMP7733Ho7jMHjwYCZOnNjo84l0dvb9JbB1E+bsyzEDDvOCSXFxVQNMPn+9X567ZmaSU+kpVq617CgMszm/tEpQaXN+KdsLyqoElTIS/fROjePoXsl0S/KTmRSgW6K/Yjk5zqn/y3tiD5h8cjN6on0wPh/+vofgJKXCOO/fLOu6sH2rNx/Qd+uw363HfvQevPOG14+OA736esGnvgMgL9d7MqA/gAlejJnYuCcDioi0d6au9PkOzG7ZsqVFKvY9fCel27fiu+n+Fql/f5JZ7YZHmkb9GBvqx9jpCH1ZWFhYMU9Re+X3+wmHw23djHajpr9ZdK4n/TTdvrTYPVhb/9vivvEi5OXizL6o1c9ty8pwb/oJdEnD+dX/NjkjY3tBGVlFPtZs3lERXNqSX0pJZO/3gQS/Q+/UAL27xNMrNUDv1Hh6p8ZxYJcASYF2+Ej4NtSQ/yattbBjW/QJa+ux362Fb9d5k1sDZuwE78mAXTPqrKcza+v/tzsT9WVsqB/r1pj7L4XQG8lJStZT6kRERET2IzZchn0lBIV7sEeMqfFJay16/n8vgh3bcM67slHBpt0lET7LLuDTrYWs2lrAlt3e07scA92TA/ROjWN4jyR6dYmjd6r3ykisP0tKGs4YA5k9ILMH5qhjgGgQalculJZgeughCSLSeSng1EgmMQmKNKROREREZL/x+SdQuAf8AdzQfC/LKAZPJWsIW1LiBbsGDYWhI+ssWxJ2WbO9iFVbvSDTutxiLF7W0rDuicwYnM5xh/YixS0i4FNQqa0YY0ATgovIfkABp0YyiUlQogwnERERkf2FXbkMkrtgTr8Q+/gfsCuXYcZOaJ1zv73QG8p36fX7ZB5FXMv6ncWs+r6QVdkFrNlWRJlr8Rk4NDORM4dnckTPJAZlJuJ3vGMzM1PIyWnaUyZFREQaQwGnRjKJyVBaio1EMD6NYxcRERHpzGxJMfY/K7y5do6ZjF36KvafT2CPGoeJi2/ZcxcXYl99HoYeiRk8DGstW3aXsWprAau2FvBZdiEFpS4A/bvGc9Lgrozomczh3ZNIDLROBpaIiEhtFHBqJJMYnbC0uAiSO8ZjXEVERESkaeyqld5cO6OPxzgOzuyLcP/3V9hFCzAzgy177jdfgj35RE45hyXrdvHimlyy8koB6J7sZ9xBXTiiZzIjeibRNUG39SIi0r7ok6mRnKRkb0EBJxEREZFOz37wDnTN8OZQAsyhw2DkWOyrz2OPnYpJS2+Z8xbsYc9br/LGmPN4eZXDzqKtHJwez+WjejDywGR6pgQ0ubeIiLRryrVtpL0ZTpo4XEREmiYrK4tJkyY1u5733nuPDz74IAYtqv88559/frPLiHQ0tnAPrP4I84PjMM7eqRSc0y+EcCn2X39vkfPmFJbxl5c/4NKR1/Bk4nD6psXx20kHcd+M/swYnM6BXeIUbBIRkXZPGU6NZBIqDakTEZFOL+VPf6L0iCMoHT++Ylvcu+8St2oVe664og1bBsuXLyc5OZlRo0a1aTtEOiv78XIIhzGjj6+y3fTohZlwEnbxK9hJP8T07heT8323q4R/rsll2YY8XLcHx/A9P54xngEZCTGpX0REpDUpw6mRTOUhdSIi0umVHnEE6ZdfTty77wJesCn98sspPeKIZtUbDoe5+uqrmTJlCpdccglFRd7nyqeffsppp53G1KlTOfvss8nOzgZg/vz5TJgwgSlTpvCTn/yErKwsnnzySebNm8fUqVNZsWJFlfrvvfderr76as466yzGjBnDwoUL+d3vfsfkyZM555xzKCsrA+Cdd95h2rRpTJ48meuuu46SkhIAlixZwvHHH8+sWbN49dVXK+otLCzkuuuu46STTmLatGm8/vrrzeoHkfbMrlwGB/SE/gP32WdOPhMSk3Cffax557CWz7MLuW1JFle9soF/f5vPiWzhwZX3cP2JhynYJCIiHZYynBpJQ+pERDqX1JtvJvDFF3WWifTsSbezzybSowe+7GzKBg+my333wX331Vi+bOhQ8m+9tc46161bx7333suoUaO47rrrePzxx7n44ou58cYb+ctf/kKPHj14/vnnueuuu5g7dy4PPvggy5cvJz4+nry8PNLS0jjvvPNITk7m8ssvr/Ec3377Lc8++yxff/01p5xyCvPmzePGG2/k4osv5q233mLChAlce+21PPPMMwwYMICf/vSnPPHEE5x33nn87Gc/IxQKcfDBB1ep/4EHHmD8+PHMnTuXvLw8Zs6cyXHHHVdPL4t0PDZvJ3z5GWbG6TUOXzPJXTA/PAMbmo9d/RFm2NGNqj/iWlZs2s0LX+TyzY5iUuN9nDUikxkHuKTc8ivMmOMxPXvH6nJERERanQJOjVQ+abgtLkIj50VE9g82LY1Ijx74N28m3Ls3Ni2t2XX26tWrYijcj3/8Yx577DEmTJjAV199xZlnnokxhkgkQvfu3QEYMmQIc+bMYfr06UyfPr1B55g4cSKBQIAhQ4bgui4TJ04E4LDDDiMrK4t169bRt29fBgwYAMDs2bN5/PHHGTduHH379uWQQw4B4LTTTuNvf/sbAMuWLWPRokU8/PDDAJSUlLB58+Zm94dIe2M/fBesu89wusrMxJOwSxfiPvsXnCEjMT5frWXLlYRdFq/PY8GXuXy/u4yeKQEuH9WDSYekEe93cJ/8E9ZazA/PjOXliIiItDoFnBqpYg6nImU4iYh0BvVlIsHeYXS7r7mGpCeeYPe111aZ06kpqmdMGGOw1jJ48GBeeukl/H4/4XC4Yv8TTzzB+++/zxtvvMH999/PkiVL6j1HfHw8AI7j4Pf7K87pOA6RSARrbYPbV85ayyOPPMLAgVWHGG3fvr3e9oh0JPaDZdC7H6Z331rLGH8A57QLcB+6E/vuIszxtQeDyyKWf67Zwctf7iSvJMLAjARuOO4Axvbpgs/x/n+z27d69Rx3IiazR8yvSUREpDVpDqdG2jukTnM4iYjsD8qDTTsffpjdP/sZOx9+uMqcTk21efNmPvzwQwAWLFjAqFGjGDBgALm5uRXby8rK+Oqrr3Bdly1btjB+/HhuvPFG8vPzKSgoIDk5mT179jS5DQMHDiQrK4sNGzYA8PzzzzN27FgGDhzId999x8aNGwF48cUXK4454YQT+Mtf/lIRrFq9enWTzy/SXtmcbFj3ZZ3ZTRWOHAeDhmJffApby5QLpRGXO5Zt4qlVOQzslsDvphzE/07vx/i+qRXBJgD70j/A8WFmzo7VpYiIiLQZBZwayQQC4A8o4CQisp+IW7WKnQ8/XJHRVDp+PDsffpi4VauaVe+gQYN49tlnmTJlCrt27eKCCy4gLi6OP//5z9x+++1MnDiRadOm8eGHHxKJRLjqqquYPHkyJ554IpdccglpaWlMnTqV1157rcZJwxsiISGBuXPnctlllzF58mQcx+G8884jISGBu+++m/PPP59Zs2bRp0+fimOuueYaysrKmDJlCpMmTeLuu+9uVj+ItEf2g3cAMKPqn5/MGIMz+2LYnYd99fl99heHXW5buomPtxRwxeie3DzxIIb3SN4ni9B+n4V9/23MxJMwXbvF5kJERETakKkrnb4Ds1u2bGmRijMzM8k+bzrm6GNwzm3bx2F3dJmZmeTk5LR1Mzo89WNsqB9jpyP0ZWFhIUlJSW3djDpVH1K3v6vpb9arVy9AUyq2My16D9Za/7ZEbvkpxMXj++U9DT7GffRe7MfLcW57CNPtAAAKyyLctmQTX+YUcdXYA5l0SO3zv7kP34Vd/THOHY9gujR/nrjadIR/ozsK9WVsqB9jR30ZG+rHujXm/ksZTk2RmKQMJxEREZFOyG75DjZtbNhwukrMj873jv/nEwDsLolw81tZfJVTxPXje9UZbLLfrcd+9C5mysktGmwSERFpTa02aXgwGJwOPAD4gEdDodCd1fbHA08ARwM7gDNCodDGYDB4DvCzSkVHAEeFQqH/tE7LaxCfiFXASURERKTTsR+8A8bB/ODYRh1nuh2AmXoqduGz7Dr+ZH67NkBWXik/P743Y/p0qfNYd8FTkJSMmTarOU0XERFpV1olwykYDPqAB4EZwFDgrGAwOLRasYuBnaFQaCBwH3AXQCgUeioUCo0MhUIjgfOAjW0abAJISFSGk4iIiEgnY63FrlwGhw3HpKU3+ngz4zRy03tz43s72Jxfyq9PqD/YZNd9CZ9+gJn2I0xSSlObLiIi0u601pC60cDaUCi0PhQKlQJPA6dWK3Mq8Hh0+TlgcjAYrD4u8CzgHy3a0obQkDoRkQ6tk85f2Knpbyat4tu1sO37Bk0WXpOcSICbjr6C7U4SN/XO46he9QeQ3AVPQZc0zOSTm3ROERGR9qq1Ak69gaxK65ui22osEwqFwkAeUP0RHWfQDgJOJiERimp+7K2IiLR/juNoQu4OJBwO4ziadlJanl25DHx+zFHHNPrYrbtL+dWib8kz8fxmy0sc/uqj2HBZ3ef78lNYswoz43Tv/lJERKQTaa05nGqawbz6T5V1lgkGg2OAwlAotLqmEwSDwUuBSwFCoRCZmZlNbGrd/H4/CV3TKVlb0mLn2F/4/X71YQyoH2ND/Rg7HaEvrbXk5ua266CT67rK6okKBAL06NFjn8fIi8SSdV3sB/+GYUdhkhs3tG1Tfgk3vZlFWcTltsl9GTD0JNwHfotdshAztXpSf/R81uK++DfomoE5YXosLkFERKRdaa2A0ybgoErrfYDqz8wtL7MpGAz6gTQgt9L+M6kjuykUCj0CPBJdtS31GMPMzEyKMdjCAj0qsZn0uMnYUD/GhvoxdjpSX/p8vrZuQq06Uj+2NGstO3bs2Gd79LG8IrHxzRewawfm9AsbddjGncXcvNhL5P/dlL70T0+AbkfB4UdiX34Ge8wkTHIN8zit/hjWfYk55yeYuPgYXICIiEj70lr56R8Ag4LB4MHBYDAOL3j0r2pl/gVcEF0+HVgcCoUsQDAYdIDZeHM/tb2ERCgpxrqRtm6JiIiIiMSAXbkM4uIxI8c0+Jh1ucXc+OZ3+I3h9qnRYFOUM/siKCrEvrTv7WtFdlO37phjp8Sk/SIiIu1NqwSconMyzQFeB9Z4m0KfB4PBW4PB4CnRYvOBbsFgcC1wHfCLSlUcD2wKhULrW6O99UpI8t6Li9u2HSIiIiLSbDYcxn78LuaI0Zj4hPoPAL7cXsRNb35HYsDH7VP70ie1apaS6d0Pc9xU7NKF2Oxqif2fLIfv1mFOOQvjD8TqMkRERNqV1hpSRygUWggsrLbt5krLxXhZTDUduxQY25Lta5TySR2LCyEpuW3bIiIiIiLNs+Y/sGc3ZvTxDSr+WXYBv1u6ifREP7dN7ssByTUHjcypZ2NXLMN9/q/4rvgVANaN4L74FPTsjRkzIVZXICIi0u7okS9NURFwKmrbdoiIiIhIs9mVy7wfEQ8/qt6yn3xfwK1LNnFAcoDbp/arNdgEYFLTMTNOg0/ex361Onqud+D7LMwp52Da8TxyIiIizaWAUxOYxPIhdQo4iYiIiHRktqQE+8kKzFHHYAJ1D29buWk3v1u6id6pcfx+Sl8yEusfLGCmngoZmbjPPoYNl2H/9XfoczDm6GNidQkiIiLtkgJOTRFfaUidiIiIiHRcqz+EkqJ6h9OtyNrNncs2c3B6PL+b3Je0hIbNTGHi4jE/Oh++XYv74O9h+1acWedgHN2Gi4hI56ZPuqZQhpOIiIhIp+CuXAZp6XDosFrLWGt57ONt9O0az62TDyIlvnFD4czo46H/IFj9MRw8GEaMam6zRURE2j0FnJoiOoeTLVLASURERKSjsoUF8OmHmB8ci3FqDyJ9sb2IrXvKOPWwDJICjZ93yTgOzhn/DaldcU67EGNMc5otIiLSIbTaU+o6FU0aLiIiItLh2f+8D+EyzKjj6iy3eH0eCX6HcX27NPlcZuAQnHv+qqF0IiKy39AnXlMklA+p0xxOIiIiIh2VXbkMunWHQw6ttUxx2OXdb3czvm8XEvzNu3VWsElERPYn+tRrAhMIgM+vDCcRERGRDsruzoM1qzCjj6tziNv7WbspCrtMPiStFVsnIiLS8Sng1FSJiQo4iYiIiHRQ9sN3wXXrfTrd4vV59EgJMKR7Yiu1TEREpHPQHE5NFa+Ak4iIiLRvwWBwOvAA4AMeDYVCd1bb3w94DDgAyAXODYVCm6L77gJmRoveFgqFnmm1hrcCu3IZHHgQ9O5fa5ntBWV8urWQM4dn4miibxERkUZRhlNTJSRiNYeTiIiItFPBYNAHPAjMAIYCZwWDwaHViv0v8EQoFBoB3ArcET12JnAUMBIYA/wsGAymtlbbW5rdsR3WfoEZfXydw+mWbsjDAhMP6TSXLiIi0moUcGqqxCRlOImIiEh7NhpYGwqF1odCoVLgaeDUamWGAm9Fl5dU2j8UeDsUCoVDoVABsAqY3gptbhX2w3cAMKNrfzqdtZbF6/MY1j2RHilxrdU0ERGRTkND6poqIRF257d1K0RERERq0xvIqrS+CS9bqbJVwGl4w+5+BHQJBoPdott/EwwG5wJJwETgi5pOEgwGLwUuBQiFQmRmZsbyGir4/f6Y1b3j4/dg4BC6DR1Ra5nV3+ezZXcZF47p32LX1BZi2Y/7O/VlbKgfY0d9GRvqx9hRwKmJTEISNie7rZshIiIiUpuaxorZauvXA38MBoMXAsuAzUA4FAq9EQwGRwHvAduB5UC4ppOEQqFHgEfK68/JyYlB0/eVmZlJLOq2Wzfhrv8aE7y4zvpe+Hgr8T7D8Axict72Ilb9KOrLWFE/xo76MjbUj3Xr1atXg8sq4NRUCZo0XERERNq1TcBBldb7AFsqFwiFQluAHwMEg8EU4LRQKJQX3fd74PfRfX8HvmmFNrc4u/IdMAYz6thay5SEXf79bT7j+nYhKeBrxdaJiIh0Hgo4NVVCIhQp4CQiIiLt1gfAoGAweDBe5tKZwNmVCwSDwUwgNxQKucAv8Z5YVz7heNdQKLQjGAyOAEYAb7Rm41uCtRb7wTIYPAzTtVut5VZu2kNBmcukQ9JasXUiIiKdiyYNb6qEJCgpwrpuW7dEREREZB+hUCgMzAFeB9Z4m0KfB4PBW4PB4CnRYhOAr4LB4NdAD6IZTUAAeCcYDH6BN1zu3Gh9HVvWeti6uc7JwgEWr88jM8nP8B5JrdQwERGRzkcZTk2VkOi9lxR7T6wTERERaWdCodBCYGG1bTdXWn4OeK6G44rxnlTXqdiVy8Dnwxx1TK1ldhSW8Z+tBZw2tBuOqWkaLBEREWkIZTg1VWI04KR5nERERETaPeu62A/egaFHYlJSay339oZ8XIuG04mIiDSTAk5NFa+Ak4iIiEiHse5LyM2pczidtZbFG/I4LDORXqlxrdg4ERGRzkcBpyYyCdFhdMWFbdsQEREREamXXbkMAnGYkWNqLbM2t5isvFImD1B2k4iISHMp4NRUGlInIiIi0iHYcBj70buYEaP2/mhYg8Xr84jzGcb37dKKrRMREemcFHBqqgQFnERERETaO+u62CcfhN15mPGTay1XFnFZtjGfsX26kBzna8UWioiIdE4KODVVNOBkizSkTkRERKQ9stZi//EI9r23MCefiRn+g1rLfrB5D3tKXSYeUvuE4iIiItJwCjg1VXk6dokynERERETaG2st9vm/YpcuxEz7Eebks+osv3h9HhmJfo7omdxKLRQREencFHBqqvIhdcpwEhEREWl37EtPY1//J2bCSZjTL8QYU2vZXUVhPtpSwISDU/E5tZcTERGRhlPAqakCceDzaQ4nERERkXbGff2f2Jf+gTlmMuasS+sMNgG8vTEf18KkQ/R0OhERkVhRwKmJjDEQn6iAk4iIiEg74i5ZiH3uL5hRx2EumINx6r/dXbw+j0HdEjgoLb4VWigiIrJ/UMCpORISoVhD6kRERETaA/fdt7B/fxiOGI256FqMU//T5tbnFrNxV4mym0RERGJMAafmSEzCKsNJREREpM25H7yDffwPMHQkzmU3YPz+Bh23eH0efsdwXD89nU5ERCSWFHBqjgQNqRMRERFpa/Y/K7Dz58LAw3Cu+DUmENeg48oilrc35jO6Twpd4uvPhhIREZGGU8CpORRwEhEREWlT9vNPcP98F/QdgHPVzZj4hs/D9PGWPeSXRJis4XQiIiIxp4BTcyjgJCIiItJm7Nercf/0e+h5EM7Vv8EkJjXq+MUb8uia4OPIA5NbqIUiIiL7LwWcmsEkJEKRJg0XERERaW12w9e4/3cbZHTHufYWTHKXRh2fXxzmw817OKF/Kj7HtFArRURE9l8KODVHQhKUKMNJREREpDXZrA249/8GUtNwrrsNk9q10XUs+zafsIueTiciItJCFHBqjuiQOmttW7dEREREZL9gv8/Cve9mSEj0gk3p3ZpUz+L1eRySHk//9IQYt1BERERAAafmSUwCa6GkuK1bIiIiItLp2W3f4869CYzBufY2TGaPJtWzcWcx63JLlN0kIiLSghRwao74RO9dE4eLiIiItKhITrYXbAqXeZlNPXs3ua4lG/LxGTi+f2oMWygiIiKVKeDUHAnlASdNHC4iIiLSUmzeTnbe/FMoLMC55lZM735NriviWpZuyOMHvVNIS/DHsJUiIiJSmQJOzVDx6F1lOImIiIi0GPfxP+DuzMG5+jeYfgOaVdcn3xewqzii4XQiIiItTD/rNEeChtSJiIiItDTn3J+QFi4hv3ufZte1eH0eqfE+ju6VEoOWiYiISG2U4dQcCeUZThpSJyIiItJSTMYBxA0d2ex69pREWLFpD8f3TyXgMzFomYiIiNRGAafmiGY4WWU4iYiIiLR773ybT9i1Gk4nIiLSChRwao7yIXVFCjiJiIiItHeL1+fRr2s8h6THt3VTREREOj0FnJojQZOGi4iIiHQEm/JK+HpHMZMOScUYDacTERFpaQo4NUdcHBhHAScRERGRdm7x+jwcAyf013A6ERGR1qCAUzMYY7xhdZo0XERERKTdiriWJRvyOerAZNIT9ZBmERGR1qCAU3MlJirDSURERKQd25RfSm5RmPH9Utu6KSIiIvsNBZyaKz4RqwwnERERkXaroDQCQIaym0RERFpNq33qBoPB6cADgA94NBQK3VltfzzwBHA0sAM4IxQKbYzuGwH8GUgFXGBUKBQqbq221ykxSRlOIiIiIu1YUZkLQGJAv7WKiIi0llb51A0Ggz7gQWAGMBQ4KxgMDq1W7GJgZygUGgjcB9wVPdYP/A24PBQKHQ5MAMpao90NkqAhdSIiIiLtWaECTiIiIq2utTKcRgNrQ6HQeoBgMPg0cCrwRaUypwK/jS4/B/wxGAwaYBrwaSgUWgUQCoV2tFKbGyYhEXa2ryaJiIiIyF5FYS/glKSAk4iISKtprU/d3kBWpfVN0W01lgmFQmEgD+gGDAZsMBh8PRgMfhwMBm9ohfY2mElIghJlOImIiIi0VxVD6vwKOImIiLSW1spwMjVssw0s4weOBUYBhcBbwWDwo1Ao9FblgsFg8FLgUoBQKERmZmazG10Tv99fpe789HSKi4tb7HydWfW+lKZRP8aG+jF21JexoX4UiZ3CMm/S8AQFnERERFpNawWcNgEHVVrvA2yppcym6LxNaUBudPvboVAoByAYDC4EjgKqBJxCodAjwCPRVZuTkxPrawAgMzOTynW71sEWFbJ9+3aMqSlmJrWp3pfSNOrH2FA/xo76MjbUj/Xr1atXWzdBOoiiMpcEv8Hn6F5NRESktbRWwOkDYFAwGDwY2AycCZxdrcy/gAuA5cDpwOJQKGSDweDrwA3BYDAJKAVOwJtUvH1ISATrQmkpxMe3dWtEREREpJrCMpfEgK+tmyEiIrJfaZW84uicTHOA14E13qbQ58Fg8NZgMHhKtNh8oFswGFwLXAf8InrsTmAuXtDqP8DHoVDoldZod4MkJHrvxYVt2w4RERERqVFhmasJw0VERFpZa2U4EQqFFgILq227udJyMTC7lmP/BvytRRvYVInlAaciSEtv27aIiIiIyD6Kw64mDBcREWll+uRtJpNQKeAkIiIiIu2OMpxERERanz55myshyXvXkDoRERGRdsmbw0m3vSIiIq1Jn7zNpQwnERERkXatSAEnERGRVqdP3uaKBpxskTKcRERERNqjorKIhtSJiIi0Mn3yNlfFkDplOImIiIi0N9ZaijRpuIj8P3t3HyZXXR7+/312Z7MzSx7JhkAICcEAEh4UHwBFKeATCIqPH9FqsV8rfbK2ttrq71ut+msLWqvyrbZKkVbp1+pHfCgKQtWgVEUFFERABAKSJx6SQEgys5vZnfP9Y2ZhXTbJbvbMnLOb9+u6cs3Omc+ZvffIdd3jPffnPpI6zsw7VSNb6gYtOEmSJBVNvZEy1IC+nu68Q5EkaZ9iwWmqesuQJOCWOkmSpMKp1hsAznCSJKnDzLxTlCRJs8vJLXWSJEmFU7PgJElSLsy8WeitwIAdTpIkSUUzUnByaLgkSZ1l5s1CpY/UDidJkqTCscNJkqR8mHmz4JY6SZKkQqra4SRJUi7MvFmw4CRJklRI1fowYIeTJEmdZubNggUnSZKkQqoNtbbUlfzYK0lSJ5l5M5BYcJIkSSqkJ7bUdecciSRJ+xYLTlko90HNu9RJkiQVTa3eIAHKpSTvUCRJ2qdYcMpCuQKDNdI0zTsSSZIkjVKrN6j0dJEkFpwkSeokC05ZKFdgeBjqO/OORJIkSaNUWwUnSZLUWWbfLFT6mo/OcZIkSSqU2lDDgeGSJOXA7JuF3krz0YKTJElSoVTrDfrscNmbe+MAACAASURBVJIkqePMvhlIHu9wcnC4JElSkdTqwxacJEnKgdk3C2U7nCRJkoqo5gwnSZJyYfbNwkjBqWbBSZIkqUiaQ8O78w5DkqR9TinvAGaEcnNLXTpQxRvuSpKkIgkhnAFcBHQDl8QYLxzz+nLgUmARsAV4Y4xxXeu1DwNn0fyS8lvAn8YY0w6GP2W1ITucJEnKg9k3CyMdToN2OEmSpOIIIXQDnwTOBFYBrw8hrBqz7CPA52KMxwEfBC5onftc4GTgOOAY4NnAb3Uo9EykaUqt3qDPu9RJktRxZt8sVNxSJ0mSCukE4O4Y45oY407gC8A5Y9asAr7T+vnaUa+nQBmYBfQCPcCDbY84Q4PDKY0Uh4ZLkpQDt9RlYVa5+ejQcEmSVCwHA2tHPV8HnDhmzS3Aq2luu3slMCeEsDDGeH0I4VpgI5AAn4gx3jH2F4QQzgfOB4gx0t/fn/1fAZRKpUm/9+YdOwFYtGBu2+KabvbmOmp8XstseB2z47XMhtcxOxacMpB0dUFvBQaqeYciSZI02njjJcfOYHon8IkQwpuB64D1wFAIYSVwFLC0te5bIYRTYozXjT45xngxcPHIe2/atCmr2H9Df38/k33v9Y81C07Dg9VJnztT7c111Pi8ltnwOmbHa5kNr+PuLVmyZMJrLThlpVKxw0mSJBXNOuCQUc+XAhtGL4gxbgBeBRBCmA28Osa4tdW59KMY4/bWa98ETqJZlJoWqvVhAIeGS5KUAwtOWSlbcJIkSYVzA3B4CGEFzc6lc4E3jF4QQugHtsQYG8B7aN6xDuB+4K0hhAtodkr9FvDxTgWehVq9ATjDSZKkPJh9s1LuI3VLnSRJKpAY4xDwNuAa4I7moXhbCOGDIYSXt5adCtwZQvgVsBj4u9bxy4F7gFtpznm6Jcb49U7GP1VPFJy6c45EkqR9jx1OWbHDSZIkFVCM8SrgqjHH3jfq58tpFpfGnjcM/H7bA2yj2lCz4FQp+R2rJEmdZvbNigUnSZKkQqm6pU6SpNyYfTOSlPug5pY6SZKkohgpODk0XJKkzjP7ZqVcgUE7nCRJkoqiVm/QlcCs7iTvUCRJ2udYcMpKuQI1C06SJElFUasP09fTRZJYcJIkqdMsOGWlXIHhIdJ6Pe9IJEmSRHNLnQPDJUnKhxk4K+W+5qODwyVJkgqhNtSgr6c77zAkSdonWXDKSqXSfBxwcLgkSVIRVOsNB4ZLkpQTM3BGkvJIwckOJ0mSpCKoWXCSJCk3ZuCsjBScanY4SZIkFUGt3qDPgpMkSbkwA2dlZIbToB1OkiRJReCWOkmS8mMGzkqrwyl1S50kSVIhuKVOkqT8mIGzMtLh5JY6SZKk3DXStHWXOj/uSpKUBzNwVhwaLkmSVBgDQw0AKiU/7kqSlAczcFbK5eajBSdJkqTc1erNglNfT3fOkUiStG+y4JSRpKsbessw4JY6SZKkvFVbBSdnOEmSlA8zcJbKFTucJEmSCuCJDic/7kqSlAczcJbKfRacJEmSCsAOJ0mS8mUGzlK5QmrBSZIkKXcjHU4ODZckKR9m4CyVK85wkiRJKoDakFvqJEnKU6lTvyiEcAZwEdANXBJjvHDM673A54BnApuB18UY7wshHArcAdzZWvqjGOMfdCruSSlXYPPDeUchSZK0z6vWhwELTpIk5aUjBacQQjfwSeBFwDrghhDCFTHG20ctewvwSIxxZQjhXOBDwOtar90TY3x6J2KdiqRcIR10S50kSVLeas5wkiQpV53KwCcAd8cY18QYdwJfAM4Zs+Yc4LOtny8HXhBCSDoUXzYqfVBzS50kSVLeqvUGpa6Enm4LTpIk5aFTGfhgYO2o5+tax8ZdE2McArYCC1uvrQgh/CyE8L0QwvPbHexe6614lzpJkqQCqNUbdjdJkpSjTs1wGq9TKZ3gmo3Ashjj5hDCM4GvhRCOjjE+NnphCOF84HyAGCP9/f0ZhP1kpVJpl++9feFCdgzVWThvHklPT1t+/0yyu2upifM6ZsPrmB2vZTa8jtLU1OoN5zdJkpSjThWc1gGHjHq+FNiwizXrQgglYB6wJcaYAoMAMcabQgj3AEcAN44+OcZ4MXBx62m6adOmzP8IgP7+fnb13o3mqAA2rV9LMntuW37/TLK7a6mJ8zpmw+uYHa9lNryOe7ZkyZK8Q1CBVYcsOEmSlKdOFZxuAA4PIawA1gPnAm8Ys+YK4DzgeuA1wOoYYxpCWESz8DQcQjgMOBxY06G4J6dcaT4O1MCCkyRJUm5q9QaVkgUnSZLy0pEs3JrJ9DbgGuCO5qF4WwjhgyGEl7eWfQZYGEK4G/hz4N2t46cAPw8h3EJzmPgfxBi3dCLuyUoeLzg5OFySJClPVWc4SZKUq051OBFjvAq4asyx9436eQB47TjnfRn4ctsDzEK5r/no4HBJkqRc1erDLJnjTE1JkvLi1z5ZGr2lTpIkSbnxLnWSJOXLLJylVodTWrPgJEmSlKdqvUFfT3feYUiStM+y4JQlZzhJkiTlbriRMjicOjRckqQcmYWzNFJwGrTDSZIkKS+1oQaAW+okScqRWThLIwUnt9RJkiTlplZvFpz6LDhJkpQbs3CGku5umDXLoeGSJEk5Gik42eEkSVJ+zMJZK/c5w0mSJClHVTucJEnKnVk4a+WKHU6SJEk5qtaHATucJEnKk1k4a+UKqQUnSZKk3Dw+NNy71EmSlBuzcNbcUidJkpSrJ4aGd+cciSRJ+y4LTllzS50kSVKuHBouSVL+zMIZS8p9ULPDSZIkKS9VC06SJOXOLJw1O5wkSZJyVa03mNWdUOpK8g5FkqR9lgWnrJUrMGjBSZIkKS+1esPuJkmScmYmzlqlAjt3kg4P5x2JJEnSPqlWb9BnwUmSpFyZibNWrjQf3VYnSZKUi2p9mErJj7mSJOXJTJy1cl/zccDB4ZIkKRshhK4QwkF5xzFd1IbscJIkKW+lvAOYaZJyhRTscJIkSVMWQpgP/DPwGqAO7BdCeDlwQozxr3MNrsCq9Qb9fT15hyFJ0j7Nr36y5pY6SZKUnU8BW4HlwM7WseuB1+UW0TTg0HBJkvJnJs7ayJa6mlvqJEnSlL0AeHuMcSM0m6hjjA8DB+QaVcE5NFySpPyZibM20uE0aIeTJEmasq1A/+gDIYRlwMZ8wpkeqhacJEnKnZk4a62CU+qWOkmSNHWXAF8OIZwGdIUQngN8luZWO42jPpxSb6TepU6SpJxNeGh4COHPgdUxxptDCCcBERgCfjvGeH27Apx2Km6pkyRJmfkQMAB8EugBLgU+DVyUZ1BFVhtqADjDSZKknE3mLnXvAD7T+vkC4KPANuDjwIkZxzV99To0XJIkTV0IoRs4D/iXGOPH845nuqjVhwELTpIk5W0ymXhejHFrCGEO8DTgn2KMnwGObE9o01NSKkHPLBiww0mSJO29GOMw8NEY42DesUwntXqzw8kZTpIk5WsymXhtCOG5wLnAdTHG4RDCXGC4PaFNY+WKHU6SJCkLXw8hvCzvIKaT6uMFp+6cI5Ekad82mS117wIuB3YCr24dOxv4SdZBTXsWnCRJUjbKwOUhhOuBtUA68kKM8Xdyi6rARjqc3FInSVK+JlxwijFeBSwZc/hLrX8arVzxLnWSJCkLv2j90wRVLThJklQIk7lL3Spgc4zxwRDCbJodT8PAR4B6m+KbnuxwkiRJGYgxfiDvGKabx+9SV7LgJElSniazpe7zwOuAB2kWmY6keZveTwNvyj60aazcB1sfyTsKSZI0A4QQTqP5WetgYD3wHzHG1flGVVwODZckqRgmk4kPjTHeGUJIgFcCrwVeA7ykLZFNY4kdTpIkKQMhhN8Dvgg8AHwF2Ah8PoTw1lwDK7BqvXk/m7IdTpIk5WoyHU6DIYQ5wCpgbYxxUwihRHOYpUYrV2DQgpMkSZqyvwReFGO8ZeRACOGLwJeBf80tqgKr1RuUSwndXUneoUiStE+b7Ja61cAc4BOtY88A7s06qGmv0ge1at5RSJKk6W8hcPuYY3cC++cQy7RQrTeo9HTnHYYkSfu8CfcaxxjfAfxv4A9jjCMFpwbwjnYENq31VmDnIGljOO9IJEnS9PZ94KMhhD6AEMJ+wD8AP8w1qgKr1hvOb5IkqQAm0+FEjPG/QwjLQgjPAdbHGG9sU1zTW6Wv+TgwAH375RuLJEmazv4A+AKwNYSwhWZn0w+BN0zk5BDCGcBFQDdwSYzxwjGvLwcuBRYBW4A3xhjXtQaVf2zU0qcC58YYvzbFv6ftBoYa3qFOkqQCmHDBKYRwEM0PPCfR/ECyMIRwPfD6GOOGNsU3PZUrzceBqgUnSZK012KMG4HfCiEsBZYAG2KM6yZybgihG/gk8CJgHXBDCOGKGOPoLXofAT4XY/xsCOF04ALgTTHGa4Gnt95nf+Bu4L+z+rvayQ4nSZKKYTIdTv8C3AK8NMa4o9XS/ffAp4CXtyO4aevxgpODwyVJ0t4LIbwYuC/G+CuaRSNCCEcCy2KM39rD6ScAd8cY17TO+wJwDr85E2oVT4xHuBYYr4PpNcA3Y4zTYkBltd7gwNk9eYchSdI+bzIFp+cBB8UY6wCtotNfAuvbEtk0lpT7SMHB4ZIkaao+CZwy5ti21vEj9nDuwcDaUc/XASeOWXML8Gqa2+5eCcwJISyMMW4eteZc4KO7+iUhhPOB8wFijPT39+8hrL1TKpUm9N47G/eyYHalbXFMdxO9jtozr2U2vI7Z8Vpmw+uYnckUnB6h+S3YLaOOHQk8mmlEM8FIh9OgHU6SJGlKDmhtqxttI3DgBM5NxjmWjnn+TuATIYQ3A9fR/CJxaOTF1kiFY4FrdvVLYowXAxePvP+mTZsmENrk9ff3M5H33j5Qp6tRn9DafdFEr6P2zGuZDa9jdryW2fA67t6SJUsmvHYyBacPA98OIXwG+DWwHPhd4L2Tim5fUHFLnSRJysSaEMLpMcbVo46dCtw7gXPXAYeMer4U+I25m605nK8CCCHMBl4dY9w6akkAvjrS4V50aZpSc2i4JEmFMOGCU4zxX0MI99C8K8pxND+wvH7MByAB9DYLTmmtNu5Xi5IkSRP0fuArrS/87gGeQvMLv9+dwLk3AIeHEFbQ7Fw6lzF3twsh9ANbYowN4D0071g32utbx6eFeiNlqAF9Pd15hyJJ0j5vMh1OtIpLFpj2pNzXfLTDSZIkTUGM8b9ag8P/F3AWzZlML4kx3jCBc4dCCG+juR2uG7g0xnhbCOGDwI0xxitodktdEEJIaW6p++OR80MIh9LskPpetn9V+1TrDQAq3qVOkqTc7bbg1PpAskcxxvdlE84M8fhd6hwaLkmSpibG+BPgJ3t57lXAVWOOvW/Uz5cDl+/i3PtoDh6fNmoWnCRJKow9dTgdsofX4cnDJ/d5SU8PlEp2OEmSpCkJIfw5sDrGeHMI4UTgSzSHev92jPH6fKMrnpGCU58FJ0mScrfbglOMcSLzAR4XQnh9jPE/pxbSDFGuWHCSJElT9Q7gM62fLwQ+CmwDPg6cmFdQRWWHkyRJxZF1Nv50xu83fZX73FInSZKmal6McWsIYQ7wNOCfYoyfAY7MOa5CqtrhJElSYUxqaPgEeFO2EeUKqR1OkiRpataGEJ4LHA1cF2McDiHMBYZzjquQqvXmZbHDSZKk/GVdcHKe04hyH9TscJIkSVPyLppDvXcCr24dO5u9HCI+09WGWlvqShacJEnKW9YFJ40oV2Db1ryjkCRJ01jrLnNLxhz+UuufxnhiS113zpFIkqSOFZxCCGcAFwHdwCUxxgvHvN4LfA54JrAZeF3rdrwjry8DbgfeH2P8SKfi3ltJuUK66YG8w5AkSTNMjLGedwxFVas3SIByySkPkiTlLet+4/vHOxhC6AY+CZwJrAJeH0JYNWbZW4BHYowrgY8BHxrz+seAb2YbbhtV+qDmDCdJkqROqdUbVHq6SBILTpIk5W3CHU4hhMN28dIgsDHG2IgxHrOLNScAd8cY17Te6wvAOTQ7lkacA7y/9fPlwCdCCEmMMQ0hvAJYA+yYaLy5662AQ8MlSZI6ptoqOEmSpPxNJiPfDdzV+jf65/uBwRDCl0MIi3dx7sHA2lHP17WOjbsmxjgEbAUWhhD2A/4K+MAkYs1fpQKDNdJGI+9IJEmS9gm1oYYDwyVJKojJzHB6K/BbNAs/a4FlwHuBHwLfo7kF7p954g4qo43X1zz2jna7WvMB4GMxxu0hhF0GF0I4HzgfIMZIf3//7v6WvVYqlSb03jsW9rMdWDh7P7r69mtLLNPdRK+lds/rmA2vY3a8ltnwOu7bQgj/a5zDdeDXwI9ijDs7HNK0UK036LPDSZKkQphMwekDwMoY40Dr+d0hhD8EfhVj/HQI4c00O57Gsw44ZNTzpcCGXaxZF0IoAfOALcCJwGtCCB8G5gONEMJAjPETo0+OMV4MXNx6mm7atGkSf9rE9ff3M5H3bgw3O5s2r19HsmBhW2KZ7iZ6LbV7XsdseB2z47XMhtdxz5YsGXvzthnlTeMc6wGWA/UQwktjjL/scEyFV6sPu6VOkqSCmEzBqQs4FBj94WYZzbvOAWzfzfvdABweQlgBrAfOBd4wZs0VwHnA9cBrgNUxxhR4/siCEML7ge1ji02FVO5rPg5UAQtOkiRp4mKMp+3qtRDCX9K8mcqZnYtoeqjVG+xf6dhNmCVJ0m5MJiN/HFgdQvg3mlvqlgK/2zoOcBbNYtGTxBiHQghvA66hWaC6NMZ4Wwjhg8CNMcYrgM8Al4UQ7qbZ2XTu3vxBRZGUK809gw4OlyRJ2fo48M68gyii5tDw7j0vlCRJbTfhglOM8cMhhJ8DrwWeAWwE3hJjvLr1+teAr+3m/KuAq8Yce9+onwda7727GN4/0Xhz93iHkwUnSZKUqR5gOO8giqg25F3qJEkqikn1HLeKS1e3KZaZpVxpPtaq+cYhSZJmjBBCH3Ah8D95x1I0aZpSqzfo8y51kiQVwoQLTiGEHuCvaQ6xXEJz6PdlwN95p5RxtApO6UBt3NvvSZIk7UoIYS1PvqNvD9AP/Bh4XceDKrjB4ZRGinepkySpICbT4fRh4ATgD2jeknc58F5gLvCO7EOb5iqjh4ZLkiRNyhvHOTYE3B9jXNvpYKaDWr15h2C31EmSVAyTKTi9FnhajHFz6/mdIYSfArdgwenJRrbUOcNJkiRNUozxe6OfhxC6gMXAg/lEVHxVC06SJBXKZApOu9oZ5o6x8ZR6oLtkwUmSJO21EMIc4JM0t9D1APUQwheAt8cYt+YaXMFU68056hacJEkqhskUnL4EfD2E8AHgfppb6v66dVxjJEnS7HJyS50kSdp7/wTsBxzLEyMN/g74P8B5OcZVOCNb6pzhJElSMUym4PSXNAtMn6Q5NHw98AXg/29DXDNDuWKHkyRJmoozgMNijCPfYP0qhPC7wD05xlRITxScunOORJIkwR4KTiGE08cc+m7rX8ITd055HrA668BmhHKF1IKTJEnaewPAIprdTSP6gcF8wimu2lBrhlPJDidJkopgTx1On9nF8ZFi00jh6bDMIppJ7HCSJElTcwnwrRDCR3liS907gItzjaqAqm6pkySpUHZbcIoxruhUIDNSuQLVHXlHIUmSpq+/AzYAb6A50mAD8GHg0jyDKiLvUidJUrFMZoaTJikp95FufjjvMCRJ0jQVY0xpFpcsMO1Brd6gK4FZ3d5AWZKkIrDg1E5uqZMkSXthnDmaTxJjdIbmKLX6MH09Xc07BUuSpNxZcGqnSh8MWnCSJEmTtqs5miOcoTlGtd5wYLgkSQViwamdWh1OaZr6bZskSZow52hOXm2oQV9Pd95hSJKkFr8GaqdyBdIUBgfyjkSSJGlGq9YbDgyXJKlAzMrtVO5rPg5U841DkiRphqtZcJIkqVDMyu1UrjQfHRwuSZLUVrV6gz4LTpIkFYZZuY2SxzucLDhJkiS1k1vqJEkqFrNyO410ONXcUidJktRObqmTJKlYzMrtNFJwGrTDSZIkqV0aadq6S50fbSVJKgqzcjtVmgWntGbBSZIkqV0GhhoAVEp+tJUkqSjMyu3k0HBJkqS2q9WbBae+nu6cI5EkSSMsOLWTQ8MlSZLartoqODnDSZKk4jArt1PPLOjqggGHhkuSJLXLEx1OfrSVJKkozMptlCRJc1udHU6SJEltY4eTJEnFY1Zut3KfBSdJkqQ2Gulwcmi4JEnFYVZut3KF1C11kiRJbVMbckudJElFY1Zut4odTpIkSe1UrQ8DFpwkSSoSs3K79VagZoeTJElSu9Sc4SRJUuGYldvNoeGSJEltVa03KHUl9HT70VaSpKIwK7dZUqnAoAUnSZKkdqnVG3Y3SZJUMGbmdiv3Qc2CkyRJUrvU6g3nN0mSVDBm5nbrbW6pS9M070gkSZJmpOqQBSdJkorGzNxulQqkDdg5mHckkiRJM1Kt3qBS8mOtJElFYmZut3Kl+ejgcEmSpLaoOsNJkqTCMTO3W7mv+WjBSZIkqS1q9WELTpIkFYyZuc2SxzucqvkGIkmSNEM5NFySpOIxM7ebW+okSZLaqlpv0NfTnXcYkiRpFAtO7VZxS50kSVK7DDdSBodTh4ZLklQwZuZ26212OKU1t9RJkiRlrTbUAHCGkyRJBWNmbjc7nCRJktqmVm8WnJzhJElSsZiZ282h4ZIkSW0zUnCyw0mSpGIxM7fbrF5IuuxwkiRJaoOqHU6SJBWSmbnNkiRpdjlZcJIkScpctT4M4NBwSZIKxszcCeWKW+okSZLawKHhkiQVk5m5E8oVUjucJEmSMvfE0PDunCORJEmjWXDqhHIFahacJEmSsubQcEmSiqmUdwD7hHIFBi04SZKkzgohnAFcBHQDl8QYLxzz+nLgUmARsAV4Y4xxXeu1ZcAlwCFACrw0xnhf56KfmKoFJ0mSCsnM3AmVPoeGS5KkjgohdAOfBM4EVgGvDyGsGrPsI8DnYozHAR8ELhj12ueAf4gxHgWcADzU/qgnr1pvMKs7odSV5B2KJEkaxQ6nDkh6K6Q1h4ZLkqSOOgG4O8a4BiCE8AXgHOD2UWtWAe9o/Xwt8LXW2lVAKcb4LYAY4/ZOBT1ZtXrD7iZJkgqoYwWnCbR099L8Ju2ZwGbgdTHG+0IIJwAXt5YlwPtjjF/tVNyZsMNJkiR13sHA2lHP1wEnjllzC/Bqmp/RXgnMCSEsBI4AHg0hfAVYAXwbeHeMcXjsLwkhnA+cDxBjpL+/P+u/A4BSqTTueze6NjOnt6dtv3em2dV11OR5LbPhdcyO1zIbXsfsdKTgNKql+0U0P+zcEEK4IsY4+hu2twCPxBhXhhDOBT4EvA74BfCsGONQCOEg4JYQwtdjjEOdiD0T5QoMVEnTlCSx3VuSJHXEeB860jHP3wl8IoTwZuA6YD0wRPMz4vOB44H7gS8CbwY+M/YNY4wX88SXg+mmTZsyCP3J+vv7Ge+9H9leZVZXOu5rerJdXUdNntcyG17H7Hgts+F13L0lS5ZMeG2nOpwm0tJ9DvD+1s+X0/zwk8QYR+9FK/PkD0rFV65AowH1nTCrN+9oJEnSvmEdzYHfI5YCG0YviDFuAF4FEEKYDbw6xrg1hLAO+Nmoz25fA05inIJT3mpDDfrcUidJUuF0quA0kZbux9e0upm2AguBTSGEE2neQWU58KbxupvybufenWr/IrYBC/sqdM3fvy1xTUe2KmbD65gNr2N2vJbZ8DoqAzcAh4cQVtDsXDoXeMPoBSGEfmBLjLEBvIfm562RcxeEEBbFGB8GTgdu7Fjkk1CtN+jv68k7DEmSNEanCk4Taene5ZoY44+Bo0MIRwGfDSF8M8Y4MHph3u3cu9MYat6ud/OGdSStn2WrYla8jtnwOmbHa5kNr+OeTaale1/U+gLvbcA1NGdoXhpjvC2E8EHgxhjjFcCpwAUhhJTmlro/bp07HEJ4J/CdEEIC3AT8ax5/x544NFySpGLqVMFpjy3do9asCyGUgHnAltELYox3hBB2AMdQ0G/ZxpOUK83KmYPDJUlSB8UYrwKuGnPsfaN+vpzmKIPxzv0WcFxbA8xAre6WOkmSiqhTBac9tnQDVwDnAdcDrwFWxxjT1jlrW9/SLQeOBO7rUNzZKFeajxacJEmSMlWtN6iULDhJklQ0HcnOrZlLIy3ddzQPNVu6Qwgvby37DLAwhHA38OfAu1vHn0fzznQ3A18F/ijGOL32GJT7mo81C06SJElZqQ+n1BupHU6SJBVQpzqcJtLSPQC8dpzzLgMua3uA7VRpdjilA9VxB1VJkiRp8mqt2ZjOcJIkqXjMzp3gljpJkqTM1erDgAUnSZKKyOzcCRacJEmSMlerNzuc3FInSVLxmJ07YVYZksSCkyRJUoaq9ZEtdd05RyJJksay4NQBSVcX9JZhoJp3KJIkSTOGHU6SJBWX2blTyn12OEmSJGXoiQ4nP9JKklQ0ZudOKVegZoeTJElSVh6/S13Jj7SSJBWN2blTyhXSQTucJEmSsuKWOkmSisvs3CkVt9RJkiRlqVofBqBsh5MkSYVjdu6UXrfUSZIkZalWb1AuJXR3JXmHIkmSxrDg1CFJpWKHkyRJUoaq9QaVnu68w5AkSeOw4NQpZQtOkiRJWarWGw4MlySpoMzQnWLBSZIkKVMDQw0HhkuSVFBm6E4p98HwEGm9nnckkiRJM0K1bsFJkqSiMkN3SrnSfBxwcLgkSVIWmjOc/DgrSVIRmaE7pdzXfHRbnSRJUiZqFpwkSSosM3SHJI93OFlwkiRJykKtPuzQcEmSCsoM3SkjBaeaW+okSZKmKk1Tag4NlySpsMzQnVJpbakbtMNJkiRpquqNlKEG9PV05x2KJEkahwWnTml1OKV2OEmSJE1Ztd4AcIaTJEkFZYbulF5nOEmSJGWlZsFJkqRCM0N3SsW71EmSJGVlpODkDCdJvTSeGgAAIABJREFUkorJDN0pveXm44Bb6iRJkqbKDidJkorNDN0hSVdXc1udHU6SJElTVrXDSZKkQjNDd1LZgpMkSVIWqvVhAColP85KklREZuhOsuAkSZKUidqQW+okSSoyM3QnlSukFpwkSZKm7Iktdd05RyJJksZjwamTyhWoOTRckiRpqmr1BglQLiV5hyJJksZhwamTKn1uqZMkScpArd6g0tNFklhwkiSpiCw4dVBSrsCgBSdJkqSpqtYbDgyXJKnAzNKd5JY6SZKkTNSGGg4MlySpwMzSnVR2S50kSVIWqvUGfRacJEkqLLN0J5UrMFQnHarnHYkkSdK0VqsP2+EkSVKBmaU7qdzXfLTLSZIkaUpqdjhJklRoZulOKleajxacJEmSpqRab1Dp6c47DEmStAsWnDooebzg5OBwSZKkqXBouCRJxWaW7qSKHU6SJElTlaZpc0tdyY+ykiQVlVm6k3pbBaeaBSdJkqS9NTic0kixw0mSpAIzS3dSpTk0PLXDSZIkaa/V6g0Ah4ZLklRgZulOcoaTJEnSlFVbBSc7nCRJKi6zdCd5lzpJkqQpq9aHAQtOkiQVmVl6kurDjb0/2YKTJEnSlLmlTpKk4jNLT8L1a7dx7mdv4pHa0F6dn3R1w6xet9RJkiRNwUjBqVLqzjkSSZK0KxacJmHp3Flsqe7kX37yAGma7t2b9M2GzQ9nG5gkSdI+pDZkh5MkSUVnlp6EQ+b1cv5zl/Pjddv57r2P7dV7JMefSHrLj0m3bc04OkmSpH1D1S11kiQVnll6ksLTD+aoRRX+9aYH2VytT/r85LSzYWiI9H/+uw3RSZIkzXzepU6SpOIzS09Sd1fC2086iPpwyid/PPmtdclBS+Gop5F+75ukw8NtilKSJGnmqtUbdCUwqzvJOxRJkrQLFpz2wpK5szjv+EXctGEH31kz+a1xXaefBVs2wS0/bkN0kiRJM1utPkxfTxdJYsFJkqSisuC0l156xAKOWdzHJTc+xMM7Jrm17rhnw8IDaKy+sj3BSZIkzWDVeoNKyY+xkiQVmZl6L3UlCW8/6UBSUv7pRxsntbUu6eomOfVMuPNW0vW/bmOUkiRJM09tqEFfT3feYUiSpN0odeoXhRDOAC4CuoFLYowXjnm9F/gc8ExgM/C6GON9IYQXARcCs4CdwLtijKs7FffuLJ49izcffwCfuuFBrr7rUc48YsGEz02e9yLSK/6TdPWVJG/6ozZGKUmSNLNU6w3KDgyXJKnQOpKpQwjdwCeBM4FVwOtDCKvGLHsL8EiMcSXwMeBDreObgJfFGI8FzgMu60TME3XG4fN52oF9/PvPHuKBbTsnfF4yey7JCaeQ/uha0ur2NkYoSZI0s9TqDfosOEmSVGidytQnAHfHGNfEGHcCXwDOGbPmHOCzrZ8vB14QQkhijD+LMW5oHb8NKLe6oQohSRL+5KSD6EoS/ulHG2lMZmvd6WfBzkHSH36njRFKkiTNLBacJEkqvk5l6oOBtaOer2sdG3dNjHEI2AosHLPm1cDPYoyDbYpzryzar4e3PPMAfvFQjSvvfGTC5yXLngIrjyK99irSRqONEUqSJM0c1XqDigUnSZIKrVMznMa7Z+3YVqDdrgkhHE1zm92Lx/sFIYTzgfMBYoz09/fvXaR7UCqVxn3v1y1cyI0PDHLZLZt44dGHcMiCyoTeb+Dl57L1o3/D3LX30PvM52QdbqHt6lpqcryO2fA6ZsdrmQ2vo7RrNQtOkiQVXqcKTuuAQ0Y9Xwps2MWadSGEEjAP2AIQQlgKfBX4nRjjPeP9ghjjxcDFrafppk2bsot+lP7+fnb13m89fn/+ZMNW3n/V7fz9i5bR3TVeDe03pYcfA/P259GvfZ7u5YdnHW6h7e5aauK8jtnwOmbHa5kNr+OeLVmyJO8QlINGmlIbalApWXCSJKnIOpWpbwAODyGsCCHMAs4Frhiz5gqaQ8EBXgOsjjGmIYT5wJXAe2KMP+hQvHtlYV8Pb33mYn65qcbX79wyoXOSUg/JKS+BX9xE+tDYGpwkSZJGGxhqjiFwhpMkScXWkUzdmsn0NuAa4I7moXhbCOGDIYSXt5Z9BlgYQrgb+HPg3a3jbwNWAu8NIdzc+ndAJ+LeG6eumMuJS2fzHzdvYu3WiY2aSk55CXR3k177zTZHJ0mSNL3V6iMFp+6cI5EkSbvTqS11xBivAq4ac+x9o34eAF47znl/C/xt2wPMSJIk/NEJB/K2K+/lous38qEXL9/j1rpk/v4kzzyZ9AffJj3nDSTlic1/kiRJ2tdUWwUnZzhJklRsZuo2mF8p8fvPWsxdmwf46u0T3Fp32llQ20H64++1OTpJkqTp64kOJz/GSpJUZB3rcNrXPP/QuVy/dhv/eevDPOvg/Th0QXn3JzzlqbDsKaSrv0F6yktIkj0PHJckSdqdEMIZwEVAN3BJjPHCMa8vBy4FFtG8WcsbY4zrWq8NA7e2lt4fY3w5BWCHkyRJ04OZuo1+/9mL2a+nm4uu38hQI93t2iRJSE4/CzbcD7/6RYcilCRJM1UIoRv4JHAmsAp4fQhh1ZhlHwE+F2M8DvggcMGo12oxxqe3/hWi2ARPdDh5lzpJkorNTN1G88ol/vDEA1nzyCCX/2LzHtcnz34+zJ5DY/U3OhCdJEma4U4A7o4xrokx7gS+AJwzZs0q4Dutn68d5/XCqXmXOkmSpgW31LXZcw6ZwymHziX+YhMnLJ3NYfvvemtdMquX5HkvJr3mq6SbHyZZuKiDkUqSpBnmYGDtqOfrgBPHrLkFeDXNbXevBOaEEBbGGDcD5RDCjcAQcGGM8Wvj/ZIQwvnA+QAxRvr7+7P9K1pKpRL9/f0k63YCcPDiRSzo62nL75rJRq6jps5rmQ2vY3a8ltnwOmbHglMHnP+sxdz6wA4+fv1G/vGM5fR07/obueTUM5sFp+uuJnnlmzoYpSRJmmHGGwg5do//O4FPhBDeDFwHrKdZYAJYFmPcEEI4DFgdQrg1xnjP2DeMMV4MXDzy/ps2bcok+LH6+/vZtGkTmx59DICBbY+wqWqX02SNXEdNndcyG17H7Hgts+F13L0lS5ZMeK1ZugPm9HbzxycexK8fHeSLt+5+a12y8AB42gmk111DWt/ZoQglSdIMtA44ZNTzpcCG0QtijBtijK+KMR4P/O/Wsa0jr7Ue1wDfBY7vQMx7VK03KHUlu/0CT5Ik5c9M3SHPXjqb0w+bx5dv38xdm2u7Xdt1+lmw/THSG77foegkSdIMdANweAhhRQhhFnAucMXoBSGE/hDCyOfB99C8Yx0hhAUhhN6RNcDJwO0di3w3avWGd6iTJGkaMFt30FueeQDzyyU+9ZMHaaS7uWvdU4+Dgw4hXf0N0t2tkyRJ2oUY4xDwNuAa4I7moXhbCOGDIYSRu86dCtwZQvgVsBj4u9bxo4AbQwi30BwmfmGMsTAFJweGS5JUfM5w6qDZs7p509MXcdH1G/nBr7fx/EPnjrsuSRKS084i/fyn4N5fwWFHdjhSSZI0E8QYrwKuGnPsfaN+vhy4fJzzfggc2/YA90J1qEGlZMFJkqSiM1t32G8dOpfl83v5j1sepj686+6l5DmnQrlCuvobnQtOkiSp4OxwkiRpejBbd1h3V8Kbj1/EA9vrXH3XI7tcl5T7SE5+IemNPyB9bNfrJEmS9iVVZzhJkjQtmK1zcPxB+3Hc4j6++IvN7Ng5vMt1yakvheEh0uv+u4PRSZIkFVetPmzBSZKkacBsnYMkSTjv+APYNjjMV27fsut1Bx4MRx9P+r1vkg4NdTBCSZKkYnJLnSRJ04PZOicrF5Y55dC5XPHLLWyu1ne5ruu0s+HRLXDzjzoYnSRJUjFV6w36errzDkOSJO2BBaccvfFp/TRS+PzPN+160bHPgP7FNBweLkmS9nHDjZTB4dS71EmSNA2YrXO0ePYsXnrEfFav2cqvHx0cd03S1U1y2kvhrttJ197b4QglSZKKozbUAHCGkyRJ04DZOmevPaafSqmLz/3soV2uSU5+IcyaRXrtlR2MTJIkqVhqdQtOkiRNF2brnM3t7ebVRy/kxg07uPXBHeOuSfabQ3LiqaQ//i7pjm0djlCSJKkYRgpODg2XJKn4zNYFcPaRC1jYV+KzP3uYNE3HXZOcdhbs3En6g293ODpJkqRiqFpwkiRp2jBbF0BvqYvfPq6fuzYP8IP7x+9gSg5ZAYevIr32KtLGcIcjlCRJyl+13vwM5NBwSZKKz2xdEKeumMfy+b1cdvPD1IfH73LqOv1s2PQg3PrTDkcnSZKUP4eGS5I0fZitC6K7K+G8py/ige11rrn7kfEXPf0kmL+QxupvdDY4SZKkAnBouCRJ04fZukCesWQ/jlvcxxdv3cyOnU/eNpeUSiSnngm3/4zGFZ/f5bwnSZKkmeiJoeHdOUciSZL2xIJTgSRJwnnHH8Bjg8N85fYt4695yStJnvsC0q9/gfQzHyWt1zscpSRJUj6qdjhJkjRtmK0LZuXCMqcsn8sVv9zC5uqTi0lJqYfkzW8necUbSX/8PRoffS/ptsdyiFSSJKmzqvUGs7oTSl1J3qFIkqQ9sOBUQG98ej+NNOXzP9807utJktB1ViA5/11w3100Lngn6QPrJ/TeO3YO8+8/fYgfrR3/bniSJElFVas37G6SJGmaMGMX0OLZszjziAWsXrOV+x8d3OW6rmc/n653/h0M1Ghc8C7SO3+x2/e99cEd/OmV9/LVO7ZwwXXrufSmBxlqOAdKkiRND7V6gz4LTpIkTQtm7IIKx/RTKXXxuZsf2u265ClPpes9/wBz59P42Pto/HD1k9bsHG7wbz99iPd+ey093Ql//6JlnHXEfP7rl4/w3m/fP+7WPUmSpKKp1oeplPz4KknSdGDGLqi5vd286uiF3LB+B794sLrbtcmiA+l6z4fh8FWk//ZxGv/1fx+/g919jwzwzqt/zdfu2MJLDp/Px166gqMP6OP8Zx/IX5y8hHu2DPCOb97Hzx/Y0Yk/S5Ikaa/VhuxwkiRpujBjF9jLjlzAwr4S//6zhx4vIO1K0jebrj/9G5KTX0j6jS8ydMk/8pVbH+Ivrv41WweGeO+pS/nDEw6kPOpbwVMOnctHzjyUObO6+ZvVa7n8ts009vB7JEmS8lJ1hpMkSdOGGbvAektd/PZx/dy1eYAf3L/nId9JqYfkvD/h4XPewvt2ruKzP9/Csw6YxT+dtYJnHTx73HOWzevlH85YznOXzeGymx/m77+3nu2Dw1n/KZIkSVPWHBrenXcYkiRpAiw4FdypK+axfH4vl938MPXh3XcfpWnKtfc+xp/tOIp79z+UP/nVl3nXdy5gziMP7Pa8vp5u3nnyEt76rAP46Ybt/PnV97Fmy0CWf4YkSdKUOTRckqTpw4xdcN1dCec9fREPbK9zzd2P7HLdYwNDfOh/NnDR9RtZsaCXi162khec9xqSgRqNC99F+suf7/b3JEnC2Ufuz9+/aDlDjZS/vObXfOvuR7P+cyRJkvZatd5waLgkSdOEGXsaeMaS/Th2cR9fvHUz1fqTt7v9dMN23n7lvdywfhvnPX0Rf/vCZSyePYvksCPp+v8+AvP2p/Hxv6Hxg+/s8Xc9dVGFj515KKsOqPCJHz/A/7l+I4NDjXb8WZIkSRNWH25Qb6R2OEmSNE2YsaeBJEk47/hFPDY4zFdu2/L48cGhBp/6yQN84Np1zO0t8ZEzDuVVRy+kuyt54tz+xXS9+0NwxDGk/34Rja9eRtrYfQFpXrnE35x2COGYhXxnzVb+6r9/zcZtO9v290mSJO1JdWfzSzeHhkuSND2YsaeJwxdWeP7yOfzXL7ewuVrnV5tq/NlV93H1XY/yiqP25yNnLmfFgvK45yZ9s+l6+9+QPP/FpFd9ifSSfyTdObjb39fdlfDbT1vE+05dyqYddf7im/fxo7V7HlwuSZLUDjssOEmSNK2YsaeRNz5tEY005YPXruOv/vvX7Bxu8MEXHMLvPuMAZnXv/n/KpFQiedMfk7zmzaQ3/A+Nd/8ejSs+T7p113OhAJ558Gz+8cxDOWjOLC64bj2f