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+The link for the dataset used in this project: https://www.kaggle.com/datasets/andrewmvd/leukemia-classification
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+{"cells":[{"cell_type":"markdown","metadata":{},"source":["## Leukaemia Classification using DL"]},{"cell_type":"markdown","metadata":{},"source":["#### Imports"]},{"cell_type":"code","execution_count":44,"metadata":{},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from PIL import Image\n","import os\n","import cv2\n","import shutil\n","from shutil import copyfile\n","import random\n","\n","import keras\n","import tensorflow as tf\n","from tensorflow import keras\n","from keras.preprocessing import image\n","from keras.preprocessing.image import ImageDataGenerator \n","from sklearn.model_selection import train_test_split\n","from keras.utils import load_img, img_to_array\n","from tensorflow import keras\n","from keras.applications.vgg16 import VGG16\n","from keras.applications import ResNet50\n","from keras.preprocessing.image import ImageDataGenerator\n","from keras.layers import Dense, MaxPooling2D, BatchNormalization, Activation, Dropout, Flatten, Conv2D, GlobalAveragePooling2D, Embedding, GRU\n","from keras.models import Model\n","from keras.applications.vgg16 import preprocess_input\n","from keras.preprocessing import image\n","from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n","from keras.optimizers import Adam"]},{"cell_type":"markdown","metadata":{},"source":["#### Data Preparation"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":["train_dir = '../Dataset/train'\n","test_dir = '../Dataset/test'"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Found 7108 files belonging to 2 classes.\n","\n","Found 3553 files belonging to 2 classes.\n"]}],"source":["#use generators\n","#resize image\n","train_ds = keras.utils.image_dataset_from_directory(\n","    directory=train_dir,\n","    labels = 'inferred',\n","    label_mode = 'int',\n","    batch_size = 32,\n","    image_size = (180,180)\n",")\n","\n","validation_ds = keras.utils.image_dataset_from_directory(\n","    directory=test_dir,\n","    labels = 'inferred',\n","    label_mode = 'int',\n","    batch_size = 32,\n","    image_size = (180,180)\n",")"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[],"source":["def process(image, label):\n","    image = tf.cast(image/255,tf.float32)\n","    return image, label\n","\n","train_ds = train_ds.map(process)\n","validation_ds = validation_ds.map(process)"]},{"cell_type":"code","execution_count":1,"metadata":{"execution":{"iopub.execute_input":"2024-05-17T10:23:42.559618Z","iopub.status.busy":"2024-05-17T10:23:42.558715Z","iopub.status.idle":"2024-05-17T10:23:42.575059Z","shell.execute_reply":"2024-05-17T10:23:42.574170Z","shell.execute_reply.started":"2024-05-17T10:23:42.559578Z"},"trusted":true},"outputs":[],"source":["# function to plote training history\n","def plot_history(history):\n","    # store results\n","    acc = history.history['accuracy']\n","    val_acc = history.history['val_accuracy']\n","    loss = history.history['loss']\n","    val_loss = history.history['val_loss']\n","\n","    # plot results\n","    # accuracy\n","    plt.figure(figsize=(5, 8))\n","    plt.rcParams['figure.figsize'] = [8, 4]\n","    plt.rcParams['font.size'] = 10\n","    plt.rcParams['axes.grid'] = True\n","    plt.rcParams['figure.facecolor'] = 'white'\n","    plt.subplot(2, 1, 1)\n","    plt.plot(acc, label='Training Accuracy')\n","    plt.plot(val_acc, label='Validation Accuracy')\n","    plt.legend(loc='lower right')\n","    plt.ylabel('Accuracy')\n","    plt.title(f'\\nTraining and Validation Accuracy. \\nTrain Accuracy: {str(round(acc[-1],3))}\\nValidation Accuracy: {str(round(val_acc[-1],3))}')\n","    \n","    # loss\n","    plt.subplot(2, 1, 2)\n","    plt.plot(loss, label='Training Loss')\n","    plt.plot(val_loss, label='Validation Loss')\n","    plt.legend(loc='upper right')\n","    plt.ylabel('Cross Entropy')\n","    plt.title(f'Training and Validation Loss. \\nTrain Loss: {str(round(loss[-1],3))}\\nValidation Loss: {str(round(val_loss[-1],3))}')\n","    plt.xlabel('epoch')\n","    plt.tight_layout(pad=3.0)\n","    plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["#### Approach 1: CNN"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[],"source":["# Model Definition\n","INPUT_SHAPE = (180, 180, 3)\n","\n","model = keras.Sequential()\n","\n","model.add(Conv2D(32, (3, 3), input_shape = INPUT_SHAPE))\n","model.add(Activation('relu'))\n","model.add(MaxPooling2D(pool_size=(2, 2)))\n","\n","model.add(Conv2D(32, (3, 3), kernel_initializer = 'he_uniform'))\n","model.add(Activation('relu'))\n","model.add(MaxPooling2D(pool_size=(2, 2)))\n","\n","model.add(Conv2D(32, (3, 3), kernel_initializer = 'he_uniform'))\n","model.add(Activation('relu'))\n","model.add(MaxPooling2D(pool_size=(2, 2)))\n","\n","model.add(Flatten())\n","\n","model.add(Dense(64))\n","model.add(Activation('relu'))\n","model.add(Dropout(0.5))\n","\n","model.add(Dense(1))\n","model.add(Activation('sigmoid'))"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[],"source":["opt = tf.keras.optimizers.legacy.Adam(learning_rate=0.0001)\n","model.compile(loss='binary_crossentropy',\n","             optimizer=opt,\n","             metrics=['accuracy'])"]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"sequential\"\n","\n","_________________________________________________________________\n","\n"," Layer (type)                Output Shape              Param #   \n","\n","=================================================================\n","\n"," conv2d (Conv2D)             (None, 178, 178, 32)      896       \n","\n","                                                                 \n","\n"," activation (Activation)     (None, 178, 178, 32)      0         \n","\n","                                                                 \n","\n"," max_pooling2d (MaxPooling2  (None, 89, 89, 32)        0         \n","\n"," D)                                                              \n","\n","                                                                 \n","\n"," conv2d_1 (Conv2D)           (None, 87, 87, 32)        9248      \n","\n","                                                                 \n","\n"," activation_1 (Activation)   (None, 87, 87, 32)        0         \n","\n","                                                                 \n","\n"," max_pooling2d_1 (MaxPoolin  (None, 43, 43, 32)        0         \n","\n"," g2D)                                                            \n","\n","                                                                 \n","\n"," conv2d_2 (Conv2D)           (None, 41, 41, 32)        9248      \n","\n","                                                                 \n","\n"," activation_2 (Activation)   (None, 41, 41, 32)        0         \n","\n","                                                                 \n","\n"," max_pooling2d_2 (MaxPoolin  (None, 20, 20, 32)        0         \n","\n"," g2D)                                                            \n","\n","                                                                 \n","\n"," flatten (Flatten)           (None, 12800)             0         \n","\n","                                                                 \n","\n"," dense (Dense)               (None, 64)                819264    \n","\n","                                                                 \n","\n"," activation_3 (Activation)   (None, 64)                0         \n","\n","                                                                 \n","\n"," dropout (Dropout)           (None, 64)                0         \n","\n","                                                                 \n","\n"," dense_1 (Dense)             (None, 1)                 65        \n","\n","                                                                 \n","\n"," activation_4 (Activation)   (None, 1)                 0         \n","\n","                                                                 \n","\n","=================================================================\n","\n","Total params: 838721 (3.20 MB)\n","\n","Trainable params: 838721 (3.20 MB)\n","\n","Non-trainable params: 0 (0.00 Byte)\n","\n","_________________________________________________________________\n"]}],"source":["model.summary()"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/10\n"]},{"name":"stderr","output_type":"stream","text":["2023-06-28 00:15:50.574347: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] PluggableGraphOptimizer failed: INVALID_ARGUMENT: Unparseable tensorflow.GraphDef proto\n","\n","2023-06-28 00:15:50.586606: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] PluggableGraphOptimizer failed: INVALID_ARGUMENT: Unparseable tensorflow.GraphDef proto\n"]},{"name":"stdout","output_type":"stream","text":["223/223 [==============================] - ETA: 0s - loss: 0.4518 - accuracy: 0.8087"]},{"name":"stderr","output_type":"stream","text":["2023-06-28 00:16:03.194159: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] PluggableGraphOptimizer failed: INVALID_ARGUMENT: Unparseable tensorflow.GraphDef proto\n","\n","2023-06-28 00:16:03.198725: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] PluggableGraphOptimizer failed: INVALID_ARGUMENT: Unparseable tensorflow.GraphDef proto\n"]},{"name":"stdout","output_type":"stream","text":["223/223 [==============================] - 15s 66ms/step - loss: 0.4518 - accuracy: 0.8087 - val_loss: 0.5206 - val_accuracy: 0.7473\n","\n","Epoch 2/10\n","\n","223/223 [==============================] - 15s 65ms/step - loss: 0.4179 - accuracy: 0.8255 - val_loss: 0.5328 - val_accuracy: 0.7518\n","\n","Epoch 3/10\n","\n","223/223 [==============================] - 15s 66ms/step - loss: 0.4104 - accuracy: 0.8324 - val_loss: 0.5387 - val_accuracy: 0.7580\n","\n","Epoch 4/10\n","\n","223/223 [==============================] - 14s 64ms/step - loss: 0.4016 - accuracy: 0.8341 - val_loss: 0.5321 - val_accuracy: 0.7605\n","\n","Epoch 5/10\n","\n","223/223 [==============================] - 15s 65ms/step - loss: 0.3971 - accuracy: 0.8384 - val_loss: 0.5186 - val_accuracy: 0.7667\n","\n","Epoch 6/10\n","\n","223/223 [==============================] - 15s 66ms/step - loss: 0.3924 - accuracy: 0.8362 - val_loss: 0.5108 - val_accuracy: 0.7670\n","\n","Epoch 7/10\n","\n","223/223 [==============================] - 14s 64ms/step - loss: 0.3801 - accuracy: 0.8481 - val_loss: 0.5027 - val_accuracy: 0.7729\n","\n","Epoch 8/10\n","\n","223/223 [==============================] - 14s 64ms/step - loss: 0.3744 - accuracy: 0.8516 - val_loss: 0.5083 - val_accuracy: 0.7740\n","\n","Epoch 9/10\n","\n","223/223 [==============================] - 15s 68ms/step - loss: 0.3700 - accuracy: 0.8499 - val_loss: 0.5142 - val_accuracy: 0.7819\n","\n","Epoch 10/10\n","\n","223/223 [==============================] - 17s 76ms/step - loss: 0.3608 - accuracy: 0.8541 - val_loss: 0.5065 - val_accuracy: 0.7841\n"]}],"source":["history = model.fit(train_ds,\n","                  batch_size = 32,\n","                  verbose=1,\n","                  epochs=10,\n","                  validation_data=validation_ds,\n","                  shuffle=False)"]},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAbQAAALgCAYAAAD8w4I6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd1yTxx8H8E8SIOy9EUEQJ7gYtlrFrajUrTjBWeuu1boqKq7WiaJ11VEH7v1zIq46wYF7K6LgAmSvkNzvjzSPxgQZAoHwfb9evGou9zx3z5Hmy91zzx2PMcZACCGElHN8VVeAEEIIKQ4U0AghhKgFCmiEEELUAgU0QgghaoECGiGEELVAAY0QQohaoIBGCCFELVBAI4QQohYooBFCCFELFNAIIYSoBQpohBBC1AIFNEIIIWqBAhohhBC1QAGNEEKIWqCARgghRC1QQCOEEKIWKKARQghRCxTQCCGEqAUKaIQQQtQCBTRCCCFqgQIaIYQQtUABjRBCiFqggEYIIUQtUEAjhBCiFiigEUIIUQsU0AghhKgFCmiEEELUAgU0QgghaoECGiGEELVAAY0QQohaoIBGCiUgIACOjo5FOnbmzJng8XjFW6EyJjo6GjweD5s2bSr1snk8HmbOnMm93rRpE3g8HqKjo/M91tHREQEBAcVan2/5rBBSFBTQ1ASPxyvQz9mzZ1Vd1QpvzJgx4PF4ePr0aZ55pk2bBh6Ph9u3b5dizQovLi4OM2fORFRUlKqrotSDBw/A4/Ggra2NpKQkVVeHlDAKaGpiy5Ytcj+tW7dWml6zZs1vKmfdunV49OhRkY79/fffkZmZ+U3lq4O+ffsCAEJDQ/PMs337dri5uaFOnTpFLqd///7IzMyEg4NDkc+Rn7i4OMyaNUtpQPuWz0px2bp1K6ytrQEAe/bsUWldSMnTUHUFSPHo16+f3OsrV64gLCxMIf1LGRkZ0NXVLXA5mpqaRaofAGhoaEBDgz5yDRs2RNWqVbF9+3YEBgYqvH/58mW8ePECf/zxxzeVIxAIIBAIvukc3+JbPivFgTGG0NBQ9OnTBy9evMC2bdswZMgQldYpL+np6dDT01N1Nco96qFVIM2aNYOrqyuuX7+Opk2bQldXF1OnTgUAHDx4EB06dICtrS2EQiGcnZ0xe/ZsiMViuXN8eV9Eds9o0aJFWLt2LZydnSEUCuHp6YnIyEi5Y5XdQ+PxeBg1ahQOHDgAV1dXCIVC1K5dG8ePH1eo/9mzZ+Hh4QFtbW04OztjzZo1Bb4v9++//6JHjx6oXLkyhEIh7O3t8csvvyj0GAMCAqCvr4/Y2Fh07twZ+vr6sLCwwIQJExTaIikpCQEBATAyMoKxsTH8/f0LPKzVt29fPHz4EDdu3FB4LzQ0FDweD71790ZOTg4CAwPh7u4OIyMj6OnpoUmTJjhz5ky+ZSi7h8YYw5w5c1CpUiXo6uqiefPmuHfvnsKxiYmJmDBhAtzc3KCvrw9DQ0P4+Pjg1q1bXJ6zZ8/C09MTADBw4EBuWFt2/1DZPbT09HT8+uuvsLe3h1AoRPXq1bFo0SIwxuTyFeZzkZeLFy8iOjoafn5+8PPzw/nz5/H69WuFfBKJBMuWLYObmxu0tbVhYWGBdu3a4dq1a3L5tm7dCi8vL+jq6sLExARNmzbFyZMn5er8+T1MmS/vT8p+L+fOncOIESNgaWmJSpUqAQBevnyJESNGoHr16tDR0YGZmRl69Oih9D5oUlISfvnlFzg6OkIoFKJSpUoYMGAA4uPjkZaWBj09PYwdO1bhuNevX0MgEGD+/PkFbMnyg/5crmASEhLg4+MDPz8/9OvXD1ZWVgCk/5Pp6+tj/Pjx0NfXx+nTpxEYGIiUlBQsXLgw3/OGhoYiNTUVP/30E3g8HhYsWICuXbvi+fPn+f6lfuHCBezbtw8jRoyAgYEBli9fjm7duiEmJgZmZmYAgJs3b6Jdu3awsbHBrFmzIBaLERQUBAsLiwJd9+7du5GRkYGff/4ZZmZmiIiIQEhICF6/fo3du3fL5RWLxWjbti0aNmyIRYsW4dSpU1i8eDGcnZ3x888/A5AGhk6dOuHChQsYPnw4atasif3798Pf379A9enbty9mzZqF0NBQNGjQQK7sXbt2oUmTJqhcuTLi4+Px999/o3fv3hg6dChSU1Oxfv16tG3bFhEREahXr16BypMJDAzEnDlz0L59e7Rv3x43btxAmzZtkJOTI5fv+fPnOHDgAHr06IEqVarg3bt3WLNmDby9vXH//n3Y2tqiZs2aCAoKQmBgIIYNG4YmTZoAABo1aqS0bMYYfvzxR5w5cwaDBw9GvXr1cOLECUycOBGxsbFYunSpXP6CfC6+Ztu2bXB2doanpydcXV2hq6uL7du3Y+LEiXL5Bg8ejE2bNsHHxwdDhgxBbm4u/v33X1y5cgUeHh4AgFmzZmHmzJlo1KgRgoKCoKWlhatXr+L06dNo06ZNgdv/cyNGjICFhQUCAwORnp4OAIiMjMSlS5fg5+eHSpUqITo6GqtWrUKzZs1w//59bjQlLS0NTZo0wYMHDzBo0CA0aNAA8fHxOHToEF6/fo169eqhS5cu2LlzJ5YsWSLXU9++fTsYY9zQt1phRC2NHDmSffnr9fb2ZgDY6tWrFfJnZGQopP30009MV1eXZWVlcWn+/v7MwcGBe/3ixQsGgJmZmbHExEQu/eDBgwwAO3z4MJc2Y8YMhToBYFpaWuzp06dc2q1btxgAFhISwqX5+voyXV1dFhsby6U9efKEaWhoKJxTGWXXN3/+fMbj8djLly/lrg8ACwoKkstbv3595u7uzr0+cOAAA8AWLFjApeXm5rImTZowAGzjxo351snT05NVqlSJicViLu348eMMAFuzZg13zuzsbLnjPn78yKysrNigQYPk0gGwGTNmcK83btzIALAXL14wxhh7//4909LSYh06dGASiYTLN3XqVAaA+fv7c2lZWVly9WJM+rsWCoVybRMZGZnn9X75WZG12Zw5c+Tyde/enfF4PLnPQEE/F3nJyclhZmZmbNq0aVxanz59WN26deXynT59mgFgY8aMUTiHrI2ePHnC+Hw+69Kli0KbfN6OX7a/jIODg1zbyn4vP/zwA8vNzZXLq+xzevnyZQaAbd68mUsLDAxkANi+ffvyrPeJEycYAHbs2DG59+vUqcO8vb0VjlMHNORYwQiFQgwcOFAhXUdHh/t3amoq4uPj0aRJE2RkZODhw4f5nrdXr14wMTHhXsv+Wn/+/Hm+x7Zq1QrOzs7c6zp16sDQ0JA7ViwW49SpU+jcuTNsbW25fFWrVoWPj0++5wfkry89PR3x8fFo1KgRGGO4efOmQv7hw4fLvW7SpInctRw9ehQaGhpcjw2Q3rMaPXp0geoDSO97vn79GufPn+fSQkNDoaWlhR49enDn1NLSAiAdGktMTERubi48PDyUDld+zalTp5CTk4PRo0fLDdOOGzdOIa9QKASfL/16EIvFSEhIgL6+PqpXr17ocmWOHj0KgUCAMWPGyKX/+uuvYIzh2LFjcun5fS6+5tixY0hISEDv3r25tN69e+PWrVtyQ6x79+4Fj8fDjBkzFM4ha6MDBw5AIpEgMDCQa5Mv8xTF0KFDFe5xfv45FYlESEhIQNWqVWFsbCzX7nv37kXdunXRpUuXPOvdqlUr2NraYtu2bdx7d+/exe3bt/O9t15eUUCrYOzs7LgvyM/du3cPXbp0gZGREQwNDWFhYcF96JOTk/M9b+XKleVey4Lbx48fC32s7HjZse/fv0dmZiaqVq2qkE9ZmjIxMTEICAiAqakpd1/M29sbgOL1ye6j5FUfQHqvw8bGBvr6+nL5qlevXqD6AICfnx8EAgE32zErKwv79++Hj4+P3B8H//zzD+rUqQNtbW2YmZnBwsICR44cKdDv5XMvX74EALi4uMilW1hYyJUHSIPn0qVL4eLiAqFQCHNzc1hYWOD27duFLvfz8m1tbWFgYCCXLpt5K6ufTH6fi6/ZunUrqlSpAqFQiKdPn+Lp06dwdnaGrq6u3Bf8s2fPYGtrC1NT0zzP9ezZM/D5fNSqVSvfcgujSpUqCmmZmZkIDAzk7jHK2j0pKUmu3Z89ewZXV9evnp/P56Nv3744cOAAMjIyAEiHYbW1tbk/mNQNBbQK5vO/AGWSkpLg7e2NW7duISgoCIcPH0ZYWBj+/PNPANIvt/zkNZuOfXGzv7iPLQixWIzWrVvjyJEjmDRpEg4cOICwsDBu8sKX11daMwMtLS3RunVr7N27FyKRCIcPH0ZqaqrcvY2tW7ciICAAzs7OWL9+PY4fP46wsDC0aNGiQL+Xopo3bx7Gjx+Ppk2bYuvWrThx4gTCwsJQu3btEi33c0X9XKSkpODw4cN48eIFXFxcuJ9atWohIyMDoaGhxfbZKogvJxPJKPt/cfTo0Zg7dy569uyJXbt24eTJkwgLC4OZmVmR2n3AgAFIS0vDgQMHuFmfHTt2hJGRUaHPVR7QpBCCs2fPIiEhAfv27UPTpk259BcvXqiwVp9YWlpCW1tb6YPIX3s4WebOnTt4/Pgx/vnnHwwYMIBLDwsLK3KdHBwcEB4ejrS0NLleWmGfu+rbty+OHz+OY8eOITQ0FIaGhvD19eXe37NnD5ycnLBv3z654S1lQ2QFqTMAPHnyBE5OTlz6hw8fFHo9e/bsQfPmzbF+/Xq59KSkJJibm3OvCzPk5uDggFOnTiE1NVWulyYb0i6u5+X27duHrKwsrFq1Sq6ugPT38/vvv+PixYv44Ycf4OzsjBMnTiAxMTHPXpqzszMkEgnu37//1Uk4JiYmCrNcc3Jy8ObNmwLXfc+ePfD398fixYu5tKysLIXzOjs74+7du/mez9XVFfXr18e2bdtQqVIlxMTEICQkpMD1KW+oh0a4v4Q//6s1JycHf/31l6qqJEcgEKBVq1Y4cOAA4uLiuPSnT58q3HfJ63hA/voYY1i2bFmR69S+fXvk5uZi1apVXJpYLC70l0Xnzp2hq6uLv/76C8eOHUPXrl2hra391bpfvXoVly9fLnSdW7VqBU1NTYSEhMidLzg4WCGvQCBQ6MXs3r0bsbGxcmmyZ6cK8rhC+/btIRaLsWLFCrn0pUuXgsfjFfh+aH62bt0KJycnDB8+HN27d5f7mTBhAvT19blhx27duoExhlmzZimcR3b9nTt3Bp/PR1BQkEIv6fM2cnZ2lrsfCgBr167Ns4emjLJ2DwkJUThHt27dcOvWLezfvz/Pesv0798fJ0+eRHBwMMzMzLh2jo+Px8OHD7nhSHVAPTSCRo0awcTEBP7+/tyyTFu2bCnVYZn8zJw5EydPnkTjxo3x888/c1+Mrq6u+S67VKNGDTg7O2PChAmIjY2FoaEh9u7dW6B7MXnx9fVF48aNMXnyZERHR6NWrVrYt29foe8v6evro3Pnztx9tC+nUnfs2BH79u1Dly5d0KFDB7x48QKrV69GrVq1kJaWVqiyZM/TzZ8/Hx07dkT79u1x8+ZNHDt2TKEn07FjRwQFBWHgwIFo1KgR7ty5g23btsn17ADpl7ixsTFWr14NAwMD6OnpoWHDhkrvD/n6+qJ58+aYNm0aoqOjUbduXZw8eRIHDx7EuHHj5CaAFFVcXBzOnDmjMPFERigUom3btti9ezeWL1+O5s2bo3///li+fDmePHmCdu3aQSKR4N9//0Xz5s0xatQoVK1aFdOmTcPs2bPRpEkTdO3aFUKhEJGRkbC1teWe5xoyZAiGDx+Obt26oXXr1rh16xZOnDih0LZf07FjR2zZsgVGRkaoVasWLl++jFOnTik8pjBx4kTs2bMHPXr0wKBBg+Du7o7ExEQcOnQIq1evRt26dbm8ffr0wW+//Yb9+/fj559/5h6jWbFiBWbNmoUzZ86gWbNmhWzpMqqUZ1WSUpLXtP3atWsrzX/x4kX23XffMR0dHWZra8t+++03btrvmTNnuHx5TdtfuHChwjnxxTTmvKbtjxw5UuHYL6c6M8ZYeHg4q1+/PtPS0mLOzs7s77//Zr/++ivT1tbOoxU+uX//PmvVqhXT19dn5ubmbOjQodw08M+nnPv7+zM9PT2F45XVPSEhgfXv358ZGhoyIyMj1r9/f3bz5s0CT9uXOXLkCAPAbGxslE4LnzdvHnNwcGBCoZDVr1+f/e9//1P4PTCW/7R9xhgTi8Vs1qxZzMbGhuno6LBmzZqxu3fvKrR3VlYW+/XXX7l8jRs3ZpcvX2be3t4KU74PHjzIatWqxT1CIbt2ZXVMTU1lv/zyC7O1tWWamprMxcWFLVy4UG76u+xaCvq5+NzixYsZABYeHp5nnk2bNjEA7ODBg4wx6aMRCxcuZDVq1GBaWlrMwsKC+fj4sOvXr8sdt2HDBla/fn0mFAqZiYkJ8/b2ZmFhYdz7YrGYTZo0iZmbmzNdXV3Wtm1b9vTp0zyn7UdGRirU7ePHj2zgwIHM3Nyc6evrs7Zt27KHDx8qve6EhAQ2atQoZmdnx7S0tFilSpWYv78/i4+PVzhv+/btGQB26dIlLk32mf78/+/yjsdYGfoznJBC6ty5M+7du4cnT56ouiqElFldunTBnTt3CnTPuTyje2ik3PhymaonT57g6NGj6jNcQkgJePPmDY4cOYL+/furuioljnpopNywsbFBQEAAnJyc8PLlS6xatQrZ2dm4efOmwrNVhFR0L168wMWLF/H3338jMjISz54943YeUFc0KYSUG+3atcP27dvx9u1bCIVCfP/995g3bx4FM0KUOHfuHAYOHIjKlSvjn3/+UftgBlAPjRBCiJqge2iEEELUAgU0QgghaoECGqmQlG0+SQgp3yigkTJFtutxfj9nz55VdVXzdPToUfB4PNja2pbaQr7qLDY2Fj179oSxsTEMDQ3RqVOnAm0hA0gXnl69ejXq1asHfX19WFlZwcfHB5cuXZLLd/bs2Tw/a1euXMnz/ElJSbC0tASPx8OePXu+6TrJt6NZjqRM2bJli9zrzZs3IywsTCFdtuVIUa1bt67Egs22bdvg6OiI6OhonD59Gq1atSqRciqCtLQ0NG/eHMnJyZg6dSo0NTWxdOlSeHt7IyoqKt+dqydOnIglS5agX79+GDFiBJKSkridty9evAgvLy+5/GPGjIGnp6dc2te2KAoMDFSrtRDLPVUuU0JIfpQt4aVMenp6KdQmf2lpaUxPT48tX76c1a9fnwUEBKi6SnlKS0tTdRXy9eeffzIALCIigkt78OABEwgEbMqUKV89ViQSMR0dHda9e3e59OfPnyvsUn3mzBkGgO3evbvAdbtz5w7T0NBgQUFBhT6WlAwaciTlTrNmzeDq6orr16+jadOm0NXVxdSpUwEABw8eRIcOHWBrawuhUAhnZ2fMnj1bYbXyL++hRUdHg8fjYdGiRVi7di2cnZ0hFArh6emJyMjIAtdt//79yMzMRI8ePeDn58dtZfKlrKwszJw5E9WqVYO2tjZsbGzQtWtXPHv2jMsjkUiwbNkyuLm5cZuOtmvXDteuXZOrs2xft8/xeDzMnDmTez1z5kzweDzcv38fffr0gYmJCX744QcAwO3bt7kH1rW1tWFtbY1BgwYhISFB4byxsbEYPHgw175VqlTBzz//jJycHDx//hw8Hg9Lly5VOO7SpUvg8XjYvn07twt6fHx8vu25Z88eeHp6yvWaatSogZYtW2LXrl1fPVYkEiEzMxNWVlZy6ZaWluDz+Ur3IwOkO7bn5ubmW7exY8eiS5cu3O7sRPUooJFyKSEhAT4+PqhXrx6Cg4PRvHlzAMCmTZugr6+P8ePHY9myZXB3d0dgYCAmT55coPOGhoZi4cKF+OmnnzBnzhxER0eja9euEIlEBTp+27ZtaN68OaytreHn54fU1FQcPnxYLo9YLEbHjh0xa9YsuLu7Y/HixRg7diySk5Pl9rgaPHgwxo0bB3t7e/z555+YPHkytLW1v3pPJz89evRARkYG5s2bh6FDhwKQ7gv3/PlzDBw4ECEhIfDz88OOHTvQvn17uR0X4uLi4OXlhR07dqBXr15Yvnw5+vfvj3PnziEjIwNOTk5o3Lix3I7Qn7eLgYEBOnXqhIiICNSsWVNhG5kvSSQS3L59Gx4eHgrveXl54dmzZ0hNTc3zeB0dHTRs2BCbNm3Ctm3bEBMTwwVvExMTDBs2TOGYgQMHwtDQENra2mjevDn3x8OXdu/ejUuXLmHBggVfvQZSylTdRSTka/LaNQAAW716tUL+jIwMhbSffvqJ6erqsqysLC4tr10DzMzMWGJiIpd+8OBBBoAdPnw437q+e/eOaWhosHXr1nFpjRo1Yp06dZLLt2HDBgaALVmyROEcslXnT58+rTAs9mUeWZ2VreyPPHY66N27t0JeZW22fft2BoCdP3+eSxswYADj8/lKV4mX1WnNmjUMAHvw4AH3Xk5ODjM3N+dWi5cN731eP2U+fPjAALCgoCCF91auXMkAsIcPH371HE+ePGENGjRgALgfJycnheMuXrzIunXrxtavX88OHjzI5s+fz8zMzJi2tja7ceOGXN6MjAxWuXJlbsizKMOVpGRQD42US0KhEAMHDlRI/3wYKTU1FfHx8WjSpAk3zJWfXr16wcTEhHstG04qyKy6HTt2gM/no1u3blxa7969cezYMbm91/bu3Qtzc3OMHj1a4RyyHaD37t0LHo+ndGfqwuwS/aXhw4crpH3eZllZWYiPj8d3330HALhx4wYAaW/pwIED8PX1VdpjktWpZ8+e0NbWluulnThxAvHx8ejXrx8A6ZAxY0xuSFQZ2WLUQqFQ4T3ZJqhfLlj9JQMDA9SuXRsjR47Evn378NdffyE3NxedO3eWG/Js1KgR9uzZg0GDBuHHH3/E5MmTceXKFfB4PEyZMkXunH/88QdEIhE3zE3KDgpopFyys7ODlpaWQvq9e/fQpUsXGBkZwdDQEBYWFtwXaUE236xcubLca1lwK8hmoFu3boWXlxcSEhLw9OlTPH36FPXr10dOTg52797N5Xv27BmqV68ODY28Jxk/e/YMtra2MDU1zbfcwlC28WZiYiLGjh0LKysr6OjowMLCgssna7MPHz4gJSUFrq6uXz2/sbExfH19uQ1LAelwo52dHVq0aFGousoCbXZ2tsJ7svuSed0HA4Dc3Fy0atUKRkZGWLFiBbp06YKff/4Zp06dwrNnz7Bw4cKvll+1alV06tQJZ86c4e7BRkdHY+HChZg7dy709fULdT2k5NG0fVIuKfsiS0pKgre3NwwNDREUFARnZ2doa2vjxo0bmDRpUoGm6QsEAqXpLJ8lT588ecJNHlG2WPK2bduU3rP5Fnn11L6cAPM5Ze3Ws2dPXLp0CRMnTuSe15JIJNzuzYU1YMAA7h6Tm5sbDh06hBEjRoDPL9zfz6amphAKhXjz5o3Ce7I0W1vbPI8/f/487t69iyVLlsilu7i4oGbNmrh48WK+dbC3t0dOTg7S09NhaGiIwMBA2NnZoVmzZoiOjgYAvH37FoA06EdHR6Ny5cqFvlZSPCigEbVx9uxZJCQkYN++fWjatCmX/uLFixIve9u2bdDU1MSWLVsUguKFCxewfPlyxMTEoHLlynB2dsbVq1chEomgqamp9HzOzs44ceIEEhMT8+ylyXqPSUlJcukvX74scL0/fvyI8PBwzJo1C4GBgVz6lxumWlhYwNDQUG7SSl7atWsHCwsLbNu2DQ0bNkRGRkaR9uLi8/lwc3NTOjHj6tWrcHJygoGBQZ7Hv3v3DoDyAC8SiQo0k/H58+fQ1tbmemMxMTF4+vQpnJycFPKOGDECgLRNjY2N8z03KX70ZwRRG7JA8nlvKicnB3/99VeJl71t2zY0adIEvXr1Qvfu3eV+Jk6cCADYvn07AKBbt26Ij49XOstPVvdu3bqBMYZZs2blmcfQ0BDm5uY4f/683PuFuV5lbQYAwcHBcq/5fD46d+6Mw4cPKw0wnx+voaGB3r17Y9euXdi0aRPc3NxQp04d7v3CTNvv3r07IiMj5cp89OgRTp8+jR49esjlffjwIWJiYrjX1apVAyC9t/m5Gzdu4NGjR6hfvz6X9uHDB4Wyb926hUOHDqFNmzZcj2vOnDnYv3+/3M/s2bMBAL/99hv2798PPT29fK+LlAzqoRG10ahRI5iYmMDf3x9jxowBj8fDli1b8h0u/FZXr17F06dPMWrUKKXv29nZoUGDBti2bRsmTZqEAQMGYPPmzRg/fjwiIiLQpEkTpKen49SpUxgxYgQ6deqE5s2bo3///li+fDmePHnCDf/9+++/aN68OVfWkCFD8Mcff2DIkCHw8PDA+fPn8fjx4wLX3dDQEE2bNsWCBQsgEolgZ2eHkydPKu3Vzps3DydPnoS3tzeGDRuGmjVr4s2bN9i9ezcuXLgg1ysZMGAAli9fjjNnzuDPP/+UO09ERASaN2+OGTNm5DsxZMSIEVi3bh06dOiACRMmQFNTE0uWLIGVlRV+/fVXubw1a9aEt7c3tyyau7s7WrdujX/++QcpKSlo06YN3rx5g5CQEOjo6GDcuHHcsb169YKOjg4aNWoES0tL3L9/H2vXroWuri7++OMPLp/s2b3Pya7b09MTnTt3/ur1kBKmsvmVhBRAXtP2a9eurTT/xYsX2Xfffcd0dHSYra0t++2339iJEycYAHbmzBkuX17T9hcuXKhwTuQzxXz06NEMAHv27FmeeWbOnMkAsFu3bjHGpFO/p02bxqpUqcI0NTWZtbU16969u9w5cnNz2cKFC1mNGjWYlpYWs7CwYD4+Puz69etcnoyMDDZ48GBmZGTEDAwMWM+ePdn79+/znLb/4cMHhbq9fv2adenShRkbGzMjIyPWo0cPFhcXp/S6X758yQYMGMAsLCyYUChkTk5ObOTIkSw7O1vhvLVr12Z8Pp+9fv1aLr2g0/ZlXr16xbp3784MDQ2Zvr4+69ixI3vy5IlCPgDM29tbLi0jI4MFBQWxWrVqMR0dHWZkZMQ6duzIbt68KZdv2bJlzMvLi5mamjINDQ1mY2PD+vXrp7ScL9G0/bKDNvgkhJSI+vXrw9TUFOHh4aquCqkg6B4aIaTYXbt2DVFRURgwYICqq0IqEOqhEUKKzd27d3H9+nUsXrwY8fHx3CxBQkoD9dAIIcVmz549GDhwIEQiEbZv307BjJQq6qERQghRC9RDI4QQohYooJEyRdkeX7K9vAriy33AikOzZs3QrFmzYj0nIaT4UUAjRfbjjz9CV1f3q3tS9e3bF1paWko3iyxL7t+/j5kzZ3Lr85U1R48eBY/Hg62tbZHWVyTyYmNj0bNnTxgbG8PQ0BCdOnUq0I4Ksj+48vqR7TEn8+TJE/j5+aFSpUrQ1dVFjRo1EBQUhIyMjDzLSEpKgqWlJXg8Hvbs2fPN11qR0EohpMj69u2Lw4cPY//+/UqnZ2dkZODgwYNo164dzMzMilzO77//XuANOovq/v37mDVrFpo1aya3kzUAnDx5skTLLoht27bB0dER0dHROH36NFq1aqXqKpVbaWlpaN68OZKTkzF16lRoampi6dKl8Pb2RlRU1Fc/qxYWFtiyZYtC+vHjx7Ft2za0adOGS3v16hW8vLxgZGSEUaNGwdTUFJcvX8aMGTNw/fp1HDx4UGkZgYGBXw145CtU+VQ3Kd8yMjKYgYEBa9u2rdL3Q0NDGQC2Y8eOAp/za5tWFgQKsQLF53bv3q2wmkhZkZaWxvT09Njy5ctZ/fr1WUBAgKqrlKe0tDRVVyFff/75JwPAIiIiuLQHDx4wgUDAbdpZWC1btmSGhoYsMzOTS5s7dy4DwO7evSuXd8CAAQyA3EayMnfu3GEaGhosKCiIVh8pAhpyJEWmo6ODrl27Ijw8HO/fv1d4PzQ0FAYGBvjxxx+RmJiICRMmwM3NDfr6+jA0NISPjw9u3bqVbznK7qFlZ2fjl19+gYWFBVfG69evFY59+fIlRowYgerVq0NHRwdmZmbo0aOH3NDipk2buIVumzdvzg0fydYEVHYP7f379xg8eDCsrKygra2NunXr4p9//pHLIxueWrRoEdauXQtnZ2cIhUJ4enpyW80UxP79+5GZmYkePXrAz88P+/bt4/YD+1xWVhZmzpyJatWqQVtbGzY2NujatSuePXvG5ZFIJFi2bBnc3Nygra0NCwsLtGvXjlv8V9k9TJkv70/Kfi/3799Hnz59YGJiwq11ePv2bQQEBMDJyQna2tqwtrbGoEGDlA49x8bGYvDgwbC1tYVQKESVKlXw888/IycnB8+fPwePx8PSpUsVjrt06RJ4PB62b99eqAWP9+zZA09PT3h6enJpNWrUQMuWLbFr1658j//SmzdvcObMGXTt2lXuMYWUlBQAgJWVlVx+Gxsb8Pl8pfv5jR07Fl26dOE2liWFQwGNfJO+ffsiNzdX4YsgMTERJ06cQJcuXaCjo4Pnz5/jwIED6NixI5YsWYKJEyfizp078Pb2RlxcXKHLHTJkCIKDg9GmTRv88ccf0NTURIcOHRTyRUZG4tKlS/Dz88Py5csxfPhwhIeHo1mzZtywTtOmTTFmzBgAwNSpU7FlyxZs2bIFNWvWVFp2ZmYmmjVrhi1btqBv375YuHAhjIyMEBAQgGXLlinkDw0NxcKFC/HTTz9hzpw5iI6ORteuXSESiQp0rdu2bUPz5s1hbW0NPz8/pKam4vDhw3J5xGIxOnbsiFmzZsHd3R2LFy/G2LFjkZycLLfly+DBgzFu3DjY29vjzz//xOTJk6GtrY0rV64UqC7K9OjRAxkZGZg3bx53DyksLAzPnz/HwIEDERISAj8/P+zYsQPt27eXWyw6Li4OXl5e2LFjB3r16oXly5ejf//+OHfuHDIyMuDk5ITGjRvL7YD9ebsYGBigU6dOiIiIQM2aNZXuYPA5iUSC27dvK91128vLC8+ePfvqPWFlduzYAYlEgr59+8qly/4IGjx4MKKiovDq1Svs3LkTq1atwpgxYxRW5ZftIbdgwYJClU8+o+ouIinfcnNzmY2NDfv+++/l0levXs0AsBMnTjDGGMvKymJisVguz4sXL5hQKGRBQUFyafhiyFG2sK5MVFQUA8BGjBghd74+ffooDDlmZGQo1Pny5csMANu8eTOX9rUhR29vb7lFb4ODgxkAtnXrVi4tJyeHff/990xfX5+lpKTIXYuZmZnc8NLBgwcZAHb48GGFsr707t07pqGhwdatW8elNWrUiHXq1Eku34YNGxgAtmTJEoVzSCQSxhhjp0+fZgDYmDFj8szztSHfL9tW9nvp3bu3Ql5l7b59+3YGgJ0/f55LGzBgAOPz+SwyMjLPOq1Zs4YBYA8ePODey8nJYebm5szf358xVvAFjz98+MAAyH3mZFauXMkAsIcPH371HF9yd3dnNjY2Cp9vxhibPXs209HRYQC4n2nTpinky8jIYJUrV+aGPGnB46KhHhr5JgKBAH5+frh8+bLcMF5oaCisrKzQsmVLAIBQKOT2lBKLxUhISIC+vj6qV6+OGzduFKrMo0ePAgDXq5L5fDsQmc93aBaJREhISEDVqlVhbGxc6HI/L9/a2hq9e/fm0jQ1NTFmzBikpaXh3Llzcvl79erFbcYJgBtOKsisuh07doDP56Nbt25cWu/evXHs2DF8/PiRS9u7dy/Mzc0xevRohXPIhmv37t0LHo+HGTNm5JmnKIYPH66Q9nm7Z2VlIT4+Ht999x0AcO0ukUhw4MAB+Pr6Ku0xyerUs2dPaGtry/XSTpw4gfj4ePTr1w+AtDfEGMv3kY3MzEwA0s/jl2TDhbI8BfH48WNcv34dfn5+SnepdnR0RNOmTbF27Vrs3bsXgwYNwrx58xR6kn/88QdEIhGmTp1a4LKJIgpo5JvJhlpCQ0MBAK9fv8a///4LPz8/bgNJiUSCpUuXwsXFBUKhEObm5rCwsMDt27eRnJxcqPJevnwJPp8PZ2dnufTq1asr5M3MzERgYCDs7e3lyk1KSip0uZ+X7+LiovAFJhui/HLH6MqVK8u9lgW3zwNSXrZu3QovLy8kJCTg6dOnePr0KerXr4+cnBzs3r2by/fs2TNUr14dGhp5T1x+9uwZbG1t89wBu6iqVKmikJaYmIixY8fCysoKOjo6sLCw4PLJ2v3Dhw9ISUmBq6vrV89vbGwMX19f7vMFSIcb7ezs0KJFi0LVVRZos7OzFd6T3Zf8PBjnRxZkvxxuBKR/jAwbNgx///03hg4diq5du2L9+vXw9/fHpEmTuPuJ0dHRWLhwIebOncvtjE2KhgIa+Wbu7u6oUaMGtyPz9u3bwRiT+5983rx5GD9+PJo2bYqtW7fixIkTCAsLQ+3atUv0uarRo0dj7ty56NmzJ3bt2oWTJ08iLCwMZmZmpfY8lyyof4nls+rckydPEBkZiQsXLsDFxYX7kU28UHZf6Vvl1VMTi8V5HqMsAPTs2RPr1q3D8OHDsW/fPpw8eRLHjx8HgCK1+4ABA/D8+XNcunQJqampOHToEHr37q20V/Q1pqamEAqFePPmjcJ7sjRbW9sCny80NBTVq1eHu7u7wnt//fUX6tevj0qVKsml//jjj8jIyMDNmzcBSKfp29nZoVmzZoiOjkZ0dDTevn0LQBr0o6Oj6dnDAqLn0Eix6Nu3L6ZPn47bt28jNDQULi4ucrPI9uzZg+bNm2P9+vVyxyUlJcHc3LxQZTk4OEAikXC9EplHjx4p5N2zZw/8/f2xePFiLi0rKwtJSUly+Qoz5Obg4IDbt29DIpHIfaE+fPiQe784bNu2DZqamtiyZYtCULxw4QKWL1+OmJgYVK5cGc7Ozrh69SpEIhE0NTWVns/Z2RknTpxAYmJinr00We/xy/b5stf5NR8/fkR4eDhmzZqFwMBALv3Jkydy+SwsLGBoaCg3aSUv7dq1g4WFBbZt24aGDRsiIyMD/fv3L3CdZPh8Ptzc3LhZnZ+7evUqnJycYGBgUKBzyXYqDwoKUvr+u3fv5IaaZWSTgXJzcwEAMTExePr0KZycnBTyjhgxAoC0TT/fEZwoRz00UixkvbHAwEBERUUpDMEIBAKFHsnu3bsRGxtb6LJ8fHwAAMuXL5dLDw4OVsirrNyQkBCFHodsxtmXX+TKtG/fHm/fvsXOnTu5tNzcXISEhEBfXx/e3t4FuYx8bdu2DU2aNEGvXr3QvXt3uZ+JEycCANcr7tatG+Lj45XO8pNdf7du3cAYw6xZs/LMY2hoCHNzc5w/f17u/b/++qvA9ZYF3y/b/cvfD5/PR+fOnXH48GGlAebz4zU0NNC7d2/s2rULmzZtgpubG+rUqcO9X5hp+927d0dkZKRcmY8ePcLp06e5xzdkHj58iJiYGKXnkQ2B9unTR+n71apVw82bN/H48WO59O3bt4PP53P1nzNnDvbv3y/3M3v2bADAb7/9hv379yvMiCR5UNVsFKJ+GjVqxM3k+nLr+sDAQAaABQQEsLVr17LRo0czU1NT5uTkJDeDsCCzHBljrHfv3gwA69u3L1u5ciXr2rUrq1OnjsJMtwEDBjCBQMDGjh3L1qxZwwICAlilSpWYmZkZN0OOMcbevHnDBAIB++6779imTZvY9u3b2bt37xhjirMcMzIyWM2aNZmWlhb79ddfWUhICPP29mYAWHBwsMK1LFy4UKGtvqznl65cuaJwvi+5u7szNzc3xph0tmmzZs0YAObn58dWrlzJFixYwNq0acMOHDjAHdO/f38GgPn4+LBly5axpUuXsq5du7KQkBAuz+TJkxkANnjwYLZq1SrWu3dv5u7unucsxw8fPijUrWnTpkxXV5dNmzaN/fXXX6xz586sbt26Cud4/fo1s7a2Zrq6umzcuHFszZo1bObMmax27drs48ePcue8du0a9/n6888/5d4r6CxHxhhLSUlhzs7OzNLSki1YsIAtXbqU2dvbM1tbW/b+/Xu5vADkfvcyubm5zMrKin333Xd5lnPu3DkmEAiYpaUlCwoKYitXrmQ+Pj4MABsyZMhX60izHIuGAhopNrJpz15eXgrvZWVlsV9//ZXZ2NgwHR0d1rhxY3b58mWFYFHQgJaZmcnGjBnDzMzMmJ6eHvP19WWvXr1S+FL7+PEjGzhwIDM3N2f6+vqsbdu27OHDh8zBwUEuoDHG2Lp165iTkxMTCARyU/i/rCNj0un0svNqaWkxNzc3hanu3xLQRo8ezQCwZ8+e5Zln5syZDAC7desWY0waaKdNm8aqVKnCNDU1mbW1NevevbvcOXJzc9nChQtZjRo1mJaWFrOwsGA+Pj7s+vXrXJ6MjAw2ePBgZmRkxAwMDFjPnj3Z+/fvCxXQXr9+zbp06cKMjY2ZkZER69GjB4uLi1N63S9fvmQDBgxgFhYWTCgUMicnJzZy5EiWnZ2tcN7atWszPp/PXr9+LZdemIDGGGOvXr1i3bt3Z4aGhkxfX5917NhR4Y8wxvIOaMePH2cA2PLly79aztWrV5mPjw+ztrZmmpqarFq1amzu3LlMJBJ99TgKaEVD+6ERQsqN+vXrw9TUFOHh4aquCimD6B4aIaRcuHbtGqKiopQuhE0IQDtWE0LKuLt37+L69etYvHgx4uPj8fz5c7k1EwmRoR4aIaRM27NnDwYOHAiRSITt27dTMCN5oh4aIYQQtUA9NEIIIWqBAhohhBC1QEtfFZFEIkFcXBwMDAy+aaVyQgghX8cYQ2pqKmxtbb+6ficFtCKKi4uDvb29qqtBCCEVxqtXrxQWe/4cBbQiki1g+urVKxgaGhbqWJFIhJMnT6JNmzZ5LiRbUVHbKEftohy1S97UqW1SUlJgb2+f78LRFNCKSDbMaGhoWKSApqurC0NDw3L/QStu1DbKUbsoR+2SN3Vsm/xu79CkEEIIIWqBAhohhBC1QAGNEEKIWqCARgghRC1QQCOEEKIWKKARQghRCzRtnxBCSInIzBEj6lUSrkUnggEY09KlRMujgEYIIaRYJKRl49rLj7gWnYjI6I+4G5uMXIl0QxczPS2MblG1RJcKpIBGCCGk0BhjeJmQgcjoRFyL/ojIl4l4/iFdIZ+VoRCejqbwdDSFSMygpUEBjRBCiArliiV48CYVEdGJXA8sPi1bIV81K314OJrC09EEHg6mqGSiU2oLuFNAI4QQoiA9OxdRr5K4HtiNmI/IyBHL5dES8FGnkhEXwNwdTGCsq6WiGlNAI4SQb5IlEuPR21TcjUvGs/fp0NfWgIWBEBb6QlgYCGFpIP2vtqZA1VX9qvepWbge/RGR0R9x7WUi7sWlQPzf/S8ZQ20NeDiawsPRBJ6OpnCzMypT10UBjRBCCig9OxcP3qTgbmwy7sZJ//vkfZrCF78yBkJpoDP/L8DJAp7FZ68tDYQw1dOChqBkn6hijOF5fDo3dHgtOhHRCRkK+eyMdaRDh//dA3Ox1AefX3b3f6SARgghSiRninAvLhn3YlNwNy4Zd2OT8Tw+HUxJ7DLT04KrnRGqWekjUyTGh9Rs6U9aNt6nZCM7V4LU7FykZufiebzixInP8XjS85krC3iG2nKB0FBbo0D3p0RiCe7GJksnb0Qn4vrLj0hIz1Eot7qVATz/64F5OJrCzlinUG2mahTQCCEVXkJaNtfjuheXjLuxKYhJVOyxAIC1oTZc7QzhamcEV1sjuNoZwcpQmGdgYYwhLTsXH1Kz8V4W6P4Ldh++eJ2Qlg0JA+LTchCfloOHb1O/Wm8tDb7Snp6FgRD6Wnwci+Fj+4ZIRL1ORpZIonBsPXtjrgfWoLIJjHTK9zYzFNAIIRUGYwzvUrL/GzKUBq57ccl4k5ylNL+9qQ4XtGrbGqK2rREsDISFKpPH48FAWxMG2ppwstD/al6xhCExPSfPgPchNYt7nZKVi5xcCWKTMhGblJnHGfkAPgIAjHU14eFgygUwVztDCDXKzv2v4kABjRCilhhjeP0xUyF4xaflKOTl8YAq5nr/BS9DuNoaobatEYx0S7fHIuDzuJ5WfrJEYsSn5d3jS0jLBj8jEZ0au+I7Z3M4mZft+1/FgQIaIaTck0gYnn9Iw924FNz7LIAlZ4oU8gr4PLhY6qO2LHjZGaGmjSH0heXr61BbU4BKJrqoZKKr9H2RSISjR4+ivUcltdmxOj/l6zdICCH/YYzh2N23WH5XgKnXTyP9i2ekAOlzUtWtDeBqZ/hfADNCDWuDMjXVnBQfCmiEkHLnfUoWph+8ixP33gHgARBDW5OPmjaG3LBhbVsjVLMygJYGbSpSUVBAI4SUG4wx7LsRi6D/3UdypggafB5a2ogxtssPqGZjVOLPb5GyjQIaIaRciEvKxNT9d3D20QcAgJudEeZ1roXnN/6Fi5U+BTNCAY0QUrYxxrA94hXmHX2AtOxcaGnwMa6VC4Y1cQKTiPFc1RUkZQYFNEJImRWTkIHJ+27j0rMEAECDysZY0L0OqloaAABEEsWJIKTiooBGCClzJBKGzZej8efxR8gUSSd8TGxbAwGNHCFQ82epSNFRQCOElCnPP6Rh0t7biIyWrnDRsIopFnSvAwczPRXXjJR1FNAIIWVCrliC9RdeYEnYY2TnSqCnJcDk9jXR16uy2q9wQYoHBTRCiMo9epuK3/bcwq3XyQCAJi7mmN/VLc9VMAhRhgIaIURlRGIJVp99huWnn0AkZjDQ1sD0jrXQw71SgbZFIeRzFNAIISpxNzYZv+25jftvUgAArWpaYm4XN1gZaqu4ZqS8ooBGCClV2blihIQ/xapzzyCWMJjoamLmj7XxY11b6pWRb1IuHq1fuXIlHB0doa2tjYYNGyIiIuKr+YODg1G9enXo6OjA3t4ev/zyC7KylO939Mcff4DH42HcuHElUHNCyOduxnxEx+UXsOLMU4glDB3cbHDyF290qmdHwYx8szLfQ9u5cyfGjx+P1atXo2HDhggODkbbtm3x6NEjWFpaKuQPDQ3F5MmTsWHDBjRq1AiPHz9GQEAAeDwelixZIpc3MjISa9asQZ06dUrrcgipkLJEYiw++QjrL7yAhAHm+lqY3ckVPm42qq4aUSNlvoe2ZMkSDB06FAMHDkStWrWwevVq6OrqYsOGDUrzX7p0CY0bN0afPn3g6OiINm3aoHfv3gq9urS0NPTt2xfr1q2DiYlJaVwKIRVSxItE+Cz7F+v+lQazrvXtEPaLNwUzUuzKdA8tJycH169fx5QpU7g0Pp+PVq1a4fLly0qPadSoEbZu3YqIiAh4eXnh+fPnOHr0KPr37y+Xb+TIkejQoQNatWqFOXPm5FuX7OxsZGdnc69TUqQ3skUiEUQixU0Ev0aWv7DHVQTUNsqVx3ZJz87F4rAn2HL1FQDAykCIoE610KK6BYDiuZby2C6lRZ3apqDXUKYDWnx8PMRiMaysrOTSrays8PDhQ6XH9OnTB/Hx8fjhhx/AGENubi6GDx+OqVOncnl27NiBGzduIDIyssB1mT9/PmbNmqWQfvLkSejqFu1ZmbCwsCIdVxFQ2yhXXtrlURIPO57zkZgtvS/2vaUEPzqkI+tZJI4+K/7yyku7qII6tE1GRkaB8pXpgFYUZ8+exbx58/DXX3+hYcOGePr0KcaOHYvZs2dj+vTpePXqFcaOHYuwsDBoaxd8evCUKVMwfvx47nVKSgrs7e3Rpk0bGBoaFqqOIpEIYWFhaN26dYXZGr2gqG2UKy/tkpolwp8nHmPng1gAgJ2xNuZ0qo0fqpqVSHnlpV1UQZ3aRjYilp8yHdDMzc0hEAjw7t07ufR3797B2tpa6THTp09H//79MWTIEACAm5sb0tPTMWzYMEybNg3Xr1/H+/fv0aBBA+4YsViM8+fPY8WKFcjOzoZAoLg9u1AohFAoVEjX1NQs8oflW45Vd9Q20gV6n7xPQ2R0IiKeJ+BpDB9Xcp+gkqkeKpnowNZY+mNlICwTe4GdfvgOU/fdxdsU6YziAd87YFK7GtATlvzXDH1e8qYObVPQ+pfpgKalpQV3d3eEh4ejc+fOAACJRILw8HCMGjVK6TEZGRng8+X/55YFKMYYWrZsiTt37si9P3DgQNSoUQOTJk1SGswIKQ1ZIjHuxCYjMjoR16I/4lp0IlKycj/Lwcf9yNcKx/F5gLWhNhfgbI11YGcs/9pQW6PEpsUnZeQg6PB97Lsp7ZU5muniz2510NCpZHplhOSlTAc0ABg/fjz8/f3h4eEBLy8vBAcHIz09HQMHDgQADBgwAHZ2dpg/fz4AwNfXF0uWLEH9+vW5Icfp06fD19cXAoEABgYGcHV1lStDT08PZmZmCumElKTkDBGuxyQiMvojIl8k4vbrZOSIJXJ5dDQFaOBgjAb2Rnj/8gnMK7vgbUoO4pIyEZeciTdJWcgRSxCXnIW45Czg5UelZekLNWD7WZCzM9aRvjaSvrY20oZmEXp5x+++we8H7iE+LRt8HjD4hyoY37o6dLToD0NS+sp8QOvVqxc+fPiAwMBAvH37FvXq1cPx48e5iSIxMTFyPbLff/8dPB4Pv//+O2JjY2FhYQFfX1/MnTtXVZdACBhjiE3KxLXoj1wP7NG7VIV85vpa8HQ0hYejKTwdTVDTxhCaAj5EIhGOHn2M9i2ryg2/SCQM8enZiEvKkga5pEy8/pjJBby4pCwkpucgLTsXj9+l4fG7NKX14/EAKwNtLujZfda7szXWhp2xDox0NLleXnxaNmYcuocjt98AAKpa6mNB9zpoUJkegSGqU+YDGgCMGjUqzyHGs2fPyr3W0NDAjBkzMGPGjAKf/8tzEPKtxBKGR29Tce2ltAd2LToRb5IVV6txMteDh6MJPB1N4eloCgcz3UINDfL5PFgaaMPSQBv17I2V5snMEf8X3KQ/sZ8FP+mPtJf3NiULb1OycCMmSel5dLUEXJC78zoJHzNEEPB5GO7thDEtXSDUoF4ZUa1yEdAIKeuyRGJEvUrCtWhpALvx8iNSs3Pl8mjweahtZwRPBxN4OJrCw9EE5vqKE42Km46WAM4W+nC20Ff6vkTCkJCe81nAy/zU4/svEMan5SAjR4yn79Pw9L20l1fD2gCLetSFq51RiV8DIQVBAY2QIkhMz8H1l9Lhw8joRNyNTYZIzOTy6GkJ0MDB5L8hRBPUszeGrlbZ+1+Oz+fBwkAICwMh6ubRy8sSifEmOYsLeEINPnxcbaClofrZlYTIlL3/uwgpYxhjeJWYKb339d8QoqyX8jlLAyE8q5hyPbAa1gZlYjp9cdDWFKCKuR6qmOupuiqE5IkCGiGQ3vNKTM/Bh9RsfEjLxofUbLxPzcK92BRERififWq2wjEulvrc5A1PR1NUMtGhFeMJUSEKaERtMcaQmp0rDVKf/6Qpvk5Iy4aE5X0uTQEPdSoZSydwOJjC3cEEJnpapXcxhJB8UUAj5U6WSIz4tK8HKNm/s3Ml+Z/wPzweYKYn5O4nWegL4WShBw8HE9S1N4a2Js3iI6Qso4BGypSXiRl4mMRD9s04JGbmKg1UyZmFWz3cQFuDC1BcsFLy2lRXS23ueRFSEVFAIyqXnCnC4Vtx2H3tFW69TgYgAB7c/eoxWgI+LAyEMFcSmCy/CFjUsyKkYqCARlRCImG48iIBu6+9xtE7b7ihQQ0+DxbaEjjbmsPSUFuhJ2VpIISFvjYMdUpubUJCSPlEAY2UqtikTOy9/hq7r7/Cq8RMLr2alT56etijo5sVrp47hfbt3cv9CuGEkNJFAY2UuCyRGGH332HXtVe48DQe7L/ZhAZCDfxYzxY9PexRp5IReDyeWuyuSwhRDQpopMTci0vGrshXOBAVJzeR43snM/T0rIR2tW1oVXZCSLGhgEaKVVJGDg5GxWHXtVe4F/dpl1lbI210d6+E7u72qGymq8IaEkLUFQU08s3EEoaLT+Ox69ornLz3jtvTS0vAR5vaVujpYY/GVc0h4NMkDkJIyaGARoosJiEDe66/wp7rr6WbS/6nlo0hennao1M9Wxjr0moahJDSQQGNFEqWSIzjd99i17VXuPQsgUs30tFE53q26OFhT9uJEEJUggIayRdjDLdfJ2PXtVc4dCsOqVnSfb54POCHqubo6WGP1rWs6AFmQohKUUAjeUpIy8b+m7HYfe01Hr1L5dIrmeigp4c9urlXgp2xjgprSAghn1BAI3JyxRKcf/IBuyJfI/zhO27TSumGjtbo6WmP76qYgU8TPAghZQwFNAIAeBGfjt3XXmHvjdd4l/Jp76+6lYzQw8MevnVtYaRDK3cQQsouCmgV3LMPaZi67w6uvkjk0kz1tNClvh16eFRCDWtDFdaOEEIKjgJaBXY3Nhn+GyKQkJ4DPg9oVt0SPT0qoUUNK2hp0DYqhJDyhQJaBRUZnYhBGyORmp0LVztDrOnvQRM8CCHlGgW0Cujc4w/4acs1ZIkk8HI0xd8BHjDUpvtjhJDyjQJaBXP0zhuM3XETIjFDs+oWWNXXnRYIJoSoBQpoFciuyFeYvO82JAzoUMcGS3vWo3tlhBC1QQGtglh/4QVm/+8+AMDP0x5zu7jRYsGEELVCAU3NMcYQfOoJloU/AQAMbVIFU9vXBI9HwYwQol7KxXjTypUr4ejoCG1tbTRs2BARERFfzR8cHIzq1atDR0cH9vb2+OWXX5CV9Wk1+Pnz58PT0xMGBgawtLRE586d8ejRo5K+jFInkTAE/e8+F8wmtKlGwYwQorbKfEDbuXMnxo8fjxkzZuDGjRuoW7cu2rZti/fv3yvNHxoaismTJ2PGjBl48OAB1q9fj507d2Lq1KlcnnPnzmHkyJG4cuUKwsLCIBKJ0KZNG6Snp5fWZZW4XLEEk/bexsaL0QCAmb61MKqFCwUzQojaKvNDjkuWLMHQoUMxcOBAAMDq1atx5MgRbNiwAZMnT1bIf+nSJTRu3Bh9+vQBADg6OqJ37964evUql+f48eNyx2zatAmWlpa4fv06mjZtWoJXUzqyc8UYtyMKx+6+BZ8HLOxeF93cK6m6WoQQUqLKdEDLycnB9evXMWXKFC6Nz+ejVatWuHz5stJjGjVqhK1btyIiIgJeXl54/vw5jh49iv79++dZTnJyMgDA1NQ0zzzZ2dnIzv60xmFKSgoAQCQSQSQSFeq6ZPkLe1xBZOTkYuT2W7jwNAGaAh6Ce9ZBm1pWJVJWSSjJtinPqF2Uo3bJmzq1TUGvoUwHtPj4eIjFYlhZWcmlW1lZ4eHDh0qP6dOnD+Lj4/HDDz+AMYbc3FwMHz5cbsjxcxKJBOPGjUPjxo3h6uqaZ13mz5+PWbNmKaSfPHkSurq6hbiqT8LCwop0XF4ycoG1DwV4kcqDFp9hSDUxcqOv42h0sRZTKoq7bdQFtYty1C55U4e2ycjIKFC+Mh3QiuLs2bOYN28e/vrrLzRs2BBPnz7F2LFjMXv2bEyfPl0h/8iRI3H37l1cuHDhq+edMmUKxo8fz71OSUmBvb092rRpA0PDwi3gKxKJEBYWhtatW0NTs3hW6EhIy8bAf27gRWoqDLU18Hf/Bqhf2bhYzl2aSqJt1AG1i3LULnlTp7aRjYjlp0wHNHNzcwgEArx7904u/d27d7C2tlZ6zPTp09G/f38MGTIEAODm5ob09HQMGzYM06ZNA5//aR7MqFGj8L///Q/nz59HpUpfv8ckFAohFAoV0jU1NYv8YfmWYz8Xl5SJfuuv4Xl8Osz1tbB5UEPUsi3fq+QXV9uoG2oX5ahd8qYObVPQ+pfpWY5aWlpwd3dHeHg4lyaRSBAeHo7vv/9e6TEZGRlyQQsABALp0k6MMe6/o0aNwv79+3H69GlUqVKlhK6g5D3/kIYeqy/jeXw6bI20seun78t9MCOEkKIo0z00ABg/fjz8/f3h4eEBLy8vBAcHIz09nZv1OGDAANjZ2WH+/PkAAF9fXyxZsgT169fnhhynT58OX19fLrCNHDkSoaGhOHjwIAwMDPD27VsAgJGREXR0ys+K8/fjUjBgw1XEp+XAyVwPW4Y0pBXzCSEVVpkPaL169cKHDx8QGBiIt2/fol69ejh+/Dg3USQmJkauR/b777+Dx+Ph999/R2xsLCwsLODr64u5c+dyeVatWgUAaNasmVxZGzduREBAQIlfU3G4/jIRAzdGIiUrF7VsDLF5sBfM9RWHRAkhpKIo8wENkN7rGjVqlNL3zp49K/daQ0MDM2bMwIwZM/I8n2zosby68CQeQzdfQ6ZIDA8HE6wP8ISRTvkeIyeEkG9VLgIa+eT43bcYs/0mcsQSNHExx5r+7tDVol8jIYTQN2E5svf6a/y29zbEEgYfV2sE+9WDUIP2MiOEEKCEZjk6OjoiKCgIMTExJXH6CmnTxRf4dfctiCUM3d0rIaR3fQpmhBDymRIJaOPGjcO+ffvg5OSE1q1bY8eOHXLLRpGCY4whJPwJZh6W7mU2sLEjFnSrAw1BmX7ighBCSl2JBbSoqChERESgZs2aGD16NGxsbDBq1CjcuHGjJIpUS4wxzDv6AIvDHgMAxrVyQWDHWuDTxpyEEKKgRP/Mb9CgAZYvX464uDjMmDEDf//9Nzw9PVGvXj1s2LCh3M82LEliCcOUfXew7t8XAIDpHWthXKtqtP0LIYTkoUQnhYhEIuzfvx8bN25EWFgYvvvuOwwePBivX7/G1KlTcerUKYSGhpZkFcqlnFwJftkVhSO334DPA/7oWgc9Pe1VXS1CCCnTSiSg3bhxAxs3bsT27dvB5/MxYMAALF26FDVq1ODydOnSBZ6eniVRfLmWmSPGz9uu4+yjD9AU8LDMrz7au9moulqEEFLmlUhA8/T0ROvWrbFq1Sp07txZ6cKSVapUgZ+fX0kUX26lZIkwZNM1REQnQluTjzX9PeBdzULV1SKEkHKhRALa8+fP4eDg8NU8enp62LhxY0kUXy4lpudgwIaruBubAgOhBjYM9ISnY94bjhJCCJFXIpNC3r9/j6tXryqkX716FdeuXSuJIsu1t8lZ6LnmMu7GpsBMTwvbh31HwYwQQgqpRALayJEj8erVK4X02NhYjBw5siSKLLei49PRffUlPH2fBhsjbez86Xu42hmpulqEEFLulMiQ4/3799GgQQOF9Pr16+P+/fslUWS59OhtKgZuvoEPqdlwNNPF1iENUclEV9XVIoSQcqlEemhCoVBhl2kAePPmDTQ0aPlIAIhOBfpuiMSH1GzUsDbAruHfUzAjhJBvUCIBrU2bNpgyZQqSk5O5tKSkJEydOhWtW7cuiSLLlSvPE7HyvgDJmbmoX9kYO4d9D0sDbVVXixBCyrUS6S4tWrQITZs2hYODA+rXrw8AiIqKgpWVFbZs2VISRZYr6y9GI0fCQyMnU6zz94SekHqthBDyrUqkh2ZnZ4fbt29jwYIFqFWrFtzd3bFs2TLcuXMH9va04kVwzzpobSfB2n71KZgRQkgxKbFvUz09PQwbNqykTl+u6Qk10LGyBEJN2v6FEEKKS4l2D+7fv4+YmBjk5OTIpf/4448lWSwhhJAKqMRWCunSpQvu3LkDHo/HraovWyleLBaXRLGEEEIqsBK5hzZ27FhUqVIF79+/h66uLu7du4fz58/Dw8MDZ8+eLYkiCSGEVHAl0kO7fPkyTp8+DXNzc/D5fPD5fPzwww+YP38+xowZg5s3b5ZEsYQQQiqwEumhicViGBgYAADMzc0RFxcHAHBwcMCjR49KokhCCCEVXIn00FxdXXHr1i1UqVIFDRs2xIIFC6ClpYW1a9fCycmpJIokhBBSwZVIQPv999+Rnp4OAAgKCkLHjh3RpEkTmJmZYefOnSVRJCGEkAquRAJa27ZtuX9XrVoVDx8+RGJiIkxMTLiZjoQQQkhxKvZ7aCKRCBoaGrh7965cuqmpKQUzQgghJabYA5qmpiYqV65Mz5oRQggpVSUyy3HatGmYOnUqEhMTi+V8K1euhKOjI7S1tdGwYUNERER8NX9wcDCqV68OHR0d2Nvb45dffkFWVtY3nZMQQkjZViL30FasWIGnT5/C1tYWDg4O0NPTk3v/xo0bBT7Xzp07MX78eKxevRoNGzZEcHAw2rZti0ePHsHS0lIhf2hoKCZPnowNGzagUaNGePz4MQICAsDj8bBkyZIinZMQQkjZVyIBrXPnzsV2riVLlmDo0KEYOHAgAGD16tU4cuQINmzYgMmTJyvkv3TpEho3bow+ffoAABwdHdG7d29cvXq1yOckhBBS9pVIQJsxY0axnCcnJwfXr1/HlClTuDQ+n49WrVrh8uXLSo9p1KgRtm7dioiICHh5eeH58+c4evQo+vfvX+RzAkB2djays7O51ykpKQCkk2BEIlGhrkuWv7DHVQTUNspRuyhH7ZI3dWqbgl5Dmd6MKz4+HmKxGFZWVnLpVlZWePjwodJj+vTpg/j4ePzwww9gjCE3NxfDhw/H1KlTi3xOAJg/fz5mzZqlkH7y5Eno6uoW9tIAAGFhYUU6riKgtlGO2kU5ape8qUPbZGRkFChfiQQ0Pp//1Sn6JTkD8uzZs5g3bx7++usvNGzYEE+fPsXYsWMxe/ZsTJ8+vcjnnTJlCsaPH8+9TklJgb29Pdq0aQNDQ8NCnUskEiEsLAytW7eGpqZmkeukjqhtlKN2UY7aJW/q1DayEbH8lEhA279/v9xrkUiEmzdv4p9//lHay8mLubk5BAIB3r17J5f+7t07WFtbKz1m+vTp6N+/P4YMGQIAcHNzQ3p6OoYNG4Zp06YV6ZwAIBQKIRQKFdI1NTWL/GH5lmPVHbWNctQuylG75E0d2qag9S+RgNapUyeFtO7du6N27drYuXMnBg8eXKDzaGlpwd3dHeHh4dxEE4lEgvDwcIwaNUrpMRkZGeDz5Z9GEAikO0Mzxop0TkIIIWVfqd5D++677zBs2LBCHTN+/Hj4+/vDw8MDXl5eCA4ORnp6OjdDccCAAbCzs8P8+fMBAL6+vliyZAnq16/PDTlOnz4dvr6+XGDL75yEEELKn1ILaJmZmVi+fDns7OwKdVyvXr3w4cMHBAYG4u3bt6hXrx6OHz/OTeqIiYmR65H9/vvv4PF4+P333xEbGwsLCwv4+vpi7ty5BT4nIYSQ8qdEAtqXixAzxpCamgpdXV1s3bq10OcbNWpUnsOBX+6AraGhgRkzZuT76MDXzkkIIaT8KZGAtnTpUrmAxufzYWFhgYYNG8LExKQkiiSEEFLBlUhACwgIKInTEkIIIXkqkcWJN27ciN27dyuk7969G//8809JFEkIIaSCK5GANn/+fJibmyukW1paYt68eSVRJCGEkAquRAJaTEwMqlSpopDu4OCAmJiYkiiSEEJIBVciAc3S0hK3b99WSL916xbMzMxKokhCCCEVXIkEtN69e2PMmDE4c+YMxGIxxGIxTp8+jbFjx8LPz68kiiSEEFLBlcgsx9mzZyM6OhotW7aEhoa0CIlEggEDBtA9NEIIISWiRAKalpYWdu7ciTlz5iAqKgo6Ojpwc3ODg4NDSRRHCCGElOzSVy4uLnBxcSnJIgghhBAAJXQPrVu3bvjzzz8V0hcsWIAePXqURJGEEEIquBIJaOfPn0f79u0V0n18fHD+/PmSKJIQQkgFVyIBLS0tDVpaWgrpmpqaBd55lBBCCCmMEglobm5u2Llzp0L6jh07UKtWrZIokhBCSAVXIpNCpk+fjq5du+LZs2do0aIFACA8PByhoaHYs2dPSRRJCCGkgiuRgObr64sDBw5g3rx52LNnD3R0dFC3bl2cPn0apqamJVEkIYSQCq7Epu136NABHTp0AACkpKRg+/btmDBhAq5fvw6xWFxSxRJCCKmgSuQemsz58+fh7+8PW1tbLF68GC1atMCVK1dKskhCCCEVVLH30N6+fYtNmzZh/fr1SElJQc+ePZGdnY0DBw7QhBBCCCElplh7aL6+vqhevTpu376N4OBgxMXFISQkpDiLIIQQQpQq1h7asWPHMGbMGPz888+05BUhhJBSVaw9tAsXLiA1NRXu7u5o2LAhVqxYgfj4+OIsghBCCFGqWAPad999h3Xr1uHNmzf46aefsGPHDtja2kIikSAsLAypqanFWRwhhBDCKZFZjnp6ehg0aBAuXLiAO3fu4Ndff8Uff/wBS0tL/PjjjyVRJCGEEFUTi4DEF8Dzc8CNLcDpucC+n4ANPsC2kl+YvkS3jwGA6tWrY8GCBZg/fz4OHz6MDRs2lHSRhBBCSoJYBCS/BpJilP+kxgFMovxYbeMSr16JBzQZgUCAzp07o3PnzqVVJCGEkMIQi4CUWMVA9fFl/gFLRkMbMK6s5McBYAzg8Uqs+qUW0AghhKhYXgFL9pMS+20By7gyoGdRokHrq1VTSamEEEJKTk46ePcPo/qbYxAcOgKkvC54wBIIvx6w9C1VFrDyUy4C2sqVK7Fw4UK8ffsWdevWRUhICLy8vJTmbdasGc6dO6eQ3r59exw5cgSAdL+2yZMn48CBA0hISECVKlUwZswYDB8+vESvgxBCSlRyLBCxFri+CRpZSagBAG+/yJNfwNKzAPgluipiiSnzAW3nzp0YP348Vq9ejYYNGyI4OBht27bFo0ePYGlpqZB/3759yMnJ4V4nJCSgbt266NHj0wyb8ePH4/Tp09i6dSscHR1x8uRJjBgxAra2tjQLkxBS/ry+Blz5C7h3AGDSxd+ZsSNeCqrA3q0xBGZV1CJg5afMX9WSJUswdOhQDBw4ELVq1cLq1auhq6ub52xJU1NTWFtbcz9hYWHQ1dWVC2iXLl2Cv78/mjVrBkdHRwwbNgx169ZFREREaV0WIYR8G7EIuLsX+LsV8HdL6b+ZGHBsAviFIvfnq7hVeSAkjccBbt0Be0/AwEptgxlQxntoOTk5uH79OqZMmcKl8fl8tGrVCpcvXy7QOdavXw8/Pz/o6elxaY0aNcKhQ4cwaNAg2Nra4uzZs3j8+DGWLl2a53mys7ORnZ3NvU5JSQEAiEQiiESiQl2XLH9hj6sIqG2Uo3ZRrkK2S+ZH8G9uAf/a3+ClxgEAmEALrHY3iD2HAdZuANSrbQp6DWU6oMXHx0MsFsPKykou3crKCg8fPsz3+IiICNy9exfr16+XSw8JCcGwYcNQqVIlaGhogM/nY926dWjatGme55o/fz5mzZqlkH7y5Eno6uoW8IrkhYWFFem4ioDaRjlqF+UqQrvoZ8XB6cNJ2CdegEAiva2SpWGIaPOWiDZvgWyBEXDjFYBXcsepQ9tkZGQUKF+ZDmjfav369XBzc1OYQBISEoIrV67g0KFDcHBwwPnz5zFy5EjY2tqiVatWSs81ZcoUjB8/nnudkpICe3t7tGnTBoaGhoWql0gkQlhYGFq3bg1NTc3CX5gao7ZRjtpFObVvF8bAe3EW/Ig14D879SnZ0hVir58gqN0VzhpCOCs5VJ3aRjYilp8yHdDMzc0hEAjw7t07ufR3797B2tr6q8emp6djx44dCAoKkkvPzMzE1KlTsX//fm5H7Tp16iAqKgqLFi3KM6AJhUIIhUKFdE1NzSJ/WL7lWHVHbaMctYtyatcuORnA7Z3A1dXAB9loFA+o3h747mfwHH+ARgGnzqtD2xS0/mU6oGlpacHd3R3h4eHcCiMSiQTh4eEYNWrUV4/dvXs3srOz0a9fP7l02T0v/hc3RgUCASSSfJ7PIISQkpQSB0T+DVzbCGQmStO09IH6/YGGwwBTJ9XWr4wr0wENkE6x9/f3h4eHB7y8vBAcHIz09HQMHDgQADBgwADY2dlh/vz5csetX78enTt3hpmZmVy6oaEhvL29MXHiROjo6MDBwQHnzp3D5s2bsWTJklK7LkII4cReB66sAu7tByS50jTjykDD4UD9foC2kWrrV06U+YDWq1cvfPjwAYGBgXj79i3q1auH48ePcxNFYmJiFHpbjx49woULF3Dy5Eml59yxYwemTJmCvn37IjExEQ4ODpg7dy49WE0IKT3iXODhYWkge3X1U7pDY+C7n6XDi3yB6upXDpX5gAYAo0aNynOI8ezZswpp1atXB2Msz/NZW1tj48aNxVU9QggpuMwk4MZm6Yoeyf/NSORrAq7dgO+GA7b1VVq98qxcBDRCCCn34p9KJ3lEhQKidGmarhngMRjwHAwYfH2iG8kfBTRCCCkpjAHPz0qHFZ+c+JRuWVs6rOjWA9DUVln11A0FNEIIKW6iTODObmkge3//U3q1dtJAVsW7zK5YX55RQCOEkOKS+va/afcbgIwEaZqmHlC/L+D1E2BeVbX1U3MU0AghREYiAXJSgazkQvwkffbvFAD/TUgzsgca/iR9hkzHWIUXVXFQQCOEqA+JBMhJK2DwyScgFZX9d9JhxRodAQF9xZYmam1CSPkjkQCvI8C/uw/eD09A48V0aUDKTsl/R+aCEGgB2sbSB5qV/eh8+d5/r3VMAD3zby+fFAkFNEJI+SARAzGXgfsHgfuHgLS3EAAwBoDML/LyNT8LOsZ5B6a83qeZh+USBTRCSNklzgVeXpAGsQeHgfQPn94TGkLi0hbX06xQ37sDNPTNPgUkDW2aRVgBUUAjhJQtYhHw4rw0iD3836fZgoA0WNXoCNTqBDg1g5jxEXf0KOpV8gTK+Yry5NtRQCOEqF5ujvQBZFkQy0r69J6OKVDzvyDm2BTQ0Pr0nhrsxkyKDwU0QohqiLKA52eAeweAR8eA7ORP7+lZADV9pUHM4QeaLUgKhD4lhJDSI8oEnp6S9sQeHZc+8yWjbwXU/PG/INaIVponhUYBjRBSsnLSgScnpUHs8clPC/MCgIGtNIDV6gTYNwS+2AqKkMKggEZIOcHj8ZCdnQ2xWKzqquQvJx2IviDtjb28DIizpOlCU8DMFajaHHBuBVjV/hTEcnIKXYxIJIKGhgaysrLKR7uUovLUNpqamhAIvr1HTgGNkDKOMYZ3797BxsYGMTEx4JXV6ehMIh1SlP1AG7DpKP3hawCautIf2aSOLAAvX35bkYzB2toar169KrvtoiLlrW2MjY1hbW39TXWlgEZIGff27VukpKTA2toapqamxfKXbLER5/639mGqdJIH+AD0pD98LUDbENAykD6oXAJfqhKJBGlpadDX11fYub6iKy9twxhDRkYG3r9/DwCwsbEp8rkooBFShonFYiQlJcHCwgKamprQ0dEpni8n2Y7ujEG6diH7bwlDlk/af8fkZkmn1menfUrXAKChI115Q8e4VB5ulkgkyMnJgba2dpn+0laF8tQ2Ojo6AID379/D0tKyyH+0UUAjpAwT5eQAudnQY2nQzMoGLzcecoElv8DDpSl5v7ho6HxaZkpTp3jPTSoMXV1dANJ7fxTQCFEXjAHv7gF39wLPLgJ1fgXfSAJNDR5Q+HkTRcCT/vD++zfvv9fAp3/zBZ/WQaR1D0kxKI77fBTQCCkr4p8C9/YBd/YA8Y+kafr2AI8PpmWIDIEWdHT1pP/jc0Emn8DDfUl89r4s/+fvK+QnpPyhgEaIKiW9kgaxu3uBN7c+pQuEgEtroFYvQMMWzNBWej9ExxC8Mn4/pKQ4Ojpi3LhxGDduXIHynz17Fs2bN8fHjx9hbGxconUjZQMFNEJKW+o74P4BaRB7dfVTOk8AOLcAXLsBNdpLh/SysoAXL1RW1aLIb+hoxowZmDlzZqHPGxkZCT09vQLnb9SoEd68eQMjI6NCl1VUNWrUwIsXL/Dy5UtYW1uXWrlEigIaIaUhI1G6/cndvUD0v59tQskDHH8AXLsCNTsBemYqrWZxePPmDffvnTt3IjAwEI8ePeLS9PX1uX8zxiAWi6Ghkf9XkYWFRaHqoaWlVapB5cKFC8jMzET37t3xzz//YNKkSaVWtjKiCrhwc8UcuyCkNGSnArd2Att6AotcgMNjgBfnpMGskifQ7g9g/AMg4H+Ax6ACBzPGGDJyckv9h7GCzY60trbmfoyMjMDj8bjXDx8+hIGBAY4dOwZ3d3cIhUJcuHABz549Q6dOnWBlZQV9fX14enri1KlTcud1dHREcHAw95rH4+Hvv/9Gv379oK+vDxcXFxw6dIh7/+zZs+DxeEhKSgIAbNq0CcbGxjhx4gRq1qwJfX19tGvXTi4A5+bmYsyYMTA2NoaZmRkmTZoEf39/dO7cOd/rXr9+Pfr06YP+/ftjw4YNCu+/fv0avXv3hqmpKfT09ODh4YGrVz/10A8fPgxPT09oa2vD3NwcXbp0kbvWAwcOyJ3P2NgYmzZtAgBER0eDx+Nh586d8Pb2hra2NrZt24bExET06dMHdnZ20NXVhZubG7Zv3y53HolEggULFqBq1aoQCoWoXLky5s6dCwBo0aIFRo0aJZf/w4cP0NLSQnh4eL5tUtqoh0ZIcRJlAo9PSHtiT05Kn9eSsXaTDifW7gKYOBa5iEyRGLUCT3x7XQvpflBb6GoVz1fG5MmTsWjRIjg5OcHExASvXr1C+/btMXfuXAiFQmzevBm+vr549OgRKleunOd5Zs+ejRkzZmDJkiVYuXIl+vbti5cvX8LU1FRp/oyMDCxatAhbtmwBn89Hv379MGHCBGzbtg0A8Oeff2Lbtm3YuHEjatasiWXLluHAgQNo3rz5V68nNTUVu3fvxtWrV1GjRg0kJyfj33//RZMmTQAAaWlp8Pb2hp2dHQ4dOgRra2vcuHEDEom0p37kyBF06dIF06ZNw+bNm5GTk4OjR48WqV0XL16M+vXrQ0tLC8nJyXB3d8fkyZNhaGiII0eOoH///nB2doaXlxcAYMqUKVi3bh2WLl2KH374AW/evMHDhw8BAEOGDMGoUaOwePFiCIVCAMDWrVthZ2eHFi1aFLp+JY0CGiHfKjdHug3KnT3Ao6NATtqn98xcpEHMtRtgUU11dSxjgoKC0Lp1a+61qakp6taty72ePXs29u/fj0OHDin0ED7n7++P7t27w9DQEPPmzcPy5csRERGBdu3aKc0vEomwevVqODs7AwBGjRqFoKAg7v2QkBBMmTKF6x2tWLGiQIFlx44dcHFxQe3atQEAfn5+WL9+PRfQQkND8eHDB0RGRnLBtmrVqtzxc+fOhZ+fH2bNmsWlfd4eBTVu3Dh07doVgLTnpaenh19//ZV7sHr06NE4ceIEdu3aBS8vL6SmpmLZsmVYsWIF/P39AQDOzs744YcfAABdu3bFqFGjcPDgQfTs2ROAtKcbEBBQJpfTooBGSFFIxNJ7YXf3AvcPyW9IaVRZek/MtZu0V1bM/+PraApwP6htsZ6zoOUWFw8PD7nXaWlpmDlzJo4cOYI3b94gNzcXmZmZiImJ+ep53NzcuH/r6enB0NCQW0JJGV1dXS6YAdJllmT5k5OT8e7dO67nAgACgQDu7u5cTyovGzZsQL9+/bjX/fr1g7e3N0JCQmBgYICoqCjUr18/z55jVFQUhg4d+tUyCuLLdhWLxZgzZw52796N2NhY5OTkIDs7m3uI+cGDB8jOzkbLli2Vnk9bW5sbQu3Zsydu3LiBu3fvyg3tliUU0AgpKIkEeB0hDWL3DgDpn31x6ltLhxJduwGVPEr0eS4ej1dsQ3+q8uVsxQkTJiAsLAyLFi1C1apVoaOjg+7duyMnnxX4NTU15V7zeLyvBh9l+Qt6bzAv9+/fx5UrVxARESE3EUQsFmPHjh0YOnQot7RTXvJ7X1k9lU36+LJdly9fjpUrVyI4OBhubm7Q09PDuHHjuHbNr1xAOuxYr149vH79Ghs3bkSLFi3g4OCQ73GqUC4mhaxcuRKOjo7Q1tZGw4YNERERkWfeZs2agcfjKfx06NBBLt+DBw/w448/wsjICHp6evD09Mz3r0FSATEGxEUBJ38Hgt2ADW2BiLXSYKZjCrgPBPz/B4y/D/j8Adh70sPJRXDx4kUEBASgS5cucHNzg7W1NaKjo0u1DkZGRrCyskJkZCSXJhaLcePGja8et379ejRt2hS3bt1CVFQU9zN+/HisX78eAFCnTh1ERUUhMTFR6Tnq1Knz1UkWFhYWcpNXnjx5goyMjHyv6erVq/jxxx/Rr18/1K1bF05OTnj8+DH3vouLC3R0dL5atpubGzw8PLBu3TqEhoZi0KBB+ZarKmX+z7ydO3di/PjxWL16NRo2bIjg4GC0bdsWjx49gqWlpUL+ffv2yf1Vl5CQgLp166JHjx5c2rNnz/DDDz9g8ODBmDVrFgwNDXHv3j1oa9MSPhWaRAwkvwYSn0t/Ep5KJ3gkPvuUR8sAqNkRcO0OOHkDAs28z0cKzMXFBfv27YOvry94PB6mT5+e7zBfSRg9ejTmz5+PqlWrokaNGggJCcHHjx/zvF8kEomwZcsWBAUFwdXVVe69IUOGYMmSJbh37x569+6NefPmoXPnzpg/fz5sbGxw8+ZN2Nra4vvvv8eMGTPQsmVLODs7w8/PD7m5uTh69CjX42vRogVWrFiB77//HmKxGJMmTVLobSrj7OyMw4cP49KlSzAxMcGSJUvw7t071KpVC4B0SHHSpEn47bffoKWlhcaNG+PDhw+4d+8eBg8eLHcto0aNgp6entzsy7KmzAe0JUuWYOjQoRg4cCAAYPXq1Thy5Ag2bNiAyZMnK+T/cox6x44d0NXVlQto06ZNQ/v27bFgwQIu7fNxdWWys7ORnZ3NvU5JSQEg/UAX9nkPWf6K+JxIfkq8bcQiIPkVeB9fgJf4Avj4AryPz8H7+AL4+BI8iWK5TEMHzKUNJLW6glVtKV1FHgAkAJTkL04ikQiMMW64iTGmki/6opLVVdl/P7+ORYsWYciQIWjUqBHMzc3x22+/ISUlReF687r+z9Nl5/7a67zqN3HiRLx58wYDBgyAQCDA0KFD0aZNGwgEAqXlHjhwAAkJCejUqZPC+9WrV0fNmjXx999/Y/HixTh+/DgmTJiA9u3bIzc3F7Vq1UJISAgkEgmaNm2KnTt3Yu7cufjjjz9gaGiIJk2acOdcuHAhBg0ahCZNmsDW1hZLly7F9evX87xWWZtMmDABr1+/Rtu2baGrq4uhQ4eiU6dOSE5O5vJNmzYNAoEAgYGBiIuLg42NDX766Se56+nVqxfGjRsHPz8/aGlplchnUCKRgDGmdHHign4f8Ni3DiCXoJycHOjq6mLPnj1yz4H4+/sjKSkJBw8ezPccbm5u+P7777F27VoA0kYzMjLCb7/9hgsXLuDmzZuoUqUKpkyZ8tVnTWbOnCk3A0kmNDSUu8FKyga+RATdnHjoZb/77Oc99LLfQjcnHnzk/T+jmKeBDKEl0rSskC60RJKuE94a1YdYoJreu4aGBqytrWFvbw8tLS2V1KEik0gkaNiwITp37oxp06apujoqExMTg/r16+P06dNFmn1ZEDk5OXj16hXevn2L3NxcufcyMjLQp08fJCcnw9DQMM9zlOmAFhcXBzs7O1y6dAnff/89l/7bb7/h3Llzcg8lKhMREYGGDRvi6tWr3Mylt2/fwsbGBrq6upgzZw6aN2+O48ePY+rUqThz5gy8vb2VnktZD83e3h7x8fFfbWBlRCIRwsLC0Lp16wING1QkBW4bUSaQ9BK8RFnv6sWnXlfKa/BY3kGLaegAJo5gJlXATKsAJlXATJ3ATJwAAxvpSvJlRFZWFl69egUHBweIRCIYGBiUyenSqsIYQ2pqarG1y8uXL3Hy5El4e3sjOzsbK1euxKZNm3Dz5k3UrFmzGGpceoqjbUQiERISEjBx4kRER0fj33//LeZafpKVlYXo6GjY29sr3P5JSUmBubl5vgGtzA85fov169fDzc1NbhqurKvcqVMn/PLLLwCAevXq4dKlS1i9enWeAU0oFHIPFn5OU1OzyEHpW45Vd5qamtCUZAMfX3y6p5X4HEj873VK7NdPoKUPmFYBTJ0Ufnj61gCfj/IQFsRiMTexCZDOdivrmzWWJtn/z8XVLhoaGti8eTN+++03MMbg6uqKU6dOcc+XlSfF0TaXL19G8+bNUa1aNezZs6dEP3t8Ph88Hk/p92JBvyfLdEAzNzeHQCDAu3fv5NLfvXuX7xpt6enp2LFjh9xDk7JzamhocDdFZWrWrIkLFy4UT8VJ4Ylzgejz4N87iB8eX4LG4wny0+KVERp9ClpmzvKBS8+CZhuSQrO3t8fFixdVXY0yo1mzZt/8WENpKtMBTUtLC+7u7ggPD+fub0kkEoSHh3919QAA2L17N7Kzs+UedpSd09PTU26xVAB4/PhxmX22Qm1JJMCrK5+e68qIhwCA3IqGOqZKe1kwdQJ0TSloEUI4ZTqgAcD48ePh7+8PDw8PeHl5ITg4GOnp6dysxwEDBsDOzg7z58+XO279+vXo3LkzzMwUF3ydOHEievXqhaZNm3L30A4fPoyzZ8+WxiVVbIwBcTf/C2L75YcOdUwhruGLm4m6qNeiKzQsXQAdE9XVlRBSrpT5gNarVy98+PABgYGBePv2LerVq4fjx4/DysoKgHT2zZfjuo8ePcKFCxdw8uRJpefs0qULVq9ejfnz52PMmDGoXr069u7dy61fRkrAu/vSIHZ3r/S+mIzQEKjRUbrChpM3JBIg9uhR1LWtD9D9RUJIIZT5gAZIFxDNa4hRWa+qevXq+Y77Dho0qEw/8a4WEp4Bd//bjfnDg0/pGjpAdR9pEKvaCtD8bEZTCT/XRQhRX+UioJFyJPm1dCjxzh7gTdSndIEWULW1dNHeau0AoX6epyCEkKKggEa+Xdp74P5BaU8s5vKndJ5AujyUazfpsKKOscqqSAhRf/RACymazI/Ajc3A5k7A4urA0QmfgplDY6DDYuDXR0D//UD9fhTMSKE1a9YM48aN415/uWO1Msp2di6K4joPKV3UQyMFl50GPDom7Yk9PSV/v8u2AeDWHajVGTCyU1kVier5+vpCJBLh+PHjCu/9+++/3Mr0derUKdR5IyMjFbZH+VYzZ87EgQMHEBUVJZf+5s0bmJiUzgzbzMxM2NnZgc/nIzY2VukCDqRgKKCRrxNlAk/CpEHs8QkgN/PTe5a1/9vIsqv0uTBCAAwePBjdunXD69evUalSJbn3Nm7cCA8Pj0IHM0C6hUppyW/hhuK0d+9e1K5dG4wxHDhwAL169Sq1sr/EGINYLIaGRvkMDTTkSBSJRdIgtu8nYKELsKs/cP+ANJiZOgFNfwNGXAFGXAKaTqBgVtoYA3LSS/+ngCtGdOzYERYWFti0aZNcelpaGnbv3o3BgwcjISEBvXv3hp2dHXR1deHm5obt27d/9bxfDjk+efIEzZo1g7W1NVxdXREWFqZwzKRJk1CtWjXo6urCyckJ06dP51Zu37RpE2bNmoVbt25xy4vJ6vzlkOOdO3fQokUL6OjowMzMDMOGDUNaWhr3fkBAADp37oxFixbBxsYGZmZmGDlyZIFWiV+/fj369euHfv36cfunfe7evXvo2LEjDA0NYWBggCZNmuDZs09bGm3YsAG1a9eGUCiEjY0NNyM8OjoaJiYmcr3PpKQk8Hg8bnb42bNnwePxcOzYMbi7u0MoFOLChQt49uwZOnXqBCsrK+jr68PT0xOnTp2Sq1d2djYmTZoEe3t7CIVCVK1aFevXrwdjDFWrVsWiRYvk8kdFRYHH4+Hp06f5tklRlc8wTIqfRAy8vCjtid0/KL1HJmNYCXDtIt0DzKYurc6haqIMYJ5t6Zc7NQ7Qyn/IT0NDAwMGDMCmTZswbdo0bh3K3bt3QywWo3fv3khLS4O7uzsmTZoEQ0NDHDlyBP3794ezs7Pc2qt5kUgk6Nq1K6ysrBAWFgaxWIzx48cr5DMwMMCmTZtga2uLO3fuYOjQoTAwMMBvv/2GXr164e7duzh+/Dj3ZW1kZKRwjvT0dLRt2xbff/89IiMj8f79e25/sM+D9pkzZ2BjY4MzZ87g6dOn6NWrF+rVq4ehQ4fmeR3Pnj3D5cuXsW/fPjDG8Msvv+Dly5fcqkWxsbFo2rQpmjVrhtOnT8PQ0BAXL17kVqNftWoVxo8fjz/++AM+Pj5ITk4u0tJdkydPxqJFi+Dk5AQTExO8evUK7du3x9y5cyEUCrF582b4+vri0aNHqFy5MgDpohaXL1/G8uXLUbduXbx48QLx8fHg8XgYNGgQNm7ciAkTJnBlbNy4EU2bNkXVqlULXb+CooBWkWUmATFXgGenpT2wtM/WzNSzAGp3kc5QrOQF0IK4pBAGDRqEhQsX4ty5c2jWrBkA6Rdat27dYGRkBCMjI7kvu9GjR+PEiRPYtWtXgQLaqVOn8PDhQxw7dgz6+vowNDTEvHnz4OPjI5fv999/5/7t6OiICRMmYMeOHfjtt9+go6MDfX19bouevISGhiIrKwubN2/m7uGtWLECvr6++PPPP7lFHkxMTLBixQoIBALUqFEDHTp0QHh4+FcD2oYNG+Dj48Pdr2vbti02btyImTNnAgBWrlwJIyMj7Nixg1ugt1q1atzxc+bMwa+//oqxY8dyaZ6envm235eCgoLQunVr7rWpqancNjGzZ8/G/v37cejQIYwaNQqPHz/Grl27EBYWhlatWgEAnJw+jdQEBAQgMDAQERER8PLygkgkQmhoqEKvrbhRQKtIZAEs+l8g+gLw9jbw+TYr2sZArR+lQczhB0BAH48ySVNX2ltSRbkFVKNGDTRq1AgbNmxAs2bN8PTpU/z777/cYuFisRjz5s3Drl27EBsbi5ycHGRnZxd4b8EHDx7A3t4etra23Ga7n28xJbNz504sX74cz549Q1paGnJzcwu93dODBw9Qt25duQkpjRs3hkQiwaNHj7iAVrt2bbmNKW1sbHDnzp08zysWi/HPP/9g2bJlXFq/fv0wYcIEBAYGgs/nIyoqCk2aNFG62vz79+8RFxeHli1bFup6lPHw8JB7nZaWhpkzZ+LIkSN48+YNcnNzkZmZiZiYGADS4UOBQJDn7iS2trbo0KEDNmzYAC8vLxw+fBjZ2dlyGy2XBPrGUmdZycDLy3kHMAAwdQYcfwCqtwecWwAatIlkmcfjFWjoT9UGDx6M0aNHY+XKldi4cSOcnZ25L8CFCxdi2bJlCA4OhpubG/T09DBu3Djk5OQUW/mXL19G3759MWvWLLRt25br6SxevLjYyvjcl0GHx+N9dWfnEydOIDY2VmESiFgsRnh4OFq3bg0dHZ08j//aewC4JQE/XzUpr3t6X84enTBhAsLCwrBo0SJUrVoVOjo66N69O/f7ya9sABgyZAj69++PpUuXYuPGjejVq1eJb4ZMAU2dFCaAOTYBHBsDhiq4F0MqhJ49e2Ls2LEIDQ3F5s2b8fPPP3P30y5evIhOnTpxu2FIJBI8fvxYYVunvNSsWROvXr3CmzdvuC/jK1euyOW5dOkSHBwc5HaafvnypVweLS0tiMXifMvatGkT0tPTubIuXrwIPp+P6tWrF6i+yqxfvx5+fn4KO2HPnTsX69evR+vWrVGnTh38888/EIlECgHTwMAAjo6OCA8PR/PmzRXOL5sV+ubNGy7ty8cT8nLx4kUEBASgS5cuAKQ9tujoaO59Nzc3SCQSnDt3jhty/FL79u2hp6eHVatW4fjx4zh//nyByv4WFNDKMwpgpAzT19dHr169MGXKFKSkpCAgIIB7z8XFBXv27MGlS5dgYmKCJUuW4N27dwUOaK1atUK1atW4ezUSiUQhMLi4uCAmJgY7duyAp6cnjhw5gv3798vlcXR0xIsXLxAVFYVKlSrBwMBA4Tmwvn37YsaMGfD398fMmTPx4cMHjB49Gv379+eGGwvrw4cPOHz4MA4dOgRXV1e59wYMGIAuXbogMTERo0aNQkhICPz8/DBlyhQYGRnhypUr8PLyQvXq1TFz5kwMHz4clpaW8PHxQWpqKi5evIjRo0dDR0cHnp6eWLBgAZydnfH+/Xu5e4pf4+Lign379sHX1xc8Hg/Tp0+X6206OjrC398fgwYN4iaFvHz5Eu/fv0fPnj0BAAKBAAEBAZgyZQpcXFyUDgkXN7rTX55kJUufBTv5O7C2GfCnI7C9F3B5hXTdRCaRBrAG/kDXv4HxD4AxN4AflwN1elAwI6Vu8ODB+PjxI9q2bQtb20+fv99//x0NGjRA27Ztuan3sj0PC4LP52P//v3IyspCq1atMGzYMMydO1cuz48//ohffvkFo0aN4nalnz59ulyebt26oV27dmjevDksLCyUPjqgq6uLEydOIDExEZ6enujevTtatmyJFStWFK4xPiObYKLs/lfLli2ho6ODrVu3wszMDKdPn0ZaWhq8vb3h7u6OdevWcb01f39/BAcH46+//kLt2rXRsWNHPHnyhDtXSEgIcnNz4e7ujnHjxmHOnDkFqt+SJUtgYmKCRo0awdfXF23btkWDBg3k8qxatQrdu3fHiBEjUKNGDQwdOhTp6elyeQYPHoycnBxuu6+SxmPlaTvSMiQlJQVGRkZITk4u9E1mkUiEo0ePon379l/fWjwrWX4Sx5tbSnpgTp96YA6Ny/0qHQVumwoiKysLL168gIODA3JycmBoaKiwXVJFJpFIkJKSQu2iRFlom3///RctW7bEq1ev8u3Nyj7rVapUgba2ttx7Bf2+pSHHsqQCBjBCiPrJzs7Ghw8fMHPmTPTo0aPIQ7OFRQFNlbJSgBfXKIARQtTK9u3bMXjwYNSrVw+bN28utXIpoKkAP/JvNH20FhpR0YoBzKSKNIBVaUoBjBBSLgUEBMhNAiotFNBUISkaJhnPpf+WBTDZLESjSl8/lhBCiFIU0FRA4tYTUe8Y6vw4AppmjqquDikHaO4WUXfF8RmnaUGqYF0Hr00bA4Y0nEi+TjbTMyMjQ8U1IaRkyT7j3zK7mXpohJRhAoEAxsbG+PDhAwwMDKCpqSm3XmBFJ5FIkJOTg6ysLJq2/4Xy0jaMMWRkZOD9+/cwNjb+ps83BTRCyjhra2uIxWK8efMGqamp3PJRRPplmJmZCR0dHWqXL5S3tjE2Nv7mjVUpoBFSxvF4PFhZWeHGjRto0aJFud1NuCSIRCKcP38eTZs2pQfxv1Ce2qa4Rh7o/wxCygnGGIRCYZn/cipNAoEAubm50NbWpnb5QkVsm7I7sEoIIYQUAgU0QgghaoECGiGEELVA99CKSPYQoGz798IQiUTIyMhASkpKhRnbLihqG+WoXZSjdsmbOrWN7Hs2v4evKaAVUWpqKgDA3t5exTUhhJCKITU1FUZGRnm+T/uhFZFEIkFcXBwMDAwK/YxHSkoK7O3t8erVq0LvpabuqG2Uo3ZRjtolb+rUNowxpKamwtbW9qsPiVMPrYj4fD4qVfq2hYQNDQ3L/QetpFDbKEftohy1S97UpW2+1jOToUkhhBBC1AIFNEIIIWqBApoKCIVCzJgxA0KhUNVVKXOobZSjdlGO2iVvFbFtaFIIIYQQtUA9NEIIIWqBAhohhBC1QAGNEEKIWqCARgghRC1QQFOBlStXwtHREdra2mjYsCEiIiJUXSWVmj9/Pjw9PWFgYABLS0t07twZjx49UnW1ypw//vgDPB4P48aNU3VVyoTY2Fj069cPZmZm0NHRgZubG65du6bqaqmUWCzG9OnTUaVKFejo6MDZ2RmzZ8/Odw1EdUEBrZTt3LkT48ePx4wZM3Djxg3UrVsXbdu2xfv371VdNZU5d+4cRo4ciStXriAsLAwikQht2rRBenq6qqtWZkRGRmLNmjWoU6eOqqtSJnz8+BGNGzeGpqYmjh07hvv372Px4sUwMTFRddVU6s8//8SqVauwYsUKPHjwAH/++ScWLFiAkJAQVVetVNC0/VLWsGFDeHp6YsWKFQCka0La29tj9OjRmDx5soprVzZ8+PABlpaWOHfuHJo2barq6qhcWloaGjRogL/++gtz5sxBvXr1EBwcrOpqqdTkyZNx8eJF/Pvvv6quSpnSsWNHWFlZYf369Vxat27doKOjg61bt6qwZqWDemilKCcnB9evX0erVq24ND6fj1atWuHy5csqrFnZkpycDAAwNTVVcU3KhpEjR6JDhw5yn5uK7tChQ/Dw8ECPHj1gaWmJ+vXrY926daqulso1atQI4eHhePz4MQDg1q1buHDhAnx8fFRcs9JBixOXovj4eIjFYlhZWcmlW1lZ4eHDhyqqVdkikUgwbtw4NG7cGK6urqqujsrt2LEDN27cQGRkpKqrUqY8f/4cq1atwvjx4zF16lRERkZizJgx0NLSgr+/v6qrpzKTJ09GSkoKatSoAYFAALFYjLlz56Jv376qrlqpoIBGypSRI0fi7t27uHDhgqqronKvXr3C2LFjERYWBm1tbVVXp0yRSCTw8PDAvHnzAAD169fH3bt3sXr16god0Hbt2oVt27YhNDQUtWvXRlRUFMaNGwdbW9sK0S4U0EqRubk5BAIB3r17J5f+7t07WFtbq6hWZceoUaPwv//9D+fPn//mrXnUwfXr1/H+/Xs0aNCASxOLxTh//jxWrFiB7OxsCAQCFdZQdWxsbFCrVi25tJo1a2Lv3r0qqlHZMHHiREyePBl+fn4AADc3N7x8+RLz58+vEAGN7qGVIi0tLbi7uyM8PJxLk0gkCA8Px/fff6/CmqkWYwyjRo3C/v37cfr0aVSpUkXVVSoTWrZsiTt37iAqKor78fDwQN++fREVFVVhgxkANG7cWOHRjsePH8PBwUFFNSobMjIyFDbAFAgEkEgkKqpR6aIeWikbP348/P394eHhAS8vLwQHByM9PR0DBw5UddVUZuTIkQgNDcXBgwdhYGCAt2/fApBu6Kejo6Pi2qmOgYGBwn1EPT09mJmZVfj7i7/88gsaNWqEefPmoWfPnoiIiMDatWuxdu1aVVdNpXx9fTF37lxUrlwZtWvXxs2bN7FkyRIMGjRI1VUrHYyUupCQEFa5cmWmpaXFvLy82JUrV1RdJZUCoPRn48aNqq5amePt7c3Gjh2r6mqUCYcPH2aurq5MKBSyGjVqsLVr16q6SiqXkpLCxo4dyypXrsy0tbWZk5MTmzZtGsvOzlZ11UoFPYdGCCFELdA9NEIIIWqBAhohhBC1QAGNEEKIWqCARgghRC1QQCOEEKIWKKARQghRCxTQCCGEqAUKaIQQQtQCBTRCCCFqgQIaIYQQtUABjRBCiFqggEYIIUQtUEAjhBCiFiigEUIIUQsU0AghhKgFCmiEEELUAgU0QgghaoECGiGEELVAAY0QQohaoIBGCCFELVBAI4QQohYooBFCCFELFNAIIYSoBQpohBBC1AIFNEIIIWqBAhohhBC1QAGNEEKIWqCARgghRC1QQCOEEKIWKKARQghRCxTQCCGEqAUKaIQQQtQCBTRCCCFqgQIaIYQQtUABjRBCiFqggEYIIUQtUEAjhBCiFiigEUIIUQsU0AghhKgFCmiEEELUAgU0QgghaoECGiGEELVAAY0QQohaoIBGCCFELVBAIyoREBAAR0fHIh07c+ZM8Hi84q1QGRMdHQ0ej4dNmzaVetk8Hg8zZ87kXm/atAk8Hg/R0dH5Huvo6IiAgIBirc+3fFZIxUIBjcjh8XgF+jl79qyqq1rhjRkzBjweD0+fPs0zz7Rp08Dj8XD79u1SrFnhxcXFYebMmYiKilJ1VTiyPyoWLVqk6qqQAtJQdQVI2bJlyxa515s3b0ZYWJhCes2aNb+pnHXr1kEikRTp2N9//x2TJ0/+pvLVQd++fRESEoLQ0FAEBgYqzbN9+3a4ubmhTp06RS6nf//+8PPzg1AoLPI58hMXF4dZs2bB0dER9erVk3vvWz4rpGKhgEbk9OvXT+71lStXEBYWppD+pYyMDOjq6ha4HE1NzSLVDwA0NDSgoUEf3YYNG6Jq1arYvn270oB2+fJlvHjxAn/88cc3lSMQCCAQCL7pHN/iWz4rpGKhIUdSaM2aNYOrqyuuX7+Opk2bQldXF1OnTgUAHDx4EB06dICtrS2EQiGcnZ0xe/ZsiMViuXN8eV/k8+GdtWvXwtnZGUKhEJ6enoiMjJQ7Vtk9NB6Ph1GjRuHAgQNwdXWFUChE7dq1cfz4cYX6nz17Fh4eHtDW1oazszPWrFlT4Pty//77L3r06IHKlStDKBTC3t4ev/zyCzIzMxWuT19fH7GxsejcuTP09fVhYWGBCRMmKLRFUlISAgICYGRkBGNjY/j7+yMpKSnfugDSXtrDhw9x48YNhfdCQ0PB4/HQu3dv5OTkIDAwEO7u7jAyMoKenh6aNGmCM2fO5FuGsntojDHMmTMHlSpVgq6uLpo3b4579+4pHJuYmIgJEybAzc0N+vr6MDQ0hI+PD27dusXlOXv2LDw9PQEAAwcO5Ia1ZfcPld1DS09Px6+//gp7e3sIhUJUr14dixYtAmNMLl9hPhdF9f79ewwePBhWVlbQ1tZG3bp18c8//yjk27FjB9zd3WFgYABDQ0O4ublh2bJl3PsikQizZs2Ci4sLtLW1YWZmhh9++AFhYWHFVld1R3/mkiJJSEiAj48P/Pz80K9fP1hZWQGQfvnp6+tj/Pjx0NfXx+nTpxEYGIiUlBQsXLgw3/OGhoYiNTUVP/30E3g8HhYsWICuXbvi+fPn+f6lfuHCBezbtw8jRoyAgYEBli9fjm7duiEmJgZmZmYAgJs3b6Jdu3awsbHBrFmzIBaLERQUBAsLiwJd9+7du5GRkYGff/4ZZmZmiIiIQEhICF6/fo3du3fL5RWLxWjbti0aNmyIRYsW4dSpU1i8eDGcnZ3x888/A5AGhk6dOuHChQsYPnw4atasif3798Pf379A9enbty9mzZqF0NBQNGjQQK7sXbt2oUmTJqhcuTLi4+Px999/o3fv3hg6dChSU1Oxfv16tG3bFhEREQrDfPkJDAzEnDlz0L59e7Rv3x43btxAmzZtkJOTI5fv+fPnOHDgAHr06IEqVarg3bt3WLNmDby9vXH//n3Y2tqiZs2aCAoKQmBgIIYNG4YmTZoAABo1aqS0bMYYfvzxR5w5cwaDBw9GvXr1cOLECUycOBGxsbFYunSpXP6CfC6KKjMzE82aNcPTp08xatQoVKlSBbt370ZAQACSkpIwduxYAEBYWBh69+6Nli1b4s8//wQAPHjwABcvXuTyzJw5E/Pnz8eQIUPg5eWFlJQUXLt2DTdu3EDr1q2/qZ4VBiPkK0aOHMm+/Jh4e3szAGz16tUK+TMyMhTSfvrpJ6arq8uysrK4NH9/f+bg4MC9fvHiBQPAzMzMWGJiIpd+8OBBBoAdPnyYS5sxY4ZCnQAwLS0t9vTpUy7t1q1bDAALCQnh0nx9fZmuri6LjY3l0p48ecI0NDQUzqmMsuubP38+4/F47OXLl3LXB4AFBQXJ5a1fvz5zd3fnXh84cIABYAsWLODScnNzWZMmTRgAtnHjxnzr5OnpySpVqsTEYjGXdvz4cQaArVmzhjtndna23HEfP35kVlZWbNCgQXLpANiMGTO41xs3bmQA2IsXLxhjjL1//55paWmxDh06MIlEwuWbOnUqA8D8/f25tKysLLl6MSb9XQuFQrm2iYyMzPN6v/ysyNpszpw5cvm6d+/OeDye3GegoJ8LZWSfyYULF+aZJzg4mAFgW7du5dJycnLY999/z/T19VlKSgpjjLGxY8cyQ0NDlpubm+e56tatyzp06PDVOpGvoyFHUiRCoRADBw5USNfR0eH+nZqaivj4eDRp0gQZGRl4+PBhvuft1asXTExMuNeyv9afP3+e77GtWrWCs7Mz97pOnTowNDTkjhWLxTh16hQ6d+4MW1tbLl/VqlXh4+OT7/kB+etLT09HfHw8GjVqBMYYbt68qZB/+PDhcq+bNGkidy1Hjx6FhoYG12MDpPesRo8eXaD6ANL7nq9fv8b58+e5tNDQUGhpaaFHjx7cObW0tAAAEokEiYmJyM3NhYeHh9Lhyq85deoUcnJyMHr0aLlh2nHjxinkFQqF4POlXzNisRgJCQnQ19dH9erVC12uzNGjRyEQCDBmzBi59F9//RWMMRw7dkwuPb/Pxbc4evQorK2t0bt3by5NU1MTY8aMQVpaGs6dOwcAMDY2Rnp6+leHD42NjXHv3j08efLkm+tVUVFAI0ViZ2fHfUF+7t69e+jSpQuMjIxgaGgICwsLbkJJcnJyvuetXLmy3GtZcPv48WOhj5UdLzv2/fv3yMzMRNWqVRXyKUtTJiYmBgEBATA1NeXui3l7ewNQvD5tbW2FoczP6wMAL1++hI2NDfT19eXyVa9evUD1AQA/Pz8IBAKEhoYCALKysrB//374+PjI/XHwzz//oE6dOtz9GQsLCxw5cqRAv5fPvXz5EgDg4uIil25hYSFXHiANnkuXLoWLiwuEQiHMzc1hYWGB27dvF7rcz8u3tbWFgYGBXLps5q2sfjL5fS6+xcuXL+Hi4sIF7bzqMmLECFSrVg0+Pj6oVKkSBg0apHAfLygoCElJSahWrRrc3NwwceLEMv+4RVlDAY0Uyec9FZmkpCR4e3vj1q1bCAoKwuHDhxEWFsbdMyjI1Ou8ZtOxL272F/exBSEWi9G6dWscOXIEkyZNwoEDBxAWFsZNXvjy+kprZqClpSVat26NvXv3QiQS4fDhw0hNTUXfvn25PFu3bkVAQACcnZ2xfv16HD9+HGFhYWjRokWJTomfN28exo8fj6ZNm2Lr1q04ceIEwsLCULt27VKbil/Sn4uCsLS0RFRUFA4dOsTd//Px8ZG7V9q0aVM8e/YMGzZsgKurK/7++280aNAAf//9d6nVs7yjSSGk2Jw9exYJCQnYt28fmjZtyqW/ePFChbX6xNLSEtra2kofRP7aw8kyd+7cwePHj/HPP/9gwIABXPq3zEJzcHBAeHg40tLS5Hppjx49KtR5+vbti+PHj+PYsWMIDQ2FoaEhfH19uff37NkDJycn7Nu3T26YcMaMGUWqMwA8efIETk5OXPqHDx8Uej179uxB8+bNsX79ern0pKQkmJubc68Ls/KLg4MDTp06hdTUVLlemmxIW1a/0uDg4IDbt29DIpHI9dKU1UVLSwu+vr7w9fWFRCLBiBEjsGbNGkyfPp0bITA1NcXAgQMxcOBApKWloWnTppg5cyaGDBlSatdUnlEPjRQb2V/Cn//lm5OTg7/++ktVVZIjEAjQqlUrHDhwAHFxcVz606dPFe675HU8IH99jDG5qdeF1b59e+Tm5mLVqlVcmlgsRkhISKHO07lzZ+jq6uKvv/7CsWPH0LVrV2hra3+17levXsXly5cLXedWrVpBU1MTISEhcucLDg5WyCsQCBR6Qrt370ZsbKxcmp6eHgAU6HGF9u3bQywWY8WKFXLpS5cuBY/HK/D90OLQvn17vH37Fjt37uTScnNzERISAn19fW44OiEhQe64/7d33+FRFG8Ax793KZfeSYNASCgJHRISilIkVAFRkKoUEWyIgKhgoamggogURVHAAoKC5acgEkIRIRTBICVEWggthZJO2t3+/jhycORCEghcEt7P8+xDbm52d3Zy3JuZnZ1Rq9WGh91zc3NN5nFwcKBOnTqG90HfrX306NHb7q6t6qSFJspNmzZtcHV1ZdiwYYZpmb755pt72rVTkmnTprFx40batm3Lc889Z/hibNSoUYnTLgUFBREYGMjEiRM5d+4cTk5OrF279o7uxfTq1Yu2bdsyadIk4uPjadCgAT/++GOZv7AcHBzo06eP4T7ajd2NAD179uTHH3/k0Ucf5eGHH+bUqVMsXryYBg0akJmZWaZzFT5PN2vWLHr27EmPHj34559/+P33341aXYXnnTFjBiNGjKBNmzYcPHiQFStWGLXsAAIDA3FxcWHx4sU4Ojpib29PeHg4tWvXLnL+Xr160bFjR9544w3i4+Np2rQpGzdu5JdffmHcuHFGA0DKQ1RUFDk5OUXS+/Tpw+jRo/nss88YPnw4+/btw9/fnzVr1rBjxw7mzZtnaEE+/fTTXL58mYceeogaNWpw+vRpFixYQLNmzQz32xo0aECHDh0ICQnBzc2Nv//+mzVr1jBmzBjDOX/66SdGjBjBsmXLyn3OzCrBPIMrRWVR3LD9hg0bmsy/Y8cOpVWrVoqtra3i6+urvPrqq8off/yhAMqWLVsM+Yobtm9qiDQ3DSMvbtj+Cy+8UGTfWrVqGQ0jVxRFiYqKUpo3b65YW1srgYGByhdffKG8/PLLio2NTTG1cN2RI0eUiIgIxcHBQfHw8FBGjRplGAZ+45DzYcOGKfb29kX2N1X2S5cuKU8++aTi5OSkODs7K08++aTyzz//lHrYfqF169YpgOLj41NkqLxOp1Nmzpyp1KpVS9FoNErz5s2V3377rcjvQVFKHravKIqi1WqV6dOnKz4+Poqtra3SoUMH5dChQ0XqOycnR3n55ZcN+dq2batER0cr7du3V9q3b2903l9++UVp0KCB4RGKwms3VcaMjAxl/Pjxiq+vr2JlZaXUrVtXmT17ttFjBIXXUtrPxc0KP5PFbd98842iKIqSlJSkjBgxQvHw8FCsra2Vxo0bF/m9rVmzRunSpYvi6empWFtbKzVr1lSeeeYZ5cKFC4Y877zzjhIWFqa4uLgotra2SlBQkPLuu+8qeXl5RX4XZflc3E9UilKB/nwWwkz69OkjQ6aFqOTkHpq479w8TdWxY8dYv349HTp0ME+BhBDlQlpo4r7j4+PD8OHDCQgI4PTp03z66afk5ubyzz//FHm2SghRecigEHHf6datG9999x2JiYloNBpat27NzJkzJZgJUclJC00IIUSVIPfQhBBCVAkS0IQQQlQJEtCEuEOmFqAUQtx7EtBElVW48nFJ29atW81dVCNbt25FpVKxZs0acxfltp07d47+/fvj4uKCk5MTjzzySKmXa5k5cyatWrWiWrVq2NjYULduXcaNG0dKSorJ/CdOnGDw4MF4enpia2tL3bp1eeONN4zy7Nmzh+eff56QkBCsrKzKNHekqDxklKOosr755huj119//TWRkZFF0gunHrpdS5YsuWczx1cGmZmZdOzYkbS0NF5//XWsrKz46KOPaN++PTExMSWuEr1v3z6aNWvGwIEDcXR0JDY2liVLlrBu3TpiYmIM8z4CxMTE0KFDB6pXr87LL7+Mu7s7CQkJnDlzxuiY69ev54svvqBJkyYEBATw33//3ZVrF2ZmzmlKhLiXTE3jZUpWVtY9KE3xtmzZogDKDz/8YNZy3K73339fAZQ9e/YY0mJjYxULCwtl8uTJt3XMNWvWKIDy3XffGdK0Wq3SqFEjJTw83ORK4jdKTEw05Cnt50BUPtLlKO5rHTp0oFGjRuzbt4927dphZ2fH66+/DsAvv/zCww8/jK+vLxqNhsDAQN5++220Wq3RMW6+hxYfH49KpWLOnDl8/vnnBAYGotFoaNmyJXv37i23sp88eZLHH38cNzc37OzsaNWqFevWrSuSb8GCBTRs2BA7OztcXV0JDQ01TGIM+pXFx40bh7+/PxqNxrC+2o0rSheuOH7x4sUSy7VmzRpatmxJy5YtDWlBQUF06tSJ77///rautbB+b5yNf+PGjRw6dIipU6dia2tLdnZ2kd9NIS8vL5Nr+ImqRQKauO9dunSJ7t2706xZM+bNm0fHjh0BWL58OQ4ODkyYMIGPP/6YkJAQpkyZwqRJk0p13JUrVzJ79myeeeYZ3nnnHeLj43nsscfIz8+/4zInJSXRpk0b/vjjD55//nneffddcnJy6N27Nz/99JMh35IlSxg7diwNGjRg3rx5TJ8+nWbNmrF7925DnmeffZZPP/2Uvn378sknnzBx4kRsbW2JjY015NmzZw/BwcFFlmy5mU6n499//yU0NLTIe2FhYZw4cYKMjIwSr09RFC5evEhiYiLbt29n7NixWFhYGE1PtmnTJgA0Gg2hoaHY29tjZ2fHwIEDuXz5connEFWQuZuIQtwrxa0cACiLFy8ukt9UN9Yzzzyj2NnZKTk5OYa04lYOcHd3Vy5fvmxI/+WXXxRA+fXXX29ZztJ0OY4bN04BlO3btxvSMjIylNq1ayv+/v6G2fYfeeSRYldGKOTs7GxyRnpTZbpxFn5TUlJSFECZMWNGkfcWLVqkAMrRo0dveQxFUZQLFy4YzWxfo0YNZfXq1UZ5evfubajnIUOGKGvWrFHeeustxdLSUmnTpk2RmfcLSZdj1SUtNHHf02g0jBgxokj6jV1UGRkZXLx4kQcffNDQ/VaSAQMG4Orqanj94IMPApR6tN+trF+/nrCwMB544AFDmoODA6NHjyY+Pp4jR44A4OLiwtmzZ2/Z1eni4sLu3buNFj29WYcOHVAUhWnTpt2yXIUTP2s0miLvFS44evPk0Ka4ubkRGRnJr7/+yowZM/Dw8Ciyblvh65YtW/Ltt9/St29fZsyYwdtvv83OnTuJiooq8TyiapGAJu571atXx9raukj64cOHefTRR3F2dsbJyYlq1arxxBNPAJRqAc6aNWsavS4MbneyIGih06dPU79+/SLphSM2T58+DcBrr72Gg4MDYWFh1K1blxdeeIEdO3YY7fPBBx9w6NAh/Pz8CAsLY9q0abcddAv/CLhxleVChYtkluZelrW1NREREfTs2ZO33nqLRYsWMXLkSH777bci5xo0aJDRvoMHDwZg586dt3UNovKSgCbue6a+YFNTU2nfvj0HDhxgxowZ/Prrr0RGRvL+++8DlGqYvoWFhcl05R5OnxocHExcXByrVq3igQceYO3atTzwwANMnTrVkKd///6cPHmSBQsW4Ovry+zZs2nYsCG///57mc/n5uaGRqPhwoULRd4rTPP19S3zcdu0aYOPjw8rVqwwpBUex8vLyyivp6cnUD5/OIjKRQKaECZs3bqVS5cusXz5cl566SV69uxJRESEUReiOdWqVYu4uLgi6YVdobVq1TKk2dvbM2DAAJYtW0ZCQgIPP/ywYRBJIR8fH55//nl+/vlnTp06hbu7O++++26Zy6VWq2ncuDF///13kfd2795NQEAAjo6OZT4u6Ft4N7aMQ0JCAP1D3Dcq7DqtVq3abZ1HVF4S0IQwobB1dWNrKi8vj08++cRcRTLSo0cP9uzZQ3R0tCEtKyuLzz//HH9/fxo0aADoR3DeyNramgYNGqAoCvn5+Wi12iLdp56envj6+hp1G5Zl2H6/fv3Yu3evUVCLi4tj8+bNPP7440Z5jx49SkJCgtE1ZGdnFznm2rVruXLlitHoyUceeQSNRsOyZcuMWsxffPEFAJ07dy6xrKJqkZlChDChTZs2uLq6MmzYMMaOHYtKpeKbb765p92Fa9euNTn4ZNiwYUyaNInvvvuO7t27M3bsWNzc3Pjqq684deoUa9euRa3W/63apUsXvL29adu2LV5eXsTGxrJw4UIefvhhHB0dSU1NpUaNGvTr14+mTZvi4ODApk2b2Lt3Lx9++KHhnHv27KFjx45MnTq1xIEhzz//PEuWLOHhhx9m4sSJWFlZMXfuXLy8vHj55ZeN8gYHB9O+fXvD9GPHjh0jIiKCAQMGEBQUhFqt5u+//+bbb7/F39+fl156ybCvt7c3b7zxBlOmTKFbt2706dOHAwcOsGTJEgYNGmT0HNzp06cNM8QUBtp33nkH0Ldmn3zyyVL+VkSFZtYxlkLcQ8UN2y9uWPuOHTuUVq1aKba2toqvr6/y6quvKn/88YcCKFu2bDHkK27Y/uzZs4sck1IMfS8cIl/cVjhU/8SJE0q/fv0UFxcXxcbGRgkLC1N+++03o2N99tlnSrt27RR3d3dFo9EogYGByiuvvKKkpaUpiqIoubm5yiuvvKI0bdpUcXR0VOzt7ZWmTZsqn3zyickylVT2QmfOnFH69eunODk5KQ4ODkrPnj2VY8eOmayP9u3bG16npKQoo0ePVoKCghR7e3vF2tpaqVu3rjJu3DglJSWlyP46nU5ZsGCBUq9ePcXKykrx8/NT3nzzTSUvL6/UdXrj+UXlJgt8CiGEqBLkHpoQQogqQQKaEEKIKkECmhBCiCpBApoQQogqQQKaEEKIKkECmqiyCtclW758uSFt2rRpqFSqUu2vUqlKfOaqrDp06GC0BIoQovxIQBMVQu/evbGzs7vlWllDhgzB2tq6yOwXFc2RI0eYNm0a8fHx5i6KwdatW1GpVKxZs8bcRblt586do3///ri4uODk5MQjjzxS6kmUO3TogEqlKrJ169atSN7c3Fxee+01fH19sbW1JTw8nMjISJPHzcvLY+bMmQQFBWFjY4OXlxcPP/wwZ8+evaNrFbdHZgoRFcKQIUP49ddf+emnnxg6dGiR97Ozs/nll1/o1q0b7u7ut32eN998s9QLdN6uI0eOMH36dDp06GC0kjXoV1kWZZeZmUnHjh1JS0vj9ddfx8rKio8++oj27dsTExNTqs9EjRo1mDVrllGaqYmShw8fzpo1axg3bhx169Zl+fLl9OjRgy1bthgt15Ofn8/DDz/Mzp07GTVqFE2aNOHKlSvs3r2btLQ0atSocecXLspEApqoEHr37o2joyMrV640GdB++eUXsrKyGDJkyB2dx9LSEktL833sTS1TI0r2ySefcOzYMfbs2WOY0qp79+40atSIDz/8kJkzZ5Z4DGdnZ8PyP8XZs2cPq1atYvbs2UycOBGAoUOH0qhRI1599VWjJWk++ugjtm3bxl9//UVYWNgdXJ0oL9LlKCoEW1tbHnvsMaKiokhOTi7y/sqVK3F0dKR3795cvnyZiRMn0rhxYxwcHHBycqJ79+4cOHCgxPOYuoeWm5vL+PHjqVatmuEcprqMTp8+zfPPP0/9+vWxtbXF3d2dxx9/3Khrcfny5YYJeDt27Gjo2iqcq9DUPbTk5GRGjhyJl5cXNjY2NG3alK+++sooT+H9wDlz5vD5558TGBiIRqOhZcuWt1y8s6xOnjzJ448/jpubG3Z2drRq1Yp169YVybdgwQIaNmyInZ0drq6uhIaGsnLlSsP7GRkZjBs3Dn9/fzQaDZ6ennTu3Jn9+/cb8pRlwuM1a9bQsmVLo/kZg4KC6NSpE99//32pr6+goKDIQqE3n8fCwoLRo0cb0mxsbBg5ciTR0dGcOXMG0C8f9PHHH/Poo48SFhZGQUGByUmVxb0lAU1UGEOGDKGgoKDIF9Tly5f5448/ePTRR7G1teXkyZP8/PPP9OzZk7lz5/LKK69w8OBB2rdvf8tVl4vz9NNPM2/ePLp06cJ7772HlZUVDz/8cJF8e/fuZefOnQwcOJD58+fz7LPPEhUVRYcOHQxfZu3atWPs2LEAvP7663zzzTd88803hoU3b3b16lU6dOjAN998w5AhQ5g9ezbOzs4MHz6cjz/+uEj+lStXMnv2bJ555hneeecd4uPjeeyxx8jPzy/zdd8sKSmJNm3a8Mcff/D8888blpjp3bs3P/30kyHfkiVLGDt2LA0aNGDevHlMnz6dZs2asXv3bkOeZ599lk8//ZS+ffvyySefMHHiRGxtbYmNjTXk2bNnD8HBwSxcuPCW5dLpdPz7779GM+0XCgsL48SJE7e891rov//+w97eHkdHR7y9vXnrrbeK1Ns///xDvXr1cHJyKnIegJiYGEDfrXz+/HmaNGnC6NGjsbe3x97eniZNmrBly5YSyyLuEnNPJilEoYKCAsXHx0dp3bq1UfrixYsVQPnjjz8URVGUnJwcRavVGuU5deqUotFolBkzZhilAcqyZcsMaVOnTjWaoDgmJkYBlOeff97oeIMHDy4yGW92dnaRMkdHRyuA8vXXXxvSfvjhhyITGBdq37690WS48+bNUwDl22+/NaTl5eUprVu3VhwcHJT09HSja3F3d1cuX75syPvLL78ogPLrr78WOdeNCifn/eGHH4rNM27cOKPJjxVFUTIyMpTatWsr/v7+hjp/5JFHip3QuZCzs7PywgsvlKpMJU14nJKSogBGv9tCixYtUgDl6NGjtzzGU089pUybNk1Zu3at8vXXXyu9e/dWAKV///5G+Ro2bKg89NBDRfY/fPiwAiiLFy9WFEVRfvzxR8Pvo27dusqyZcuUZcuWKXXr1lWsra2VAwcO3LI84u6QFpqoMCwsLBg4cCDR0dFG3XgrV67Ey8uLTp06AaDRaAzLo2i1Wi5duoSDgwP169c36tIqjfXr1wMYWlWFxo0bVyTvjStb5+fnc+nSJerUqYOLi0uZz3vj+b29vRk0aJAhzcrKirFjx5KZmcm2bduM8g8YMMBokdEHH3wQoNSj/UoqS1hYmNHABwcHB0aPHk18fDxHjhwBwMXFhbNnz96yq9PFxYXdu3ffssXcoUMHFEUp8dGIq1evAvrf+81sbGyM8hTnyy+/ZOrUqTz22GM8+eST/PLLL4waNYrvv/+eXbt2GZ2rNOcp7LbMyMggKiqK4cOHM3z4cDZt2oSiKHzwwQe3LI+4OySgiQqlcNBH4f2Ys2fPsn37dgYOHGhYdFOn0/HRRx9Rt25dNBoNHh4eVKtWjX///bfIYpUlOX36NGq1msDAQKP0+vXrF8l79epVpkyZgp+fn9F5U1NTy3zeG89ft25dQ4AuVNhFefr0aaP0mjVrGr0uDG5Xrly5rfPfXBZT131zWV577TUcHBwICwujbt26vPDCC+zYscNonw8++IBDhw7h5+dHWFgY06ZNu+2gW/iHxI0LjhYqXHX7xj82SqtwbbZNmzYZnas05yn8t23btvj5+Rny1axZkwceeMBo8Ii4dySgiQolJCSEoKAgvvvuOwC+++47FEUxGt04c+ZMJkyYQLt27fj222/5448/iIyMpGHDhkYrF5e3F198kXfffZf+/fvz/fffs3HjRiIjI3F3d7+r571RYVC/mXIPV4EKDg4mLi6OVatW8cADD7B27VoeeOABpk6dasjTv39/Tp48yYIFC/D19WX27Nk0bNiQ33//vcznc3NzQ6PRcOHChSLvFaaZGn5fksJAdPnyZUOaj49Pqc5T+K+Xl1eRvJ6enuXyB4YoOxm2LyqcIUOG8NZbb/Hvv/+ycuVK6tatazS6bc2aNXTs2JEvv/zSaL/U1FQ8PDzKdK5atWqh0+k4ceKEUeskLi6uSN41a9YwbNgwo5Wcc3JySE1NNcpX2plICs//77//otPpjFpphStV16pVq9THulO1atUyed2mymJvb8+AAQMYMGAAeXl5PPbYY7z77rtMnjzZ0D3n4+PD888/z/PPP09ycjItWrTg3XffpXv37mUql1qtpnHjxoaVpm+0e/duAgICcHR0LNMx4Xo3bbVq1QxpzZo1Y8uWLaSnpxsNDCkc8NKsWTMAGjdujJWVFefOnSty3PPnzxsdU9w70kITFU5ha2zKlCnExMQUefbMwsKiSIvkhx9+MPnlUpLCL9f58+cbpc+bN69IXlPnXbBgAVqt1ijN3t4eoEigM6VHjx4kJiayevVqQ1pBQQELFizAwcGB9u3bl+YyykWPHj3Ys2cP0dHRhrSsrCw+//xz/P39adCgAUCRmVqsra1p0KABiqKQn5+PVqst0gXr6emJr6+vUXdeWYbt9+vXj7179xoFtbi4ODZv3mx4TKLQ0aNHSUhIMLxOT08v0o2oKArvvPMOAF27djU6j1ar5fPPPzek5ebmsmzZMsLDww2tOkdHR3r06MHOnTsNAR8gNjaWnTt30rlz5xKvSZQ/aaGJCqd27dq0adOGX375BaBIQOvZsyczZsxgxIgRtGnThoMHD7JixQoCAgLKfK5mzZoxaNAgPvnkE9LS0mjTpg1RUVEcP368SN6ePXvyzTff4OzsTIMGDYiOjmbTpk1FZqlo1qwZFhYWvP/++6SlpaHRaHjooYfw9PQscszRo0fz2WefMXz4cPbt24e/vz9r1qxhx44dzJs377ZaHreydu1aoy/gQsOGDWPSpEl89913dO/enbFjx+Lm5sZXX33FqVOnWLt2raEF2aVLF7y9vWnbti1eXl7ExsaycOFCHn74YRwdHUlNTaVGjRr069ePpk2b4uDgwKZNm9i7d69R63bPnj107NiRqVOnljgw5Pnnn2fJkiU8/PDDTJw4ESsrK+bOnYuXl5fhXlih4OBg2rdvb3j2b//+/QwaNIhBgwZRp04drl69yk8//cSOHTsYPXo0LVq0MOwbHh7O448/zuTJk0lOTqZOnTp89dVXxMfHF+kRmDlzJlFRUTz00EOGQUXz58/Hzc2N119/vdS/E1GOzDfAUojiFQ7HDgsLK/JeTk6O8vLLLys+Pj6Kra2t0rZtWyU6OrrIkPjSDNtXFEW5evWqMnbsWMXd3V2xt7dXevXqpZw5c6bIkPIrV64oI0aMUDw8PBQHBwela9euytGjR5VatWopw4YNMzrmkiVLlICAAMXCwsJoCP/NZVQURUlKSjIc19raWmncuLFRmW+8ltmzZxepj5vLaUrhEPnitsKh+idOnFD69eunuLi4KDY2NkpYWJjy22+/GR3rs88+U9q1a6e4u7srGo1GCQwMVF555RUlLS1NURRFyc3NVV555RWladOmiqOjo2Jvb680bdpU+eSTT0yWqaSyFzpz5ozSr18/xcnJSXFwcFB69uypHDt2zGR93FjHJ0+eVB5//HHF399fsbGxUezs7JSQkBBl8eLFik6nK7L/1atXlYkTJyre3t6KRqNRWrZsqWzYsMFkmfbt26dEREQo9vb2iqOjo/LII48o//33X6muR5Q/laLcw7vJQgghxF0i99CEEEJUCRLQhBBCVAkS0IQQQlQJEtCEEEJUCRLQhBBCVAkS0IQQQlQJ8mD1bdLpdJw/fx5HR8cyTXUkhBCibBRFISMjA19f3yITed9IAtptOn/+vNEs20IIIe6uM2fOUKNGjWLfl4B2mwqnJDpz5kyR1W1Lkp+fz8aNG+nSpQtWVlZ3o3iVltSNaVIvpkm9FK8q1U16ejp+fn4lTgUnAe02FXYzOjk53VZAs7Ozw8nJqdJ/0Mqb1I1pUi+mSb0UryrWTUm3d2RQiBBCiCpBApoQQogqQQKaEEKIKkHuoQkhSk2r1ZKfn2/uYhjk5+djaWlJTk5OkYVW73eVqW6srKywsLC44+NIQBNClEhRFBITE0u1Cve9pCgK3t7enDlzRp4HvUllqxsXFxe8vb3vqKwS0IQQJSoMZp6entjZ2VWYL0idTkdmZiYODg63fOD2flRZ6kZRFLKzs0lOTgbAx8fnto8lAU2Yn6JAagJcOID6wkFqpyTChepQvSlYVI3hxpWZVqs1BDN3d3dzF8eITqcjLy8PGxubCv2lbQ6VqW5sbW0BSE5OxtPT87a7HyWgiXtLp4PLJ+DCAeMtJxUAC6AJwNKvwcoOfJtDjZb6zS8MHDzNWPj7U+E9Mzs7OzOXRFRlhZ+v/Px8CWiiAtIWwMU448CVeBDyMovmVVuBZzA6zwaknDqMZ95pVLnpcHqHfivkUhNqhOmDW41Q8GoMltb37pruYxWlm1FUTeXx+ZKAJspHQS4kHzEOXkmHoSCnaF5LW/BuBD5Nr2/VgsHSGm1+PrvWr6dH925YpZ6Cs3vh7B44+zckx+q7JlMT4NCaa8eyAZ9m4NdSH+hqtASn2++DF0JUXhLQRNnlZUHiIePglRILuoKiea0dwaeJcfByrwsWJXz0VGrwDNJvLZ7Up+Wkwbl9+uB2Zo8+2OWkwpld+q2Qs5++9VYY4HyagKWm3C5f3N/8/f0ZN24c48aNK1X+rVu30rFjR65cuYKLi8tdLdv9TgKauLWrqfpuwhuD18X/AKVoXltXfWvJp+m1INYMXGtDed2QtnGGwIf0G+gHk1w6rg9shQEu+QikndFvh3/S57Ow1pelRstrLbmW4Fz8jN2iaiipC2vq1KlMmzatzMfdu3cv9vb2pc7fpk0bLly4gLOzc5nPVRYSOCWgiRtlpkDiTYM1rsSbzuvgbdzq8mmqDxL38j6LSgUedfVbs8H6tNwMOLf/WlfltS370rVuyz1Q2JBz9L0e3GqE6ctvZXPvyi7uugsXLhh+Xr16NVOmTCEuLs6Q5uDgYPhZURS0Wi2WliV/JVarVq1M5bC2tsbb27tM+4jbU7HHcoq7S5sPR/4Hq4bA3AYwpw582xeiZsCRX64HM5eaENwLOr4Jg3+Al+NgYhwM+R4eegOCe4KL370NZsXROEJAe2g3EQavhldOwIv74dHPoOXT4N0EVBaQcV5/jRvfhKVdYFYNWPIQ/D4JDq7R36dTTLRCRaXh7e1t2JydnVGpVIbXR48exdHRkd9//52QkBA0Gg1//fUXJ06c4JFHHsHLywsHBwdatmzJpk2bjI7r7+/PvHnzDK9VKhVffPEFjz76KHZ2dtStW5f//e9/hve3bt2KSqUyPJS+fPlyXFxc+OOPPwgODsbBwYFu3boZBeCCggLGjh2Li4sL7u7uvPbaawwbNow+ffrcdn1cuXKFoUOH4urqip2dHd27d+fYsWOG90+fPk2vXr1wdXXF3t6ehg0bsn79esO+Q4YMoVq1atja2lK3bl2WLVt222W5W6SFdj9KOwf7v4L9X0PGBeP33OsYt7q8m4Cdm3nKWR5UKnAP1G9NB+rT8rLg/D/XuiqvDTrJStHfnzu3D3Zf29fBC+p0hlbP6QexCANFUbiab57plGytLMptxOWkSZOYM2cOAQEBuLq6cubMGXr06MG7776LRqPh66+/plevXsTFxVGzZs1ijzN9+nQ++OADZs+ezYIFCxgyZAinT5/Gzc30/53s7GzmzJnDN998g1qt5oknnmDixImsWLECgPfff58VK1awbNkygoOD+fjjj/n555/p2LHjbV/r8OHDOXbsGP/73/9wcnLitddeo0ePHhw5cgQrKyteeOEF8vLy+PPPP7G3t+fIkSOGVuxbb73FkSNH+P333/Hw8OD48eNcvXr1tstyt0hAu1/odHBqK+z9EuJ+B+Xal5Gdh37QRd0u4NUIbMq2tlulZG0P/g/oN7j2YPfpa8HtWoBLPAiZSRDzrX4L6AhtxkBgp4rREjWzq/laGkz5wyznPjKjK3bW5fPVNWPGDDp37mx47ebmRtOmTQ2v3377bX766Sf+97//MWbMmGKPM3z4cAYNGgTAzJkzmT9/Pnv27KFbt24m8+fn57N48WICAwMBGDNmDDNmzDC8v2DBAiZPnsyjjz4KwMKFCw2tpdtRGMh27NhBmzZtAFixYgV+fn78/PPPPP744yQkJNC3b18aN24MQEBAgGH/hIQEmjdvTmhoKKBvpVZEEtCquuzLELMC/l4Kl09eT6/VFkKf0ncl3u8jAFUqcPXXb00e16flZcO5v/X1duQXOLlFv3k2hNYvQON+Um9VQOEXdKHMzEymTZvGunXruHDhAgUFBVy9epWEhIRbHqdJkyaGn+3t7XFycjJM5WSKnZ2dIZiBfrqnwvxpaWkkJSURFhZmeN/CwoKQkBB0Ol2Zrq9QbGwslpaWhIeHG9Lc3d2pX78+sbGxAIwdO5bnnnuOjRs3EhERQd++fQ3X9dxzz9G3b1/2799Ply5d6NOnjyEwViQS0KoiRdG3NP5eCod+BG2uPl3jpO92C30KPIPNW8aKztoOarfTb1fiYddifRdt8mH45XmImg5ho/V1WZm7ZG+TrZUFR2Z0Ndu5y8vNoxUnTpxIZGQkc+bMoU6dOtja2tKvXz/y8vJueZybV4RWqVS3DD6m8itmvmf79NNP07VrV9atW8fGjRuZNWsWH374IS+++CLdu3fn9OnTrF+/nsjISDp16sQLL7zAnDlzzFrmm8mgkKokN1MfxBY/CF92hgPf6YOZdxPo9TFMiIUesyWYlZWrP3R/DyYchohp4Oij747c/DZ81BDWv2Lc+r0PqFQq7KwtzbLdzRlLduzYwfDhw3n00Udp3Lgx3t7exMfH37XzmeLs7IyXlxd79+41pGm1Wvbv33/bxwwODqagoIDdu3cb0i5dukRcXBwNGjQwpPn5+fHss8/y448/8vLLL7NkyRLDe9WqVWPYsGF8++23zJs3j88///y2y3O3SAutKkg6An9/CQdWQ16GPs3SBho+Bi1HQvUQue9THmxd4YHx0OoFOPwj7FwISQdhz+ewZ4l+tGfrF6FmeMnHEhVS3bp1+fHHH+nVqxcqlYq33nrrtrv57sSLL77IrFmzqFOnDkFBQSxYsIArV66UKpgfPHgQR0dHdDodWVlZODg40Lx5cx555BFGjRrFZ599hqOjI5MmTaJ69eo88sgjAIwbN47u3btTr149rly5wpYtWwgO1v/xO2XKFEJCQmjYsCG5ubn89ttvhvcqErO30BYtWoS/vz82NjaEh4ezZ8+eYvMuX74clUpltNnYXH92KD8/n9dee43GjRtjb2+Pr68vQ4cO5fz580bH8ff3L3Kc9957765d411RkAv//gBLu8GnrWHvF/pg5hYIXd7Vt8Ye/VQ/Y4YEs/Jlaa3vun12Owz9BepEAArE/qp/BOCLzvr7brqKvaiiKGru3Lm4urrSpk0bevXqRdeuXWnRosU9L8drr73GoEGDGDp0KK1bt8bBwYGuXbsafd8Vp127djRv3pyQkBDatWtHy5YtAVi2bBkhISH07NmT1q1boygK69evN3R/arVaXnjhBYKDg+nWrRv16tXjk08+AfTP0k2ePJkmTZrQrl07LCwsWLVq1d2rgNulmNGqVasUa2trZenSpcrhw4eVUaNGKS4uLkpSUpLJ/MuWLVOcnJyUCxcuGLbExETD+6mpqUpERISyevVq5ejRo0p0dLQSFhamhISEGB2nVq1ayowZM4yOk5mZWaayp6WlKYCSlpZW5uvOy8tTfv75ZyUvL6/M+yqXTynKximK8n6Aokx10m/TXBVl1RBFOb5ZUbTash+zArmjujGnpCOK8vPzijLD4/rvZV4TRdn1maLklu2zZYo56+Xq1avKkSNHlKtXr97zc5dEq9UqV65cUbSV/HNfEq1Wq9SrV0958803y7RPZaqbW33OSvt9a9Yux7lz5zJq1ChGjBgBwOLFi1m3bh1Lly5l0qRJJvcpfDjSFGdnZyIjI43SFi5cSFhYGAkJCUbPkTg6Olaep/d1Wji2UT/k/vgmDNNOOfpAyHBoMRScfM1ZQuEZDI8sgoemwN4l+hbzlXj4/RXY8q5+8Ej4M+BYST5zwqxOnz7Nxo0bad++Pbm5uSxcuJBTp04xePBgcxetQjNbQMvLy2Pfvn1MnjzZkKZWq4mIiCA6OrrY/TIzM6lVqxY6nY4WLVowc+ZMGjZsWGz+tLQ0VCpVkbnN3nvvPd5++21q1qzJ4MGDGT9+/C2nvcnNzSU3N9fwOj09HdB3cxauF1VahflL3C8zCXXMCtT/fI0q/awhWVe7A7oWI1DqdQW1ZeFBy1SGiqrUdVNR2bjBg69B+BjU/65GvedTVFdOwV9zUXYuQGnUD234c+DZoORj3cCc9ZKfn4+iKOh0OrPcT7oV5drIwMLyVSXLly9n4sSJKIpCo0aN2LhxI/Xr1y/1dVa2utHpdCiKYnI9tNJ+7lWKYp6xoufPn6d69ers3LmT1q1bG9JfffVVtm3bZjQap1B0dDTHjh2jSZMmpKWlMWfOHP78808OHz5MjRpFJ5vNycmhbdu2BAUFGZ7AB33LsEWLFri5ubFz504mT57MiBEjmDt3brHlnTZtGtOnTy+SvnLlyvJd+FBRcM88Su2LUfik7kON/j5MnoU9Ce7tiHfvSJaN/JVfaSg6vNP+oU7yetyzrk8zlOzYiOOe3UlxbFTh73FaWlri7e2Nn58f1tay9py4O/Ly8jhz5gyJiYkUFBiv3JGdnc3gwYNJS0vDyan4yR8qVUC7WX5+PsHBwQwaNIi33367yHt9+/bl7NmzbN269ZaVsHTpUp555hkyMzPRaEw/LGuqhebn58fFixdveeziyh0ZGUnnzp2vP4+Sk4b64GrU+5ejuvifIa+ueqi+NRbcG6xsy3Seyshk3VQRqnP7UO/+BNXRX1Ep+r+YFc8GaMOfR2n4mH5VgGKYs15ycnI4c+aMYfBWRaIoChkZGTg6OsoCpDepbHWTk5NDfHw8fn5+RT5n6enpeHh4lBjQzNbl6OHhgYWFBUlJSUbpSUlJpb63ZWVlRfPmzTl+/LhRen5+Pv379+f06dNs3ry5xIATHh5OQUEB8fHx1K9f32QejUZjMthZWVnd9heMlZUVVimH9PfGDq2F/Oxrb9jrZ6wIHYnap4n5h6KawZ3Ua4Xl30q/3fCgtir5CJa/joEt70D4tQe1bV2LPYQ56kWr1aJSqVCr1ajLaymgclLYlVZYPnFdZasbtVqNSqUy+Rkv7WfebFdpbW1NSEgIUVFRhjSdTkdUVJRRi+1WtFotBw8exMfn+grFhcHs2LFjbNq0CXd39xKPExMTg1qtxtPTs+wXcjvys6l56U8slnaGzzvAP9/og1m1YOgxB16O1T8I7dOkxEOJSsjkg9qJ+lUO5jaE9a/C5VPmLqUQlY5ZRzlOmDCBYcOGERoaSlhYGPPmzSMrK8sw6nHo0KFUr16dWbNmAfqJRFu1akWdOnVITU1l9uzZnD59mqeffhrQB7N+/fqxf/9+fvvtN7RaLYmJiYB+0lFra2uio6PZvXs3HTt2xNHRkejoaMaPH88TTzyBq2vxfxmXJ4u1T9E84dqSFGoraPCI/gHomq0r/P0UUY6KPKi9AJIOwZ7P9CMlg3pCm7H6dduEECUya0AbMGAAKSkpTJkyhcTERJo1a8aGDRvw8vIC9DM839hUvnLlCqNGjSIxMRFXV1dCQkLYuXOnYeqWc+fOGdYhatasmdG5tmzZQocOHdBoNKxatYpp06aRm5tL7dq1GT9+PBMmTLg3Fw3oGvXl6pkYbNo+h0XIMHAo24KBooopfFC7yQA4uRWiF+ofz4j9n37zC0cV9pzJRcKFENeZbVBIZZeeno6zs3OJNylNyc+9yvrfN9Dj4Z5V7z7RHcrPz2f9+vX06NHj/q6b5Fh9YPv3e9DqJ8a9auWKJrgb6sCO+kmTnXxKOEj5yMnJ4dSpU9SuXbvCDQrR6XSkp6fj5ORUKe4T3UuVrW5u9Tkr7fdtxb/KqkhtCSqpenELhQ9qjzsED05EsXXFNv8K6n+/g59Gw9wgWNgS1k3UT7l19Yq5S1xldejQgXHjxhle37xitSkqlYqff/75js9dXse5X8i3qhAVmaMXdHqLghf/ZWfgq2hbjwXf5oAKLv6nv9e2+gn4IEA/wChyKpzYrF/P7T7Xq1evYhfY3L59OyqVin///bfMx927dy+jR4++0+IZmTZtWpHbJAAXLlyge/fu5Xqumy1fvrzIxBOVlcy2L0RlYGVLilMjdA/1wMLKSt8ii/8LTm6DU9v0we38P/ptxzz9M21+4VC7vb57snoLsLi/unBHjhxpeBb15okXli1bRmhoqNHCnKVVrdq9u+ddaabnqyCkhSZEZWTrql9t/OE5MGbvtdUVPoOmg8Gpuv6+W/x2/fNtS7vA+7VhRX+IXgSJh6ASTIV0p3r27Em1atVYvny5UXpmZiY//PADI0eO5NKlSwwaNIjq1atjZ2dH48aN+e6772553Ju7HI8dO0a7du2wsbGhQYMGReaTBf3s+fXq1cPOzo6AgADeeustw3ROy5cvZ/r06Rw4cMCw+kdhmW/ucjx48CAPPfQQtra2uLu7M3r0aDIzMw3vDx8+nD59+jBnzhyqV69OQEAAY8aMuaMp0xISEnjkkUdwcHDAycmJ/v37Gz0/fODAAcOocScnJ0JCQvj7778B/ZyUvXr1wtXVFXt7exo2bMj69etvuywlkRaaEFWBk69+pGTTgfoVyy+dgFNb9S24+O36Ft2xP/QbgJ3H9RW5A9qDa+2yPTKiKNcnArjXrOxKVVZLS0uGDh3K8uXLeeONNwyzZfzwww9otVoGDRpEZmYmISEhvPbaazg5ObFu3TqefPJJAgMDCQsLK/EcOp2Oxx57DC8vL3bv3k1aWprR/bZCjo6OLF++HF9fXw4ePMioUaNwdHTk1VdfZcCAARw6dIgNGzawaZP+cR5nZ+cix8jKyqJr1660bt2avXv3kpyczNNPP82YMWP0AVCnA10BW7ZsxsfNkc1rl3L0RAJPPDuB5k0aMerZ50u8HlPXVxjMtm3bRkFBAS+88AIDBgxg69atAAwZMoTmzZvz6aefYmFhQUxMjGFA1wsvvEBeXh5//vkn9vb2HDlyBAcHhzKXo7QkoAlR1ahU4FFHv7V8Wv9Fl3Twevfk6Z2QfVH/7NvhH/X7ONeEgHZQu4M+yDl63foc+dkw00wrPLx+HqztS5X1qaeeYvbs2Wzbto0OHToA+u7Gvn374uzsjLOzMxMnTjTkf/HFF/njjz/4/vvvSxXQNm3axNGjR/njjz/w9dXXx8yZM4vc93rzzTcNP/v7+zNx4kRWrVrFq6++iq2tLQ4ODoY5M4uzcuVKcnJy+Prrr7G3tYG6/iyc/S69+j/B+xOfxsvNHnLScXVyYOH0l7CwsCDY35uHH2pL1O+/MOrR9qBxBGsH/b+l6IKOiori4MGDnDp1Cj8/PwC+/vprGjZsyN69e2nZsiUJCQm88sorBAUFAfpFUgslJCTQt29fGjduDEBAQECJ57wTEtCEqOrUavBpqt/ajoWCPDj39/UAd3YvpCXAP9/qN9DPWhPQXn8PzqfyPtgdFBREmzZtWLp0KR06dOD48eNs376dGTNmAPrZhmbOnMn333/PuXPnyMvLIzc3t9QTjsfGxuLn52cIZoDJmY5Wr17N/PnzOXHiBJmZmRQUFJT+cR+dFnIziD2wj6YN62OflQBpOQC0bVgdnU5H3H9H8WqlX5m+YVA9LJx9USw15Gam4e3jzaHDR/Xd0NmX9BvoV7W3drjlAKLC6ysMZgANGjTAxcWF2NhYWrZsyYQJE3j66af55ptviIiI4PHHHycwMBCAsWPH8txzz7Fx40YiIiLo27fvbd23LC0JaELcbyytoVYb/dZxMuRmQkK0/qHuU9sg8SCkxOq33YvBoRa0mw+Z9qBy1c81amWnbymZg1XZVrcYOXIkL774IosWLWLZsmUEBgbSvn17AGbPns3HH3/MvHnzDCvdjxs3jry8vHIrbnR0NEOGDGH69Ol07doVZ2dnVq1axYcfflg0s7ZA3/rNv3q9Szf1NFw6DnmZ+vcL9MEMtRVorgVFRx/wbAi2rljZq8DJF0WnIyfPApWtKzorW/1q9rkZ+pXt86/qj1OQA1kpoGgh+ai+5aZx0Ac6tUXR8pkwbdo0Bg8ezLp16/j999+ZOnUqq1at4tFHH+Xpp5+ma9eurFu3jo0bNzJr1iw+/PBDXnzxxXKo2aIkoAlxv9M4QN3O+g0g65L+vtupbfpWXF4eaHP13ZR5lwCVvstP46jfSnlPy1z69+/PSy+9xMqVK/n666957rnnDPfTduzYwSOPPMITTzwB6O8Z/ffff4bZh0oSHBzMmTNnuHDhgmFO2V27dhnl2blzJ7Vq1eKNN94wpJ0+fVr/Q0465GdjXZCJNjdb3zVsioU1wQ0asPyHdWRpvLB3qQYWVuxYvx61Wk39JqH6P1SKpQIbJ/0GoCvQ/yGTl3m967Hgqn7LSta/trIjuKYnZ86c4czpePxq+QNw5MgRUlNTjeqoXr161KtXj/HjxzNo0CCWLVvGo48+CoCfnx/PPvsszz77LJMnT2bJkiUS0IQQ94i9OzTso98AUuLh/EXQOINyFXT5+i/CvEzIuAAqi+vBTeMIlqaXYDIXBwcHBgwYwOTJk0lPT2f48OGG9+rWrcuaNWvYuXMnrq6uzJ07l6SkpFIHtIiICOrVq8ewYcOYPXs26enpRoELoG6dOiQkJLDq66W0bBrMuvW/89OPa/StossnAPD3ceNUwlliDsVRo0YNHF090Di46A/gUgu8GjJkdG2mfrCQYc+MZdq0aaSkpPDiiy/y5JNPGqYLLDW1Jdi66DcnX7Q6iEnIgPwsfRekNh+NxoqIlvVoHFSHIQMeY967UyhQW/P8xDdp3749oaGhXL16lVdeeYV+/fpRu3Ztzp49y969e+nbty8A48aNo3v37tSrV48rV66wZcsWgoODy1bWslzWXTuyEKJqcPTWt8icq4NXQ/39Naca+gCnUuu/mHNSIe0MJB+BpMOQmqAfWaktKPHw98LIkSO5cuUKXbt2Nbrf9eabb9KiRQu6du1Khw4d8Pb2pk+fPqU+rlqt5qeffuLq1auEhYXx9NNP8+6Mafo3sy/DpRP0Dg9g/KjBjBn3Ms3aRrBz127eekk/oToWGrB1pe/AJ+jWtQsdBzxHtYYP8t0fu/T1DoauPzs7O/744w8uX75My5Yt6devH506dWLhwoV3XD+ZmZk0b92e5u160DyiH827DqLXU6+gsnPnl+ULcHV2ol3vwUT07k+Arwer50+BS8exyL7IpZQkhg4dSr169ejfvz/du3c3LIas1Wp54YUXCA4Oplu3btSrV49PPvnkjstbHJnL8Tbd0VyOMl9hsaRuTDNnvdxyLkdFp/+LPi/j2v2ZbIrMomxld731Zm1frtO+mXW+QkUHBbnX7nnlXL/3pWhN57e00deFle31f0t5n+p2lFvdKIq+yzk389rvOFPfZXkjlcW10ZPXRlBa2pS5G7o85nKULkchxO1Tqa99iTnoBybotPovvNxrAa6g8Is+GzKTADVobrj/Zmlboe+/GegKrg3UuGEryMH0EgiqayMIbwheljZ3NXjdVapr12NpA/Ye+gBXkHPtd3yt61nRQm6afgN9l2ZhgLO+1g19D37PEtCEEOVHbQE2zvoN9EPFC4Nbbsa1wQjXfgb9F5+h9eZYwsCGe0BR9GU2BK1r/2qLGfWosrgWtGz1wdnKFqxsqvbk4yrV9Wt28Lz+kH3hHzJ5Wfrfc06qfoNrIzIdwdlP/xjJXSIBTQhx91hYg527fjP6y/6GrqurV66vFmChAZtrwU3jeHdbNYpO31VYcFPLq7guQwvrG4LWtc3CunK0MO8m1bVRr9b24OB1Qzf0jQHu2kCiu9wtLAFNCHFvFPnLXqf/sisMcPnZ+ns1WbmQdVG/j9UN3ZPWdrff8tEWFA1cJXUZWt0UvNTydVkqRt3Q3vqZavKz9N3Rd5n8hoQQpVLu48dU6uvBCq4/G1UY4LS5+i/C/CzITNTnL5y2qXDgQdFC3tBlmH09eOmKmZz3xi5Dw/0uTdXuMrzX1Orrv+NbKI/PlwQ0IcQtFY6qzM7OxtbW9u6d6MZno0A/gvDG+2+KFnLT9RuA2gqVxhHrAlClZ1y/56UUs5KAhbXxCENLW/1Dxfd7l2EFkZ2tnxnlTkbxmj2gLVq0iNmzZ5OYmEjTpk1ZsGBBsZOCLl++nBEjRhilaTQacnJyDK8VRWHq1KksWbKE1NRU2rZty6effmo0Yebly5d58cUX+fXXX1Gr1fTt25ePP/74rs4CLURlZWFhgYuLC8nJ+hkk7OzsDDNt3P2TO4CdA9h6Xxsan6XvpszPBvIg7xJqIPfqjTup9K0sSxuwsAErjelRhgXXht1XUTqdjry8PHJycu79Iw1loCgK2dnZJCcn4+LigoXF7d83NWtAW716NRMmTGDx4sWEh4czb948unbtSlxcHJ6enib3cXJyIi4uzvD65v9YH3zwAfPnz+err76idu3avPXWW3Tt2pUjR44Ynm0YMmQIFy5cIDIykvz8fEaMGMHo0aNZuXLl3btYISqxwlngC4Oa2SkaKMhFKchBl5+D2kqDysJa3wpTW15rdeVe2+5PiqJw9epVbG1t790fIHfAxcXljhc0NWtAmzt3LqNGjTK0uhYvXsy6detYunQpkyZNMrmPSqUq9qIVRWHevHm8+eabPPLII4B+qQMvLy9+/vlnBg4cSGxsLBs2bGDv3r2EhoYCsGDBAnr06MGcOXOMZhEQQuipVCp8fHzw9PS8o8Uiy1t+fj5//vkn7dqFyoP4N7leN+0qfN1YWVndUcuskNkCWl5eHvv27WPy5MmGNLVaTUREBNHR0cXul5mZSa1atdDpdLRo0YKZM2fSsGFDAE6dOkViYiIRERGG/M7OzoSHhxMdHc3AgQOJjo7GxcXFEMxAPx+bWq1m9+7dhgk1b5abm0tu7vW/9tLT9f34+fn5Zf4PXpi/In0xVBRSN6ZVpHopjy+e8qLT6SgoKMDCwqJClasiqEx1o9Pp0N1iFfXSfu7NFtAuXryIVqstMqmml5cXR48eNblP/fr1Wbp0KU2aNCEtLY05c+bQpk0bDh8+TI0aNUhMTDQc4+ZjFr6XmJhYpDvT0tISNzc3Qx5TZs2aZZif7EYbN24s9dpJNzO1VLvQk7oxTerFNKmX4lWFuikcMFISsw8KKYvWrVsbLZ7Xpk0bgoOD+eyzz3j77bfv6rknT57MhAkTDK/T09Px8/OjS5cutzWXY2RkJJ07d67wXQH3mtSNaVIvpkm9FK8q1U1hj1hJzBbQPDw8sLCwICkpySg9KSmp1DcGraysaN68OcePHweu37hOSkoyrE1U+LpZs2aGPDff2C4oKODy5cu3PK9Go0GjKboshpWV1W1/WO5k36pO6sY0qRfTpF6KVxXqprTlN9tYTmtra0JCQoiKijKk6XQ6oqKiTC5hbopWq+XgwYOG4FW7dm28vb2Njpmens7u3bsNx2zdujWpqans27fPkGfz5s3odDrCw8PL49KEEEKYgVm7HCdMmMCwYcMIDQ0lLCyMefPmkZWVZRj1OHToUKpXr86sWbMAmDFjBq1ataJOnTqkpqYye/ZsTp8+zdNP69cWUqlUjBs3jnfeeYe6desahu37+voa1jgqXJdn1KhRLF68mPz8fMaMGcPAgQNlhKMQQlRiZg1oAwYMICUlhSlTppCYmEizZs3YsGGDYVBHQkKC0QOBV65cYdSoUSQmJuLq6kpISAg7d+40Wl321VdfJSsri9GjR5OamsoDDzzAhg0bjNbXWbFiBWPGjKFTp06GB6vnz59/7y5cCCFEuTP7oJAxY8YwZswYk+9t3brV6PVHH33ERx99dMvjqVQqZsyYwYwZM4rN4+bmJg9RCyFEFVNx50MRQgghykACmhBCiCpBApoQQogqQQKaEEKIKkECmhBCiCpBApoQQogqocwBzd/fnxkzZpCQkHA3yiOEEELcljIHtHHjxvHjjz8SEBBA586dWbVqldGyKkIIIYQ53FZAi4mJYc+ePQQHB/Piiy/i4+PDmDFj2L9//90ooxBCCFGi276H1qJFC+bPn8/58+eZOnUqX3zxBS1btqRZs2YsXboURVHKs5xCCCHELd321Ff5+fn89NNPLFu2jMjISFq1asXIkSM5e/Ysr7/+Ops2bZLppYQQQtwzZQ5o+/fvZ9myZXz33Xeo1WqGDh3KRx99RFBQkCHPo48+SsuWLcu1oEIIIcStlDmgtWzZks6dO/Ppp5/Sp08fkwuv1a5dm4EDB5ZLAYUQQojSKHNAO3nyJLVq1bplHnt7e5YtW3bbhRJCCCHKqswBrTCY/f3338TGxgL6RTNDQ0PLt2RCCCFEGZQ5oJ09e5ZBgwaxY8cOXFxcAEhNTaVNmzasWrWKGjVqlHcZhRBCiBKVedj+008/TX5+PrGxsVy+fJnLly8TGxuLTqfj6aefvhtlFEIIIUpU5hbatm3b2LlzJ/Xr1zek1a9fnwULFvDggw+Wa+GEEEKI0ipzC83Pz4/8/Pwi6VqtFl9f3zIXYNGiRfj7+2NjY0N4eDh79uwp1X6rVq1CpVLRp08fo3SVSmVymz17tiGPv79/kfffe++9MpddCCFExVHmgDZ79mxefPFF/v77b0Pa33//zUsvvcScOXPKdKzVq1czYcIEpk6dyv79+2natCldu3YlOTn5lvvFx8czceJEky3CCxcuGG1Lly5FpVLRt29fo3wzZswwyvfiiy+WqexCCCEqljJ3OQ4fPpzs7GzCw8OxtNTvXlBQgKWlJU899RRPPfWUIe/ly5dveay5c+cyatQoRowYAcDixYtZt24dS5cuZdKkSSb30Wq1DBkyhOnTp7N9+3ZSU1ON3vf29jZ6/csvv9CxY0cCAgKM0h0dHYvkFUIIUXmVOaDNmzevXE6cl5fHvn37mDx5siFNrVYTERFBdHR0sfvNmDEDT09PRo4cyfbt2295jqSkJNatW8dXX31V5L333nuPt99+m5o1azJ48GDGjx9vCNCm5ObmGq0qkJ6eDuinADPVBXsrhfnLut/9QOrGNKkX06ReileV6qa011DmgDZs2LAyF8aUixcvotVq8fLyMkr38vLi6NGjJvf566+/+PLLL4mJiSnVOb766iscHR157LHHjNLHjh1LixYtcHNzY+fOnUyePJkLFy4wd+7cYo81a9Yspk+fXiR948aN2NnZlao8N4uMjLyt/e4HUjemSb2YJvVSvKpQN9nZ2aXKd1uTE2u1Wn7++WfDg9UNGzakd+/eWFhY3M7hSiUjI4Mnn3ySJUuW4OHhUap9li5dypAhQ7CxsTFKnzBhguHnJk2aYG1tzTPPPMOsWbPQaDQmjzV58mSj/dLT0/Hz86NLly44OTmV6Vry8/OJjIykc+fOJqcOu59J3Zgm9WKa1EvxqlLdFPaIlaTMAe348eP06NGDc+fOGYbuz5o1Cz8/P9atW0dgYGCpjuPh4YGFhQVJSUlG6UlJSSbvbZ04cYL4+Hh69eplSNPpdPqLsLQkLi7O6Nzbt28nLi6O1atXl1iW8PBwCgoKiI+PN3oc4UYajcZksLOysrrtD8ud7FvVSd2YJvVimtRL8apC3ZS2/GUe5Th27FgCAwM5c+YM+/fvZ//+/SQkJFC7dm3Gjh1b6uNYW1sTEhJCVFSUIU2n0xEVFUXr1q2L5A8KCuLgwYPExMQYtt69e9OxY0diYmLw8/Mzyv/ll18SEhJC06ZNSyxLTEwMarUaT0/PUpdfCCFExXJbD1bv2rULNzc3Q5q7uzvvvfcebdu2LdOxJkyYwLBhwwgNDSUsLIx58+aRlZVlGPU4dOhQqlevzqxZs7CxsaFRo0ZG+xdOvXVzenp6Oj/88AMffvhhkXNGR0eze/duOnbsiKOjI9HR0YwfP54nnngCV1fXMpVfCCFExVHmgKbRaMjIyCiSnpmZibW1dZmONWDAAFJSUpgyZQqJiYk0a9aMDRs2GAaKJCQkoFaXfVHtVatWoSgKgwYNMln+VatWMW3aNHJzc6lduzbjx483uj8mhBCi8ilzQOvZsyejR4/myy+/JCwsDIDdu3fz7LPP0rt37zIXYMyYMYwZM8bke1u3br3lvsuXLzeZPnr0aEaPHm3yvRYtWrBr166yFFEIIUQlUObmz/z58wkMDKR169bY2NhgY2ND27ZtqVOnDh9//PHdKKMQQghRojK10BRFIT09nVWrVnHu3Dmj9dDq1KlzVwoohBBClEaZA1qdOnU4fPgwdevWlSAmhBCiwihTl6NaraZu3bpcunTpbpVHCCGEuC1lvof23nvv8corr3Do0KG7UR4hhBDitpR5lOPQoUPJzs6madOmWFtbY2tra/R+STPsCyGEEHdDmQPaRx99hEqluhtlEUIIIW7bba2HJoQQQlQ0Zb6HZmFhYXJF6UuXLt3V2faFEEKIWylzQFMUxWR6bm5umae+EkIIIcpLqbsc58+fD4BKpeKLL77AwcHB8J5Wq+XPP/8kKCio/EsohBBClEKpA9pHH30E6FtoixcvNupetLa2xt/fn8WLF5d/CYUQQohSKHVAO3XqFAAdO3bkxx9/lKVWhBBCVChlHuW4ZcuWu1EOIYQQ4o6UOaBptVqWL19OVFQUycnJ6HQ6o/c3b95cboUTQgghSqvMAe2ll15i+fLlPPzwwzRq1EgeshZCCFEhlDmgrVq1iu+//54ePXrcjfIIIYQQt6XMz6FZW1vLsjFCCCEqnDIHtJdffpmPP/642AeshRBCCHMoc0D766+/WLFiBYGBgfTq1YvHHnvMaCurRYsW4e/vj42NDeHh4ezZs6dU+61atQqVSkWfPn2M0ocPH45KpTLaunXrZpTn8uXLDBkyBCcnJ1xcXBg5ciSZmZllLvvtOn05m4WH1SSm59yzcwohRFVX5oDm4uLCo48+Svv27fHw8MDZ2dloK4vVq1czYcIEpk6dyv79+2natCldu3Y1OVfkjeLj45k4cSIPPvigyfe7devGhQsXDNt3331n9P6QIUM4fPgwkZGR/Pbbb/z555+MHj26TGW/E5N/OsyxdDWjvt5Pek7+PTuvEEJUZWUeFLJs2bJyO/ncuXMZNWoUI0aMAGDx4sWsW7eOpUuXMmnSJJP7aLVahgwZwvTp09m+fTupqalF8mg0Gry9vU3uHxsby4YNG9i7dy+hoaEALFiwgB49ejBnzhx8fX3L5+Ju4YPHGvHIgj85mpTJc9/uY9nwMKwty/y3hRBCiBuUOqAlJyfj6elZ7PsFBQXs37+fsLCwUh0vLy+Pffv2MXnyZEOaWq0mIiKC6OjoYvebMWMGnp6ejBw5ku3bt5vMs3XrVjw9PXF1deWhhx7inXfewd3dHYDo6GhcXFwMwQwgIiICtVrN7t27efTRR00eMzc3l9zcXMPr9PR0APLz88nPL1sry8vBkmeCtSw6qmHH8UtM/D6GOf3kEQjAUJdlrdOqTurFNKmX4lWluintNZQ6oPn4+HDhwgVDUGvcuDHr16/Hz88P0C8f07p1a7RabamOd/HiRbRaLV5eXkbpXl5eHD161OQ+f/31F19++SUxMTHFHrdbt2489thj1K5dmxMnTvD666/TvXt3oqOjsbCwIDExsUhgtrS0xM3NjcTExGKPO2vWLKZPn14kfePGjdjZ2d3iSk2rYQ9DA/P4PFbN//69QPalc/SqqSt5x/tEZGSkuYtQIUm9mCb1UryqUDfZ2dmlylfqgHbzqMb4+PgiUfNujnzMyMjgySefZMmSJXh4eBSbb+DAgYafGzduTJMmTQgMDGTr1q106tTpts8/efJkJkyYYHidnp6On58fXbp0wcnJqUzHys/PJzIykrGPR1DrYDKTfzrMpnNqHmjRkCFhfrddxqqgsG46d+6MlZWVuYtTYUi9mCb1UryqVDeFPWIlKfM9tFspS5eZh4cHFhYWJCUlGaUnJSWZvP914sQJ4uPj6dWrlyGtcNotS0tL4uLiCAwMLLJfQEAAHh4eHD9+nE6dOuHt7V1k0ElBQQGXL18u9r4b6O/LaTSaIulWVla3/WGxsrJiULg/yRn5fLTpP2b8Fouvix1dGhZfjvvFndRrVSb1YprUS/GqQt2UtvxmG4lgbW1NSEgIUVFRhjSdTkdUVBStW7cukj8oKIiDBw8SExNj2Hr37k3Hjh2JiYkxdH3e7OzZs1y6dAkfHx8AWrduTWpqKvv27TPk2bx5MzqdjvDw8HK+ytIZ26kOA1v6oVNg7Kp/2J9wxSzlEEKIyqzULTSVSkVGRgY2NjYoioJKpSIzM9PQFCxtk/BGEyZMYNiwYYSGhhIWFsa8efPIysoyjHocOnQo1atXZ9asWdjY2NCoUSOj/V1cXAAM6ZmZmUyfPp2+ffvi7e3NiRMnePXVV6lTpw5du3YFIDg4mG7dujFq1CgWL15Mfn4+Y8aMYeDAgfdkhKMpKpWKd/o0Iik9hy1xKTz91d+sfa4NtT3szVIeIYSojMp0D61evXpGr5s3b270uqyj9AYMGEBKSgpTpkwhMTGRZs2asWHDBsNAkYSEBNTq0jciLSws+Pfff/nqq69ITU3F19eXLl268Pbbbxt1F65YsYIxY8bQqVMn1Go1ffv2NazIbS6WFmoWDm7BwM93cfBcGsOX7WHtc23wcCjazSmEEKKoUge0u7UO2pgxYxgzZozJ97Zu3XrLfZcvX2702tbWlj/++KPEc7q5ubFy5crSFvGesddYsnR4Sx77dAenL2UzcvlevhvdCjvrcr3VKYQQVVKpvynbt29/N8shrqnmqOGrEWH0/XQnB86m8eLKf/jsyRAsLeTBayGEuBX5lqyAAqo58MWwUDSWaqKOJvPWL4dlMmghhCiBBLQKKqSWGx8PbI5KBd/tSeCTrSfMXSQhhKjQJKBVYN0aeTOtV0MAZv8Rx9p9Z81cIiGEqLgkoFVww9r480z7AABeW/sv24+lmLlEQghRMd1xQEtPT+fnn38mNja2PMojTHitaxC9m/pSoFN47tv9HD6fZu4iCSFEhVPmgNa/f38WLlwIwNWrVwkNDaV///40adKEtWvXlnsBBajVKmY/3oRWAW5k5hYwYtlezqVeNXexhBCiQilzQPvzzz8NC2v+9NNPKIpCamoq8+fP55133in3Ago9jaUFnz0ZSn0vR5Izchm+dA9p2ZV/WQghhCgvZQ5oaWlpuLm5AbBhwwb69u2LnZ0dDz/8MMeOHSv3AorrnG2tWDaiJd5ONhxLzmTUN3+Tk1+65XqEEKKqK3NA8/PzIzo6mqysLDZs2ECXLl0AuHLlCjY2NuVeQGHM18WW5U+1xFFjyZ5Tl3n5hwPodPKMmhBClDmgjRs3jiFDhlCjRg18fX3p0KEDoO+KbNy4cXmXT5gQ5O3EZ0+GYGWhYt2/F5j1uwzIEUKIMge0559/nujoaJYuXcpff/1lmDw4ICBA7qHdQ23qeDC7X1MAlmw/xdK/Tpm5REIIYV63NettaGgooaGhAGi1Wg4ePEibNm1wdXUt18KJW+vTvDoX0nJ4f8NR3l53BB9nG7o39jF3sYQQwixuq8vxyy+/BPTBrH379rRo0QI/P78SZ8cX5e/Z9gE82aoWigIvrY5hb/xlcxdJCCHMoswBbc2aNTRtqu/q+vXXXzl16hRHjx5l/PjxvPHGG+VeQHFrKpWKab0b0rmBF3kFOp7+6m+OJ2eau1hCCHHPlTmgXbx4EW9vbwDWr1/P448/Tr169Xjqqac4ePBguRdQlMxCrWL+wOY083Mh7Wo+w5buITkjx9zFEkKIe6rMAc3Ly4sjR46g1WrZsGEDnTt3BiA7OxsLC4tyL6AoHVtrC74cFoq/ux3nUq8yYtleMnMLzF0sIYS4Z8oc0EaMGEH//v1p1KgRKpWKiIgIAHbv3k1QUFC5F1CUnruDhq+eCsPd3prD59N5fsV+8rU6cxdLCCHuiTIHtGnTpvHFF18wevRoduzYgUajAcDCwoJJkyaVuQCLFi3C398fGxsbwsPD2bNnT6n2W7VqFSqVij59+hjS8vPzee2112jcuDH29vb4+voydOhQzp8/b7Svv78/KpXKaHvvvffKXPaKqJa7PUuHt8TWyoI//0vh9R8PyuKgQoj7wm0N2+/Xr1+RtGHDhpX5OKtXr2bChAksXryY8PBw5s2bR9euXYmLi8PT07PY/eLj45k4caJhTslC2dnZ7N+/n7feeoumTZty5coVXnrpJXr37s3ff/9tlHfGjBmMGjXK8NrR0bHM5a+omvq5sHBwc0Z9/Tc/7DuLr4st4zvXM3exhBDirrqt5WO2bdtGr169qFOnDnXq1KF3795s3769zMeZO3cuo0aNYsSIETRo0IDFixdjZ2fH0qVLi91Hq9UyZMgQpk+fTkBAgNF7zs7OREZG0r9/f+rXr0+rVq1YuHAh+/btIyEhwSivo6Mj3t7ehs3e3r7M5a/IOgV78U4f/cwtH0cdY/XehBL2EEKIyq3MLbRvv/2WESNG8NhjjzF27FgAduzYQadOnVi+fDmDBw8u1XHy8vLYt28fkydPNqSp1WoiIiKIjo4udr8ZM2bg6enJyJEjSxVE09LSUKlUuLi4GKW/9957vP3229SsWZPBgwczfvx4LC2Lr47c3Fxyc3MNr9PT0wF9N2d+ftlmvS/MX9b9yurxFj6cvZzFJ9tO8vpPh3Czs6RDvWp39Zx36l7VTWUj9WKa1EvxqlLdlPYaVEoZb7AEBwczevRoxo8fb5Q+d+5clixZUuqFPs+fP0/16tXZuXMnrVu3NqS/+uqrbNu2jd27dxfZ56+//mLgwIHExMTg4eHB8OHDSU1N5eeffzZ5jpycHNq2bUtQUBArVqwwKmuLFi1wc3Nj586dTJ48mREjRjB37txiyztt2jSmT59eJH3lypXY2dmV6prNQVFgxQk1e1PUWKsVXmyopaaDuUslhBCll52dzeDBg0lLS8PJyanYfGVuoZ08eZJevXoVSe/duzevv/56WQ9XahkZGTz55JMsWbIEDw+PEvPn5+fTv39/FEXh008/NXpvwoQJhp+bNGmCtbU1zzzzDLNmzTIMcrnZ5MmTjfZLT0/Hz8+PLl263LKCiytbZGQknTt3xsrKqkz73o7OBTpGf/sPO05cYvlJO74fHUZNt4oZhO913VQWUi+mSb0UryrVTWGPWEnKHND8/PyIioqiTp06RumbNm3Cz8+v1Mfx8PDAwsKCpKQko/SkpCTDg9s3OnHiBPHx8UbBVKfTD0m3tLQkLi6OwMBA4HowO336NJs3by4x4ISHh1NQUEB8fDz169c3mUej0ZgMdlZWVrf9YbmTfct2Hlj8ZAgDPtvFkQvpjPrmH9Y+1wZXe+u7fu7bda/qprKRejFN6qV4VaFuSlv+Mge0l19+mbFjxxITE0ObNm0A/T205cuX8/HHH5f6ONbW1oSEhBAVFWUYeq/T6YiKimLMmDFF8gcFBRWZieTNN98kIyODjz/+2BBMC4PZsWPH2LJlC+7u7iWWJSYmBrVafcuRlZWdo41+cdDHPtnJyYtZjPxqLytHtcLGSh6GF0JUDWUOaM899xze3t58+OGHfP/994D+vtrq1at55JFHynSsCRMmMGzYMEJDQwkLC2PevHlkZWUxYsQIAIYOHUr16tWZNWsWNjY2NGrUyGj/woEehen5+fn069eP/fv389tvv6HVaklMTATAzc0Na2troqOj2b17Nx07dsTR0ZHo6GjGjx/PE088UeVXC/BysmH5iJb0/XQn+xNSeWnVP3wyJAQLtcrcRRNCiDtWpoBWUFDAzJkzeeqpp/jrr7/u+OQDBgwgJSWFKVOmkJiYSLNmzdiwYQNeXl4AJCQkGNZbK41z587xv//9D4BmzZoZvbdlyxY6dOiARqNh1apVTJs2jdzcXGrXrs348eON7o9VZXW9HFkyNJQnv9zDH4eTmPHrYab1bohKJUFNCFG5lSmgWVpa8sEHHzB06NByK8CYMWNMdjECJS5Hs3z5cqPX/v7+Jc6K0aJFC3bt2lWWIlY54QHuzB3QlDEr/+Gr6NNUd7VldLtAcxdLCCHuSJkfrO7UqRPbtm27G2UR91DPJr68+XAwADPXH+W1Nf/yvwPnSU6XWfqFEJVTme+hde/enUmTJnHw4EFCQkKKzLDRu3fvciucuLuefjCA86k5LN1xitV/n2H132cACKxmT6sAd1oFuBMe4Iano42ZSyqEECUrc0B7/vnnAUw+hKxSqdBqtXdeKnHPvNUzmHb1PNh+7CK7Tl7iyIV0TqRkcSIlixW79dNlSYATQlQGZQ5ohc9+iapBpVLRob4nHerrH1lIy85nT/xldp28VGKAax3oTnhtd6o5mn4YXQgh7qXbmm1fVF3OdlZ0buBF5wb6kaap2XnsOXWZXSf1QS42sWiAq+PpQKsAN30LTgKcEMJMSh3QNm/ezJgxY9i1a1eRmTfS0tJo06YNn376Ke3atSv3QgrzcbGzpktDb7o01M/eYirAHU/O5HhyJt/ukgAnhDCfUge0efPmMWrUKJPTSDk7O/PMM8/w0UcfSUCr4ooLcNEnL7Hr5GViLxQf4FoHeBAe4IaHgwQ4IUT5K3VAO3DgAO+//36x73fp0oU5c+aUS6FE5XFzgLuSlXfDPTjTAa6up4PRIBMJcEKI8lDqgJaUlHTLCSItLS1JSUkpl0KJysvV3pquDb3peosAdyw5k2PJmXyz6zRgHOBC/KrOyuFCiHur1AGtevXqHDp0qMgs+4X+/fdffHx8yq1gomowFeB2n7o+ivJoYkaRAFfdzoKj1sfo3NCHpjVcZK5JIUSplDqg9ejRg7feeotu3bphY2P8HNLVq1eZOnUqPXv2LPcCiqrF1d6abo286dao+AB3LlvFp9tO8em2U7jbW9Ohviedgj15sK4HjjaVexkMIcTdU+qA9uabb/Ljjz9Sr149xowZY1g37OjRoyxatAitVssbb7xx1woqqqabA1xiahYL10Rx2caX7ccucSkrj7X7z7J2/1ks1SrCA9zoWN+TTsFe1PawL+HoQoj7SakDmpeXFzt37uS5555j8uTJhkmAVSoVXbt2ZdGiRYZZ8oW4Xe721rSsptCjR1NQW7A3/jKbY5PZfDSZkxez2HH8EjuOX+KddbEEeNjzUJAnDwV70tLfDSuLMk9NKoSoQsr0YHWtWrVYv349V65c4fjx4yiKQt26dav8OmLCPKws1LQJ9KBNoAdv9mzAqYtZbD6azOajSew+eZmTF7M4+dcpvvjrFI4aS9rVr8ZD9T3pUL8a7jJyUoj7zm3NFOLq6krLli3LuyxC3FJtD3tGPlCbkQ/UJj0nn7+OXSQqNpmtcclcyspj3b8XWPfvBVQqaO7nQqdgLx4K8iTI21HWexPiPiBTX4lKycnGih6NfejR2AetTuHA2VS2HE0mKjaZIxfS2Z+Qyv6EVGb/EYevsw0PBXvSKciL1oHu2FhZmLv4Qoi7QAKaqPQs1Cpa1HSlRU1XXu5Sn/OpV9kSl8zm2GT+On6R82k5fLsrgW93JWBjpaZtoAcPBXvyUJAnPs625i6+EKKcSEATVY6viy1DwmsxJLwWV/O0RJ+8qL/3FpvM+bQcoo4mE3U0GYAGPk50uhbcmtZwQS3PvAlRaZl9WNiiRYvw9/fHxsaG8PBw9uzZU6r9Vq1ahUqlok+fPkbpiqIwZcoUfHx8sLW1JSIigmPHjhnluXz5MkOGDMHJyQkXFxdGjhxJZmZmeV2SqEBsrS14KMiLd/o0Zsekh/j9pQd5pWt9WtR0QaWCIxfSWbD5OI9+spOW727i5e8PsP7gBTJy8s1ddCFEGZm1hbZ69WomTJjA4sWLCQ8PZ968eXTt2pW4uDg8PT2L3S8+Pp6JEyfy4IMPFnnvgw8+YP78+Xz11VfUrl2bt956i65du3LkyBHDA+FDhgzhwoULREZGkp+fz4gRIxg9ejQrV668a9cqzE+lUhHs40SwjxMvdKzDpcxctsalsDkumT/jUoyeebOyUBFW240wf3dqV7MnwMMefw97HDTSqSFERWXW/51z585l1KhRjBgxAoDFixezbt06li5dyqRJk0zuo9VqGTJkCNOnT2f79u2kpqYa3lMUhXnz5vHmm2/yyCOPAPD111/j5eXFzz//zMCBA4mNjWXDhg3s3buX0NBQABYsWECPHj2YM2cOvr6+d/eiRYXh7qChb0gN+obUIF+rK/aZtxtVc9RQ28Oe2u721K5mT20PfbDzc7OTwSZCmJnZAlpeXh779u1j8uTJhjS1Wk1ERATR0dHF7jdjxgw8PT0ZOXIk27dvN3rv1KlTJCYmEhERYUhzdnYmPDyc6OhoBg4cSHR0NC4uLoZgBhAREYFarWb37t08+uijJs+bm5tLbm6u4XV6ejoA+fn55OeXrXuqMH9Z97sfmLNuWtZ0pmVNZ17rWpdTF7PY+t9F4pIyiL+YTfylbC5l5ZGSkUtKRi57Tl022lelgurONvh72OPvboe/ux21Peyp5W5HdWcbLO/woW/5zJgm9VK8qlQ3pb0GswW0ixcvotVqi8wu4uXlxdGjR03u89dff/Hll18SExNj8v3ExETDMW4+ZuF7iYmJRbozLS0tcXNzM+QxZdasWUyfPr1I+saNG7Gzsyt2v1uJjIy8rf3uBxWhbrwALw20qw5Uh+wCSMmBlKsqUnJUJF+FlBwVKTmQo1VxNjWHs6k5/HVTq85CpeBhA9VsFKrZgKet/t9qNgrO1vpgWFoVoV4qIqmX4lWFusnOzi5VvkpzQyAjI4Mnn3ySJUuW4OHhcc/PP3nyZCZMmGB4nZ6ejp+fH126dDG56Omt5OfnExkZSefOnW+5JM/9qDLWjaIoXMrKI/5SNqcuZhN/KYv4S9n6lt3lbPIKdCRdhaSrRSOXnbUFNd3sqO1uh7/HtZaduz3+Hna42lkb8lXGerkXpF6KV5XqprBHrCRmC2geHh5YWFiQlJRklJ6UlIS3t3eR/CdOnCA+Pp5evXoZ0nQ6HaBvYcXFxRn2S0pKMlrKJikpiWbNmgHg7e1NcnKy0bELCgq4fPmyyfMW0mg0aDRFp1OysrK67Q/Lnexb1VW2uvGxtsbH1YHWN62upNMpXEjP4VRKFqcuZnLqYva1f7M4c+Uq2XlajiZmcDQxo8gxnW2trt+jc7Uh7aKKwEs51PHWyP26m1S2z8u9VBXqprTlN1tAs7a2JiQkhKioKMPQe51OR1RUFGPGjCmSPygoiIMHDxqlvfnmm2RkZPDxxx/j5+eHlZUV3t7eREVFGQJYeno6u3fv5rnnngOgdevWpKamsm/fPkJCQgDYvHkzOp2O8PDwu3fB4r6kVquo7mJLdRdbHqhr3LOQr9Vx5nI2py5mGW3xF7M4n5ZD2tV8Ys6kEnMm9doeFiw/Fo1aBX5udtSp5kAdTwcCPfX/1vF0wEmW1xH3MbN2OU6YMIFhw4YRGhpKWFgY8+bNIysryzDqcejQoVSvXp1Zs2ZhY2NDo0aNjPZ3cXEBMEofN24c77zzDnXr1jUM2/f19TUEzeDgYLp168aoUaNYvHgx+fn5jBkzhoEDB8oIR3FPWVmoCajmQEA1hyLvXc3T6rsuL2Zx8mIWJ5Iz2H/sHJcLrEjPKeD0pWxOX8o2PCBeyNNRYwhugdWuBzpPR43MZymqPLMGtAEDBpCSksKUKVNITEykWbNmbNiwwTCoIyEhAbW6bKPDXn31VbKyshg9ejSpqak88MADbNiwwWhR0hUrVjBmzBg6deqEWq2mb9++zJ8/v1yvTYg7YWttYXhmDvT3Q9avT6B79y6k5uo4npzJieRMjidncjxF/29Sei7JGfpt5wnjgSmONpZGAa6wdefnZicrgosqw+yDQsaMGWOyixFg69att9x3+fLlRdJUKhUzZsxgxowZxe7n5uYmD1GLSkmlUuHpaIOnow1tAo27MNNz8jmZkqUPcte2EymZnL6URUZOwU3dl3rWFmpqe9gbd11WcyCgmr3cpxOVjtkDmhCifDjZWNHMz4Vmfi5G6bkFWuIvZhsCXGGwO3kxk5x8HXFJGcQlGQ9KUanAz9XOqEUX6GlPnWqOONvJfTpRMUlAE6KK01haUN/bkfrejkbpOp3CudSrRi26wu7LtKv5JFzOJuFyNptvuk/n4aChoa8TfUNq0K2hN9aWZp8SVghAApoQ9y21WoWfmx1+bnZ0DLo+2UDhc3U3d10eT87kQloOFzNz2fZfCtv+S8HDwZoBLf0YFFaTGq63N8GAEOVFApoQwohKpcLDQYOHg4ZWAe5G72XmFnAiOZPNR5P5bk8CyRm5LNpygk+2nuCh+p480aoW7epVk4EmwiwkoAkhSs1BY0lTPxea+rkw5qE6bDqSxLe7T7Pj+CXDOnM1XG0ZHF6T/qF+eDgUnYxAiLtFApoQ4rZYWajp3tiH7o19OJmSyYrdCazZd5azV67ywYY4Por8j+6NfHiydS1Ca7nKc3DirpOAJoS4YwHVHHirZwNe6VqfXw+c59vdCRw4k8r/DpznfwfOU9/LkSda1aRP8+o4ymwm4i6RgCaEKDc2VhY8HurH46F+HDybxordp/k55hxxSRm89cthZv1+lD7Nq/NEeC0a+JZtUm8hSiIBTQhxVzSu4cx7NZowuUcwP+4/y7e7TnMiJYuVuxNYuTuBFjVdeKJVLXo09pGHuEW5kIAmhLirnG2tGNG2NsPb+LPr5GW+3X2aPw4lsj8hlf0Jqbz92xEeD/VjSHhNarnbm7u4ohKTgCaEuCdUKhWtA91pHehOckYO3+89w3d7znAu9Sqf/3mSz/88yYN1PXiiVS06BXne8Srf4v4jAU0Icc95Otow5qG6PNehDluOJvPt7tNs+y+F7ccusv3YRXycbRgUVpOBLf3wdLIp+YBCIAFNCGFGFmoVEQ28iGjgRcKlbFbsOc0Pf5/lQloOcyP/Y37UMbo09OKJ8Fq0DnSXof/iliSgCSEqhJrudkzuHsyEzvX4/WAi3+46zd+nr7D+YCLrDyYSUM2eIeG16NeihkyQLEySgCaEqFA0lhb0aV6dPs2rczQxnW93nean/ec4mZLF278dYfYfR+nVxJcnWtWigbcMIhHXSUATQlRYQd5OvNOnMZO6B/PzP+f4dtdpjiZm8MO+s/yw7yyNfJ1oaKMiKCWLOt7OMofkfU4CmhCiwnPQWPJEq1oMCa/J/oQrfLsrgXX/XuDQ+XQOYcHq+Tuws7YgyNuRhr7ONPB1oqGvE/W8HOUZt/uIBDQhRKWhUqkIqeVGSC033urZgFV74vl+x38k5VqQnac1PNtWyEKtok41Bxr6OtHg2tbQx1nuwVVREtCEEJWSm701ox6oTfX0WLp268LZtDyOXEjn8Pk0jpxP5/D5dC5n5RlW5P7xn3OGfau72BpacYUtOl9nGxlFWcmZPaAtWrSI2bNnk5iYSNOmTVmwYAFhYWEm8/7444/MnDmT48ePk5+fT926dXn55Zd58sknDXmK+0B+8MEHvPLKKwD4+/tz+vRpo/dnzZrFpEmTyumqhBD3koVaRR1PB+p4OtC7qS+gX6g0KT3XKMAduZBOwuVszqVe5VzqVSKPJBmO4WJnRQMf4yAX4GEvD3hXImYNaKtXr2bChAksXryY8PBw5s2bR9euXYmLi8PT07NIfjc3N9544w2CgoKwtrbmt99+Y8SIEXh6etK1a1cALly4YLTP77//zsiRI+nbt69R+owZMxg1apThtaOj8fL0QojKTaVS4e1sg7ezDZ2CvQzpaVfzib2Qbghyh8+ncTw5k9TsfHaeuMTOE5cMeTWWaoK8HWng62zotgz2dsLWWu7LVURmDWhz585l1KhRjBgxAoDFixezbt06li5darK11KFDB6PXL730El999RV//fWXIaB5e3sb5fnll1/o2LEjAQEBRumOjo5F8gohqj5nWytaBbgbrcadW6DlWFLmtSCXxuHz6cReSCcrT8uBs2kcOJtmyKtWQW0PexreEOQa+jrjZm9tjssRNzBbQMvLy2Pfvn1MnjzZkKZWq4mIiCA6OrrE/RVFYfPmzcTFxfH++++bzJOUlMS6dev46quvirz33nvv8fbbb1OzZk0GDx7M+PHjsbQsvjpyc3PJzc01vE5PTwcgPz+f/Pz8Est7o8L8Zd3vfiB1Y5rUi2nlVS9qoL6nHfU97Xi0mf4PXZ1OIeFKNrEXMjhyIYMjF9KJvZBBSmYeJ1KyOJGSxf8OnDccw8tJQ7C3I4HV7PF3t6e2hx213O3wctSY5d5cVfrMlPYazBbQLl68iFarxcvLyyjdy8uLo0ePFrtfWloa1atXJzc3FwsLCz755BM6d+5sMu9XX32Fo6Mjjz32mFH62LFjadGiBW5ubuzcuZPJkydz4cIF5s6dW+x5Z82axfTp04ukb9y4ETs7u1tdarEiIyNva7/7gdSNaVIvpt3tegkGgj0AD0jPg7NZKs5lX/s3S0VKjoqk9FyS0nPZ+t9Fo32t1QrVbKCajUI1W/2/nrb6NHtLuNuxrip8ZrKzs0uVz+yDQsrK0dGRmJgYMjMziYqKYsKECQQEBBTpjgRYunQpQ4YMwcbGeHLTCRMmGH5u0qQJ1tbWPPPMM8yaNQuNRmPyvJMnTzbaLz09HT8/P7p06YKTU9kWKszPzycyMpLOnTtjZSXDh28kdWOa1ItpFaVeMnMLiEvM4GhiBqcuZRN/MZv4S9mcTb1Kng7OZcO57KKRy8nGEn93O/zd7fX/ethde213xyt7V5S6KQ+FPWIlMVtA8/DwwMLCgqSkJKP0pKSkW97bUqvV1KlTB4BmzZoRGxvLrFmzigS07du3ExcXx+rVq0ssS3h4OAUFBcTHx1O/fn2TeTQajclgZ2VlddsfljvZt6qTujFN6sU0c9eLq5UVrerY0qqO8WC2fK2OM5ezib+UxcmULOIvZXHqYhbxF/UjLdNzCvj3XDr/niv6he3hYH2t69Iefw97Aq796+9uX6ZBKeaum/JQ2vKbLaBZW1sTEhJCVFQUffr0AUCn0xEVFcWYMWNKfRydTmd0b6vQl19+SUhICE2bNi3xGDExMajVapMjK4UQ4nZZWagJqOZAQDUHHgoyfi8nX8vpS9mcupjJqYv6f+MvZnPyYhYXM3O5mJnHxcw8/j59pchxfZxtjAOdu/7nmm52WFvev48ZmLXLccKECQwbNozQ0FDCwsKYN28eWVlZhlGPQ4cOpXr16syaNQvQ38cKDQ0lMDCQ3Nxc1q9fzzfffMOnn35qdNz09HR++OEHPvzwwyLnjI6OZvfu3XTs2BFHR0eio6MZP348TzzxBK6urnf/ooUQArCxsqC+tyP1vYs+MpSRk0/8xWxOXcoi/qK+VVe4pV3N50JaDhfScoweMQD9CMwarnbU9rCnlpstFpdVtMstwLWSt9BKy6wBbcCAAaSkpDBlyhQSExNp1qwZGzZsMAwUSUhIQK2+/tdGVlYWzz//PGfPnsXW1pagoCC+/fZbBgwYYHTcVatWoSgKgwYNKnJOjUbDqlWrmDZtGrm5udSuXZvx48cb3R8TQghzcrSxonENZxrXcC7y3pWsPE5evCHQXcri1LXuzOw8LQmXs0m4XDiIwoJvZ22hdaAHnRt4ERHsiY+z7b29mHtIpSiKYu5CVEbp6ek4OzuTlpZ2W4NC1q9fT48ePSp933Z5k7oxTerFNKmX6xRFITkj19CSiz2fxvqY01zMMR6M0ri6MxHBXnRu4EWwj2OlmO6rtN+3lW6UoxBCiKJUKhVeTjZ4OdnQKsCd/Px8WnCSoLD2bPnvEptik9ifcIWD59I4eC6Njzb9R3UX22stNy/CartV+vtvEtCEEKKKUqkgsJo9Qb4uPNchkJSMXLYcTSYyNontx1I4l3qV5TvjWb4zHkcbSzrU9yQi2JMO9T1xtq18LV4JaEIIcZ+o5qihf0s/+rf042qelh3HLxJ5JImoo0lczMzj1wPn+fXAeSzVKsID3IgI1rfe/Nxub/KIe00CmhBC3IdsrS2IaOBFRAMvdDqFmLOpRB5JYtORJI4lZ7Lj+CV2HL/E9F+PEOTtSJdreRtXd66w990koAkhxH1OrVbRoqYrLWq68lq3IOIvZrEpNonII0nsjb/M0WuzoMzffBwvJ41hUEnrQHc0lhVn5QEJaEIIIYz4e9jz9IMBPP1gAFey8tgSl8ym2CS2xaWQlJ7Lit0JrNidgL21Be3qVaNzAy861vfE1cwrDkhAE0IIUSxXe2sea1GDx1rUICdfy66Tl/Rdk7FJJKXn8vuhRH4/lIiFWkVoLVfDqEl/D/t7XlYJaEIIIUrFxsqCDvX1oyDf6dOIQ+fSiTySSGRsMrEX0tl96jK7T13mnXWx1PV0IKKBvmuyWQ0X1Oq7f99NApoQQogyU6lUhtlMJnSpz5nL2UTFJrEpNpldJy9xLDmTY8mZfLr1BB4OGiKCPZnaq+FdXe1bApoQQog75udmx/C2tRnetjZpV/PZ9l8KkUeS2Ho0mYuZufx1/CI2Vnf3wW0JaEIIIcqVs60VvZv60rupL3kFOvbGXyYjJ/+uD/eXgCaEEOKusbZU07aOxz05V+WeuEsIIYS4RgKaEEKIKkECmhBCiCpBApoQQogqQQKaEEKIKkECmhBCiCpBApoQQogqQZ5Du02KogCQnp5e5n3z8/PJzs4mPT0dK6vKtyrs3SR1Y5rUi2lSL8WrSnVT+D1b+L1bHAlotykjIwMAPz8/M5dECCHuDxkZGTg7Oxf7vkopKeQJk3Q6HefPn8fR0bHM07mkp6fj5+fHmTNncHJyukslrJykbkyTejFN6qV4ValuFEUhIyMDX19f1Ori75RJC+02qdVqatSocUfHcHJyqvQftLtF6sY0qRfTpF6KV1Xq5lYts0IyKEQIIUSVIAFNCCFElSABzQw0Gg1Tp05Fo9GYuygVjtSNaVIvpkm9FO9+rBsZFCKEEKJKkBaaEEKIKkECmhBCiCpBApoQQogqQQKaEEKIKkECmhksWrQIf39/bGxsCA8PZ8+ePeYuklnNmjWLli1b4ujoiKenJ3369CEuLs7cxapw3nvvPVQqFePGjTN3USqEc+fO8cQTT+Du7o6trS2NGzfm77//NnexzEqr1fLWW29Ru3ZtbG1tCQwM5O233y5xDsSqQgLaPbZ69WomTJjA1KlT2b9/P02bNqVr164kJyebu2hms23bNl544QV27dpFZGQk+fn5dOnShaysLHMXrcLYu3cvn332GU2aNDF3USqEK1eu0LZtW6ysrPj99985cuQIH374Ia6uruYumlm9//77fPrppyxcuJDY2Fjef/99PvjgAxYsWGDuot0TMmz/HgsPD6dly5YsXLgQ0M8J6efnx4svvsikSZPMXLqKISUlBU9PT7Zt20a7du3MXRyzy8zMpEWLFnzyySe88847NGvWjHnz5pm7WGY1adIkduzYwfbt281dlAqlZ8+eeHl58eWXXxrS+vbti62tLd9++60ZS3ZvSAvtHsrLy2Pfvn1EREQY0tRqNREREURHR5uxZBVLWloaAG5ubmYuScXwwgsv8PDDDxt9bu53//vf/wgNDeXxxx/H09OT5s2bs2TJEnMXy+zatGlDVFQU//33HwAHDhzgr7/+onv37mYu2b0hkxPfQxcvXkSr1eLl5WWU7uXlxdGjR81UqopFp9Mxbtw42rZtS6NGjcxdHLNbtWoV+/fvZ+/eveYuSoVy8uRJPv30UyZMmMDrr7/O3r17GTt2LNbW1gwbNszcxTObSZMmkZ6eTlBQEBYWFmi1Wt59912GDBli7qLdExLQRIXywgsvcOjQIf766y9zF8Xszpw5w0svvURkZCQ2NjbmLk6FotPpCA0NZebMmQA0b96cQ4cOsXjx4vs6oH3//fesWLGClStX0rBhQ2JiYhg3bhy+vr73Rb1IQLuHPDw8sLCwICkpySg9KSkJb29vM5Wq4hgzZgy//fYbf/755x0vzVMV7Nu3j+TkZFq0aGFI02q1/PnnnyxcuJDc3FwsLCzMWELz8fHxoUGDBkZpwcHBrF271kwlqhheeeUVJk2axMCBAwFo3Lgxp0+fZtasWfdFQJN7aPeQtbU1ISEhREVFGdJ0Oh1RUVG0bt3ajCUzL0VRGDNmDD/99BObN2+mdu3a5i5ShdCpUycOHjxITEyMYQsNDWXIkCHExMTct8EMoG3btkUe7fjvv/+oVauWmUpUMWRnZxdZANPCwgKdTmemEt1b0kK7xyZMmMCwYcMIDQ0lLCyMefPmkZWVxYgRI8xdNLN54YUXWLlyJb/88guOjo4kJiYC+gX9bG1tzVw683F0dCxyH9He3h53d/f7/v7i+PHjadOmDTNnzqR///7s2bOHzz//nM8//9zcRTOrXr168e6771KzZk0aNmzIP//8w9y5c3nqqafMXbR7QxH33IIFC5SaNWsq1tbWSlhYmLJr1y5zF8msAJPbsmXLzF20Cqd9+/bKSy+9ZO5iVAi//vqr0qhRI0Wj0ShBQUHK559/bu4imV16erry0ksvKTVr1lRsbGyUgIAA5Y033lByc3PNXbR7Qp5DE0IIUSXIPTQhhBBVggQ0IYQQVYIENCGEEFWCBDQhhBBVggQ0IYQQVYIENCGEEFWCBDQhhBBVggQ0IYQQVYIENCHELW3duhWVSkVqaqq5iyLELUlAE0IIUSVIQBNCCFElSEATooLT6XTMmjWL2rVrY2trS9OmTVmzZg1wvTtw3bp1NGnSBBsbG1q1yhtAHQAAA4NJREFUasWhQ4eMjrF27VoaNmyIRqPB39+fDz/80Oj93NxcXnvtNfz8/NBoNNSpU4cvv/zSKM++ffsIDQ3Fzs6ONm3aFFm+RQizM/fsyEKIW3vnnXeUoKAgZcOGDcqJEyeUZcuWKRqNRtm6dauyZcsWBVCCg4OVjRs3Kv/++6/Ss2dPxd/fX8nLy1MURVH+/vtvRa1WKzNmzFDi4uKUZcuWKba2tkarGfTv31/x8/NTfvzxR+XEiRPKpk2blFWrVimKohjOER4ermzdulU5fPiw8uCDDypt2rQxR3UIUSwJaEJUYDk5OYqdnZ2yc+dOo/SRI0cqgwYNMgSbwuCjKIpy6dIlxdbWVlm9erWiKIoyePBgpXPnzkb7v/LKK0qDBg0URVGUuLg4BVAiIyNNlqHwHJs2bTKkrVu3TgGUq1evlst1ClEepMtRiArs+PHjZGdn07lzZxwcHAzb119/zYkTJwz5blzx3M3Njfr16xMbGwtAbGwsbdu2NTpu27ZtOXbsGFqt1rD6dfv27W9ZliZNmhh+9vHxASA5OfmOr1GI8iIrVgtRgWVmZgKwbt06qlevbvSeRqMxCmq3q7SrgltZWRl+VqlUgP7+nhAVhbTQhKjAGjRogEajISEhgTp16hhtfn5+hny7du0y/HzlyhX+++8/goODAQgODmbHjh1Gx92xYwf16tXDwsKCxo0bo9Pp2LZt2725KCHuEmmhCVGBOTo6MnHiRMaPH49Op+OBBx4gLS2NHTt24OTkRK1atQCYMWMG7u7ueHl58cYbb+Dh4UGfPn0AePnll2nZsiVvv/02AwYMIDo6moULF/LJJ58A4O/vz7Bhw3jqqaeYP38+TZs25fTp0yQnJ9O/f39zXboQZWfum3hCiFvT6XTKvHnzlPr16ytWVlZKtWrVlK5duyrbtm0zDNj49ddflYYNGyrW1tZKWFiYcuDAAaNjrFmzRmnQoIFiZWWl1KxZU5k9e7bR+1evXlXGjx+v+Pj4KNbW1kqdOnWUpUuXKopyfVDIlStXDPn/+ecfBVBOnTp1ty9fiFJTKYqimDmmCiFu09atW+nYsSNXrlzBxcXF3MURwqzkHpoQQogqQQKaEEKIKkG6HIUQQlQJ0kITQghRJUhAE0IIUSVIQBNCCFElSEATQghRJUhAE0IIUSVIQBNCCFElSEATQghRJUhAE0IIUSX8H0q/rMJA2OXEAAAAAElFTkSuQmCC","text/plain":["<Figure size 500x800 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plot_history(history)"]},{"cell_type":"code","execution_count":17,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["112/112 [==============================] - 2s 21ms/step - loss: 0.5065 - accuracy: 0.7841\n","\n","Accuracy:  78.41261029243469  %\n"]}],"source":["_, acc = model.evaluate(validation_ds)\n","print(\"Accuracy: \", (acc*100.0), \" %\")"]},{"cell_type":"markdown","metadata":{},"source":["#### Approach 2: VGG16"]},{"cell_type":"code","execution_count":18,"metadata":{},"outputs":[],"source":["# Load the pre-trained VGG16 model\n","\n","base_model = VGG16(weights='imagenet', include_top=False, input_shape=(180, 180, 3))\n","base_model.trainable = False\n","last_output = base_model.output\n","x = tf.keras.layers.Flatten()(last_output)  \n","x = tf.keras.layers.Dense(1024, activation='relu')(x)   \n","x = tf.keras.layers.Dropout(0.6)(x) \n","predictions = Dense(1, activation='sigmoid')(x)  "]},{"cell_type":"code","execution_count":19,"metadata":{},"outputs":[],"source":["# Create the final model\n","vgg16_model = Model(inputs=base_model.input, outputs=predictions)"]},{"cell_type":"code","execution_count":20,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"model\"\n","\n","_________________________________________________________________\n","\n"," Layer (type)                Output Shape              Param #   \n","\n","=================================================================\n","\n"," input_1 (InputLayer)        [(None, 180, 180, 3)]     0         \n","\n","                                                                 \n","\n"," block1_conv1 (Conv2D)       (None, 180, 180, 64)      1792      \n","\n","                                                                 \n","\n"," block1_conv2 (Conv2D)       (None, 180, 180, 64)      36928     \n","\n","                                                                 \n","\n"," block1_pool (MaxPooling2D)  (None, 90, 90, 64)        0         \n","\n","                                                                 \n","\n"," block2_conv1 (Conv2D)       (None, 90, 90, 128)       73856     \n","\n","                                                                 \n","\n"," block2_conv2 (Conv2D)       (None, 90, 90, 128)       147584    \n","\n","                                                                 \n","\n"," block2_pool (MaxPooling2D)  (None, 45, 45, 128)       0         \n","\n","                                                                 \n","\n"," block3_conv1 (Conv2D)       (None, 45, 45, 256)       295168    \n","\n","                                                                 \n","\n"," block3_conv2 (Conv2D)       (None, 45, 45, 256)       590080    \n","\n","                                                                 \n","\n"," block3_conv3 (Conv2D)       (None, 45, 45, 256)       590080    \n","\n","                                                                 \n","\n"," block3_pool (MaxPooling2D)  (None, 22, 22, 256)       0         \n","\n","                                                                 \n","\n"," block4_conv1 (Conv2D)       (None, 22, 22, 512)       1180160   \n","\n","                                                                 \n","\n"," block4_conv2 (Conv2D)       (None, 22, 22, 512)       2359808   \n","\n","                                                                 \n","\n"," block4_conv3 (Conv2D)       (None, 22, 22, 512)       2359808   \n","\n","                                                                 \n","\n"," block4_pool (MaxPooling2D)  (None, 11, 11, 512)       0         \n","\n","                                                                 \n","\n"," block5_conv1 (Conv2D)       (None, 11, 11, 512)       2359808   \n","\n","                                                                 \n","\n"," block5_conv2 (Conv2D)       (None, 11, 11, 512)       2359808   \n","\n","                                                                 \n","\n"," block5_conv3 (Conv2D)       (None, 11, 11, 512)       2359808   \n","\n","                                                                 \n","\n"," block5_pool (MaxPooling2D)  (None, 5, 5, 512)         0         \n","\n","                                                                 \n","\n"," flatten_1 (Flatten)         (None, 12800)             0         \n","\n","                                                                 \n","\n"," dense_2 (Dense)             (None, 1024)              13108224  \n","\n","                                                                 \n","\n"," dropout_1 (Dropout)         (None, 1024)              0         \n","\n","                                                                 \n","\n"," dense_3 (Dense)             (None, 1)                 1025      \n","\n","                                                                 \n","\n","=================================================================\n","\n","Total params: 27823937 (106.14 MB)\n","\n","Trainable params: 13109249 (50.01 MB)\n","\n","Non-trainable params: 14714688 (56.13 MB)\n","\n","_________________________________________________________________\n"]}],"source":["vgg16_model.summary()"]},{"cell_type":"code","execution_count":22,"metadata":{},"outputs":[],"source":["vgg16_model.compile(optimizer = opt,\n","            loss = 'binary_crossentropy',\n","            metrics=['accuracy'])"]},{"cell_type":"code","execution_count":23,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/20\n"]},{"name":"stderr","output_type":"stream","text":["2023-06-28 00:19:16.674956: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] PluggableGraphOptimizer failed: INVALID_ARGUMENT: Unparseable tensorflow.GraphDef proto\n","\n","2023-06-28 00:19:16.687992: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] PluggableGraphOptimizer failed: INVALID_ARGUMENT: Unparseable tensorflow.GraphDef proto\n"]},{"name":"stdout","output_type":"stream","text":["223/223 [==============================] - ETA: 0s - loss: 0.4952 - accuracy: 0.8113"]},{"name":"stderr","output_type":"stream","text":["2023-06-28 00:20:27.793970: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] PluggableGraphOptimizer failed: INVALID_ARGUMENT: Unparseable tensorflow.GraphDef proto\n","\n","2023-06-28 00:20:27.802193: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] PluggableGraphOptimizer failed: INVALID_ARGUMENT: Unparseable tensorflow.GraphDef proto\n"]},{"name":"stdout","output_type":"stream","text":["223/223 [==============================] - 117s 523ms/step - loss: 0.4952 - accuracy: 0.8113 - val_loss: 0.6141 - val_accuracy: 0.7670\n","\n","Epoch 2/20\n","\n","223/223 [==============================] - 107s 478ms/step - loss: 0.4023 - accuracy: 0.8346 - val_loss: 0.5370 - val_accuracy: 0.7599\n","\n","Epoch 3/20\n","\n","223/223 [==============================] - 112s 503ms/step - loss: 0.3932 - accuracy: 0.8398 - val_loss: 0.5530 - val_accuracy: 0.7656\n","\n","Epoch 4/20\n","\n","223/223 [==============================] - 117s 523ms/step - loss: 0.3839 - accuracy: 0.8409 - val_loss: 0.5643 - val_accuracy: 0.7658\n","\n","Epoch 5/20\n","\n","223/223 [==============================] - 124s 555ms/step - loss: 0.3844 - accuracy: 0.8412 - val_loss: 0.5333 - val_accuracy: 0.7650\n","\n","Epoch 6/20\n","\n","223/223 [==============================] - 122s 549ms/step - loss: 0.3767 - accuracy: 0.8417 - val_loss: 0.5463 - val_accuracy: 0.7661\n","\n","Epoch 7/20\n","\n","223/223 [==============================] - 105s 471ms/step - loss: 0.3752 - accuracy: 0.8426 - val_loss: 0.5616 - val_accuracy: 0.7675\n","\n","Epoch 8/20\n","\n","223/223 [==============================] - 106s 475ms/step - loss: 0.3733 - accuracy: 0.8471 - val_loss: 0.5295 - val_accuracy: 0.7627\n","\n","Epoch 9/20\n","\n","223/223 [==============================] - 106s 475ms/step - loss: 0.3656 - accuracy: 0.8481 - val_loss: 0.5271 - val_accuracy: 0.7574\n","\n","Epoch 10/20\n","\n","223/223 [==============================] - 106s 475ms/step - loss: 0.3665 - accuracy: 0.8472 - val_loss: 0.5341 - val_accuracy: 0.7678\n","\n","Epoch 11/20\n","\n","223/223 [==============================] - 106s 477ms/step - loss: 0.3648 - accuracy: 0.8507 - val_loss: 0.5481 - val_accuracy: 0.7658\n","\n","Epoch 12/20\n","\n","223/223 [==============================] - 106s 475ms/step - loss: 0.3573 - accuracy: 0.8510 - val_loss: 0.5319 - val_accuracy: 0.7661\n","\n","Epoch 13/20\n","\n","223/223 [==============================] - 106s 475ms/step - loss: 0.3583 - accuracy: 0.8510 - val_loss: 0.5283 - val_accuracy: 0.7658\n","\n","Epoch 14/20\n","\n","223/223 [==============================] - 107s 480ms/step - loss: 0.3547 - accuracy: 0.8505 - val_loss: 0.5364 - val_accuracy: 0.7664\n","\n","Epoch 15/20\n","\n","223/223 [==============================] - 107s 478ms/step - loss: 0.3534 - accuracy: 0.8521 - val_loss: 0.5450 - val_accuracy: 0.7715\n","\n","Epoch 16/20\n","\n","223/223 [==============================] - 107s 479ms/step - loss: 0.3511 - accuracy: 0.8544 - val_loss: 0.5242 - val_accuracy: 0.7684\n","\n","Epoch 17/20\n","\n","223/223 [==============================] - 112s 504ms/step - loss: 0.3463 - accuracy: 0.8542 - val_loss: 0.5337 - val_accuracy: 0.7692\n","\n","Epoch 18/20\n","\n","223/223 [==============================] - 106s 478ms/step - loss: 0.3475 - accuracy: 0.8545 - val_loss: 0.5607 - val_accuracy: 0.7765\n","\n","Epoch 19/20\n","\n","223/223 [==============================] - 106s 478ms/step - loss: 0.3431 - accuracy: 0.8564 - val_loss: 0.5283 - val_accuracy: 0.7678\n","\n","Epoch 20/20\n","\n","223/223 [==============================] - 107s 480ms/step - loss: 0.3402 - accuracy: 0.8578 - val_loss: 0.5356 - val_accuracy: 0.7698\n"]}],"source":["# Train the model\n","history = vgg16_model.fit(train_ds,\n","                       epochs=20,\n","                       validation_data=validation_ds,\n","                       verbose=1)"]},{"cell_type":"code","execution_count":24,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 500x800 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plot_history(history)"]},{"cell_type":"code","execution_count":25,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["112/112 [==============================] - 31s 273ms/step - loss: 0.5356 - accuracy: 0.7698\n","\n","Accuracy:  76.97719931602478  %\n"]}],"source":["_, acc = vgg16_model.evaluate(validation_ds)\n","print(\"Accuracy: \", (acc*100.0), \" %\")"]},{"cell_type":"markdown","metadata":{},"source":["#### Approach 3: ResNet50"]},{"cell_type":"code","execution_count":26,"metadata":{},"outputs":[],"source":["# Load the pre-trained ResNet50 model\n","\n","base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(180, 180, 3))\n","base_model.trainable = False\n","last_output = base_model.output \n","x = tf.keras.layers.Flatten()(last_output)  \n","x = tf.keras.layers.Dense(1024, activation='relu')(x)   \n","x = tf.keras.layers.Dropout(0.6)(x) \n","predictions = Dense(1, activation='sigmoid')(x)  "]},{"cell_type":"code","execution_count":27,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"model_1\"\n","\n","__________________________________________________________________________________________________\n","\n"," Layer (type)                Output Shape                 Param #   Connected to                  \n","\n","==================================================================================================\n","\n"," input_2 (InputLayer)        [(None, 180, 180, 3)]        0         []                            \n","\n","                                                                                                  \n","\n"," conv1_pad (ZeroPadding2D)   (None, 186, 186, 3)          0         ['input_2[0][0]']             \n","\n","                                                                                                  \n","\n"," conv1_conv (Conv2D)         (None, 90, 90, 64)           9472      ['conv1_pad[0][0]']           \n","\n","                                                                                                  \n","\n"," conv1_bn (BatchNormalizati  (None, 90, 90, 64)           256       ['conv1_conv[0][0]']          \n","\n"," on)                                                                                              \n","\n","                                                                                                  \n","\n"," conv1_relu (Activation)     (None, 90, 90, 64)           0         ['conv1_bn[0][0]']            \n","\n","                                                                                                  \n","\n"," pool1_pad (ZeroPadding2D)   (None, 92, 92, 64)           0         ['conv1_relu[0][0]']          \n","\n","                                                                                                  \n","\n"," pool1_pool (MaxPooling2D)   (None, 45, 45, 64)           0         ['pool1_pad[0][0]']           \n","\n","                                                                                                  \n","\n"," conv2_block1_1_conv (Conv2  (None, 45, 45, 64)           4160      ['pool1_pool[0][0]']          \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv2_block1_1_bn (BatchNo  (None, 45, 45, 64)           256       ['conv2_block1_1_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv2_block1_1_relu (Activ  (None, 45, 45, 64)           0         ['conv2_block1_1_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv2_block1_2_conv (Conv2  (None, 45, 45, 64)           36928     ['conv2_block1_1_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv2_block1_2_bn (BatchNo  (None, 45, 45, 64)           256       ['conv2_block1_2_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv2_block1_2_relu (Activ  (None, 45, 45, 64)           0         ['conv2_block1_2_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv2_block1_0_conv (Conv2  (None, 45, 45, 256)          16640     ['pool1_pool[0][0]']          \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv2_block1_3_conv (Conv2  (None, 45, 45, 256)          16640     ['conv2_block1_2_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv2_block1_0_bn (BatchNo  (None, 45, 45, 256)          1024      ['conv2_block1_0_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv2_block1_3_bn (BatchNo  (None, 45, 45, 256)          1024      ['conv2_block1_3_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv2_block1_add (Add)      (None, 45, 45, 256)          0         ['conv2_block1_0_bn[0][0]',   \n","\n","                                                                     'conv2_block1_3_bn[0][0]']   \n","\n","                                                                                                  \n","\n"," conv2_block1_out (Activati  (None, 45, 45, 256)          0         ['conv2_block1_add[0][0]']    \n","\n"," on)                                                                                              \n","\n","                                                                                                  \n","\n"," conv2_block2_1_conv (Conv2  (None, 45, 45, 64)           16448     ['conv2_block1_out[0][0]']    \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv2_block2_1_bn (BatchNo  (None, 45, 45, 64)           256       ['conv2_block2_1_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv2_block2_1_relu (Activ  (None, 45, 45, 64)           0         ['conv2_block2_1_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv2_block2_2_conv (Conv2  (None, 45, 45, 64)           36928     ['conv2_block2_1_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv2_block2_2_bn (BatchNo  (None, 45, 45, 64)           256       ['conv2_block2_2_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv2_block2_2_relu (Activ  (None, 45, 45, 64)           0         ['conv2_block2_2_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv2_block2_3_conv (Conv2  (None, 45, 45, 256)          16640     ['conv2_block2_2_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv2_block2_3_bn (BatchNo  (None, 45, 45, 256)          1024      ['conv2_block2_3_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv2_block2_add (Add)      (None, 45, 45, 256)          0         ['conv2_block1_out[0][0]',    \n","\n","                                                                     'conv2_block2_3_bn[0][0]']   \n","\n","                                                                                                  \n","\n"," conv2_block2_out (Activati  (None, 45, 45, 256)          0         ['conv2_block2_add[0][0]']    \n","\n"," on)                                                                                              \n","\n","                                                                                                  \n","\n"," conv2_block3_1_conv (Conv2  (None, 45, 45, 64)           16448     ['conv2_block2_out[0][0]']    \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv2_block3_1_bn (BatchNo  (None, 45, 45, 64)           256       ['conv2_block3_1_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv2_block3_1_relu (Activ  (None, 45, 45, 64)           0         ['conv2_block3_1_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv2_block3_2_conv (Conv2  (None, 45, 45, 64)           36928     ['conv2_block3_1_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv2_block3_2_bn (BatchNo  (None, 45, 45, 64)           256       ['conv2_block3_2_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv2_block3_2_relu (Activ  (None, 45, 45, 64)           0         ['conv2_block3_2_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv2_block3_3_conv (Conv2  (None, 45, 45, 256)          16640     ['conv2_block3_2_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv2_block3_3_bn (BatchNo  (None, 45, 45, 256)          1024      ['conv2_block3_3_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv2_block3_add (Add)      (None, 45, 45, 256)          0         ['conv2_block2_out[0][0]',    \n","\n","                                                                     'conv2_block3_3_bn[0][0]']   \n","\n","                                                                                                  \n","\n"," conv2_block3_out (Activati  (None, 45, 45, 256)          0         ['conv2_block3_add[0][0]']    \n","\n"," on)                                                                                              \n","\n","                                                                                                  \n","\n"," conv3_block1_1_conv (Conv2  (None, 23, 23, 128)          32896     ['conv2_block3_out[0][0]']    \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv3_block1_1_bn (BatchNo  (None, 23, 23, 128)          512       ['conv3_block1_1_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv3_block1_1_relu (Activ  (None, 23, 23, 128)          0         ['conv3_block1_1_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv3_block1_2_conv (Conv2  (None, 23, 23, 128)          147584    ['conv3_block1_1_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv3_block1_2_bn (BatchNo  (None, 23, 23, 128)          512       ['conv3_block1_2_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv3_block1_2_relu (Activ  (None, 23, 23, 128)          0         ['conv3_block1_2_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv3_block1_0_conv (Conv2  (None, 23, 23, 512)          131584    ['conv2_block3_out[0][0]']    \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv3_block1_3_conv (Conv2  (None, 23, 23, 512)          66048     ['conv3_block1_2_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv3_block1_0_bn (BatchNo  (None, 23, 23, 512)          2048      ['conv3_block1_0_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv3_block1_3_bn (BatchNo  (None, 23, 23, 512)          2048      ['conv3_block1_3_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv3_block1_add (Add)      (None, 23, 23, 512)          0         ['conv3_block1_0_bn[0][0]',   \n","\n","                                                                     'conv3_block1_3_bn[0][0]']   \n","\n","                                                                                                  \n","\n"," conv3_block1_out (Activati  (None, 23, 23, 512)          0         ['conv3_block1_add[0][0]']    \n","\n"," on)                                                                                              \n","\n","                                                                                                  \n","\n"," conv3_block2_1_conv (Conv2  (None, 23, 23, 128)          65664     ['conv3_block1_out[0][0]']    \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv3_block2_1_bn (BatchNo  (None, 23, 23, 128)          512       ['conv3_block2_1_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv3_block2_1_relu (Activ  (None, 23, 23, 128)          0         ['conv3_block2_1_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv3_block2_2_conv (Conv2  (None, 23, 23, 128)          147584    ['conv3_block2_1_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv3_block2_2_bn (BatchNo  (None, 23, 23, 128)          512       ['conv3_block2_2_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv3_block2_2_relu (Activ  (None, 23, 23, 128)          0         ['conv3_block2_2_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv3_block2_3_conv (Conv2  (None, 23, 23, 512)          66048     ['conv3_block2_2_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv3_block2_3_bn (BatchNo  (None, 23, 23, 512)          2048      ['conv3_block2_3_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv3_block2_add (Add)      (None, 23, 23, 512)          0         ['conv3_block1_out[0][0]',    \n","\n","                                                                     'conv3_block2_3_bn[0][0]']   \n","\n","                                                                                                  \n","\n"," conv3_block2_out (Activati  (None, 23, 23, 512)          0         ['conv3_block2_add[0][0]']    \n","\n"," on)                                                                                              \n","\n","                                                                                                  \n","\n"," conv3_block3_1_conv (Conv2  (None, 23, 23, 128)          65664     ['conv3_block2_out[0][0]']    \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv3_block3_1_bn (BatchNo  (None, 23, 23, 128)          512       ['conv3_block3_1_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv3_block3_1_relu (Activ  (None, 23, 23, 128)          0         ['conv3_block3_1_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv3_block3_2_conv (Conv2  (None, 23, 23, 128)          147584    ['conv3_block3_1_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv3_block3_2_bn (BatchNo  (None, 23, 23, 128)          512       ['conv3_block3_2_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv3_block3_2_relu (Activ  (None, 23, 23, 128)          0         ['conv3_block3_2_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv3_block3_3_conv (Conv2  (None, 23, 23, 512)          66048     ['conv3_block3_2_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv3_block3_3_bn (BatchNo  (None, 23, 23, 512)          2048      ['conv3_block3_3_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv3_block3_add (Add)      (None, 23, 23, 512)          0         ['conv3_block2_out[0][0]',    \n","\n","                                                                     'conv3_block3_3_bn[0][0]']   \n","\n","                                                                                                  \n","\n"," conv3_block3_out (Activati  (None, 23, 23, 512)          0         ['conv3_block3_add[0][0]']    \n","\n"," on)                                                                                              \n","\n","                                                                                                  \n","\n"," conv3_block4_1_conv (Conv2  (None, 23, 23, 128)          65664     ['conv3_block3_out[0][0]']    \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv3_block4_1_bn (BatchNo  (None, 23, 23, 128)          512       ['conv3_block4_1_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv3_block4_1_relu (Activ  (None, 23, 23, 128)          0         ['conv3_block4_1_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv3_block4_2_conv (Conv2  (None, 23, 23, 128)          147584    ['conv3_block4_1_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv3_block4_2_bn (BatchNo  (None, 23, 23, 128)          512       ['conv3_block4_2_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv3_block4_2_relu (Activ  (None, 23, 23, 128)          0         ['conv3_block4_2_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv3_block4_3_conv (Conv2  (None, 23, 23, 512)          66048     ['conv3_block4_2_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv3_block4_3_bn (BatchNo  (None, 23, 23, 512)          2048      ['conv3_block4_3_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv3_block4_add (Add)      (None, 23, 23, 512)          0         ['conv3_block3_out[0][0]',    \n","\n","                                                                     'conv3_block4_3_bn[0][0]']   \n","\n","                                                                                                  \n","\n"," conv3_block4_out (Activati  (None, 23, 23, 512)          0         ['conv3_block4_add[0][0]']    \n","\n"," on)                                                                                              \n","\n","                                                                                                  \n","\n"," conv4_block1_1_conv (Conv2  (None, 12, 12, 256)          131328    ['conv3_block4_out[0][0]']    \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv4_block1_1_bn (BatchNo  (None, 12, 12, 256)          1024      ['conv4_block1_1_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv4_block1_1_relu (Activ  (None, 12, 12, 256)          0         ['conv4_block1_1_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv4_block1_2_conv (Conv2  (None, 12, 12, 256)          590080    ['conv4_block1_1_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv4_block1_2_bn (BatchNo  (None, 12, 12, 256)          1024      ['conv4_block1_2_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv4_block1_2_relu (Activ  (None, 12, 12, 256)          0         ['conv4_block1_2_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv4_block1_0_conv (Conv2  (None, 12, 12, 1024)         525312    ['conv3_block4_out[0][0]']    \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv4_block1_3_conv (Conv2  (None, 12, 12, 1024)         263168    ['conv4_block1_2_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv4_block1_0_bn (BatchNo  (None, 12, 12, 1024)         4096      ['conv4_block1_0_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv4_block1_3_bn (BatchNo  (None, 12, 12, 1024)         4096      ['conv4_block1_3_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv4_block1_add (Add)      (None, 12, 12, 1024)         0         ['conv4_block1_0_bn[0][0]',   \n","\n","                                                                     'conv4_block1_3_bn[0][0]']   \n","\n","                                                                                                  \n","\n"," conv4_block1_out (Activati  (None, 12, 12, 1024)         0         ['conv4_block1_add[0][0]']    \n","\n"," on)                                                                                              \n","\n","                                                                                                  \n","\n"," conv4_block2_1_conv (Conv2  (None, 12, 12, 256)          262400    ['conv4_block1_out[0][0]']    \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv4_block2_1_bn (BatchNo  (None, 12, 12, 256)          1024      ['conv4_block2_1_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv4_block2_1_relu (Activ  (None, 12, 12, 256)          0         ['conv4_block2_1_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv4_block2_2_conv (Conv2  (None, 12, 12, 256)          590080    ['conv4_block2_1_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv4_block2_2_bn (BatchNo  (None, 12, 12, 256)          1024      ['conv4_block2_2_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv4_block2_2_relu (Activ  (None, 12, 12, 256)          0         ['conv4_block2_2_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv4_block2_3_conv (Conv2  (None, 12, 12, 1024)         263168    ['conv4_block2_2_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv4_block2_3_bn (BatchNo  (None, 12, 12, 1024)         4096      ['conv4_block2_3_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv4_block2_add (Add)      (None, 12, 12, 1024)         0         ['conv4_block1_out[0][0]',    \n","\n","                                                                     'conv4_block2_3_bn[0][0]']   \n","\n","                                                                                                  \n","\n"," conv4_block2_out (Activati  (None, 12, 12, 1024)         0         ['conv4_block2_add[0][0]']    \n","\n"," on)                                                                                              \n","\n","                                                                                                  \n","\n"," conv4_block3_1_conv (Conv2  (None, 12, 12, 256)          262400    ['conv4_block2_out[0][0]']    \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv4_block3_1_bn (BatchNo  (None, 12, 12, 256)          1024      ['conv4_block3_1_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv4_block3_1_relu (Activ  (None, 12, 12, 256)          0         ['conv4_block3_1_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv4_block3_2_conv (Conv2  (None, 12, 12, 256)          590080    ['conv4_block3_1_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv4_block3_2_bn (BatchNo  (None, 12, 12, 256)          1024      ['conv4_block3_2_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv4_block3_2_relu (Activ  (None, 12, 12, 256)          0         ['conv4_block3_2_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv4_block3_3_conv (Conv2  (None, 12, 12, 1024)         263168    ['conv4_block3_2_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv4_block3_3_bn (BatchNo  (None, 12, 12, 1024)         4096      ['conv4_block3_3_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv4_block3_add (Add)      (None, 12, 12, 1024)         0         ['conv4_block2_out[0][0]',    \n","\n","                                                                     'conv4_block3_3_bn[0][0]']   \n","\n","                                                                                                  \n","\n"," conv4_block3_out (Activati  (None, 12, 12, 1024)         0         ['conv4_block3_add[0][0]']    \n","\n"," on)                                                                                              \n","\n","                                                                                                  \n","\n"," conv4_block4_1_conv (Conv2  (None, 12, 12, 256)          262400    ['conv4_block3_out[0][0]']    \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv4_block4_1_bn (BatchNo  (None, 12, 12, 256)          1024      ['conv4_block4_1_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv4_block4_1_relu (Activ  (None, 12, 12, 256)          0         ['conv4_block4_1_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv4_block4_2_conv (Conv2  (None, 12, 12, 256)          590080    ['conv4_block4_1_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv4_block4_2_bn (BatchNo  (None, 12, 12, 256)          1024      ['conv4_block4_2_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv4_block4_2_relu (Activ  (None, 12, 12, 256)          0         ['conv4_block4_2_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv4_block4_3_conv (Conv2  (None, 12, 12, 1024)         263168    ['conv4_block4_2_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv4_block4_3_bn (BatchNo  (None, 12, 12, 1024)         4096      ['conv4_block4_3_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv4_block4_add (Add)      (None, 12, 12, 1024)         0         ['conv4_block3_out[0][0]',    \n","\n","                                                                     'conv4_block4_3_bn[0][0]']   \n","\n","                                                                                                  \n","\n"," conv4_block4_out (Activati  (None, 12, 12, 1024)         0         ['conv4_block4_add[0][0]']    \n","\n"," on)                                                                                              \n","\n","                                                                                                  \n","\n"," conv4_block5_1_conv (Conv2  (None, 12, 12, 256)          262400    ['conv4_block4_out[0][0]']    \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv4_block5_1_bn (BatchNo  (None, 12, 12, 256)          1024      ['conv4_block5_1_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv4_block5_1_relu (Activ  (None, 12, 12, 256)          0         ['conv4_block5_1_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv4_block5_2_conv (Conv2  (None, 12, 12, 256)          590080    ['conv4_block5_1_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv4_block5_2_bn (BatchNo  (None, 12, 12, 256)          1024      ['conv4_block5_2_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv4_block5_2_relu (Activ  (None, 12, 12, 256)          0         ['conv4_block5_2_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv4_block5_3_conv (Conv2  (None, 12, 12, 1024)         263168    ['conv4_block5_2_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv4_block5_3_bn (BatchNo  (None, 12, 12, 1024)         4096      ['conv4_block5_3_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv4_block5_add (Add)      (None, 12, 12, 1024)         0         ['conv4_block4_out[0][0]',    \n","\n","                                                                     'conv4_block5_3_bn[0][0]']   \n","\n","                                                                                                  \n","\n"," conv4_block5_out (Activati  (None, 12, 12, 1024)         0         ['conv4_block5_add[0][0]']    \n","\n"," on)                                                                                              \n","\n","                                                                                                  \n","\n"," conv4_block6_1_conv (Conv2  (None, 12, 12, 256)          262400    ['conv4_block5_out[0][0]']    \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv4_block6_1_bn (BatchNo  (None, 12, 12, 256)          1024      ['conv4_block6_1_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv4_block6_1_relu (Activ  (None, 12, 12, 256)          0         ['conv4_block6_1_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv4_block6_2_conv (Conv2  (None, 12, 12, 256)          590080    ['conv4_block6_1_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv4_block6_2_bn (BatchNo  (None, 12, 12, 256)          1024      ['conv4_block6_2_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv4_block6_2_relu (Activ  (None, 12, 12, 256)          0         ['conv4_block6_2_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv4_block6_3_conv (Conv2  (None, 12, 12, 1024)         263168    ['conv4_block6_2_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv4_block6_3_bn (BatchNo  (None, 12, 12, 1024)         4096      ['conv4_block6_3_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv4_block6_add (Add)      (None, 12, 12, 1024)         0         ['conv4_block5_out[0][0]',    \n","\n","                                                                     'conv4_block6_3_bn[0][0]']   \n","\n","                                                                                                  \n","\n"," conv4_block6_out (Activati  (None, 12, 12, 1024)         0         ['conv4_block6_add[0][0]']    \n","\n"," on)                                                                                              \n","\n","                                                                                                  \n","\n"," conv5_block1_1_conv (Conv2  (None, 6, 6, 512)            524800    ['conv4_block6_out[0][0]']    \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv5_block1_1_bn (BatchNo  (None, 6, 6, 512)            2048      ['conv5_block1_1_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv5_block1_1_relu (Activ  (None, 6, 6, 512)            0         ['conv5_block1_1_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv5_block1_2_conv (Conv2  (None, 6, 6, 512)            2359808   ['conv5_block1_1_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv5_block1_2_bn (BatchNo  (None, 6, 6, 512)            2048      ['conv5_block1_2_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv5_block1_2_relu (Activ  (None, 6, 6, 512)            0         ['conv5_block1_2_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv5_block1_0_conv (Conv2  (None, 6, 6, 2048)           2099200   ['conv4_block6_out[0][0]']    \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv5_block1_3_conv (Conv2  (None, 6, 6, 2048)           1050624   ['conv5_block1_2_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv5_block1_0_bn (BatchNo  (None, 6, 6, 2048)           8192      ['conv5_block1_0_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv5_block1_3_bn (BatchNo  (None, 6, 6, 2048)           8192      ['conv5_block1_3_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv5_block1_add (Add)      (None, 6, 6, 2048)           0         ['conv5_block1_0_bn[0][0]',   \n","\n","                                                                     'conv5_block1_3_bn[0][0]']   \n","\n","                                                                                                  \n","\n"," conv5_block1_out (Activati  (None, 6, 6, 2048)           0         ['conv5_block1_add[0][0]']    \n","\n"," on)                                                                                              \n","\n","                                                                                                  \n","\n"," conv5_block2_1_conv (Conv2  (None, 6, 6, 512)            1049088   ['conv5_block1_out[0][0]']    \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv5_block2_1_bn (BatchNo  (None, 6, 6, 512)            2048      ['conv5_block2_1_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv5_block2_1_relu (Activ  (None, 6, 6, 512)            0         ['conv5_block2_1_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv5_block2_2_conv (Conv2  (None, 6, 6, 512)            2359808   ['conv5_block2_1_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv5_block2_2_bn (BatchNo  (None, 6, 6, 512)            2048      ['conv5_block2_2_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv5_block2_2_relu (Activ  (None, 6, 6, 512)            0         ['conv5_block2_2_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv5_block2_3_conv (Conv2  (None, 6, 6, 2048)           1050624   ['conv5_block2_2_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv5_block2_3_bn (BatchNo  (None, 6, 6, 2048)           8192      ['conv5_block2_3_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv5_block2_add (Add)      (None, 6, 6, 2048)           0         ['conv5_block1_out[0][0]',    \n","\n","                                                                     'conv5_block2_3_bn[0][0]']   \n","\n","                                                                                                  \n","\n"," conv5_block2_out (Activati  (None, 6, 6, 2048)           0         ['conv5_block2_add[0][0]']    \n","\n"," on)                                                                                              \n","\n","                                                                                                  \n","\n"," conv5_block3_1_conv (Conv2  (None, 6, 6, 512)            1049088   ['conv5_block2_out[0][0]']    \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv5_block3_1_bn (BatchNo  (None, 6, 6, 512)            2048      ['conv5_block3_1_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv5_block3_1_relu (Activ  (None, 6, 6, 512)            0         ['conv5_block3_1_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv5_block3_2_conv (Conv2  (None, 6, 6, 512)            2359808   ['conv5_block3_1_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv5_block3_2_bn (BatchNo  (None, 6, 6, 512)            2048      ['conv5_block3_2_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv5_block3_2_relu (Activ  (None, 6, 6, 512)            0         ['conv5_block3_2_bn[0][0]']   \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," conv5_block3_3_conv (Conv2  (None, 6, 6, 2048)           1050624   ['conv5_block3_2_relu[0][0]'] \n","\n"," D)                                                                                               \n","\n","                                                                                                  \n","\n"," conv5_block3_3_bn (BatchNo  (None, 6, 6, 2048)           8192      ['conv5_block3_3_conv[0][0]'] \n","\n"," rmalization)                                                                                     \n","\n","                                                                                                  \n","\n"," conv5_block3_add (Add)      (None, 6, 6, 2048)           0         ['conv5_block2_out[0][0]',    \n","\n","                                                                     'conv5_block3_3_bn[0][0]']   \n","\n","                                                                                                  \n","\n"," conv5_block3_out (Activati  (None, 6, 6, 2048)           0         ['conv5_block3_add[0][0]']    \n","\n"," on)                                                                                              \n","\n","                                                                                                  \n","\n"," flatten_2 (Flatten)         (None, 73728)                0         ['conv5_block3_out[0][0]']    \n","\n","                                                                                                  \n","\n"," dense_4 (Dense)             (None, 1024)                 7549849   ['flatten_2[0][0]']           \n","\n","                                                          6                                       \n","\n","                                                                                                  \n","\n"," dropout_2 (Dropout)         (None, 1024)                 0         ['dense_4[0][0]']             \n","\n","                                                                                                  \n","\n"," dense_5 (Dense)             (None, 1)                    1025      ['dropout_2[0][0]']           \n","\n","                                                                                                  \n","\n","==================================================================================================\n","\n","Total params: 99087233 (377.99 MB)\n","\n","Trainable params: 75499521 (288.01 MB)\n","\n","Non-trainable params: 23587712 (89.98 MB)\n","\n","__________________________________________________________________________________________________\n"]}],"source":["# create the final model\n","resnet50_model = Model(inputs=base_model.input, outputs=predictions)\n","resnet50_model.summary()"]},{"cell_type":"code","execution_count":28,"metadata":{},"outputs":[],"source":["resnet50_model.compile(optimizer = tf.keras.optimizers.legacy.Adam(),\n","            loss = 'binary_crossentropy',\n","            metrics=['accuracy'])"]},{"cell_type":"code","execution_count":29,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/20\n","\n","223/223 [==============================] - 15s 66ms/step - loss: 0.3541 - accuracy: 0.8573 - val_loss: 0.4978 - val_accuracy: 0.7872\n","\n","Epoch 2/20\n","\n","223/223 [==============================] - 15s 66ms/step - loss: 0.3450 - accuracy: 0.8620 - val_loss: 0.5030 - val_accuracy: 0.7917\n","\n","Epoch 3/20\n","\n","223/223 [==============================] - 15s 68ms/step - loss: 0.3470 - accuracy: 0.8609 - val_loss: 0.4814 - val_accuracy: 0.7909\n","\n","Epoch 4/20\n","\n","223/223 [==============================] - 15s 66ms/step - loss: 0.3328 - accuracy: 0.8669 - val_loss: 0.4944 - val_accuracy: 0.7920\n","\n","Epoch 5/20\n","\n","223/223 [==============================] - 15s 66ms/step - loss: 0.3287 - accuracy: 0.8708 - val_loss: 0.4936 - val_accuracy: 0.7931\n","\n","Epoch 6/20\n","\n","223/223 [==============================] - 15s 67ms/step - loss: 0.3243 - accuracy: 0.8708 - val_loss: 0.4867 - val_accuracy: 0.7931\n","\n","Epoch 7/20\n","\n","223/223 [==============================] - 16s 70ms/step - loss: 0.3193 - accuracy: 0.8751 - val_loss: 0.5300 - val_accuracy: 0.7934\n","\n","Epoch 8/20\n","\n","223/223 [==============================] - 15s 66ms/step - loss: 0.3152 - accuracy: 0.8741 - val_loss: 0.4992 - val_accuracy: 0.7971\n","\n","Epoch 9/20\n","\n","223/223 [==============================] - 15s 66ms/step - loss: 0.3068 - accuracy: 0.8772 - val_loss: 0.4897 - val_accuracy: 0.7934\n","\n","Epoch 10/20\n","\n","223/223 [==============================] - 15s 65ms/step - loss: 0.3010 - accuracy: 0.8794 - val_loss: 0.4923 - val_accuracy: 0.8010\n","\n","Epoch 11/20\n","\n","223/223 [==============================] - 15s 66ms/step - loss: 0.2958 - accuracy: 0.8841 - val_loss: 0.5217 - val_accuracy: 0.7976\n","\n","Epoch 12/20\n","\n","223/223 [==============================] - 15s 66ms/step - loss: 0.2871 - accuracy: 0.8848 - val_loss: 0.5665 - val_accuracy: 0.7945\n","\n","Epoch 13/20\n","\n","223/223 [==============================] - 15s 66ms/step - loss: 0.2884 - accuracy: 0.8846 - val_loss: 0.5192 - val_accuracy: 0.7990\n","\n","Epoch 14/20\n","\n","223/223 [==============================] - 15s 67ms/step - loss: 0.2830 - accuracy: 0.8887 - val_loss: 0.4796 - val_accuracy: 0.8069\n","\n","Epoch 15/20\n","\n","223/223 [==============================] - 15s 67ms/step - loss: 0.2741 - accuracy: 0.8901 - val_loss: 0.4866 - val_accuracy: 0.8035\n","\n","Epoch 16/20\n","\n","223/223 [==============================] - 15s 68ms/step - loss: 0.2674 - accuracy: 0.8950 - val_loss: 0.4946 - val_accuracy: 0.8047\n","\n","Epoch 17/20\n","\n","223/223 [==============================] - 15s 67ms/step - loss: 0.2634 - accuracy: 0.8965 - val_loss: 0.4945 - val_accuracy: 0.8019\n","\n","Epoch 18/20\n","\n","223/223 [==============================] - 15s 66ms/step - loss: 0.2559 - accuracy: 0.9004 - val_loss: 0.5039 - val_accuracy: 0.8038\n","\n","Epoch 19/20\n","\n","223/223 [==============================] - 15s 68ms/step - loss: 0.2559 - accuracy: 0.8986 - val_loss: 0.4974 - val_accuracy: 0.7982\n","\n","Epoch 20/20\n","\n","223/223 [==============================] - 16s 69ms/step - loss: 0.2541 - accuracy: 0.8979 - val_loss: 0.5144 - val_accuracy: 0.8016\n"]}],"source":["# Train the model\n","resnet50_history = model.fit(train_ds,\n","                       epochs=20,\n","                       validation_data=validation_ds,\n","                       verbose=1)"]},{"cell_type":"code","execution_count":30,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 500x800 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plot_history(resnet50_history)"]},{"cell_type":"markdown","metadata":{},"source":["#### Approach 4: Inception"]},{"cell_type":"code","execution_count":32,"metadata":{},"outputs":[],"source":["def create_model(base_model):\n","    \n","    x = base_model.output    \n","    x = GlobalAveragePooling2D()(x)\n","    x = Dense(128, activation = 'relu')(x)\n","    x = Dropout(0.4)(x)\n","    x = Dense(64, activation = 'relu')(x)\n","    x = Dropout(0.2)(x)\n","    \n","    outputs = Dense(1, activation='sigmoid')(x)\n","    \n","    model = Model(base_model.inputs, outputs)\n","    \n","    return model "]},{"cell_type":"code","execution_count":33,"metadata":{},"outputs":[],"source":["def fit_model(model, base_model, epochs, fine_tune = 0):\n","    \n","    early = tf.keras.callbacks.EarlyStopping( patience = 10,\n","                                              min_delta = 0.001,\n","                                              restore_best_weights = True)\n","    \n","    print(\"Unfreezing number of layers in base model = \", fine_tune)\n","    \n","    if fine_tune > 0:\n","        base_model.trainable = True\n","        for layer in base_model.layers[:-fine_tune]:\n","            layer.trainable = False       \n","        # small learning rate for fine tuning\n","        model.compile(optimizer=tf.keras.optimizers.legacy.Adam(1e-5),\n","                      loss='binary_crossentropy',\n","                      metrics=['accuracy'])\n","    else:\n","        base_model.trainable = False\n","        model.compile(optimizer=tf.keras.optimizers.legacy.Adam(),\n","                      loss='binary_crossentropy',\n","                      metrics=['accuracy'])\n","\n","    history = model.fit(train_ds,\n","                        validation_data = validation_ds,\n","                        epochs = 15,\n","                        callbacks = [early])\n","    \n","    return history"]},{"cell_type":"code","execution_count":34,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_resnet_v2/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5\n","\n","219055592/219055592 [==============================] - 59s 0us/step\n"]}],"source":["# load the InceptionResNetV2 architecture with imagenet weights as base\n","inception_base_model = tf.keras.applications.InceptionResNetV2(\n","                     include_top = False,\n","                     weights = 'imagenet',\n","                     input_shape = (180, 180, 3)\n","                     )"]},{"cell_type":"code","execution_count":35,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"model_2\"\n","\n","__________________________________________________________________________________________________\n","\n"," Layer (type)                Output Shape                 Param #   Connected to                  \n","\n","==================================================================================================\n","\n"," input_3 (InputLayer)        [(None, 180, 180, 3)]        0         []                            \n","\n","                                                                                                  \n","\n"," conv2d_3 (Conv2D)           (None, 89, 89, 32)           864       ['input_3[0][0]']             \n","\n","                                                                                                  \n","\n"," batch_normalization (Batch  (None, 89, 89, 32)           96        ['conv2d_3[0][0]']            \n","\n"," Normalization)                                                                                   \n","\n","                                                                                                  \n","\n"," activation_5 (Activation)   (None, 89, 89, 32)           0         ['batch_normalization[0][0]'] \n","\n","                                                                                                  \n","\n"," conv2d_4 (Conv2D)           (None, 87, 87, 32)           9216      ['activation_5[0][0]']        \n","\n","                                                                                                  \n","\n"," batch_normalization_1 (Bat  (None, 87, 87, 32)           96        ['conv2d_4[0][0]']            \n","\n"," chNormalization)                                                                                 \n","\n","                                                                                                  \n","\n"," activation_6 (Activation)   (None, 87, 87, 32)           0         ['batch_normalization_1[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_5 (Conv2D)           (None, 87, 87, 64)           18432     ['activation_6[0][0]']        \n","\n","                                                                                                  \n","\n"," batch_normalization_2 (Bat  (None, 87, 87, 64)           192       ['conv2d_5[0][0]']            \n","\n"," chNormalization)                                                                                 \n","\n","                                                                                                  \n","\n"," activation_7 (Activation)   (None, 87, 87, 64)           0         ['batch_normalization_2[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," max_pooling2d_3 (MaxPoolin  (None, 43, 43, 64)           0         ['activation_7[0][0]']        \n","\n"," g2D)                                                                                             \n","\n","                                                                                                  \n","\n"," conv2d_6 (Conv2D)           (None, 43, 43, 80)           5120      ['max_pooling2d_3[0][0]']     \n","\n","                                                                                                  \n","\n"," batch_normalization_3 (Bat  (None, 43, 43, 80)           240       ['conv2d_6[0][0]']            \n","\n"," chNormalization)                                                                                 \n","\n","                                                                                                  \n","\n"," activation_8 (Activation)   (None, 43, 43, 80)           0         ['batch_normalization_3[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_7 (Conv2D)           (None, 41, 41, 192)          138240    ['activation_8[0][0]']        \n","\n","                                                                                                  \n","\n"," batch_normalization_4 (Bat  (None, 41, 41, 192)          576       ['conv2d_7[0][0]']            \n","\n"," chNormalization)                                                                                 \n","\n","                                                                                                  \n","\n"," activation_9 (Activation)   (None, 41, 41, 192)          0         ['batch_normalization_4[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," max_pooling2d_4 (MaxPoolin  (None, 20, 20, 192)          0         ['activation_9[0][0]']        \n","\n"," g2D)                                                                                             \n","\n","                                                                                                  \n","\n"," conv2d_11 (Conv2D)          (None, 20, 20, 64)           12288     ['max_pooling2d_4[0][0]']     \n","\n","                                                                                                  \n","\n"," batch_normalization_8 (Bat  (None, 20, 20, 64)           192       ['conv2d_11[0][0]']           \n","\n"," chNormalization)                                                                                 \n","\n","                                                                                                  \n","\n"," activation_13 (Activation)  (None, 20, 20, 64)           0         ['batch_normalization_8[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_9 (Conv2D)           (None, 20, 20, 48)           9216      ['max_pooling2d_4[0][0]']     \n","\n","                                                                                                  \n","\n"," conv2d_12 (Conv2D)          (None, 20, 20, 96)           55296     ['activation_13[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_6 (Bat  (None, 20, 20, 48)           144       ['conv2d_9[0][0]']            \n","\n"," chNormalization)                                                                                 \n","\n","                                                                                                  \n","\n"," batch_normalization_9 (Bat  (None, 20, 20, 96)           288       ['conv2d_12[0][0]']           \n","\n"," chNormalization)                                                                                 \n","\n","                                                                                                  \n","\n"," activation_11 (Activation)  (None, 20, 20, 48)           0         ['batch_normalization_6[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," activation_14 (Activation)  (None, 20, 20, 96)           0         ['batch_normalization_9[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," average_pooling2d (Average  (None, 20, 20, 192)          0         ['max_pooling2d_4[0][0]']     \n","\n"," Pooling2D)                                                                                       \n","\n","                                                                                                  \n","\n"," conv2d_8 (Conv2D)           (None, 20, 20, 96)           18432     ['max_pooling2d_4[0][0]']     \n","\n","                                                                                                  \n","\n"," conv2d_10 (Conv2D)          (None, 20, 20, 64)           76800     ['activation_11[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_13 (Conv2D)          (None, 20, 20, 96)           82944     ['activation_14[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_14 (Conv2D)          (None, 20, 20, 64)           12288     ['average_pooling2d[0][0]']   \n","\n","                                                                                                  \n","\n"," batch_normalization_5 (Bat  (None, 20, 20, 96)           288       ['conv2d_8[0][0]']            \n","\n"," chNormalization)                                                                                 \n","\n","                                                                                                  \n","\n"," batch_normalization_7 (Bat  (None, 20, 20, 64)           192       ['conv2d_10[0][0]']           \n","\n"," chNormalization)                                                                                 \n","\n","                                                                                                  \n","\n"," batch_normalization_10 (Ba  (None, 20, 20, 96)           288       ['conv2d_13[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_11 (Ba  (None, 20, 20, 64)           192       ['conv2d_14[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_10 (Activation)  (None, 20, 20, 96)           0         ['batch_normalization_5[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," activation_12 (Activation)  (None, 20, 20, 64)           0         ['batch_normalization_7[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," activation_15 (Activation)  (None, 20, 20, 96)           0         ['batch_normalization_10[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_16 (Activation)  (None, 20, 20, 64)           0         ['batch_normalization_11[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," mixed_5b (Concatenate)      (None, 20, 20, 320)          0         ['activation_10[0][0]',       \n","\n","                                                                     'activation_12[0][0]',       \n","\n","                                                                     'activation_15[0][0]',       \n","\n","                                                                     'activation_16[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_18 (Conv2D)          (None, 20, 20, 32)           10240     ['mixed_5b[0][0]']            \n","\n","                                                                                                  \n","\n"," batch_normalization_15 (Ba  (None, 20, 20, 32)           96        ['conv2d_18[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_20 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_15[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_16 (Conv2D)          (None, 20, 20, 32)           10240     ['mixed_5b[0][0]']            \n","\n","                                                                                                  \n","\n"," conv2d_19 (Conv2D)          (None, 20, 20, 48)           13824     ['activation_20[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_13 (Ba  (None, 20, 20, 32)           96        ['conv2d_16[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_16 (Ba  (None, 20, 20, 48)           144       ['conv2d_19[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_18 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_13[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_21 (Activation)  (None, 20, 20, 48)           0         ['batch_normalization_16[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_15 (Conv2D)          (None, 20, 20, 32)           10240     ['mixed_5b[0][0]']            \n","\n","                                                                                                  \n","\n"," conv2d_17 (Conv2D)          (None, 20, 20, 32)           9216      ['activation_18[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_20 (Conv2D)          (None, 20, 20, 64)           27648     ['activation_21[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_12 (Ba  (None, 20, 20, 32)           96        ['conv2d_15[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_14 (Ba  (None, 20, 20, 32)           96        ['conv2d_17[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_17 (Ba  (None, 20, 20, 64)           192       ['conv2d_20[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_17 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_12[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_19 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_14[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_22 (Activation)  (None, 20, 20, 64)           0         ['batch_normalization_17[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," block35_1_mixed (Concatena  (None, 20, 20, 128)          0         ['activation_17[0][0]',       \n","\n"," te)                                                                 'activation_19[0][0]',       \n","\n","                                                                     'activation_22[0][0]']       \n","\n","                                                                                                  \n","\n"," block35_1_conv (Conv2D)     (None, 20, 20, 320)          41280     ['block35_1_mixed[0][0]']     \n","\n","                                                                                                  \n","\n"," custom_scale_layer (Custom  (None, 20, 20, 320)          0         ['mixed_5b[0][0]',            \n","\n"," ScaleLayer)                                                         'block35_1_conv[0][0]']      \n","\n","                                                                                                  \n","\n"," block35_1_ac (Activation)   (None, 20, 20, 320)          0         ['custom_scale_layer[0][0]']  \n","\n","                                                                                                  \n","\n"," conv2d_24 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_1_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," batch_normalization_21 (Ba  (None, 20, 20, 32)           96        ['conv2d_24[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_26 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_21[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_22 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_1_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_25 (Conv2D)          (None, 20, 20, 48)           13824     ['activation_26[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_19 (Ba  (None, 20, 20, 32)           96        ['conv2d_22[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_22 (Ba  (None, 20, 20, 48)           144       ['conv2d_25[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_24 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_19[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_27 (Activation)  (None, 20, 20, 48)           0         ['batch_normalization_22[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_21 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_1_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_23 (Conv2D)          (None, 20, 20, 32)           9216      ['activation_24[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_26 (Conv2D)          (None, 20, 20, 64)           27648     ['activation_27[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_18 (Ba  (None, 20, 20, 32)           96        ['conv2d_21[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_20 (Ba  (None, 20, 20, 32)           96        ['conv2d_23[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_23 (Ba  (None, 20, 20, 64)           192       ['conv2d_26[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_23 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_18[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_25 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_20[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_28 (Activation)  (None, 20, 20, 64)           0         ['batch_normalization_23[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," block35_2_mixed (Concatena  (None, 20, 20, 128)          0         ['activation_23[0][0]',       \n","\n"," te)                                                                 'activation_25[0][0]',       \n","\n","                                                                     'activation_28[0][0]']       \n","\n","                                                                                                  \n","\n"," block35_2_conv (Conv2D)     (None, 20, 20, 320)          41280     ['block35_2_mixed[0][0]']     \n","\n","                                                                                                  \n","\n"," custom_scale_layer_1 (Cust  (None, 20, 20, 320)          0         ['block35_1_ac[0][0]',        \n","\n"," omScaleLayer)                                                       'block35_2_conv[0][0]']      \n","\n","                                                                                                  \n","\n"," block35_2_ac (Activation)   (None, 20, 20, 320)          0         ['custom_scale_layer_1[0][0]']\n","\n","                                                                                                  \n","\n"," conv2d_30 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_2_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," batch_normalization_27 (Ba  (None, 20, 20, 32)           96        ['conv2d_30[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_32 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_27[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_28 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_2_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_31 (Conv2D)          (None, 20, 20, 48)           13824     ['activation_32[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_25 (Ba  (None, 20, 20, 32)           96        ['conv2d_28[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_28 (Ba  (None, 20, 20, 48)           144       ['conv2d_31[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_30 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_25[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_33 (Activation)  (None, 20, 20, 48)           0         ['batch_normalization_28[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_27 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_2_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_29 (Conv2D)          (None, 20, 20, 32)           9216      ['activation_30[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_32 (Conv2D)          (None, 20, 20, 64)           27648     ['activation_33[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_24 (Ba  (None, 20, 20, 32)           96        ['conv2d_27[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_26 (Ba  (None, 20, 20, 32)           96        ['conv2d_29[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_29 (Ba  (None, 20, 20, 64)           192       ['conv2d_32[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_29 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_24[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_31 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_26[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_34 (Activation)  (None, 20, 20, 64)           0         ['batch_normalization_29[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," block35_3_mixed (Concatena  (None, 20, 20, 128)          0         ['activation_29[0][0]',       \n","\n"," te)                                                                 'activation_31[0][0]',       \n","\n","                                                                     'activation_34[0][0]']       \n","\n","                                                                                                  \n","\n"," block35_3_conv (Conv2D)     (None, 20, 20, 320)          41280     ['block35_3_mixed[0][0]']     \n","\n","                                                                                                  \n","\n"," custom_scale_layer_2 (Cust  (None, 20, 20, 320)          0         ['block35_2_ac[0][0]',        \n","\n"," omScaleLayer)                                                       'block35_3_conv[0][0]']      \n","\n","                                                                                                  \n","\n"," block35_3_ac (Activation)   (None, 20, 20, 320)          0         ['custom_scale_layer_2[0][0]']\n","\n","                                                                                                  \n","\n"," conv2d_36 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_3_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," batch_normalization_33 (Ba  (None, 20, 20, 32)           96        ['conv2d_36[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_38 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_33[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_34 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_3_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_37 (Conv2D)          (None, 20, 20, 48)           13824     ['activation_38[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_31 (Ba  (None, 20, 20, 32)           96        ['conv2d_34[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_34 (Ba  (None, 20, 20, 48)           144       ['conv2d_37[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_36 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_31[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_39 (Activation)  (None, 20, 20, 48)           0         ['batch_normalization_34[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_33 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_3_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_35 (Conv2D)          (None, 20, 20, 32)           9216      ['activation_36[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_38 (Conv2D)          (None, 20, 20, 64)           27648     ['activation_39[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_30 (Ba  (None, 20, 20, 32)           96        ['conv2d_33[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_32 (Ba  (None, 20, 20, 32)           96        ['conv2d_35[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_35 (Ba  (None, 20, 20, 64)           192       ['conv2d_38[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_35 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_30[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_37 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_32[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_40 (Activation)  (None, 20, 20, 64)           0         ['batch_normalization_35[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," block35_4_mixed (Concatena  (None, 20, 20, 128)          0         ['activation_35[0][0]',       \n","\n"," te)                                                                 'activation_37[0][0]',       \n","\n","                                                                     'activation_40[0][0]']       \n","\n","                                                                                                  \n","\n"," block35_4_conv (Conv2D)     (None, 20, 20, 320)          41280     ['block35_4_mixed[0][0]']     \n","\n","                                                                                                  \n","\n"," custom_scale_layer_3 (Cust  (None, 20, 20, 320)          0         ['block35_3_ac[0][0]',        \n","\n"," omScaleLayer)                                                       'block35_4_conv[0][0]']      \n","\n","                                                                                                  \n","\n"," block35_4_ac (Activation)   (None, 20, 20, 320)          0         ['custom_scale_layer_3[0][0]']\n","\n","                                                                                                  \n","\n"," conv2d_42 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_4_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," batch_normalization_39 (Ba  (None, 20, 20, 32)           96        ['conv2d_42[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_44 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_39[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_40 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_4_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_43 (Conv2D)          (None, 20, 20, 48)           13824     ['activation_44[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_37 (Ba  (None, 20, 20, 32)           96        ['conv2d_40[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_40 (Ba  (None, 20, 20, 48)           144       ['conv2d_43[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_42 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_37[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_45 (Activation)  (None, 20, 20, 48)           0         ['batch_normalization_40[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_39 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_4_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_41 (Conv2D)          (None, 20, 20, 32)           9216      ['activation_42[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_44 (Conv2D)          (None, 20, 20, 64)           27648     ['activation_45[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_36 (Ba  (None, 20, 20, 32)           96        ['conv2d_39[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_38 (Ba  (None, 20, 20, 32)           96        ['conv2d_41[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_41 (Ba  (None, 20, 20, 64)           192       ['conv2d_44[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_41 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_36[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_43 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_38[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_46 (Activation)  (None, 20, 20, 64)           0         ['batch_normalization_41[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," block35_5_mixed (Concatena  (None, 20, 20, 128)          0         ['activation_41[0][0]',       \n","\n"," te)                                                                 'activation_43[0][0]',       \n","\n","                                                                     'activation_46[0][0]']       \n","\n","                                                                                                  \n","\n"," block35_5_conv (Conv2D)     (None, 20, 20, 320)          41280     ['block35_5_mixed[0][0]']     \n","\n","                                                                                                  \n","\n"," custom_scale_layer_4 (Cust  (None, 20, 20, 320)          0         ['block35_4_ac[0][0]',        \n","\n"," omScaleLayer)                                                       'block35_5_conv[0][0]']      \n","\n","                                                                                                  \n","\n"," block35_5_ac (Activation)   (None, 20, 20, 320)          0         ['custom_scale_layer_4[0][0]']\n","\n","                                                                                                  \n","\n"," conv2d_48 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_5_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," batch_normalization_45 (Ba  (None, 20, 20, 32)           96        ['conv2d_48[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_50 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_45[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_46 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_5_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_49 (Conv2D)          (None, 20, 20, 48)           13824     ['activation_50[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_43 (Ba  (None, 20, 20, 32)           96        ['conv2d_46[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_46 (Ba  (None, 20, 20, 48)           144       ['conv2d_49[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_48 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_43[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_51 (Activation)  (None, 20, 20, 48)           0         ['batch_normalization_46[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_45 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_5_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_47 (Conv2D)          (None, 20, 20, 32)           9216      ['activation_48[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_50 (Conv2D)          (None, 20, 20, 64)           27648     ['activation_51[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_42 (Ba  (None, 20, 20, 32)           96        ['conv2d_45[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_44 (Ba  (None, 20, 20, 32)           96        ['conv2d_47[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_47 (Ba  (None, 20, 20, 64)           192       ['conv2d_50[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_47 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_42[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_49 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_44[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_52 (Activation)  (None, 20, 20, 64)           0         ['batch_normalization_47[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," block35_6_mixed (Concatena  (None, 20, 20, 128)          0         ['activation_47[0][0]',       \n","\n"," te)                                                                 'activation_49[0][0]',       \n","\n","                                                                     'activation_52[0][0]']       \n","\n","                                                                                                  \n","\n"," block35_6_conv (Conv2D)     (None, 20, 20, 320)          41280     ['block35_6_mixed[0][0]']     \n","\n","                                                                                                  \n","\n"," custom_scale_layer_5 (Cust  (None, 20, 20, 320)          0         ['block35_5_ac[0][0]',        \n","\n"," omScaleLayer)                                                       'block35_6_conv[0][0]']      \n","\n","                                                                                                  \n","\n"," block35_6_ac (Activation)   (None, 20, 20, 320)          0         ['custom_scale_layer_5[0][0]']\n","\n","                                                                                                  \n","\n"," conv2d_54 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_6_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," batch_normalization_51 (Ba  (None, 20, 20, 32)           96        ['conv2d_54[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_56 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_51[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_52 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_6_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_55 (Conv2D)          (None, 20, 20, 48)           13824     ['activation_56[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_49 (Ba  (None, 20, 20, 32)           96        ['conv2d_52[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_52 (Ba  (None, 20, 20, 48)           144       ['conv2d_55[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_54 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_49[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_57 (Activation)  (None, 20, 20, 48)           0         ['batch_normalization_52[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_51 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_6_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_53 (Conv2D)          (None, 20, 20, 32)           9216      ['activation_54[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_56 (Conv2D)          (None, 20, 20, 64)           27648     ['activation_57[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_48 (Ba  (None, 20, 20, 32)           96        ['conv2d_51[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_50 (Ba  (None, 20, 20, 32)           96        ['conv2d_53[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_53 (Ba  (None, 20, 20, 64)           192       ['conv2d_56[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_53 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_48[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_55 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_50[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_58 (Activation)  (None, 20, 20, 64)           0         ['batch_normalization_53[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," block35_7_mixed (Concatena  (None, 20, 20, 128)          0         ['activation_53[0][0]',       \n","\n"," te)                                                                 'activation_55[0][0]',       \n","\n","                                                                     'activation_58[0][0]']       \n","\n","                                                                                                  \n","\n"," block35_7_conv (Conv2D)     (None, 20, 20, 320)          41280     ['block35_7_mixed[0][0]']     \n","\n","                                                                                                  \n","\n"," custom_scale_layer_6 (Cust  (None, 20, 20, 320)          0         ['block35_6_ac[0][0]',        \n","\n"," omScaleLayer)                                                       'block35_7_conv[0][0]']      \n","\n","                                                                                                  \n","\n"," block35_7_ac (Activation)   (None, 20, 20, 320)          0         ['custom_scale_layer_6[0][0]']\n","\n","                                                                                                  \n","\n"," conv2d_60 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_7_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," batch_normalization_57 (Ba  (None, 20, 20, 32)           96        ['conv2d_60[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_62 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_57[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_58 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_7_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_61 (Conv2D)          (None, 20, 20, 48)           13824     ['activation_62[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_55 (Ba  (None, 20, 20, 32)           96        ['conv2d_58[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_58 (Ba  (None, 20, 20, 48)           144       ['conv2d_61[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_60 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_55[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_63 (Activation)  (None, 20, 20, 48)           0         ['batch_normalization_58[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_57 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_7_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_59 (Conv2D)          (None, 20, 20, 32)           9216      ['activation_60[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_62 (Conv2D)          (None, 20, 20, 64)           27648     ['activation_63[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_54 (Ba  (None, 20, 20, 32)           96        ['conv2d_57[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_56 (Ba  (None, 20, 20, 32)           96        ['conv2d_59[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_59 (Ba  (None, 20, 20, 64)           192       ['conv2d_62[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_59 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_54[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_61 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_56[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_64 (Activation)  (None, 20, 20, 64)           0         ['batch_normalization_59[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," block35_8_mixed (Concatena  (None, 20, 20, 128)          0         ['activation_59[0][0]',       \n","\n"," te)                                                                 'activation_61[0][0]',       \n","\n","                                                                     'activation_64[0][0]']       \n","\n","                                                                                                  \n","\n"," block35_8_conv (Conv2D)     (None, 20, 20, 320)          41280     ['block35_8_mixed[0][0]']     \n","\n","                                                                                                  \n","\n"," custom_scale_layer_7 (Cust  (None, 20, 20, 320)          0         ['block35_7_ac[0][0]',        \n","\n"," omScaleLayer)                                                       'block35_8_conv[0][0]']      \n","\n","                                                                                                  \n","\n"," block35_8_ac (Activation)   (None, 20, 20, 320)          0         ['custom_scale_layer_7[0][0]']\n","\n","                                                                                                  \n","\n"," conv2d_66 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_8_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," batch_normalization_63 (Ba  (None, 20, 20, 32)           96        ['conv2d_66[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_68 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_63[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_64 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_8_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_67 (Conv2D)          (None, 20, 20, 48)           13824     ['activation_68[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_61 (Ba  (None, 20, 20, 32)           96        ['conv2d_64[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_64 (Ba  (None, 20, 20, 48)           144       ['conv2d_67[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_66 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_61[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_69 (Activation)  (None, 20, 20, 48)           0         ['batch_normalization_64[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_63 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_8_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_65 (Conv2D)          (None, 20, 20, 32)           9216      ['activation_66[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_68 (Conv2D)          (None, 20, 20, 64)           27648     ['activation_69[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_60 (Ba  (None, 20, 20, 32)           96        ['conv2d_63[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_62 (Ba  (None, 20, 20, 32)           96        ['conv2d_65[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_65 (Ba  (None, 20, 20, 64)           192       ['conv2d_68[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_65 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_60[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_67 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_62[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_70 (Activation)  (None, 20, 20, 64)           0         ['batch_normalization_65[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," block35_9_mixed (Concatena  (None, 20, 20, 128)          0         ['activation_65[0][0]',       \n","\n"," te)                                                                 'activation_67[0][0]',       \n","\n","                                                                     'activation_70[0][0]']       \n","\n","                                                                                                  \n","\n"," block35_9_conv (Conv2D)     (None, 20, 20, 320)          41280     ['block35_9_mixed[0][0]']     \n","\n","                                                                                                  \n","\n"," custom_scale_layer_8 (Cust  (None, 20, 20, 320)          0         ['block35_8_ac[0][0]',        \n","\n"," omScaleLayer)                                                       'block35_9_conv[0][0]']      \n","\n","                                                                                                  \n","\n"," block35_9_ac (Activation)   (None, 20, 20, 320)          0         ['custom_scale_layer_8[0][0]']\n","\n","                                                                                                  \n","\n"," conv2d_72 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_9_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," batch_normalization_69 (Ba  (None, 20, 20, 32)           96        ['conv2d_72[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_74 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_69[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_70 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_9_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_73 (Conv2D)          (None, 20, 20, 48)           13824     ['activation_74[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_67 (Ba  (None, 20, 20, 32)           96        ['conv2d_70[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_70 (Ba  (None, 20, 20, 48)           144       ['conv2d_73[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_72 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_67[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_75 (Activation)  (None, 20, 20, 48)           0         ['batch_normalization_70[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_69 (Conv2D)          (None, 20, 20, 32)           10240     ['block35_9_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_71 (Conv2D)          (None, 20, 20, 32)           9216      ['activation_72[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_74 (Conv2D)          (None, 20, 20, 64)           27648     ['activation_75[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_66 (Ba  (None, 20, 20, 32)           96        ['conv2d_69[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_68 (Ba  (None, 20, 20, 32)           96        ['conv2d_71[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_71 (Ba  (None, 20, 20, 64)           192       ['conv2d_74[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_71 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_66[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_73 (Activation)  (None, 20, 20, 32)           0         ['batch_normalization_68[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_76 (Activation)  (None, 20, 20, 64)           0         ['batch_normalization_71[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," block35_10_mixed (Concaten  (None, 20, 20, 128)          0         ['activation_71[0][0]',       \n","\n"," ate)                                                                'activation_73[0][0]',       \n","\n","                                                                     'activation_76[0][0]']       \n","\n","                                                                                                  \n","\n"," block35_10_conv (Conv2D)    (None, 20, 20, 320)          41280     ['block35_10_mixed[0][0]']    \n","\n","                                                                                                  \n","\n"," custom_scale_layer_9 (Cust  (None, 20, 20, 320)          0         ['block35_9_ac[0][0]',        \n","\n"," omScaleLayer)                                                       'block35_10_conv[0][0]']     \n","\n","                                                                                                  \n","\n"," block35_10_ac (Activation)  (None, 20, 20, 320)          0         ['custom_scale_layer_9[0][0]']\n","\n","                                                                                                  \n","\n"," conv2d_76 (Conv2D)          (None, 20, 20, 256)          81920     ['block35_10_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_73 (Ba  (None, 20, 20, 256)          768       ['conv2d_76[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_78 (Activation)  (None, 20, 20, 256)          0         ['batch_normalization_73[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_77 (Conv2D)          (None, 20, 20, 256)          589824    ['activation_78[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_74 (Ba  (None, 20, 20, 256)          768       ['conv2d_77[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_79 (Activation)  (None, 20, 20, 256)          0         ['batch_normalization_74[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_75 (Conv2D)          (None, 9, 9, 384)            1105920   ['block35_10_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_78 (Conv2D)          (None, 9, 9, 384)            884736    ['activation_79[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_72 (Ba  (None, 9, 9, 384)            1152      ['conv2d_75[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_75 (Ba  (None, 9, 9, 384)            1152      ['conv2d_78[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_77 (Activation)  (None, 9, 9, 384)            0         ['batch_normalization_72[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_80 (Activation)  (None, 9, 9, 384)            0         ['batch_normalization_75[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," max_pooling2d_5 (MaxPoolin  (None, 9, 9, 320)            0         ['block35_10_ac[0][0]']       \n","\n"," g2D)                                                                                             \n","\n","                                                                                                  \n","\n"," mixed_6a (Concatenate)      (None, 9, 9, 1088)           0         ['activation_77[0][0]',       \n","\n","                                                                     'activation_80[0][0]',       \n","\n","                                                                     'max_pooling2d_5[0][0]']     \n","\n","                                                                                                  \n","\n"," conv2d_80 (Conv2D)          (None, 9, 9, 128)            139264    ['mixed_6a[0][0]']            \n","\n","                                                                                                  \n","\n"," batch_normalization_77 (Ba  (None, 9, 9, 128)            384       ['conv2d_80[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_82 (Activation)  (None, 9, 9, 128)            0         ['batch_normalization_77[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_81 (Conv2D)          (None, 9, 9, 160)            143360    ['activation_82[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_78 (Ba  (None, 9, 9, 160)            480       ['conv2d_81[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_83 (Activation)  (None, 9, 9, 160)            0         ['batch_normalization_78[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_79 (Conv2D)          (None, 9, 9, 192)            208896    ['mixed_6a[0][0]']            \n","\n","                                                                                                  \n","\n"," conv2d_82 (Conv2D)          (None, 9, 9, 192)            215040    ['activation_83[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_76 (Ba  (None, 9, 9, 192)            576       ['conv2d_79[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_79 (Ba  (None, 9, 9, 192)            576       ['conv2d_82[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_81 (Activation)  (None, 9, 9, 192)            0         ['batch_normalization_76[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_84 (Activation)  (None, 9, 9, 192)            0         ['batch_normalization_79[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," block17_1_mixed (Concatena  (None, 9, 9, 384)            0         ['activation_81[0][0]',       \n","\n"," te)                                                                 'activation_84[0][0]']       \n","\n","                                                                                                  \n","\n"," block17_1_conv (Conv2D)     (None, 9, 9, 1088)           418880    ['block17_1_mixed[0][0]']     \n","\n","                                                                                                  \n","\n"," custom_scale_layer_10 (Cus  (None, 9, 9, 1088)           0         ['mixed_6a[0][0]',            \n","\n"," tomScaleLayer)                                                      'block17_1_conv[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_1_ac (Activation)   (None, 9, 9, 1088)           0         ['custom_scale_layer_10[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_84 (Conv2D)          (None, 9, 9, 128)            139264    ['block17_1_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," batch_normalization_81 (Ba  (None, 9, 9, 128)            384       ['conv2d_84[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_86 (Activation)  (None, 9, 9, 128)            0         ['batch_normalization_81[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_85 (Conv2D)          (None, 9, 9, 160)            143360    ['activation_86[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_82 (Ba  (None, 9, 9, 160)            480       ['conv2d_85[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_87 (Activation)  (None, 9, 9, 160)            0         ['batch_normalization_82[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_83 (Conv2D)          (None, 9, 9, 192)            208896    ['block17_1_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_86 (Conv2D)          (None, 9, 9, 192)            215040    ['activation_87[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_80 (Ba  (None, 9, 9, 192)            576       ['conv2d_83[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_83 (Ba  (None, 9, 9, 192)            576       ['conv2d_86[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_85 (Activation)  (None, 9, 9, 192)            0         ['batch_normalization_80[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_88 (Activation)  (None, 9, 9, 192)            0         ['batch_normalization_83[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," block17_2_mixed (Concatena  (None, 9, 9, 384)            0         ['activation_85[0][0]',       \n","\n"," te)                                                                 'activation_88[0][0]']       \n","\n","                                                                                                  \n","\n"," block17_2_conv (Conv2D)     (None, 9, 9, 1088)           418880    ['block17_2_mixed[0][0]']     \n","\n","                                                                                                  \n","\n"," custom_scale_layer_11 (Cus  (None, 9, 9, 1088)           0         ['block17_1_ac[0][0]',        \n","\n"," tomScaleLayer)                                                      'block17_2_conv[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_2_ac (Activation)   (None, 9, 9, 1088)           0         ['custom_scale_layer_11[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_88 (Conv2D)          (None, 9, 9, 128)            139264    ['block17_2_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," batch_normalization_85 (Ba  (None, 9, 9, 128)            384       ['conv2d_88[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_90 (Activation)  (None, 9, 9, 128)            0         ['batch_normalization_85[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_89 (Conv2D)          (None, 9, 9, 160)            143360    ['activation_90[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_86 (Ba  (None, 9, 9, 160)            480       ['conv2d_89[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_91 (Activation)  (None, 9, 9, 160)            0         ['batch_normalization_86[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_87 (Conv2D)          (None, 9, 9, 192)            208896    ['block17_2_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_90 (Conv2D)          (None, 9, 9, 192)            215040    ['activation_91[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_84 (Ba  (None, 9, 9, 192)            576       ['conv2d_87[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_87 (Ba  (None, 9, 9, 192)            576       ['conv2d_90[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_89 (Activation)  (None, 9, 9, 192)            0         ['batch_normalization_84[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_92 (Activation)  (None, 9, 9, 192)            0         ['batch_normalization_87[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," block17_3_mixed (Concatena  (None, 9, 9, 384)            0         ['activation_89[0][0]',       \n","\n"," te)                                                                 'activation_92[0][0]']       \n","\n","                                                                                                  \n","\n"," block17_3_conv (Conv2D)     (None, 9, 9, 1088)           418880    ['block17_3_mixed[0][0]']     \n","\n","                                                                                                  \n","\n"," custom_scale_layer_12 (Cus  (None, 9, 9, 1088)           0         ['block17_2_ac[0][0]',        \n","\n"," tomScaleLayer)                                                      'block17_3_conv[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_3_ac (Activation)   (None, 9, 9, 1088)           0         ['custom_scale_layer_12[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_92 (Conv2D)          (None, 9, 9, 128)            139264    ['block17_3_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," batch_normalization_89 (Ba  (None, 9, 9, 128)            384       ['conv2d_92[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_94 (Activation)  (None, 9, 9, 128)            0         ['batch_normalization_89[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_93 (Conv2D)          (None, 9, 9, 160)            143360    ['activation_94[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_90 (Ba  (None, 9, 9, 160)            480       ['conv2d_93[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_95 (Activation)  (None, 9, 9, 160)            0         ['batch_normalization_90[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_91 (Conv2D)          (None, 9, 9, 192)            208896    ['block17_3_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_94 (Conv2D)          (None, 9, 9, 192)            215040    ['activation_95[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_88 (Ba  (None, 9, 9, 192)            576       ['conv2d_91[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_91 (Ba  (None, 9, 9, 192)            576       ['conv2d_94[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_93 (Activation)  (None, 9, 9, 192)            0         ['batch_normalization_88[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_96 (Activation)  (None, 9, 9, 192)            0         ['batch_normalization_91[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," block17_4_mixed (Concatena  (None, 9, 9, 384)            0         ['activation_93[0][0]',       \n","\n"," te)                                                                 'activation_96[0][0]']       \n","\n","                                                                                                  \n","\n"," block17_4_conv (Conv2D)     (None, 9, 9, 1088)           418880    ['block17_4_mixed[0][0]']     \n","\n","                                                                                                  \n","\n"," custom_scale_layer_13 (Cus  (None, 9, 9, 1088)           0         ['block17_3_ac[0][0]',        \n","\n"," tomScaleLayer)                                                      'block17_4_conv[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_4_ac (Activation)   (None, 9, 9, 1088)           0         ['custom_scale_layer_13[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_96 (Conv2D)          (None, 9, 9, 128)            139264    ['block17_4_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," batch_normalization_93 (Ba  (None, 9, 9, 128)            384       ['conv2d_96[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_98 (Activation)  (None, 9, 9, 128)            0         ['batch_normalization_93[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_97 (Conv2D)          (None, 9, 9, 160)            143360    ['activation_98[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_94 (Ba  (None, 9, 9, 160)            480       ['conv2d_97[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_99 (Activation)  (None, 9, 9, 160)            0         ['batch_normalization_94[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," conv2d_95 (Conv2D)          (None, 9, 9, 192)            208896    ['block17_4_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_98 (Conv2D)          (None, 9, 9, 192)            215040    ['activation_99[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_92 (Ba  (None, 9, 9, 192)            576       ['conv2d_95[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_95 (Ba  (None, 9, 9, 192)            576       ['conv2d_98[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_97 (Activation)  (None, 9, 9, 192)            0         ['batch_normalization_92[0][0]\n","\n","                                                                    ']                            \n","\n","                                                                                                  \n","\n"," activation_100 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_95[0][0]\n","\n"," )                                                                  ']                            \n","\n","                                                                                                  \n","\n"," block17_5_mixed (Concatena  (None, 9, 9, 384)            0         ['activation_97[0][0]',       \n","\n"," te)                                                                 'activation_100[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_5_conv (Conv2D)     (None, 9, 9, 1088)           418880    ['block17_5_mixed[0][0]']     \n","\n","                                                                                                  \n","\n"," custom_scale_layer_14 (Cus  (None, 9, 9, 1088)           0         ['block17_4_ac[0][0]',        \n","\n"," tomScaleLayer)                                                      'block17_5_conv[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_5_ac (Activation)   (None, 9, 9, 1088)           0         ['custom_scale_layer_14[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_100 (Conv2D)         (None, 9, 9, 128)            139264    ['block17_5_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," batch_normalization_97 (Ba  (None, 9, 9, 128)            384       ['conv2d_100[0][0]']          \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_102 (Activation  (None, 9, 9, 128)            0         ['batch_normalization_97[0][0]\n","\n"," )                                                                  ']                            \n","\n","                                                                                                  \n","\n"," conv2d_101 (Conv2D)         (None, 9, 9, 160)            143360    ['activation_102[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_98 (Ba  (None, 9, 9, 160)            480       ['conv2d_101[0][0]']          \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_103 (Activation  (None, 9, 9, 160)            0         ['batch_normalization_98[0][0]\n","\n"," )                                                                  ']                            \n","\n","                                                                                                  \n","\n"," conv2d_99 (Conv2D)          (None, 9, 9, 192)            208896    ['block17_5_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_102 (Conv2D)         (None, 9, 9, 192)            215040    ['activation_103[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_96 (Ba  (None, 9, 9, 192)            576       ['conv2d_99[0][0]']           \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_99 (Ba  (None, 9, 9, 192)            576       ['conv2d_102[0][0]']          \n","\n"," tchNormalization)                                                                                \n","\n","                                                                                                  \n","\n"," activation_101 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_96[0][0]\n","\n"," )                                                                  ']                            \n","\n","                                                                                                  \n","\n"," activation_104 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_99[0][0]\n","\n"," )                                                                  ']                            \n","\n","                                                                                                  \n","\n"," block17_6_mixed (Concatena  (None, 9, 9, 384)            0         ['activation_101[0][0]',      \n","\n"," te)                                                                 'activation_104[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_6_conv (Conv2D)     (None, 9, 9, 1088)           418880    ['block17_6_mixed[0][0]']     \n","\n","                                                                                                  \n","\n"," custom_scale_layer_15 (Cus  (None, 9, 9, 1088)           0         ['block17_5_ac[0][0]',        \n","\n"," tomScaleLayer)                                                      'block17_6_conv[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_6_ac (Activation)   (None, 9, 9, 1088)           0         ['custom_scale_layer_15[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_104 (Conv2D)         (None, 9, 9, 128)            139264    ['block17_6_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," batch_normalization_101 (B  (None, 9, 9, 128)            384       ['conv2d_104[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_106 (Activation  (None, 9, 9, 128)            0         ['batch_normalization_101[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_105 (Conv2D)         (None, 9, 9, 160)            143360    ['activation_106[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_102 (B  (None, 9, 9, 160)            480       ['conv2d_105[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_107 (Activation  (None, 9, 9, 160)            0         ['batch_normalization_102[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_103 (Conv2D)         (None, 9, 9, 192)            208896    ['block17_6_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_106 (Conv2D)         (None, 9, 9, 192)            215040    ['activation_107[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_100 (B  (None, 9, 9, 192)            576       ['conv2d_103[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_103 (B  (None, 9, 9, 192)            576       ['conv2d_106[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_105 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_100[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_108 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_103[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," block17_7_mixed (Concatena  (None, 9, 9, 384)            0         ['activation_105[0][0]',      \n","\n"," te)                                                                 'activation_108[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_7_conv (Conv2D)     (None, 9, 9, 1088)           418880    ['block17_7_mixed[0][0]']     \n","\n","                                                                                                  \n","\n"," custom_scale_layer_16 (Cus  (None, 9, 9, 1088)           0         ['block17_6_ac[0][0]',        \n","\n"," tomScaleLayer)                                                      'block17_7_conv[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_7_ac (Activation)   (None, 9, 9, 1088)           0         ['custom_scale_layer_16[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_108 (Conv2D)         (None, 9, 9, 128)            139264    ['block17_7_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," batch_normalization_105 (B  (None, 9, 9, 128)            384       ['conv2d_108[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_110 (Activation  (None, 9, 9, 128)            0         ['batch_normalization_105[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_109 (Conv2D)         (None, 9, 9, 160)            143360    ['activation_110[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_106 (B  (None, 9, 9, 160)            480       ['conv2d_109[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_111 (Activation  (None, 9, 9, 160)            0         ['batch_normalization_106[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_107 (Conv2D)         (None, 9, 9, 192)            208896    ['block17_7_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_110 (Conv2D)         (None, 9, 9, 192)            215040    ['activation_111[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_104 (B  (None, 9, 9, 192)            576       ['conv2d_107[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_107 (B  (None, 9, 9, 192)            576       ['conv2d_110[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_109 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_104[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_112 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_107[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," block17_8_mixed (Concatena  (None, 9, 9, 384)            0         ['activation_109[0][0]',      \n","\n"," te)                                                                 'activation_112[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_8_conv (Conv2D)     (None, 9, 9, 1088)           418880    ['block17_8_mixed[0][0]']     \n","\n","                                                                                                  \n","\n"," custom_scale_layer_17 (Cus  (None, 9, 9, 1088)           0         ['block17_7_ac[0][0]',        \n","\n"," tomScaleLayer)                                                      'block17_8_conv[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_8_ac (Activation)   (None, 9, 9, 1088)           0         ['custom_scale_layer_17[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_112 (Conv2D)         (None, 9, 9, 128)            139264    ['block17_8_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," batch_normalization_109 (B  (None, 9, 9, 128)            384       ['conv2d_112[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_114 (Activation  (None, 9, 9, 128)            0         ['batch_normalization_109[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_113 (Conv2D)         (None, 9, 9, 160)            143360    ['activation_114[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_110 (B  (None, 9, 9, 160)            480       ['conv2d_113[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_115 (Activation  (None, 9, 9, 160)            0         ['batch_normalization_110[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_111 (Conv2D)         (None, 9, 9, 192)            208896    ['block17_8_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_114 (Conv2D)         (None, 9, 9, 192)            215040    ['activation_115[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_108 (B  (None, 9, 9, 192)            576       ['conv2d_111[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_111 (B  (None, 9, 9, 192)            576       ['conv2d_114[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_113 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_108[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_116 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_111[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," block17_9_mixed (Concatena  (None, 9, 9, 384)            0         ['activation_113[0][0]',      \n","\n"," te)                                                                 'activation_116[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_9_conv (Conv2D)     (None, 9, 9, 1088)           418880    ['block17_9_mixed[0][0]']     \n","\n","                                                                                                  \n","\n"," custom_scale_layer_18 (Cus  (None, 9, 9, 1088)           0         ['block17_8_ac[0][0]',        \n","\n"," tomScaleLayer)                                                      'block17_9_conv[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_9_ac (Activation)   (None, 9, 9, 1088)           0         ['custom_scale_layer_18[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_116 (Conv2D)         (None, 9, 9, 128)            139264    ['block17_9_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," batch_normalization_113 (B  (None, 9, 9, 128)            384       ['conv2d_116[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_118 (Activation  (None, 9, 9, 128)            0         ['batch_normalization_113[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_117 (Conv2D)         (None, 9, 9, 160)            143360    ['activation_118[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_114 (B  (None, 9, 9, 160)            480       ['conv2d_117[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_119 (Activation  (None, 9, 9, 160)            0         ['batch_normalization_114[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_115 (Conv2D)         (None, 9, 9, 192)            208896    ['block17_9_ac[0][0]']        \n","\n","                                                                                                  \n","\n"," conv2d_118 (Conv2D)         (None, 9, 9, 192)            215040    ['activation_119[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_112 (B  (None, 9, 9, 192)            576       ['conv2d_115[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_115 (B  (None, 9, 9, 192)            576       ['conv2d_118[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_117 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_112[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_120 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_115[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," block17_10_mixed (Concaten  (None, 9, 9, 384)            0         ['activation_117[0][0]',      \n","\n"," ate)                                                                'activation_120[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_10_conv (Conv2D)    (None, 9, 9, 1088)           418880    ['block17_10_mixed[0][0]']    \n","\n","                                                                                                  \n","\n"," custom_scale_layer_19 (Cus  (None, 9, 9, 1088)           0         ['block17_9_ac[0][0]',        \n","\n"," tomScaleLayer)                                                      'block17_10_conv[0][0]']     \n","\n","                                                                                                  \n","\n"," block17_10_ac (Activation)  (None, 9, 9, 1088)           0         ['custom_scale_layer_19[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_120 (Conv2D)         (None, 9, 9, 128)            139264    ['block17_10_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_117 (B  (None, 9, 9, 128)            384       ['conv2d_120[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_122 (Activation  (None, 9, 9, 128)            0         ['batch_normalization_117[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_121 (Conv2D)         (None, 9, 9, 160)            143360    ['activation_122[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_118 (B  (None, 9, 9, 160)            480       ['conv2d_121[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_123 (Activation  (None, 9, 9, 160)            0         ['batch_normalization_118[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_119 (Conv2D)         (None, 9, 9, 192)            208896    ['block17_10_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_122 (Conv2D)         (None, 9, 9, 192)            215040    ['activation_123[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_116 (B  (None, 9, 9, 192)            576       ['conv2d_119[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_119 (B  (None, 9, 9, 192)            576       ['conv2d_122[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_121 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_116[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_124 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_119[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," block17_11_mixed (Concaten  (None, 9, 9, 384)            0         ['activation_121[0][0]',      \n","\n"," ate)                                                                'activation_124[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_11_conv (Conv2D)    (None, 9, 9, 1088)           418880    ['block17_11_mixed[0][0]']    \n","\n","                                                                                                  \n","\n"," custom_scale_layer_20 (Cus  (None, 9, 9, 1088)           0         ['block17_10_ac[0][0]',       \n","\n"," tomScaleLayer)                                                      'block17_11_conv[0][0]']     \n","\n","                                                                                                  \n","\n"," block17_11_ac (Activation)  (None, 9, 9, 1088)           0         ['custom_scale_layer_20[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_124 (Conv2D)         (None, 9, 9, 128)            139264    ['block17_11_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_121 (B  (None, 9, 9, 128)            384       ['conv2d_124[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_126 (Activation  (None, 9, 9, 128)            0         ['batch_normalization_121[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_125 (Conv2D)         (None, 9, 9, 160)            143360    ['activation_126[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_122 (B  (None, 9, 9, 160)            480       ['conv2d_125[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_127 (Activation  (None, 9, 9, 160)            0         ['batch_normalization_122[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_123 (Conv2D)         (None, 9, 9, 192)            208896    ['block17_11_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_126 (Conv2D)         (None, 9, 9, 192)            215040    ['activation_127[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_120 (B  (None, 9, 9, 192)            576       ['conv2d_123[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_123 (B  (None, 9, 9, 192)            576       ['conv2d_126[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_125 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_120[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_128 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_123[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," block17_12_mixed (Concaten  (None, 9, 9, 384)            0         ['activation_125[0][0]',      \n","\n"," ate)                                                                'activation_128[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_12_conv (Conv2D)    (None, 9, 9, 1088)           418880    ['block17_12_mixed[0][0]']    \n","\n","                                                                                                  \n","\n"," custom_scale_layer_21 (Cus  (None, 9, 9, 1088)           0         ['block17_11_ac[0][0]',       \n","\n"," tomScaleLayer)                                                      'block17_12_conv[0][0]']     \n","\n","                                                                                                  \n","\n"," block17_12_ac (Activation)  (None, 9, 9, 1088)           0         ['custom_scale_layer_21[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_128 (Conv2D)         (None, 9, 9, 128)            139264    ['block17_12_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_125 (B  (None, 9, 9, 128)            384       ['conv2d_128[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_130 (Activation  (None, 9, 9, 128)            0         ['batch_normalization_125[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_129 (Conv2D)         (None, 9, 9, 160)            143360    ['activation_130[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_126 (B  (None, 9, 9, 160)            480       ['conv2d_129[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_131 (Activation  (None, 9, 9, 160)            0         ['batch_normalization_126[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_127 (Conv2D)         (None, 9, 9, 192)            208896    ['block17_12_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_130 (Conv2D)         (None, 9, 9, 192)            215040    ['activation_131[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_124 (B  (None, 9, 9, 192)            576       ['conv2d_127[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_127 (B  (None, 9, 9, 192)            576       ['conv2d_130[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_129 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_124[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_132 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_127[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," block17_13_mixed (Concaten  (None, 9, 9, 384)            0         ['activation_129[0][0]',      \n","\n"," ate)                                                                'activation_132[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_13_conv (Conv2D)    (None, 9, 9, 1088)           418880    ['block17_13_mixed[0][0]']    \n","\n","                                                                                                  \n","\n"," custom_scale_layer_22 (Cus  (None, 9, 9, 1088)           0         ['block17_12_ac[0][0]',       \n","\n"," tomScaleLayer)                                                      'block17_13_conv[0][0]']     \n","\n","                                                                                                  \n","\n"," block17_13_ac (Activation)  (None, 9, 9, 1088)           0         ['custom_scale_layer_22[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_132 (Conv2D)         (None, 9, 9, 128)            139264    ['block17_13_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_129 (B  (None, 9, 9, 128)            384       ['conv2d_132[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_134 (Activation  (None, 9, 9, 128)            0         ['batch_normalization_129[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_133 (Conv2D)         (None, 9, 9, 160)            143360    ['activation_134[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_130 (B  (None, 9, 9, 160)            480       ['conv2d_133[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_135 (Activation  (None, 9, 9, 160)            0         ['batch_normalization_130[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_131 (Conv2D)         (None, 9, 9, 192)            208896    ['block17_13_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_134 (Conv2D)         (None, 9, 9, 192)            215040    ['activation_135[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_128 (B  (None, 9, 9, 192)            576       ['conv2d_131[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_131 (B  (None, 9, 9, 192)            576       ['conv2d_134[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_133 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_128[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_136 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_131[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," block17_14_mixed (Concaten  (None, 9, 9, 384)            0         ['activation_133[0][0]',      \n","\n"," ate)                                                                'activation_136[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_14_conv (Conv2D)    (None, 9, 9, 1088)           418880    ['block17_14_mixed[0][0]']    \n","\n","                                                                                                  \n","\n"," custom_scale_layer_23 (Cus  (None, 9, 9, 1088)           0         ['block17_13_ac[0][0]',       \n","\n"," tomScaleLayer)                                                      'block17_14_conv[0][0]']     \n","\n","                                                                                                  \n","\n"," block17_14_ac (Activation)  (None, 9, 9, 1088)           0         ['custom_scale_layer_23[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_136 (Conv2D)         (None, 9, 9, 128)            139264    ['block17_14_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_133 (B  (None, 9, 9, 128)            384       ['conv2d_136[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_138 (Activation  (None, 9, 9, 128)            0         ['batch_normalization_133[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_137 (Conv2D)         (None, 9, 9, 160)            143360    ['activation_138[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_134 (B  (None, 9, 9, 160)            480       ['conv2d_137[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_139 (Activation  (None, 9, 9, 160)            0         ['batch_normalization_134[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_135 (Conv2D)         (None, 9, 9, 192)            208896    ['block17_14_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_138 (Conv2D)         (None, 9, 9, 192)            215040    ['activation_139[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_132 (B  (None, 9, 9, 192)            576       ['conv2d_135[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_135 (B  (None, 9, 9, 192)            576       ['conv2d_138[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_137 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_132[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_140 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_135[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," block17_15_mixed (Concaten  (None, 9, 9, 384)            0         ['activation_137[0][0]',      \n","\n"," ate)                                                                'activation_140[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_15_conv (Conv2D)    (None, 9, 9, 1088)           418880    ['block17_15_mixed[0][0]']    \n","\n","                                                                                                  \n","\n"," custom_scale_layer_24 (Cus  (None, 9, 9, 1088)           0         ['block17_14_ac[0][0]',       \n","\n"," tomScaleLayer)                                                      'block17_15_conv[0][0]']     \n","\n","                                                                                                  \n","\n"," block17_15_ac (Activation)  (None, 9, 9, 1088)           0         ['custom_scale_layer_24[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_140 (Conv2D)         (None, 9, 9, 128)            139264    ['block17_15_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_137 (B  (None, 9, 9, 128)            384       ['conv2d_140[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_142 (Activation  (None, 9, 9, 128)            0         ['batch_normalization_137[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_141 (Conv2D)         (None, 9, 9, 160)            143360    ['activation_142[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_138 (B  (None, 9, 9, 160)            480       ['conv2d_141[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_143 (Activation  (None, 9, 9, 160)            0         ['batch_normalization_138[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_139 (Conv2D)         (None, 9, 9, 192)            208896    ['block17_15_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_142 (Conv2D)         (None, 9, 9, 192)            215040    ['activation_143[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_136 (B  (None, 9, 9, 192)            576       ['conv2d_139[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_139 (B  (None, 9, 9, 192)            576       ['conv2d_142[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_141 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_136[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_144 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_139[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," block17_16_mixed (Concaten  (None, 9, 9, 384)            0         ['activation_141[0][0]',      \n","\n"," ate)                                                                'activation_144[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_16_conv (Conv2D)    (None, 9, 9, 1088)           418880    ['block17_16_mixed[0][0]']    \n","\n","                                                                                                  \n","\n"," custom_scale_layer_25 (Cus  (None, 9, 9, 1088)           0         ['block17_15_ac[0][0]',       \n","\n"," tomScaleLayer)                                                      'block17_16_conv[0][0]']     \n","\n","                                                                                                  \n","\n"," block17_16_ac (Activation)  (None, 9, 9, 1088)           0         ['custom_scale_layer_25[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_144 (Conv2D)         (None, 9, 9, 128)            139264    ['block17_16_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_141 (B  (None, 9, 9, 128)            384       ['conv2d_144[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_146 (Activation  (None, 9, 9, 128)            0         ['batch_normalization_141[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_145 (Conv2D)         (None, 9, 9, 160)            143360    ['activation_146[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_142 (B  (None, 9, 9, 160)            480       ['conv2d_145[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_147 (Activation  (None, 9, 9, 160)            0         ['batch_normalization_142[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_143 (Conv2D)         (None, 9, 9, 192)            208896    ['block17_16_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_146 (Conv2D)         (None, 9, 9, 192)            215040    ['activation_147[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_140 (B  (None, 9, 9, 192)            576       ['conv2d_143[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_143 (B  (None, 9, 9, 192)            576       ['conv2d_146[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_145 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_140[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_148 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_143[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," block17_17_mixed (Concaten  (None, 9, 9, 384)            0         ['activation_145[0][0]',      \n","\n"," ate)                                                                'activation_148[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_17_conv (Conv2D)    (None, 9, 9, 1088)           418880    ['block17_17_mixed[0][0]']    \n","\n","                                                                                                  \n","\n"," custom_scale_layer_26 (Cus  (None, 9, 9, 1088)           0         ['block17_16_ac[0][0]',       \n","\n"," tomScaleLayer)                                                      'block17_17_conv[0][0]']     \n","\n","                                                                                                  \n","\n"," block17_17_ac (Activation)  (None, 9, 9, 1088)           0         ['custom_scale_layer_26[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_148 (Conv2D)         (None, 9, 9, 128)            139264    ['block17_17_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_145 (B  (None, 9, 9, 128)            384       ['conv2d_148[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_150 (Activation  (None, 9, 9, 128)            0         ['batch_normalization_145[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_149 (Conv2D)         (None, 9, 9, 160)            143360    ['activation_150[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_146 (B  (None, 9, 9, 160)            480       ['conv2d_149[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_151 (Activation  (None, 9, 9, 160)            0         ['batch_normalization_146[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_147 (Conv2D)         (None, 9, 9, 192)            208896    ['block17_17_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_150 (Conv2D)         (None, 9, 9, 192)            215040    ['activation_151[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_144 (B  (None, 9, 9, 192)            576       ['conv2d_147[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_147 (B  (None, 9, 9, 192)            576       ['conv2d_150[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_149 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_144[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_152 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_147[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," block17_18_mixed (Concaten  (None, 9, 9, 384)            0         ['activation_149[0][0]',      \n","\n"," ate)                                                                'activation_152[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_18_conv (Conv2D)    (None, 9, 9, 1088)           418880    ['block17_18_mixed[0][0]']    \n","\n","                                                                                                  \n","\n"," custom_scale_layer_27 (Cus  (None, 9, 9, 1088)           0         ['block17_17_ac[0][0]',       \n","\n"," tomScaleLayer)                                                      'block17_18_conv[0][0]']     \n","\n","                                                                                                  \n","\n"," block17_18_ac (Activation)  (None, 9, 9, 1088)           0         ['custom_scale_layer_27[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_152 (Conv2D)         (None, 9, 9, 128)            139264    ['block17_18_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_149 (B  (None, 9, 9, 128)            384       ['conv2d_152[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_154 (Activation  (None, 9, 9, 128)            0         ['batch_normalization_149[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_153 (Conv2D)         (None, 9, 9, 160)            143360    ['activation_154[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_150 (B  (None, 9, 9, 160)            480       ['conv2d_153[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_155 (Activation  (None, 9, 9, 160)            0         ['batch_normalization_150[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_151 (Conv2D)         (None, 9, 9, 192)            208896    ['block17_18_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_154 (Conv2D)         (None, 9, 9, 192)            215040    ['activation_155[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_148 (B  (None, 9, 9, 192)            576       ['conv2d_151[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_151 (B  (None, 9, 9, 192)            576       ['conv2d_154[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_153 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_148[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_156 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_151[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," block17_19_mixed (Concaten  (None, 9, 9, 384)            0         ['activation_153[0][0]',      \n","\n"," ate)                                                                'activation_156[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_19_conv (Conv2D)    (None, 9, 9, 1088)           418880    ['block17_19_mixed[0][0]']    \n","\n","                                                                                                  \n","\n"," custom_scale_layer_28 (Cus  (None, 9, 9, 1088)           0         ['block17_18_ac[0][0]',       \n","\n"," tomScaleLayer)                                                      'block17_19_conv[0][0]']     \n","\n","                                                                                                  \n","\n"," block17_19_ac (Activation)  (None, 9, 9, 1088)           0         ['custom_scale_layer_28[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_156 (Conv2D)         (None, 9, 9, 128)            139264    ['block17_19_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_153 (B  (None, 9, 9, 128)            384       ['conv2d_156[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_158 (Activation  (None, 9, 9, 128)            0         ['batch_normalization_153[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_157 (Conv2D)         (None, 9, 9, 160)            143360    ['activation_158[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_154 (B  (None, 9, 9, 160)            480       ['conv2d_157[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_159 (Activation  (None, 9, 9, 160)            0         ['batch_normalization_154[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_155 (Conv2D)         (None, 9, 9, 192)            208896    ['block17_19_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_158 (Conv2D)         (None, 9, 9, 192)            215040    ['activation_159[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_152 (B  (None, 9, 9, 192)            576       ['conv2d_155[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_155 (B  (None, 9, 9, 192)            576       ['conv2d_158[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_157 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_152[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_160 (Activation  (None, 9, 9, 192)            0         ['batch_normalization_155[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," block17_20_mixed (Concaten  (None, 9, 9, 384)            0         ['activation_157[0][0]',      \n","\n"," ate)                                                                'activation_160[0][0]']      \n","\n","                                                                                                  \n","\n"," block17_20_conv (Conv2D)    (None, 9, 9, 1088)           418880    ['block17_20_mixed[0][0]']    \n","\n","                                                                                                  \n","\n"," custom_scale_layer_29 (Cus  (None, 9, 9, 1088)           0         ['block17_19_ac[0][0]',       \n","\n"," tomScaleLayer)                                                      'block17_20_conv[0][0]']     \n","\n","                                                                                                  \n","\n"," block17_20_ac (Activation)  (None, 9, 9, 1088)           0         ['custom_scale_layer_29[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_163 (Conv2D)         (None, 9, 9, 256)            278528    ['block17_20_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," batch_normalization_160 (B  (None, 9, 9, 256)            768       ['conv2d_163[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_165 (Activation  (None, 9, 9, 256)            0         ['batch_normalization_160[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_159 (Conv2D)         (None, 9, 9, 256)            278528    ['block17_20_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_161 (Conv2D)         (None, 9, 9, 256)            278528    ['block17_20_ac[0][0]']       \n","\n","                                                                                                  \n","\n"," conv2d_164 (Conv2D)         (None, 9, 9, 288)            663552    ['activation_165[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_156 (B  (None, 9, 9, 256)            768       ['conv2d_159[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_158 (B  (None, 9, 9, 256)            768       ['conv2d_161[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_161 (B  (None, 9, 9, 288)            864       ['conv2d_164[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_161 (Activation  (None, 9, 9, 256)            0         ['batch_normalization_156[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_163 (Activation  (None, 9, 9, 256)            0         ['batch_normalization_158[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_166 (Activation  (None, 9, 9, 288)            0         ['batch_normalization_161[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_160 (Conv2D)         (None, 4, 4, 384)            884736    ['activation_161[0][0]']      \n","\n","                                                                                                  \n","\n"," conv2d_162 (Conv2D)         (None, 4, 4, 288)            663552    ['activation_163[0][0]']      \n","\n","                                                                                                  \n","\n"," conv2d_165 (Conv2D)         (None, 4, 4, 320)            829440    ['activation_166[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_157 (B  (None, 4, 4, 384)            1152      ['conv2d_160[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_159 (B  (None, 4, 4, 288)            864       ['conv2d_162[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_162 (B  (None, 4, 4, 320)            960       ['conv2d_165[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_162 (Activation  (None, 4, 4, 384)            0         ['batch_normalization_157[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_164 (Activation  (None, 4, 4, 288)            0         ['batch_normalization_159[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_167 (Activation  (None, 4, 4, 320)            0         ['batch_normalization_162[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," max_pooling2d_6 (MaxPoolin  (None, 4, 4, 1088)           0         ['block17_20_ac[0][0]']       \n","\n"," g2D)                                                                                             \n","\n","                                                                                                  \n","\n"," mixed_7a (Concatenate)      (None, 4, 4, 2080)           0         ['activation_162[0][0]',      \n","\n","                                                                     'activation_164[0][0]',      \n","\n","                                                                     'activation_167[0][0]',      \n","\n","                                                                     'max_pooling2d_6[0][0]']     \n","\n","                                                                                                  \n","\n"," conv2d_167 (Conv2D)         (None, 4, 4, 192)            399360    ['mixed_7a[0][0]']            \n","\n","                                                                                                  \n","\n"," batch_normalization_164 (B  (None, 4, 4, 192)            576       ['conv2d_167[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_169 (Activation  (None, 4, 4, 192)            0         ['batch_normalization_164[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_168 (Conv2D)         (None, 4, 4, 224)            129024    ['activation_169[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_165 (B  (None, 4, 4, 224)            672       ['conv2d_168[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_170 (Activation  (None, 4, 4, 224)            0         ['batch_normalization_165[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_166 (Conv2D)         (None, 4, 4, 192)            399360    ['mixed_7a[0][0]']            \n","\n","                                                                                                  \n","\n"," conv2d_169 (Conv2D)         (None, 4, 4, 256)            172032    ['activation_170[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_163 (B  (None, 4, 4, 192)            576       ['conv2d_166[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_166 (B  (None, 4, 4, 256)            768       ['conv2d_169[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_168 (Activation  (None, 4, 4, 192)            0         ['batch_normalization_163[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_171 (Activation  (None, 4, 4, 256)            0         ['batch_normalization_166[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," block8_1_mixed (Concatenat  (None, 4, 4, 448)            0         ['activation_168[0][0]',      \n","\n"," e)                                                                  'activation_171[0][0]']      \n","\n","                                                                                                  \n","\n"," block8_1_conv (Conv2D)      (None, 4, 4, 2080)           933920    ['block8_1_mixed[0][0]']      \n","\n","                                                                                                  \n","\n"," custom_scale_layer_30 (Cus  (None, 4, 4, 2080)           0         ['mixed_7a[0][0]',            \n","\n"," tomScaleLayer)                                                      'block8_1_conv[0][0]']       \n","\n","                                                                                                  \n","\n"," block8_1_ac (Activation)    (None, 4, 4, 2080)           0         ['custom_scale_layer_30[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_171 (Conv2D)         (None, 4, 4, 192)            399360    ['block8_1_ac[0][0]']         \n","\n","                                                                                                  \n","\n"," batch_normalization_168 (B  (None, 4, 4, 192)            576       ['conv2d_171[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_173 (Activation  (None, 4, 4, 192)            0         ['batch_normalization_168[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_172 (Conv2D)         (None, 4, 4, 224)            129024    ['activation_173[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_169 (B  (None, 4, 4, 224)            672       ['conv2d_172[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_174 (Activation  (None, 4, 4, 224)            0         ['batch_normalization_169[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_170 (Conv2D)         (None, 4, 4, 192)            399360    ['block8_1_ac[0][0]']         \n","\n","                                                                                                  \n","\n"," conv2d_173 (Conv2D)         (None, 4, 4, 256)            172032    ['activation_174[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_167 (B  (None, 4, 4, 192)            576       ['conv2d_170[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_170 (B  (None, 4, 4, 256)            768       ['conv2d_173[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_172 (Activation  (None, 4, 4, 192)            0         ['batch_normalization_167[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_175 (Activation  (None, 4, 4, 256)            0         ['batch_normalization_170[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," block8_2_mixed (Concatenat  (None, 4, 4, 448)            0         ['activation_172[0][0]',      \n","\n"," e)                                                                  'activation_175[0][0]']      \n","\n","                                                                                                  \n","\n"," block8_2_conv (Conv2D)      (None, 4, 4, 2080)           933920    ['block8_2_mixed[0][0]']      \n","\n","                                                                                                  \n","\n"," custom_scale_layer_31 (Cus  (None, 4, 4, 2080)           0         ['block8_1_ac[0][0]',         \n","\n"," tomScaleLayer)                                                      'block8_2_conv[0][0]']       \n","\n","                                                                                                  \n","\n"," block8_2_ac (Activation)    (None, 4, 4, 2080)           0         ['custom_scale_layer_31[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_175 (Conv2D)         (None, 4, 4, 192)            399360    ['block8_2_ac[0][0]']         \n","\n","                                                                                                  \n","\n"," batch_normalization_172 (B  (None, 4, 4, 192)            576       ['conv2d_175[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_177 (Activation  (None, 4, 4, 192)            0         ['batch_normalization_172[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_176 (Conv2D)         (None, 4, 4, 224)            129024    ['activation_177[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_173 (B  (None, 4, 4, 224)            672       ['conv2d_176[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_178 (Activation  (None, 4, 4, 224)            0         ['batch_normalization_173[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_174 (Conv2D)         (None, 4, 4, 192)            399360    ['block8_2_ac[0][0]']         \n","\n","                                                                                                  \n","\n"," conv2d_177 (Conv2D)         (None, 4, 4, 256)            172032    ['activation_178[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_171 (B  (None, 4, 4, 192)            576       ['conv2d_174[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_174 (B  (None, 4, 4, 256)            768       ['conv2d_177[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_176 (Activation  (None, 4, 4, 192)            0         ['batch_normalization_171[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_179 (Activation  (None, 4, 4, 256)            0         ['batch_normalization_174[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," block8_3_mixed (Concatenat  (None, 4, 4, 448)            0         ['activation_176[0][0]',      \n","\n"," e)                                                                  'activation_179[0][0]']      \n","\n","                                                                                                  \n","\n"," block8_3_conv (Conv2D)      (None, 4, 4, 2080)           933920    ['block8_3_mixed[0][0]']      \n","\n","                                                                                                  \n","\n"," custom_scale_layer_32 (Cus  (None, 4, 4, 2080)           0         ['block8_2_ac[0][0]',         \n","\n"," tomScaleLayer)                                                      'block8_3_conv[0][0]']       \n","\n","                                                                                                  \n","\n"," block8_3_ac (Activation)    (None, 4, 4, 2080)           0         ['custom_scale_layer_32[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_179 (Conv2D)         (None, 4, 4, 192)            399360    ['block8_3_ac[0][0]']         \n","\n","                                                                                                  \n","\n"," batch_normalization_176 (B  (None, 4, 4, 192)            576       ['conv2d_179[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_181 (Activation  (None, 4, 4, 192)            0         ['batch_normalization_176[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_180 (Conv2D)         (None, 4, 4, 224)            129024    ['activation_181[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_177 (B  (None, 4, 4, 224)            672       ['conv2d_180[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_182 (Activation  (None, 4, 4, 224)            0         ['batch_normalization_177[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_178 (Conv2D)         (None, 4, 4, 192)            399360    ['block8_3_ac[0][0]']         \n","\n","                                                                                                  \n","\n"," conv2d_181 (Conv2D)         (None, 4, 4, 256)            172032    ['activation_182[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_175 (B  (None, 4, 4, 192)            576       ['conv2d_178[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_178 (B  (None, 4, 4, 256)            768       ['conv2d_181[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_180 (Activation  (None, 4, 4, 192)            0         ['batch_normalization_175[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_183 (Activation  (None, 4, 4, 256)            0         ['batch_normalization_178[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," block8_4_mixed (Concatenat  (None, 4, 4, 448)            0         ['activation_180[0][0]',      \n","\n"," e)                                                                  'activation_183[0][0]']      \n","\n","                                                                                                  \n","\n"," block8_4_conv (Conv2D)      (None, 4, 4, 2080)           933920    ['block8_4_mixed[0][0]']      \n","\n","                                                                                                  \n","\n"," custom_scale_layer_33 (Cus  (None, 4, 4, 2080)           0         ['block8_3_ac[0][0]',         \n","\n"," tomScaleLayer)                                                      'block8_4_conv[0][0]']       \n","\n","                                                                                                  \n","\n"," block8_4_ac (Activation)    (None, 4, 4, 2080)           0         ['custom_scale_layer_33[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_183 (Conv2D)         (None, 4, 4, 192)            399360    ['block8_4_ac[0][0]']         \n","\n","                                                                                                  \n","\n"," batch_normalization_180 (B  (None, 4, 4, 192)            576       ['conv2d_183[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_185 (Activation  (None, 4, 4, 192)            0         ['batch_normalization_180[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_184 (Conv2D)         (None, 4, 4, 224)            129024    ['activation_185[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_181 (B  (None, 4, 4, 224)            672       ['conv2d_184[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_186 (Activation  (None, 4, 4, 224)            0         ['batch_normalization_181[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_182 (Conv2D)         (None, 4, 4, 192)            399360    ['block8_4_ac[0][0]']         \n","\n","                                                                                                  \n","\n"," conv2d_185 (Conv2D)         (None, 4, 4, 256)            172032    ['activation_186[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_179 (B  (None, 4, 4, 192)            576       ['conv2d_182[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_182 (B  (None, 4, 4, 256)            768       ['conv2d_185[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_184 (Activation  (None, 4, 4, 192)            0         ['batch_normalization_179[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_187 (Activation  (None, 4, 4, 256)            0         ['batch_normalization_182[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," block8_5_mixed (Concatenat  (None, 4, 4, 448)            0         ['activation_184[0][0]',      \n","\n"," e)                                                                  'activation_187[0][0]']      \n","\n","                                                                                                  \n","\n"," block8_5_conv (Conv2D)      (None, 4, 4, 2080)           933920    ['block8_5_mixed[0][0]']      \n","\n","                                                                                                  \n","\n"," custom_scale_layer_34 (Cus  (None, 4, 4, 2080)           0         ['block8_4_ac[0][0]',         \n","\n"," tomScaleLayer)                                                      'block8_5_conv[0][0]']       \n","\n","                                                                                                  \n","\n"," block8_5_ac (Activation)    (None, 4, 4, 2080)           0         ['custom_scale_layer_34[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_187 (Conv2D)         (None, 4, 4, 192)            399360    ['block8_5_ac[0][0]']         \n","\n","                                                                                                  \n","\n"," batch_normalization_184 (B  (None, 4, 4, 192)            576       ['conv2d_187[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_189 (Activation  (None, 4, 4, 192)            0         ['batch_normalization_184[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_188 (Conv2D)         (None, 4, 4, 224)            129024    ['activation_189[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_185 (B  (None, 4, 4, 224)            672       ['conv2d_188[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_190 (Activation  (None, 4, 4, 224)            0         ['batch_normalization_185[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_186 (Conv2D)         (None, 4, 4, 192)            399360    ['block8_5_ac[0][0]']         \n","\n","                                                                                                  \n","\n"," conv2d_189 (Conv2D)         (None, 4, 4, 256)            172032    ['activation_190[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_183 (B  (None, 4, 4, 192)            576       ['conv2d_186[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_186 (B  (None, 4, 4, 256)            768       ['conv2d_189[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_188 (Activation  (None, 4, 4, 192)            0         ['batch_normalization_183[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_191 (Activation  (None, 4, 4, 256)            0         ['batch_normalization_186[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," block8_6_mixed (Concatenat  (None, 4, 4, 448)            0         ['activation_188[0][0]',      \n","\n"," e)                                                                  'activation_191[0][0]']      \n","\n","                                                                                                  \n","\n"," block8_6_conv (Conv2D)      (None, 4, 4, 2080)           933920    ['block8_6_mixed[0][0]']      \n","\n","                                                                                                  \n","\n"," custom_scale_layer_35 (Cus  (None, 4, 4, 2080)           0         ['block8_5_ac[0][0]',         \n","\n"," tomScaleLayer)                                                      'block8_6_conv[0][0]']       \n","\n","                                                                                                  \n","\n"," block8_6_ac (Activation)    (None, 4, 4, 2080)           0         ['custom_scale_layer_35[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_191 (Conv2D)         (None, 4, 4, 192)            399360    ['block8_6_ac[0][0]']         \n","\n","                                                                                                  \n","\n"," batch_normalization_188 (B  (None, 4, 4, 192)            576       ['conv2d_191[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_193 (Activation  (None, 4, 4, 192)            0         ['batch_normalization_188[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_192 (Conv2D)         (None, 4, 4, 224)            129024    ['activation_193[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_189 (B  (None, 4, 4, 224)            672       ['conv2d_192[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_194 (Activation  (None, 4, 4, 224)            0         ['batch_normalization_189[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_190 (Conv2D)         (None, 4, 4, 192)            399360    ['block8_6_ac[0][0]']         \n","\n","                                                                                                  \n","\n"," conv2d_193 (Conv2D)         (None, 4, 4, 256)            172032    ['activation_194[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_187 (B  (None, 4, 4, 192)            576       ['conv2d_190[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_190 (B  (None, 4, 4, 256)            768       ['conv2d_193[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_192 (Activation  (None, 4, 4, 192)            0         ['batch_normalization_187[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_195 (Activation  (None, 4, 4, 256)            0         ['batch_normalization_190[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," block8_7_mixed (Concatenat  (None, 4, 4, 448)            0         ['activation_192[0][0]',      \n","\n"," e)                                                                  'activation_195[0][0]']      \n","\n","                                                                                                  \n","\n"," block8_7_conv (Conv2D)      (None, 4, 4, 2080)           933920    ['block8_7_mixed[0][0]']      \n","\n","                                                                                                  \n","\n"," custom_scale_layer_36 (Cus  (None, 4, 4, 2080)           0         ['block8_6_ac[0][0]',         \n","\n"," tomScaleLayer)                                                      'block8_7_conv[0][0]']       \n","\n","                                                                                                  \n","\n"," block8_7_ac (Activation)    (None, 4, 4, 2080)           0         ['custom_scale_layer_36[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_195 (Conv2D)         (None, 4, 4, 192)            399360    ['block8_7_ac[0][0]']         \n","\n","                                                                                                  \n","\n"," batch_normalization_192 (B  (None, 4, 4, 192)            576       ['conv2d_195[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_197 (Activation  (None, 4, 4, 192)            0         ['batch_normalization_192[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_196 (Conv2D)         (None, 4, 4, 224)            129024    ['activation_197[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_193 (B  (None, 4, 4, 224)            672       ['conv2d_196[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_198 (Activation  (None, 4, 4, 224)            0         ['batch_normalization_193[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_194 (Conv2D)         (None, 4, 4, 192)            399360    ['block8_7_ac[0][0]']         \n","\n","                                                                                                  \n","\n"," conv2d_197 (Conv2D)         (None, 4, 4, 256)            172032    ['activation_198[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_191 (B  (None, 4, 4, 192)            576       ['conv2d_194[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_194 (B  (None, 4, 4, 256)            768       ['conv2d_197[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_196 (Activation  (None, 4, 4, 192)            0         ['batch_normalization_191[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_199 (Activation  (None, 4, 4, 256)            0         ['batch_normalization_194[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," block8_8_mixed (Concatenat  (None, 4, 4, 448)            0         ['activation_196[0][0]',      \n","\n"," e)                                                                  'activation_199[0][0]']      \n","\n","                                                                                                  \n","\n"," block8_8_conv (Conv2D)      (None, 4, 4, 2080)           933920    ['block8_8_mixed[0][0]']      \n","\n","                                                                                                  \n","\n"," custom_scale_layer_37 (Cus  (None, 4, 4, 2080)           0         ['block8_7_ac[0][0]',         \n","\n"," tomScaleLayer)                                                      'block8_8_conv[0][0]']       \n","\n","                                                                                                  \n","\n"," block8_8_ac (Activation)    (None, 4, 4, 2080)           0         ['custom_scale_layer_37[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_199 (Conv2D)         (None, 4, 4, 192)            399360    ['block8_8_ac[0][0]']         \n","\n","                                                                                                  \n","\n"," batch_normalization_196 (B  (None, 4, 4, 192)            576       ['conv2d_199[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_201 (Activation  (None, 4, 4, 192)            0         ['batch_normalization_196[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_200 (Conv2D)         (None, 4, 4, 224)            129024    ['activation_201[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_197 (B  (None, 4, 4, 224)            672       ['conv2d_200[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_202 (Activation  (None, 4, 4, 224)            0         ['batch_normalization_197[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_198 (Conv2D)         (None, 4, 4, 192)            399360    ['block8_8_ac[0][0]']         \n","\n","                                                                                                  \n","\n"," conv2d_201 (Conv2D)         (None, 4, 4, 256)            172032    ['activation_202[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_195 (B  (None, 4, 4, 192)            576       ['conv2d_198[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_198 (B  (None, 4, 4, 256)            768       ['conv2d_201[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_200 (Activation  (None, 4, 4, 192)            0         ['batch_normalization_195[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_203 (Activation  (None, 4, 4, 256)            0         ['batch_normalization_198[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," block8_9_mixed (Concatenat  (None, 4, 4, 448)            0         ['activation_200[0][0]',      \n","\n"," e)                                                                  'activation_203[0][0]']      \n","\n","                                                                                                  \n","\n"," block8_9_conv (Conv2D)      (None, 4, 4, 2080)           933920    ['block8_9_mixed[0][0]']      \n","\n","                                                                                                  \n","\n"," custom_scale_layer_38 (Cus  (None, 4, 4, 2080)           0         ['block8_8_ac[0][0]',         \n","\n"," tomScaleLayer)                                                      'block8_9_conv[0][0]']       \n","\n","                                                                                                  \n","\n"," block8_9_ac (Activation)    (None, 4, 4, 2080)           0         ['custom_scale_layer_38[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv2d_203 (Conv2D)         (None, 4, 4, 192)            399360    ['block8_9_ac[0][0]']         \n","\n","                                                                                                  \n","\n"," batch_normalization_200 (B  (None, 4, 4, 192)            576       ['conv2d_203[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_205 (Activation  (None, 4, 4, 192)            0         ['batch_normalization_200[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_204 (Conv2D)         (None, 4, 4, 224)            129024    ['activation_205[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_201 (B  (None, 4, 4, 224)            672       ['conv2d_204[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_206 (Activation  (None, 4, 4, 224)            0         ['batch_normalization_201[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," conv2d_202 (Conv2D)         (None, 4, 4, 192)            399360    ['block8_9_ac[0][0]']         \n","\n","                                                                                                  \n","\n"," conv2d_205 (Conv2D)         (None, 4, 4, 256)            172032    ['activation_206[0][0]']      \n","\n","                                                                                                  \n","\n"," batch_normalization_199 (B  (None, 4, 4, 192)            576       ['conv2d_202[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," batch_normalization_202 (B  (None, 4, 4, 256)            768       ['conv2d_205[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," activation_204 (Activation  (None, 4, 4, 192)            0         ['batch_normalization_199[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," activation_207 (Activation  (None, 4, 4, 256)            0         ['batch_normalization_202[0][0\n","\n"," )                                                                  ]']                           \n","\n","                                                                                                  \n","\n"," block8_10_mixed (Concatena  (None, 4, 4, 448)            0         ['activation_204[0][0]',      \n","\n"," te)                                                                 'activation_207[0][0]']      \n","\n","                                                                                                  \n","\n"," block8_10_conv (Conv2D)     (None, 4, 4, 2080)           933920    ['block8_10_mixed[0][0]']     \n","\n","                                                                                                  \n","\n"," custom_scale_layer_39 (Cus  (None, 4, 4, 2080)           0         ['block8_9_ac[0][0]',         \n","\n"," tomScaleLayer)                                                      'block8_10_conv[0][0]']      \n","\n","                                                                                                  \n","\n"," conv_7b (Conv2D)            (None, 4, 4, 1536)           3194880   ['custom_scale_layer_39[0][0]'\n","\n","                                                                    ]                             \n","\n","                                                                                                  \n","\n"," conv_7b_bn (BatchNormaliza  (None, 4, 4, 1536)           4608      ['conv_7b[0][0]']             \n","\n"," tion)                                                                                            \n","\n","                                                                                                  \n","\n"," conv_7b_ac (Activation)     (None, 4, 4, 1536)           0         ['conv_7b_bn[0][0]']          \n","\n","                                                                                                  \n","\n"," global_average_pooling2d (  (None, 1536)                 0         ['conv_7b_ac[0][0]']          \n","\n"," GlobalAveragePooling2D)                                                                          \n","\n","                                                                                                  \n","\n"," dense_6 (Dense)             (None, 128)                  196736    ['global_average_pooling2d[0][\n","\n","                                                                    0]']                          \n","\n","                                                                                                  \n","\n"," dropout_3 (Dropout)         (None, 128)                  0         ['dense_6[0][0]']             \n","\n","                                                                                                  \n","\n"," dense_7 (Dense)             (None, 64)                   8256      ['dropout_3[0][0]']           \n","\n","                                                                                                  \n","\n"," dropout_4 (Dropout)         (None, 64)                   0         ['dense_7[0][0]']             \n","\n","                                                                                                  \n","\n"," dense_8 (Dense)             (None, 1)                    65        ['dropout_4[0][0]']           \n","\n","                                                                                                  \n","\n","==================================================================================================\n","\n","Total params: 54541793 (208.06 MB)\n","\n","Trainable params: 54481249 (207.83 MB)\n","\n","Non-trainable params: 60544 (236.50 KB)\n","\n","__________________________________________________________________________________________________\n"]}],"source":["inception_model = create_model(inception_base_model)\n","inception_model.summary()"]},{"cell_type":"code","execution_count":36,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Unfreezing number of layers in base model =  0\n","\n","Epoch 1/15\n"]},{"name":"stderr","output_type":"stream","text":["2023-06-28 01:17:13.479624: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] PluggableGraphOptimizer failed: INVALID_ARGUMENT: Unparseable tensorflow.GraphDef proto\n","\n","2023-06-28 01:17:13.622448: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] PluggableGraphOptimizer failed: INVALID_ARGUMENT: Unparseable tensorflow.GraphDef