/dlDDDd2P7xckiQpayMdTm6pkyRpeijlHYAm7sA5szjziAV8/ZePcOqKubz1WYuZPWvitwZOkoTkJa8iXXEEjau/Qvr1L5BedTnJCc8necHLSZY/ZdzzFs+exYUvXsYlNz3EV27fwq821Xjn8w5mQcX/fCRJUmfs2DkEQKVn4p99JElSfqwYTDNvPv4AXnDYvF1un5uI5Ihj6D7iGNIH1pOu/gbpD79Dev21sHIVXS98OTz9RJLu3/ww19PdxR+ecCBP7a/wzz95gLd9Yw2nHDqX0w+bx8r9yyRJsovfJkmSNHUjN06xw0mSpOnBgtM0U+pKplRsGi058GCSN/w+6St+m/T73yZd/Q0an7oQ9l9EcvpZJM97Mcl+s3/jnNMOm8dT9i/zxV9s4tv3bOWqXz3K0rmzOG3FPH5rxVwW7deTSWy7Uq0Pc8vGKjdu2M7AUIPXHr2QQzO6HpIkqbic4SRJ0vRiwUkkfbNJXvwK0he+DG65gca3ryC9/N9Jr/hPkueeTnL6y0gOWvr4+mXze3nX8w5mx85hfnD/Nq5ds5XLbnmY/7jlYY5d3Mdph83jpENm05dRy/vGbTu5cf12bli/ndseqjLUgP16uiCBH96/jRc+ZR5vOG6RW/wkSZrBHr9LXcmCkyRJ04H/D12PS7q64fiT6D7+JNL715Cu/jrp979F+t1vwjHPoOsFL4NVx5N0NT/o7TermxevnM+LV87ngW07+e59j3Htmq1cdP1GPvWThOccMofTDpvHsYv76O6a+Ja7oUbK7Q9VuXH9dm7csIP1j+0EYOncWZx95P48++DZPHVRhVq9QfzFJq688xGuu28brzl6f17+1P3p9YOoJEkzjkPDJUmaXiw4aVzJssNI3vynpK86j/S6q0m/+00aF30ADlxK8oKzSZ5zOknvE1vZDpwzi3OP7ed1xyzkl5tqXLvmMb5//2N8977H2L9S4tQVczltxTyWze8d9/c9OjDETzfs4Mb12/nZxh1U6w1KXQnHLO7jpUfM51lLZnPgnFm/cc6c3m7e8szFnHn4Aj5780P8xy2buPquR/mdpy/i+YfOpcu5UjNetT7MtWse45q7H2VeuZvff9Zils4b/78xSdL0NjI0vOwXS5IkTQsWnLRbydz5JGefS3rGq0lv/D7pt79O+n8/RfqlS2HFkSSHryJZuQoOO5Kk0keSJBy1qI+jFvXxe886gBvWbefae7fytTu28JXbt/CU/Xs5bcU8nr98Llse2s63b9vEDeu3c9fmAVJgQaXEycvm8KyDZ/O0A/eb0JyGJXNn8Z5TlnLrgzu49KaH+OgPN/L1Ox/hLc84gKMO6Gv/RVLHbdy2kyvvfITvrNlKtd7gKfuXWbNlgD+76j7OPbafV6zan9IkuuokScVX3TlMuZRMqmtakiTlx4KTJiQp9ZCcdBrpiafCPb8kveF/SO++g/TKL5GmDUi64JBDm8WnlatIDj+KWfMXcvLyuZy8fC6PDgzxP/c9xrX3buWSmx7ikpseevy9D19Y5tzj+nn2wbNZsaB3rzuTjl28H/945qF8997HuOzmh3n3t+7n5GVzOO/4RSyePWvPb6BCS9OUmx+o8o1fbuGmDTvoSuDk5XM5+8gFHNlf4ZHaEJ++4UEuu+VhfnD/Y/zJSQdx2P4OlJekmWLHzmEqGc2HlCRJ7WfBSZOSJAmsPIpk5VEApANVWHMn6V13kN59O+n3vwWrv0EK0L+4WYA6/CjmrVzF2Ucs5WVP3Z9fPzrI9Wu3seKABRwxN8102HdXknD6YfN47rI5fO32LXzl9s38eN12Xv7UBbzm6IXsN8sPqtNNrd7g2nu3cuWdj7DusZ3MK3cTjl3IS1bOZ2HfE3dFXFAp8e5TDuaH9z/Gp294kHdefR+vWrWQcOxCZnW7/UKSprsdO4cdGC5J0jRiwUlTkpT7moPEVx0PQDo0BGvvbRaf7r6D9Lafwo+ubRag9psDK4/ikJVHsWzlUSw89GC21AbbEle51MW5x/XzopXz+I9bNvHV27fw7Xu28obj+nnxyvm2408DD2zbyZW/eoTv3LOVHa1tc3/2nIN43vI59OymgPTcZXM5dvF+XPrTB/nSbZu5fu02/uSkg3jqokoHo5ckZa26c9iB4ZI0g6RpysDAAI1Go9nYUBAPPvggg4Pt+f+p00WapnR1dVEul6f0v40FJ2UqKZVgxeEkKw6HF51Dmqbw8EbSu+6Au29vFqJu+Qkp8DBA32zoX9zshlrUeuw/sHls4QEkPT17+I27t7Cvhz99zkGcfeQCLr3pQf5fe3ceH0dx533803NIGt2S5QPZ5jS3TQzBGOPsLjYCm+CAIbiMEwh5QghO8D4cm2wgz4snBMgGlnDkeYANGAghTxK2WMI63OGIdzfsxpCEJQTMaQMyxsK27mMkzUw/f3TPaCRLtiS3NJL8fb9e/erq6p7umlJLU/pNVfWPX67j8bca+MpxUziuumhM/WET7w/bq9vaeeLtBl7e0krIgZP2L2HZ4ZUcXjX4P3Yl+WEuW1DNXx1Qyl0btnHVbz5g2eEVnD93siab7YfrutR3JKht6iKeSHHsfkV62qOIjDntXclBze0oIiLjQzweJxqNEomMrbBEJBIhHNbImEQiQTweJxYb/hf3Y+snKxOO4zgwpRpnSjUsPAUAt6UJ3nuTwtYm2j7YhLujDrZ+gPvnlyHR7fWG8l4M5ZOgagpO1VTwA1FO1VSYPA3KKnBCg2t4HlJZwA01+7NhSysPvPIJ163fwtxphcyfWcK04ihTi/OYUhTZbc8ZGTnxRIrfbmriibcbqG3qoiw/zIrZk1h6aO9hc0N1XHUx/2fZQfzsv7fz2FsNbNjSyqXzpzF3v6IASz9+JFIu21q72NLkL82dbGn20h2JVOa4wmiIvzqglFMOKeOwSXv3rYaISFDauhJUxfQ5LSIyUaRSqTEXbJIekUhkr3t6jdpP1xizFPgREAbutdbe2Gd/PvAg8GlgJ7DSWvu+MWYS8C/APOABa+2a0SqzjAynpAzmzqeoqoqOHTsy+W4qBU0NsKPOC0Jt3+ald9bhvvkaNK4H1+0JSIUjUFYB5ZVe8Km8EsoqoXySl/bzKSrBcRwcx+HEmSV8urqYp95pwP5lJ/+9rb2nXMCkwkgmAOWto0wr8dKl+WH94x2AdG+ad3fGeWdnnHfr47y1o8N/2lw+l/nD5oKad6kwGuaSedP4zAGl3PH7j/nuC7XUHFLG/zhuCsUTdE6vju4UW5o7+ai5i9qmLj5q7qS2qYttrV1kxZWYFIswoyyPxQeXMqMsnxml3uT6v93cxPrNTTzzbiMzy/KoOaSMkw8sozzA+dZERIaqvTtJrFR/h0REJgr9bzX27e3PaFQ+tY0xYeBO4FRgC/CyMebX1to3sg67CGiw1s4yxpwH3ASsBOLANcBsf5EJygmFoGISVEzCOfSoXfa73d1Qvx22b/MCUvWfQGMDblM91G3Ffesv0N7qHZv9wki0JzBVXkm4fBLLyir47H6VNMXK2RYuos4tYFsySl0c6tq6+dPHbTR0JHpdvyAS6glC+UGpkvwwXckUnQmXzmSKLn/dmUjRmXTpTKTo8ted/ay7kynCIYe8kEM0HCIv7JAX9tL5YYeov50XDvVK52XtKyvpoqW1FdeFlB+Qc11vSeGCCym8QE/Kr5iU622n66kyFmFyUZQpRVGqiqKU5IUC+wBo7Ejwbn2cd3fGebe+g3d3xmmIJwEIOXBAeT6fOaCExQeXcURVbMQ+eI6eUsjtnz2Ih17bwb9u9J509/V5U5k/s2RErjfSkimXHe3dfNTcxdaWLm/d3MWW5i52tPfcuyEH9ivJY0ZpHifOLGF6aR4zy/KYXppH4QBPezpmWhEXH5/kdx+08Nx7TfzkT9t58JXtHD+9mJpDyvh0dbHmQRORUadJw0VEJCj19fWsXLkSgO3btxMOh6msrATgmWeeITSIkTRXXHEFl156KbNmzRrwmAceeIDS0lLOOeecvS7z8uXLueGGG5g9u/+wSCKRYM6cOWzcuDGT94tf/II333yT6667bq+vPxyj9TXRCcC71tpNAMaYh4CzgOyA01nAtX76X4A7jDGOtbYN+J0xZuCfouwTnGgUplbD1GoG+lfX7er0ekk11UNjPW6jt6bJT2+txX3jVehowwHK/eWI9AnCYW9eqeJSOkvK+aR4GnVFVdTlV1AXKaGus5CPO/J4ZWuIrlT/pUgHjfLDIfIjDvmREHl+AKm4MOKlIz0BpaQL3UkvMNXlB6HS6bbu1ID7eoJq2wdfh3gjFb21Q8jxglTZvV7AC65NLoowpSjKZH/x0l5eRSxCqJ/AUHNnkvfq47yzs8MPMMXZ6Qc/HGBmWR7HVhcxqzLGrEkFHFieP6pzBeVHQlx47BQW7l/KHRs+5h/+/SM+c0AJFx8/lapRK8XQNMcTfNTiBZOyg0sft3TTneq5CwqjIapL8pg9pZAZZXmZHkvTivOIhoceHCqMhjltVjmnzSqntqmT599r4oXNTWzY0kpFQZhFB5dxysFlzCjLD/Ltioj0y3VdTRouIiKBqays5NlnnwXglltuoaioiNWrVwPeULJEIuF9Qe9Pnt2f2267bY/X+fKXvxxYmcej0Qo4TQdqs7a3APMHOsZamzDGNAGTgB0MgjHma8DX/NdTVTUy/z5GIpERO/e+ZsTqsnr6Hg9x4x0kG3aSam7EbWki1dxEqqXJS/tLtKWZop2bOGDzK6RamiDR3fN6oDGvmLZIIXnJLvJJUhANk58fJVxQiBOL4RQUEooV4sQKcQr8dSyGEy3EyffSoYIinOISQsUlOEUlhEpKcaJ5ey6/65JIuXQlUhAKk0olCQE4EPKtp1HCAAAdEklEQVQDSQ6OF1zKbPffJdJ1XRo7EmxribOtuZO6lk62tXRS52+/U9tKc7x3b69IyGFKST7T/CWeSPFmXQtbm3vG+M4sj3HczAqOmFLMEVOLOWxyMYVjZAhbVRUcP6ua//fHLTzwUi2v1XXw9b8KMbkwSiKVojvp1W93MtVrnUi6dKf3Z6WT/jEAkbBDJBQiEnL8dHoJ7brd55hEymVLY5zaxg4+bOigtrGjV92HQw7TywrYf1IxCw+JsX+Fv5THqCiMjljvsKoqOPaQ6VyeTPFfHzTwxOt1rNtYz6/eqGfOfiWccdRUFh9WRVGe95EynN/t0A9/iHv88bgnn5zJc9avx/nDH0h985tBvp1xY6T+RqZcl7auJG2dCdq6krT2WWenkymXvEi6Z2WIvHQAPZK97QyQ761L8yPkqVeK7KXulPd3eaCemSIiIkHYvHkzF110EfPmzeOVV17hpz/9KbfddhuvvfYa8XicM888kyuuuALo6XF0xBFHMGfOHC644AJeeOEFYrEYP/nJT6iqquKmm26isrKSiy++mOXLl3PCCSfw4osv0tzczK233sq8efNob2/nsssuY/PmzRx22GFs3ryZm2++ecCeTACPPPIId911F67rcuqpp3L11VePVhUNyWgFnPr7L8gdxjEDstbeA9yTft2OHYOKUw1ZVVUVI3XufU3O6zJaAJOmectuOEDIdaEzDq3N0NYCrS1UtjZT2d4G8Q7o7PDW8Q5SnXFcP01jQ8++zg7o6tpzufLyvF5WRSVQWASFxThFJX5eUWafU1gMhUVUTJ9BQ7wTCmKQVzDoidT7mhyGyRUwpyIfyAdKM/vau5PsaEvwSVs329u6s9Zd/H5nG3lhh0MqCzj1kFJmVRZwSGUBRb2CS920NzfQ3veiObbs4EKOmXQgd/z+Y2589u0hvTaaCRx5Abj0ELNkyiXhQiLpkvQDg6lB/yXzVMYiVJfmsWBGMdNLveFv1SV5TC2O9jOUrZtURzc7O4Z2jeE6shSOXDCFhrmVrN/cxHPvNXHj8+9y2/r3WHhAKTWHlDH3oP3YWV/vBTnTL3R60tlxsXRgtODgWUxdtYpP7vwnOk46ieiLL7Lfmm/wwY/uoGnzVi/gl/J64/Wk/cCgHwxMpLw6707uWuGOH4Td9fr9lcmTGZ6a3nbBxc3kuW76ODcr3XNcMgVJ1wtIZqcTLqRS6fvDSyfcvse7RKJRuru7vfI4XunTP/5Mb0XH6fUe0oHldF5Hd4r27hTt3UnaulO0d6V6TRA/kLADhXlhwo73j36337NyOK45eQbHTy8e1mv3pLq6ekTOK2NPe7d33+opdSIiE1PqobW4tZsDPacz8yBC51085Ne9/fbb3Hrrrdx0000AXH311VRUVJBIJFixYgVnnHEGhx12WK/XNDc3c+KJJ/Kd73yHa6+9loceeog1a3adftp1XZ544gl+85vfcPvtt/Pzn/+c+++/n8mTJ7N27Vpef/11li5dutvybd26lX/8x3/kqaeeoqSkhPPOO49nn32WRYsWDfm9jrTRCjhtAWZmbc8Atg5wzBZjTAQoA+pHp3giu+c4jhfQKYhB1VQvbxjncVNJiMf9AFQc4u3Q3obb3uoFstpavXmo2lr9vFbY+Qlu7SYv3RnvOZe/3tm7oJBf4Je10FvHCiE/hhOL9byH9L6CQpyCGESj3lxXmSXSs45GiYWjzCyIMLM4H8JFE2qCv/3L8vnBqQewI1VAY2MjYccPIoWdrKCSF1BKb4ecoU2gl0oHGlLpgJQfHMnOS7k4Dkwtjo6Lb/ArYhHOPmoSy4+s5K0dcZ7f1Mh/vN/CC5uagA+HccYqPn3ud/jBV7/GEyd+js///jHWfPEa/vjxVPj1pqCLP+pCDoQdh3DI66nmpR3CDv495d13Pfsgz/HmhSMryAX4Acx+8tIBMXqCYQWREIV5ISpi3pxdhVFvu6hPOhYNURQNUZgXpijq9Vrqe49nelYmewJQXX4vv568rO2Ut31AuYZdyt7rUMBJRERGyYEHHsjcuXMz2+vWreOXv/wlyWSSbdu28fbbb+8ScCooKGDx4sUAHHPMMWzYsKHfc59++ukAzJkzh9pabxDYSy+9xKWXXgrA0UcfzeGHH77b8r3yyissXLgwM+fU8uXL2bBhQ+b6feXyf7fRCji9DBxqjDkI+Ag4D/hCn2N+DVwI/BdwLvCCtXZ4X6eKjFFOKOz3WirqnT/I17uJbmhvywpMtVAcCdOy/RMveBXvgI72TG8rN53X3NjT6yreDslkzzmH80YyASk/KJWX7wWw8gt6gliZwFfvtJMf6zefgsJh987aW+GQw9FTStgR2bvHfg4k5DiEwg7jII40ZI7jcMTkGEdMjnHRp6fy0pZWkpECWltbM8dkB0C8bW8jO2CCC+7c03in7R2++vO7+cP5l3D8qqWcGMoehkhW2g8AhncNCIZDDiGyeibhBUuyy5O+Pn3S2bOjpXtfee+z5/c0lO5BlO5R5B/Qe440L8AU8oNH/c15tic57wXah+N4885NxPtYxj4FnEREJrbh9EQaKYWFhZn0pk2buPfee3niiScoKyvjb//2b+ns3PV/hry8nmlRwuEwyaz/t/o7LvuY7HbqYAx0fDgcJhQKkUgkiES8UE9jY2MmMJULoxJw8udkWgM8A4SB+621rxtjrgP+YK39NXAf8DNjzLt4PZvOS7/eGPM+3viePGPMcuC0Pk+4E9knOJEolJZ7iy9WVUXbEP4pdV3Xm48qKzBFortn6U5Astt7KmAi4ecneh+Tvd3dDd1dPQGt5kbcTz7OGkoY7/Wf/YB/Tp0QFPlDCYu9IYROcYm3nV78ua4yxxQVe723BvHPvJtKQSoFqaS3JP0llYRkimSyC7epGSJhCEe8CeT9tRPSf9iDURAJ8dcHlg47UJL34otUPPUwLZdfzrEPPsiBZ55C18KFI1BSERlv0gEnTRouIiKjqbW1leLiYkpKSqirq2P9+vWcnDXnaBBOOOEEHnvsMebPn8/GjRt5++3dT/Vx3HHHccMNN1BfX09paSnr1q3LTHg+f/58Hn30UVasWEFHRwePP/443/ve9wIt71CMVg8nrLVPAk/2yfvfWek4sGKA1x44ooUT2Yc4jgPRPG/JClztclxA13NTKeju6gluZc135XbGe3plZQ0rdFubvScLbv0QWlu816TP1/cC4YgXeIpEewJJ6cBSVkAJd/fz1uw2POI4fvApHYjKCkpFoj15efnekl+Ak1/g9eDKK4D8fMiP+evd7yOaB5Foznp75Ureiy9SsXo1DT/+MV0LF9J50km9tkVk39augJOIiOTAnDlzOPTQQ1m8eDH7778/8+bNC/waX/nKV7jsssuoqalh9uzZHH744ZSWlg54fHV1Nd/85jdZsWJFZtLwmpoaAK6//nquuuoq7rnnHlzXZeXKlSNS5sFyhtp9a5xwt27tO0VUMMbaEIfxTHUZjH2hHt1EtzeM0J+wnfYW3FZ/zqu2Zi8vmfSCPqEQhPwAUCiMN54p3H9eOJJJl5SW0tLU5PXeSib8HlBZ60Q/ef7aTae7Or2lM561dEJX715egxKJehPIR7OWvHxvvi0/7WSO8fPz8rwgVsybo8sb2hjLbPcMZYzhREbu+4bh3JPFd91F16c+1Su4lPfii+S9+iqt3/hG0EUcF/aF3+295U8aPnEmlZsYRqQN9m+bm7j1Pz/mzmUHMaNM84LtDf1tCY7qMhiqx+CMt7psb2/vNXxtrIhEIiQSiT0fGJBEIkEikaCgoIBNmzbxhS98gd/97neZYXG51N/PaCjtr9y/AxGRPXAiUSir8JZ0XsDXGOrQxKFwXdd7QmGX36OrT1DK7Yz35HV3ecd2917c7LyuTmht9oY9prcT3d7rkj0fjrsNcUXzegWg0hPMO7FCKK2AsnIoq8Tx15RWeEMaR2jSwf6CSl0LF6p3k4gAZJ6uqDmcRERkomlra2PlypWZINdNN900JoJNQZgY70JEZAxzHMcfMpcPJWW77g/wWm6iu2f4Yjxrnq54hz/PVu+8nnx//q1tW6CpwQti0SdoFY54wzD94J9TVuEHp9LpciivJFUUw3XdCfU0QxHJrXZNGi4iIhNUWVkZTz/9dK6LMSIUcBIRmUCcSBSKo1C867jvoYR/3Hg7NDV6c2n5a5oboLEBt7kBdn6Cu+ktaG0G1+0VmNoO3iTwsRjEirwhfbFCiBV5PagyeT37nMIiKPCPyy/whjw6DoT858CFHO+cTtY6nZfZ37NvX5sDS2Si6+hO4eA9nEBERETGBwWcRERkF05BoRcAmlq920CVm0xCS2NWcKqBIlzadmyHjjboaMftaPN6UDXuxP24NpNP1uNiA59N0HF6T+6+x3U/eaGQ94RCJ+TP9eXP/eWk0+n5wUK9FyfUez6x7HV41/1Or/3hzP6u8nLc5mbA8aOF6aBaVjr9Xh2n57jsdGig99o7X73RZKzr6E5RmBcmpHtVRERk3FDASUREhs0Jh6F8krdwCA5QVFVFxx7mw8rMa5UOPmWt3c5O76mCruutUy7getspt/e+XfLc3k8p7G8C+GSyZ6L37H3p4YjJhH+OlPeUxVQys505t+unkylw++wfooGCbQ1DPtNeSAfB+gal0oEwx8nqdRbKWof2uB1a/kWcgw4bzXcjE1C7H3ASERGR8UMBJxERGXW95rUqr+y9L0dlCsquQapk73VyT/uTkEpSVlpKU2NjzxMO3XTgrU86HYzLTqf3pZK42U9YTKX6CcL1E5xLZeWng3h+kM3ts913P67bE8BLvx+RvVRVFOGoqSW5LoaIiIgMgQJOIiIiAXLSQ+v2Ul5VFU4AT04c7wE8EYAvHDN53D3uW0RExrZzzz2XNWvWcPLJJ2fy1q5dy/vvv8/3v//9AV936KGH8s4777Bt2zauueYa1q5d2++5r7nmGj71qU8NeJ61a9dy/vnnE4vFALjgggu44447KCvb9SFDQ3HLLbdQVFTE6tWrBzymb/lqa2u58MILeeGFF/bq2n1p5kURERERERER2aecddZZrFu3rlfeunXrOPvsswf1+mnTpvUbbBqse++9l46Ojsz2z372s70ONo01CjiJiIiIiIiIyD7ljDPO4LnnnqOzsxPwevnU1dUxf/582traMMawZMkSTjnlFJ555pldXl9bW8vixYsB6Ojo4Otf/zo1NTWsXr2aeDyeOe6qq67i9NNPZ9GiRfzwhz8E4L777qOuro4VK1Zw7rnnAjB//nzq6+sBuPvuu1m8eDGLFy/OBLVqa2v5m7/5G771rW+xaNEiVq1a1Stg1Z+//OUvLFu2jJqaGi666CIaGxv3staGRkPqRERERERERCRn7v1DHZsb4ns+cAgOqijgq8dPHXB/ZWUlc+fOZf369SxZsoR169Zx5pln4jgO+fn53HfffZSUlFBfX8/nPvc5TjvttAGf7Pvggw8Si8V47rnneOONN1i6dGlm37e//W0qKipIJpOsXLmSN954g4suuoh77rmHhx9+mMrK3vOZ/vnPf8Zay+OPP47ruixbtowFCxZQVlbG5s2bufPOO7n55pu55JJLePLJJ/n85z8/4Hu8/PLLuf7661mwYAE333wzt956K9ddd90Qa3L41MNJRERERERERPY5y5cvzwyrW7duHcuXLwe8JyrfeOON1NTUsHLlSrZt28b27dsHPM+GDRs455xzADjqqKM48sgjM/see+wxlixZwpIlS3jrrbd45513dluml156iaVLl1JYWEhRURGnn346GzZsAGDmzJnMnj0bgGOOOYba2toBz9Pc3ExTUxMLFiwAYMWKFZnz9GegYNreUA8nEREREREREcmZ3fVEGklLly7le9/7Hq+99hrxeJw5c+YA8Ktf/YqdO3fy1FNPEY1GmT9/fmbo3UD6C9h8+OGH3H333TzxxBOUl5dz+eWX9xpu1x83/YTifuTn52fS4XB4j+caSEVFBU1NTZntxsZGKioqhnWu3VEPJxEREZEJzBiz1BjzljHmXWPMVf3sP8AY87wx5s/GmPXGmBl99pcaYz4yxtwxeqUWEREZeUVFRSxYsIArr7wy07sJoKWlhaqqKqLRKC+++CJbtmzZ7Xnmz5/Po48+CsCbb77Jxo0bM+eJxWKUlpayfft2fvvb32ZeU1xcTGtr6y7nOvHEE3nmmWfo6Oigvb2dp59+mvnz5w/5vZWWllJWVpbp1fTII49w4oknAnDSSSfxyCOPZIJbDz/8MAsXLhzyNfZEAScRERGRCcoYEwbuBE4HjgJWGWOO6nPYD4EHrbXHANcBP+iz/3rg30a6rCIiIrmwfPly3njjDc4666xM3jnnnMOrr77K6aefzqOPPsqsWbN2e44vfelLtLW1UVNTw1133cXcuXMBOProo5k9ezaLFi3iyiuvZN68eZnXfPGLX+T888/PTBqeNmfOHFasWMEZZ5zBsmXLWLVqVWYY3VDdfvvtXH/99dTU1PD6669zxRVXZK5dXFzMqaeeSk1NDW1tbaxevXpY19gdZ3fdtcYxd+vWrSNy4qqqKnbs2DEi597XqC6DoXoMhuoxOKrLYKge96y6uhog+AkHJhBjzALgWmvtEn/7agBr7Q+yjnkdWGKt3WKMcYAma22pv+/TwLeAp4HjrbVr9nBJtcHGONVjcFSXwVA9Bme81WV7ezuFhYW5LsYuIpEIiUQi18UYE/r7GQ2l/aU5nEREREQmrulA9oyiW4C+/fJfBT4P/Ag4GygxxkwCGoBbgAuAUwa6gDHma8DXAKy1VFVVBVb4bJFIZMTOvS9RPQZHdRkM1WNwxltd1tXVEYmMzZDEWC3XaMvPz9+re0q1KCIiIjJx9fcNZN/u7d8E7jDGfBn4d+AjIAF8A3jSWltrjBnwAtbae4B70uceqW/Xx9s392OV6jE4qstgqB6DM97qsrOzk3A4nOti7EI9nHp0dnbuck/5PZwGRXM4iYiIiExcW4CZWdszgF5j3qy1W62151hrjwX+l5/XBCwA1hhj3seb5+lLxpgbR6XUIiIiMu6ph5OIiIjIxPUycKgx5iC8nkvnAV/IPsAYUwXUW2tTwNXA/QDW2i9mHfNlvDmcdnnKnYiIyHBM0PmkJ5S9/Rmph5OIiIjIBGWtTQBrgGeAjV6Wfd0Yc50x5kz/sJOBt4wxbwNTge/npLAiIrJPCYVCGro2hiUSCUKhvQsZ6Sl1QzTexsWOZarLYKgeg6F6DI7qMhiqxz3TU+rGJLXBxjjVY3BUl8FQPQZnvNWl67rE43FSqRSOM3Y+zvPz8+ns7Mx1MXLKdV1CoRAFBQW7/Gz0lDoRERERERERGbMcxyEWi+W6GLsYb4G7sUxD6kREREREREREJFAKOImIiIiIiIiISKAUcBIRERERERERkUBN2EnDc10AERERGRVjZ5ZRAbXBRERE9gWDan9N1B5Ozkgtxpg/juT596VFdal6HEuL6lF1OdYW1eOgFxlb9DsxxhfVo+pyrC2qR9XlWFtUj4NaBmWiBpxERERERERERCRHFHASEREREREREZFAKeA0dPfkugATiOoyGKrHYKgeg6O6DIbqUaQ3/U4EQ/UYHNVlMFSPwVFdBkP1GJCJOmm4iIiIiIiIiIjkiHo4iYiIiIiIiIhIoCK5LsB4YoxZCvwICAP3WmtvzHGRxiVjzPtAC5AEEtba43NbovHDGHM/sAz4xFo728+rBP4ZOBB4HzDW2oZclXE8GKAerwUuBrb7h33HWvtkbko4PhhjZgIPAtOAFHCPtfZHuieHbjd1eS26L0XUBguI2mDDpzZYMNQGC4baYMFQ+2vkqYfTIBljwsCdwOnAUcAqY8xRuS3VuLbIWjtXDZ0hewBY2ifvKuB5a+2hwPP+tuzeA+xajwC3+fflXH2oDEoC+Dtr7ZHAicCl/t9F3ZNDN1Bdgu5L2cepDRY4tcGG5wHUBgvCA6gNFgS1wYKh9tcIU8Bp8E4A3rXWbrLWdgEPAWfluEyyj7HW/jtQ3yf7LOCnfvqnwPJRLdQ4NEA9yhBZaz+21v7JT7cAG4Hp6J4cst3UpYioDSZjgNpgwVAbLBhqgwVD7a+Rp4DT4E0HarO2t6Cbcbhc4DfGmD8aY76W68JMAFOttR+D90cTmJLj8oxna4wxfzbG3G+Mqch1YcYTY8yBwLHABnRP7pU+dQm6L0XUBguO2mDB0uddcPRZN0xqgwVD7a+RoYDT4Dn95OkRf8Oz0Fp7HF7X+EuNMX+d6wKJAP8EHALMBT4GbsltccYPY0wx8AhwubW2OdflGc/6qUvdlyJqgwVJbTAZi/RZN0xqgwVD7a+Ro4DT4G0BZmZtzwC25qgs45q1dqu//gR4FK+rvAxfnTFmPwB//UmOyzMuWWvrrLVJa20KWIvuy0ExxkTxPqB/bq39lZ+te3IY+qtL3ZcigNpggVEbLHD6vAuAPuuGR22wYKj9NbIUcBq8l4FDjTEHGWPygPOAX+e4TOOOMabIGFOSTgOnAX/JbanGvV8DF/rpC4F1OSzLuJX+cPadje7LPTLGOMB9wEZr7a1Zu3RPDtFAdan7UgRQGywQaoONCH3eBUCfdUOnNlgw1P4aeY7rqkfyYBljPgvcjvdI3vuttd/PcZHGHWPMwXjfqAFEgF+oHgfPGPNL4GSgCqgDvgv8K2CB/YEPgRXWWk3GuBsD1OPJeN1mXbzHyF6SHgMv/TPGfAb4D+A1vEfJAnwHb+y77skh2E1drkL3pYjaYAFQG2zvqA0WDLXBgqE2WDDU/hp5CjiJiIiIiIiIiEigNKROREREREREREQCpYCTiIiIiIiIiIgESgEnEREREREREREJlAJOIiIiIiIiIiISKAWcREREREREREQkUAo4icg+wRjjGmNm5bocIiIiIvsKtb9E9m2RXBdARPZNxpj3galAMiv7AWvtmtyUSERERGRiU/tLREaTAk4ikkufs9Y+l+tCiIiIiOxD1P4SkVGhgJOIjCnGmC8DFwN/Ar4EfAxcaq193t9fDfwY+AxQD9xkrV3r7wsD3wYuAqYAbwPLrbW1/ulrjDFPAVXAL4A11tp0V+/7gLlAN/C8tXblKLxdERERkZxT+0tERoLmcBKRsWg+sAmvYfJd4FfGmEp/3y+BLUA1cC7wD8aYU/x9VwKrgM8CpcBXgPas8y4D5gGfAgywxM+/HvgNUAHMAP7viLwrERERkbFL7S8RCZR6OIlILv2rMSaRtf0tvG+4PgFut9a6wD8bY/4OOMMYsx7vm7Vl1to48N/GmHuBC4Dnga8Cf2+tfcs/36t9rnejtbYRaDTG/BbvG7Wn/WseAFRba7cAvxuB9yoiIiIyFqj9JSKjQgEnEcml5X3nEPC7dH/kN3bSPsD7Rq0aqLfWtvTZd7yfngm8t5vrbctKtwPFfvrv8b5le8kY0wDcYq29f4jvRURERGQ8UPtLREaFhtSJyFg03RjjZG3vD2z1l0pjTEmffR/56VrgkKFezFq7zVp7sbW2GrgEuEuP8BUREZF9jNpfIhIo9XASkbFoCvA/jTF3AcuBI4EnrbU7jTH/CfzAGPNN4DC8CSrP9193L3C9MeYN4F1gDt63dTt3dzFjzArgv/zu3A2AS+/HBYuIiIhMdGp/iUigFHASkVx6zBiT3bB4FlgHbAAOBXYAdcC5WY2WVXhPSdmK1zj5rrX2WX/frUA+3gSUVcCbwNmDKMc84HZjTJl/vcustZv35o2JiIiIjFFqf4nIqHBc193zUSIio8SfQ+Cr1trP5LosIiIiIvsCtb9EZCRoDicREREREREREQmUAk4iIiIiIiIiIhIoDakTEREREREREZFAqYeTiIiIiIiIiIgESgEnEREREREREREJlAJOIiIiIiIiIiISKAWcREREREREREQkUAo4iYiIiIiIiIhIoBRwEhERERERERGRQP1/tfWGr3iF3AAAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 1440x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Visualise the results of training\n",
"# Variables stored in results.history: val_loss, val_acc, val_IoU, loss, acc, IoU, lr\n",
"fig, ax = plt.subplots(1, 2, figsize=(20, 8))\n",
"ax[0].plot(resultsF.history[\"loss\"], label=\"Training loss\")\n",
"ax[0].plot(resultsF.history[\"val_loss\"], label=\"Validation loss\")\n",
"ax[0].set_title('Learning curve')\n",
"ax[0].plot( np.argmin(resultsF.history[\"val_loss\"]), np.min(resultsF.history[\"val_loss\"]), marker=\"x\", color=\"r\", label=\"best model\")\n",
"ax[0].set_xlabel(\"Epochs\")\n",
"ax[0].set_ylabel(\"log_loss\")\n",
"ax[0].legend();\n",
"\n",
"ax[1].plot(resultsF.history[\"val_IoU\"], label=\"Training IoU\")\n",
"ax[1].plot(resultsF.history[\"IoU\"], label=\"Validation IoU\")\n",
"ax[1].set_title(\"IoU curve\")\n",
"ax[1].set_xlabel(\"Epochs\")\n",
"ax[1].set_ylabel(\"IoU score\")\n",
"ax[1].legend();"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# # Evaluate on validation set (loss, acc, IoU)\n",
"# modelF.evaluate(X_validF, y_validF, verbose=2)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"282/282 [==============================] - ETA: 40 - ETA: 19 - ETA: 12 - ETA: 8 - ETA: - ETA: - ETA: - ETA: - 13s 46ms/step\n",
"32/32 [==============================] - 0s 15ms/step\n"
]
}
],
"source": [
"# Predict on training and validations sets\n",
"preds_trainF = modelF.predict(X_trainF, verbose=1)\n",
"preds_valF = modelF.predict(X_validF, verbose=1)\n",
"\n",
"# Threshold predictions (only keep predictions with a minimum level of confidence)\n",
"preds_train_tF = (preds_trainF > 0.5).astype(np.uint8)\n",
"preds_val_tF = (preds_valF > 0.5).astype(np.uint8)"
]
}
],
"metadata": {
"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"
}
},
"nbformat": 4,
"nbformat_minor": 2
}