proto\n"]},{"name":"stdout","output_type":"stream","text":["223/223 [==============================] - ETA: 0s - loss: 0.5149 - accuracy: 0.7708"]},{"name":"stderr","output_type":"stream","text":["2023-06-28 01:18:15.830478: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] PluggableGraphOptimizer failed: INVALID_ARGUMENT: Unparseable tensorflow.GraphDef proto\n","\n","2023-06-28 01:18:15.988644: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] PluggableGraphOptimizer failed: INVALID_ARGUMENT: Unparseable tensorflow.GraphDef proto\n"]},{"name":"stdout","output_type":"stream","text":["223/223 [==============================] - 97s 408ms/step - loss: 0.5149 - accuracy: 0.7708 - val_loss: 0.5485 - val_accuracy: 0.7546\n","\n","Epoch 2/15\n","\n","223/223 [==============================] - 81s 364ms/step - loss: 0.4480 - accuracy: 0.8146 - val_loss: 0.5063 - val_accuracy: 0.7622\n","\n","Epoch 3/15\n","\n","223/223 [==============================] - 97s 436ms/step - loss: 0.4186 - accuracy: 0.8258 - val_loss: 0.5100 - val_accuracy: 0.7675\n","\n","Epoch 4/15\n","\n","223/223 [==============================] - 94s 421ms/step - loss: 0.4028 - accuracy: 0.8329 - val_loss: 0.5082 - val_accuracy: 0.7670\n","\n","Epoch 5/15\n","\n","223/223 [==============================] - 93s 418ms/step - loss: 0.4014 - accuracy: 0.8334 - val_loss: 0.5054 - val_accuracy: 0.7675\n","\n","Epoch 6/15\n","\n","223/223 [==============================] - 101s 453ms/step - loss: 0.3972 - accuracy: 0.8340 - val_loss: 0.4942 - val_accuracy: 0.7816\n","\n","Epoch 7/15\n","\n","223/223 [==============================] - 98s 439ms/step - loss: 0.3839 - accuracy: 0.8431 - val_loss: 0.4847 - val_accuracy: 0.7816\n","\n","Epoch 8/15\n","\n","223/223 [==============================] - 95s 427ms/step - loss: 0.3808 - accuracy: 0.8441 - val_loss: 0.4789 - val_accuracy: 0.7771\n","\n","Epoch 9/15\n","\n","223/223 [==============================] - 96s 429ms/step - loss: 0.3661 - accuracy: 0.8559 - val_loss: 0.4822 - val_accuracy: 0.7802\n","\n","Epoch 10/15\n","\n","223/223 [==============================] - 98s 440ms/step - loss: 0.3649 - accuracy: 0.8530 - val_loss: 0.4782 - val_accuracy: 0.7867\n","\n","Epoch 11/15\n","\n","223/223 [==============================] - 94s 423ms/step - loss: 0.3605 - accuracy: 0.8533 - val_loss: 0.4777 - val_accuracy: 0.7886\n","\n","Epoch 12/15\n","\n","223/223 [==============================] - 94s 421ms/step - loss: 0.3508 - accuracy: 0.8541 - val_loss: 0.5002 - val_accuracy: 0.7737\n","\n","Epoch 13/15\n","\n","223/223 [==============================] - 97s 437ms/step - loss: 0.3502 - accuracy: 0.8573 - val_loss: 0.4647 - val_accuracy: 0.7872\n","\n","Epoch 14/15\n","\n","223/223 [==============================] - 94s 423ms/step - loss: 0.3463 - accuracy: 0.8579 - val_loss: 0.4749 - val_accuracy: 0.7861\n","\n","Epoch 15/15\n","\n","223/223 [==============================] - 92s 412ms/step - loss: 0.3412 - accuracy: 0.8634 - val_loss: 0.4977 - val_accuracy: 0.7746\n"]}],"source":["history = fit_model(inception_model, inception_base_model, epochs = 15)"]},{"cell_type":"code","execution_count":37,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 500x800 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plot_history(history)"]},{"cell_type":"markdown","metadata":{},"source":["#### Approach 5: Xception"]},{"cell_type":"code","execution_count":38,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/xception/xception_weights_tf_dim_ordering_tf_kernels_notop.h5\n","\n","83683744/83683744 [==============================] - 24s 0us/step\n"]}],"source":["# load the xception architecture with imagenet weights as base\n","xception_base_model = tf.keras.applications.xception.Xception(\n","                                 include_top = False,\n","                                 weights = 'imagenet',\n","                                 input_shape = (180, 180, 3)\n","                                 )"]},{"cell_type":"code","execution_count":39,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"model_3\"\n","\n","__________________________________________________________________________________________________\n","\n"," Layer (type)                Output Shape                 Param #   Connected to                  \n","\n","==================================================================================================\n","\n"," input_4 (InputLayer)        [(None, 180, 180, 3)]        0         []                            \n","\n","                                                                                                  \n","\n"," block1_conv1 (Conv2D)       (None, 89, 89, 32)           864       ['input_4[0][0]']             \n","\n","                                                                                                  \n","\n"," block1_conv1_bn (BatchNorm  (None, 89, 89, 32)           128       ['block1_conv1[0][0]']        \n","\n"," alization)                                                                                       \n","\n","                                                                                                  \n","\n"," block1_conv1_act (Activati  (None, 89, 89, 32)           0         ['block1_conv1_bn[0][0]']     \n","\n"," on)                                                                                              \n","\n","                                                                                                  \n","\n"," block1_conv2 (Conv2D)       (None, 87, 87, 64)           18432     ['block1_conv1_act[0][0]']    \n","\n","                                                                                                  \n","\n"," block1_conv2_bn (BatchNorm  (None, 87, 87, 64)           256       ['block1_conv2[0][0]']        \n","\n"," alization)                                                                                       \n","\n","                                                                                                  \n","\n"," block1_conv2_act (Activati  (None, 87, 87, 64)           0         ['block1_conv2_bn[0][0]']     \n","\n"," on)                                                                                              \n","\n","                                                                                                  \n","\n"," block2_sepconv1 (Separable  (None, 87, 87, 128)          8768      ['block1_conv2_act[0][0]']    \n","\n"," Conv2D)                                                                                          \n","\n","                                                                                                  \n","\n"," block2_sepconv1_bn (BatchN  (None, 87, 87, 128)          512       ['block2_sepconv1[0][0]']     \n","\n"," ormalization)                                                                                    \n","\n","                                                                                                  \n","\n"," block2_sepconv2_act (Activ  (None, 87, 87, 128)          0         ['block2_sepconv1_bn[0][0]']  \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," block2_sepconv2 (Separable  (None, 87, 87, 128)          17536     ['block2_sepconv2_act[0][0]'] \n","\n"," Conv2D)                                                                                          \n","\n","                                                                                                  \n","\n"," block2_sepconv2_bn (BatchN  (None, 87, 87, 128)          512       ['block2_sepconv2[0][0]']     \n","\n"," ormalization)                                                                                    \n","\n","                                                                                                  \n","\n"," conv2d_206 (Conv2D)         (None, 44, 44, 128)          8192      ['block1_conv2_act[0][0]']    \n","\n","                                                                                                  \n","\n"," block2_pool (MaxPooling2D)  (None, 44, 44, 128)          0         ['block2_sepconv2_bn[0][0]']  \n","\n","                                                                                                  \n","\n"," batch_normalization_203 (B  (None, 44, 44, 128)          512       ['conv2d_206[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," add (Add)                   (None, 44, 44, 128)          0         ['block2_pool[0][0]',         \n","\n","                                                                     'batch_normalization_203[0][0\n","\n","                                                                    ]']                           \n","\n","                                                                                                  \n","\n"," block3_sepconv1_act (Activ  (None, 44, 44, 128)          0         ['add[0][0]']                 \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," block3_sepconv1 (Separable  (None, 44, 44, 256)          33920     ['block3_sepconv1_act[0][0]'] \n","\n"," Conv2D)                                                                                          \n","\n","                                                                                                  \n","\n"," block3_sepconv1_bn (BatchN  (None, 44, 44, 256)          1024      ['block3_sepconv1[0][0]']     \n","\n"," ormalization)                                                                                    \n","\n","                                                                                                  \n","\n"," block3_sepconv2_act (Activ  (None, 44, 44, 256)          0         ['block3_sepconv1_bn[0][0]']  \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," block3_sepconv2 (Separable  (None, 44, 44, 256)          67840     ['block3_sepconv2_act[0][0]'] \n","\n"," Conv2D)                                                                                          \n","\n","                                                                                                  \n","\n"," block3_sepconv2_bn (BatchN  (None, 44, 44, 256)          1024      ['block3_sepconv2[0][0]']     \n","\n"," ormalization)                                                                                    \n","\n","                                                                                                  \n","\n"," conv2d_207 (Conv2D)         (None, 22, 22, 256)          32768     ['add[0][0]']                 \n","\n","                                                                                                  \n","\n"," block3_pool (MaxPooling2D)  (None, 22, 22, 256)          0         ['block3_sepconv2_bn[0][0]']  \n","\n","                                                                                                  \n","\n"," batch_normalization_204 (B  (None, 22, 22, 256)          1024      ['conv2d_207[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," add_1 (Add)                 (None, 22, 22, 256)          0         ['block3_pool[0][0]',         \n","\n","                                                                     'batch_normalization_204[0][0\n","\n","                                                                    ]']                           \n","\n","                                                                                                  \n","\n"," block4_sepconv1_act (Activ  (None, 22, 22, 256)          0         ['add_1[0][0]']               \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," block4_sepconv1 (Separable  (None, 22, 22, 728)          188672    ['block4_sepconv1_act[0][0]'] \n","\n"," Conv2D)                                                                                          \n","\n","                                                                                                  \n","\n"," block4_sepconv1_bn (BatchN  (None, 22, 22, 728)          2912      ['block4_sepconv1[0][0]']     \n","\n"," ormalization)                                                                                    \n","\n","                                                                                                  \n","\n"," block4_sepconv2_act (Activ  (None, 22, 22, 728)          0         ['block4_sepconv1_bn[0][0]']  \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," block4_sepconv2 (Separable  (None, 22, 22, 728)          536536    ['block4_sepconv2_act[0][0]'] \n","\n"," Conv2D)                                                                                          \n","\n","                                                                                                  \n","\n"," block4_sepconv2_bn (BatchN  (None, 22, 22, 728)          2912      ['block4_sepconv2[0][0]']     \n","\n"," ormalization)                                                                                    \n","\n","                                                                                                  \n","\n"," conv2d_208 (Conv2D)         (None, 11, 11, 728)          186368    ['add_1[0][0]']               \n","\n","                                                                                                  \n","\n"," block4_pool (MaxPooling2D)  (None, 11, 11, 728)          0         ['block4_sepconv2_bn[0][0]']  \n","\n","                                                                                                  \n","\n"," batch_normalization_205 (B  (None, 11, 11, 728)          2912      ['conv2d_208[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," add_2 (Add)                 (None, 11, 11, 728)          0         ['block4_pool[0][0]',         \n","\n","                                                                     'batch_normalization_205[0][0\n","\n","                                                                    ]']                           \n","\n","                                                                                                  \n","\n"," block5_sepconv1_act (Activ  (None, 11, 11, 728)          0         ['add_2[0][0]']               \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," block5_sepconv1 (Separable  (None, 11, 11, 728)          536536    ['block5_sepconv1_act[0][0]'] \n","\n"," Conv2D)                                                                                          \n","\n","                                                                                                  \n","\n"," block5_sepconv1_bn (BatchN  (None, 11, 11, 728)          2912      ['block5_sepconv1[0][0]']     \n","\n"," ormalization)                                                                                    \n","\n","                                                                                                  \n","\n"," block5_sepconv2_act (Activ  (None, 11, 11, 728)          0         ['block5_sepconv1_bn[0][0]']  \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," block5_sepconv2 (Separable  (None, 11, 11, 728)          536536    ['block5_sepconv2_act[0][0]'] \n","\n"," Conv2D)                                                                                          \n","\n","                                                                                                  \n","\n"," block5_sepconv2_bn (BatchN  (None, 11, 11, 728)          2912      ['block5_sepconv2[0][0]']     \n","\n"," ormalization)                                                                                    \n","\n","                                                                                                  \n","\n"," block5_sepconv3_act (Activ  (None, 11, 11, 728)          0         ['block5_sepconv2_bn[0][0]']  \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," block5_sepconv3 (Separable  (None, 11, 11, 728)          536536    ['block5_sepconv3_act[0][0]'] \n","\n"," Conv2D)                                                                                          \n","\n","                                                                                                  \n","\n"," block5_sepconv3_bn (BatchN  (None, 11, 11, 728)          2912      ['block5_sepconv3[0][0]']     \n","\n"," ormalization)                                                                                    \n","\n","                                                                                                  \n","\n"," add_3 (Add)                 (None, 11, 11, 728)          0         ['block5_sepconv3_bn[0][0]',  \n","\n","                                                                     'add_2[0][0]']               \n","\n","                                                                                                  \n","\n"," block6_sepconv1_act (Activ  (None, 11, 11, 728)          0         ['add_3[0][0]']               \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," block6_sepconv1 (Separable  (None, 11, 11, 728)          536536    ['block6_sepconv1_act[0][0]'] \n","\n"," Conv2D)                                                                                          \n","\n","                                                                                                  \n","\n"," block6_sepconv1_bn (BatchN  (None, 11, 11, 728)          2912      ['block6_sepconv1[0][0]']     \n","\n"," ormalization)                                                                                    \n","\n","                                                                                                  \n","\n"," block6_sepconv2_act (Activ  (None, 11, 11, 728)          0         ['block6_sepconv1_bn[0][0]']  \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," block6_sepconv2 (Separable  (None, 11, 11, 728)          536536    ['block6_sepconv2_act[0][0]'] \n","\n"," Conv2D)                                                                                          \n","\n","                                                                                                  \n","\n"," block6_sepconv2_bn (BatchN  (None, 11, 11, 728)          2912      ['block6_sepconv2[0][0]']     \n","\n"," ormalization)                                                                                    \n","\n","                                                                                                  \n","\n"," block6_sepconv3_act (Activ  (None, 11, 11, 728)          0         ['block6_sepconv2_bn[0][0]']  \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," block6_sepconv3 (Separable  (None, 11, 11, 728)          536536    ['block6_sepconv3_act[0][0]'] \n","\n"," Conv2D)                                                                                          \n","\n","                                                                                                  \n","\n"," block6_sepconv3_bn (BatchN  (None, 11, 11, 728)          2912      ['block6_sepconv3[0][0]']     \n","\n"," ormalization)                                                                                    \n","\n","                                                                                                  \n","\n"," add_4 (Add)                 (None, 11, 11, 728)          0         ['block6_sepconv3_bn[0][0]',  \n","\n","                                                                     'add_3[0][0]']               \n","\n","                                                                                                  \n","\n"," block7_sepconv1_act (Activ  (None, 11, 11, 728)          0         ['add_4[0][0]']               \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," block7_sepconv1 (Separable  (None, 11, 11, 728)          536536    ['block7_sepconv1_act[0][0]'] \n","\n"," Conv2D)                                                                                          \n","\n","                                                                                                  \n","\n"," block7_sepconv1_bn (BatchN  (None, 11, 11, 728)          2912      ['block7_sepconv1[0][0]']     \n","\n"," ormalization)                                                                                    \n","\n","                                                                                                  \n","\n"," block7_sepconv2_act (Activ  (None, 11, 11, 728)          0         ['block7_sepconv1_bn[0][0]']  \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," block7_sepconv2 (Separable  (None, 11, 11, 728)          536536    ['block7_sepconv2_act[0][0]'] \n","\n"," Conv2D)                                                                                          \n","\n","                                                                                                  \n","\n"," block7_sepconv2_bn (BatchN  (None, 11, 11, 728)          2912      ['block7_sepconv2[0][0]']     \n","\n"," ormalization)                                                                                    \n","\n","                                                                                                  \n","\n"," block7_sepconv3_act (Activ  (None, 11, 11, 728)          0         ['block7_sepconv2_bn[0][0]']  \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," block7_sepconv3 (Separable  (None, 11, 11, 728)          536536    ['block7_sepconv3_act[0][0]'] \n","\n"," Conv2D)                                                                                          \n","\n","                                                                                                  \n","\n"," block7_sepconv3_bn (BatchN  (None, 11, 11, 728)          2912      ['block7_sepconv3[0][0]']     \n","\n"," ormalization)                                                                                    \n","\n","                                                                                                  \n","\n"," add_5 (Add)                 (None, 11, 11, 728)          0         ['block7_sepconv3_bn[0][0]',  \n","\n","                                                                     'add_4[0][0]']               \n","\n","                                                                                                  \n","\n"," block8_sepconv1_act (Activ  (None, 11, 11, 728)          0         ['add_5[0][0]']               \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," block8_sepconv1 (Separable  (None, 11, 11, 728)          536536    ['block8_sepconv1_act[0][0]'] \n","\n"," Conv2D)                                                                                          \n","\n","                                                                                                  \n","\n"," block8_sepconv1_bn (BatchN  (None, 11, 11, 728)          2912      ['block8_sepconv1[0][0]']     \n","\n"," ormalization)                                                                                    \n","\n","                                                                                                  \n","\n"," block8_sepconv2_act (Activ  (None, 11, 11, 728)          0         ['block8_sepconv1_bn[0][0]']  \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," block8_sepconv2 (Separable  (None, 11, 11, 728)          536536    ['block8_sepconv2_act[0][0]'] \n","\n"," Conv2D)                                                                                          \n","\n","                                                                                                  \n","\n"," block8_sepconv2_bn (BatchN  (None, 11, 11, 728)          2912      ['block8_sepconv2[0][0]']     \n","\n"," ormalization)                                                                                    \n","\n","                                                                                                  \n","\n"," block8_sepconv3_act (Activ  (None, 11, 11, 728)          0         ['block8_sepconv2_bn[0][0]']  \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," block8_sepconv3 (Separable  (None, 11, 11, 728)          536536    ['block8_sepconv3_act[0][0]'] \n","\n"," Conv2D)                                                                                          \n","\n","                                                                                                  \n","\n"," block8_sepconv3_bn (BatchN  (None, 11, 11, 728)          2912      ['block8_sepconv3[0][0]']     \n","\n"," ormalization)                                                                                    \n","\n","                                                                                                  \n","\n"," add_6 (Add)                 (None, 11, 11, 728)          0         ['block8_sepconv3_bn[0][0]',  \n","\n","                                                                     'add_5[0][0]']               \n","\n","                                                                                                  \n","\n"," block9_sepconv1_act (Activ  (None, 11, 11, 728)          0         ['add_6[0][0]']               \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," block9_sepconv1 (Separable  (None, 11, 11, 728)          536536    ['block9_sepconv1_act[0][0]'] \n","\n"," Conv2D)                                                                                          \n","\n","                                                                                                  \n","\n"," block9_sepconv1_bn (BatchN  (None, 11, 11, 728)          2912      ['block9_sepconv1[0][0]']     \n","\n"," ormalization)                                                                                    \n","\n","                                                                                                  \n","\n"," block9_sepconv2_act (Activ  (None, 11, 11, 728)          0         ['block9_sepconv1_bn[0][0]']  \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," block9_sepconv2 (Separable  (None, 11, 11, 728)          536536    ['block9_sepconv2_act[0][0]'] \n","\n"," Conv2D)                                                                                          \n","\n","                                                                                                  \n","\n"," block9_sepconv2_bn (BatchN  (None, 11, 11, 728)          2912      ['block9_sepconv2[0][0]']     \n","\n"," ormalization)                                                                                    \n","\n","                                                                                                  \n","\n"," block9_sepconv3_act (Activ  (None, 11, 11, 728)          0         ['block9_sepconv2_bn[0][0]']  \n","\n"," ation)                                                                                           \n","\n","                                                                                                  \n","\n"," block9_sepconv3 (Separable  (None, 11, 11, 728)          536536    ['block9_sepconv3_act[0][0]'] \n","\n"," Conv2D)                                                                                          \n","\n","                                                                                                  \n","\n"," block9_sepconv3_bn (BatchN  (None, 11, 11, 728)          2912      ['block9_sepconv3[0][0]']     \n","\n"," ormalization)                                                                                    \n","\n","                                                                                                  \n","\n"," add_7 (Add)                 (None, 11, 11, 728)          0         ['block9_sepconv3_bn[0][0]',  \n","\n","                                                                     'add_6[0][0]']               \n","\n","                                                                                                  \n","\n"," block10_sepconv1_act (Acti  (None, 11, 11, 728)          0         ['add_7[0][0]']               \n","\n"," vation)                                                                                          \n","\n","                                                                                                  \n","\n"," block10_sepconv1 (Separabl  (None, 11, 11, 728)          536536    ['block10_sepconv1_act[0][0]']\n","\n"," eConv2D)                                                                                         \n","\n","                                                                                                  \n","\n"," block10_sepconv1_bn (Batch  (None, 11, 11, 728)          2912      ['block10_sepconv1[0][0]']    \n","\n"," Normalization)                                                                                   \n","\n","                                                                                                  \n","\n"," block10_sepconv2_act (Acti  (None, 11, 11, 728)          0         ['block10_sepconv1_bn[0][0]'] \n","\n"," vation)                                                                                          \n","\n","                                                                                                  \n","\n"," block10_sepconv2 (Separabl  (None, 11, 11, 728)          536536    ['block10_sepconv2_act[0][0]']\n","\n"," eConv2D)                                                                                         \n","\n","                                                                                                  \n","\n"," block10_sepconv2_bn (Batch  (None, 11, 11, 728)          2912      ['block10_sepconv2[0][0]']    \n","\n"," Normalization)                                                                                   \n","\n","                                                                                                  \n","\n"," block10_sepconv3_act (Acti  (None, 11, 11, 728)          0         ['block10_sepconv2_bn[0][0]'] \n","\n"," vation)                                                                                          \n","\n","                                                                                                  \n","\n"," block10_sepconv3 (Separabl  (None, 11, 11, 728)          536536    ['block10_sepconv3_act[0][0]']\n","\n"," eConv2D)                                                                                         \n","\n","                                                                                                  \n","\n"," block10_sepconv3_bn (Batch  (None, 11, 11, 728)          2912      ['block10_sepconv3[0][0]']    \n","\n"," Normalization)                                                                                   \n","\n","                                                                                                  \n","\n"," add_8 (Add)                 (None, 11, 11, 728)          0         ['block10_sepconv3_bn[0][0]', \n","\n","                                                                     'add_7[0][0]']               \n","\n","                                                                                                  \n","\n"," block11_sepconv1_act (Acti  (None, 11, 11, 728)          0         ['add_8[0][0]']               \n","\n"," vation)                                                                                          \n","\n","                                                                                                  \n","\n"," block11_sepconv1 (Separabl  (None, 11, 11, 728)          536536    ['block11_sepconv1_act[0][0]']\n","\n"," eConv2D)                                                                                         \n","\n","                                                                                                  \n","\n"," block11_sepconv1_bn (Batch  (None, 11, 11, 728)          2912      ['block11_sepconv1[0][0]']    \n","\n"," Normalization)                                                                                   \n","\n","                                                                                                  \n","\n"," block11_sepconv2_act (Acti  (None, 11, 11, 728)          0         ['block11_sepconv1_bn[0][0]'] \n","\n"," vation)                                                                                          \n","\n","                                                                                                  \n","\n"," block11_sepconv2 (Separabl  (None, 11, 11, 728)          536536    ['block11_sepconv2_act[0][0]']\n","\n"," eConv2D)                                                                                         \n","\n","                                                                                                  \n","\n"," block11_sepconv2_bn (Batch  (None, 11, 11, 728)          2912      ['block11_sepconv2[0][0]']    \n","\n"," Normalization)                                                                                   \n","\n","                                                                                                  \n","\n"," block11_sepconv3_act (Acti  (None, 11, 11, 728)          0         ['block11_sepconv2_bn[0][0]'] \n","\n"," vation)                                                                                          \n","\n","                                                                                                  \n","\n"," block11_sepconv3 (Separabl  (None, 11, 11, 728)          536536    ['block11_sepconv3_act[0][0]']\n","\n"," eConv2D)                                                                                         \n","\n","                                                                                                  \n","\n"," block11_sepconv3_bn (Batch  (None, 11, 11, 728)          2912      ['block11_sepconv3[0][0]']    \n","\n"," Normalization)                                                                                   \n","\n","                                                                                                  \n","\n"," add_9 (Add)                 (None, 11, 11, 728)          0         ['block11_sepconv3_bn[0][0]', \n","\n","                                                                     'add_8[0][0]']               \n","\n","                                                                                                  \n","\n"," block12_sepconv1_act (Acti  (None, 11, 11, 728)          0         ['add_9[0][0]']               \n","\n"," vation)                                                                                          \n","\n","                                                                                                  \n","\n"," block12_sepconv1 (Separabl  (None, 11, 11, 728)          536536    ['block12_sepconv1_act[0][0]']\n","\n"," eConv2D)                                                                                         \n","\n","                                                                                                  \n","\n"," block12_sepconv1_bn (Batch  (None, 11, 11, 728)          2912      ['block12_sepconv1[0][0]']    \n","\n"," Normalization)                                                                                   \n","\n","                                                                                                  \n","\n"," block12_sepconv2_act (Acti  (None, 11, 11, 728)          0         ['block12_sepconv1_bn[0][0]'] \n","\n"," vation)                                                                                          \n","\n","                                                                                                  \n","\n"," block12_sepconv2 (Separabl  (None, 11, 11, 728)          536536    ['block12_sepconv2_act[0][0]']\n","\n"," eConv2D)                                                                                         \n","\n","                                                                                                  \n","\n"," block12_sepconv2_bn (Batch  (None, 11, 11, 728)          2912      ['block12_sepconv2[0][0]']    \n","\n"," Normalization)                                                                                   \n","\n","                                                                                                  \n","\n"," block12_sepconv3_act (Acti  (None, 11, 11, 728)          0         ['block12_sepconv2_bn[0][0]'] \n","\n"," vation)                                                                                          \n","\n","                                                                                                  \n","\n"," block12_sepconv3 (Separabl  (None, 11, 11, 728)          536536    ['block12_sepconv3_act[0][0]']\n","\n"," eConv2D)                                                                                         \n","\n","                                                                                                  \n","\n"," block12_sepconv3_bn (Batch  (None, 11, 11, 728)          2912      ['block12_sepconv3[0][0]']    \n","\n"," Normalization)                                                                                   \n","\n","                                                                                                  \n","\n"," add_10 (Add)                (None, 11, 11, 728)          0         ['block12_sepconv3_bn[0][0]', \n","\n","                                                                     'add_9[0][0]']               \n","\n","                                                                                                  \n","\n"," block13_sepconv1_act (Acti  (None, 11, 11, 728)          0         ['add_10[0][0]']              \n","\n"," vation)                                                                                          \n","\n","                                                                                                  \n","\n"," block13_sepconv1 (Separabl  (None, 11, 11, 728)          536536    ['block13_sepconv1_act[0][0]']\n","\n"," eConv2D)                                                                                         \n","\n","                                                                                                  \n","\n"," block13_sepconv1_bn (Batch  (None, 11, 11, 728)          2912      ['block13_sepconv1[0][0]']    \n","\n"," Normalization)                                                                                   \n","\n","                                                                                                  \n","\n"," block13_sepconv2_act (Acti  (None, 11, 11, 728)          0         ['block13_sepconv1_bn[0][0]'] \n","\n"," vation)                                                                                          \n","\n","                                                                                                  \n","\n"," block13_sepconv2 (Separabl  (None, 11, 11, 1024)         752024    ['block13_sepconv2_act[0][0]']\n","\n"," eConv2D)                                                                                         \n","\n","                                                                                                  \n","\n"," block13_sepconv2_bn (Batch  (None, 11, 11, 1024)         4096      ['block13_sepconv2[0][0]']    \n","\n"," Normalization)                                                                                   \n","\n","                                                                                                  \n","\n"," conv2d_209 (Conv2D)         (None, 6, 6, 1024)           745472    ['add_10[0][0]']              \n","\n","                                                                                                  \n","\n"," block13_pool (MaxPooling2D  (None, 6, 6, 1024)           0         ['block13_sepconv2_bn[0][0]'] \n","\n"," )                                                                                                \n","\n","                                                                                                  \n","\n"," batch_normalization_206 (B  (None, 6, 6, 1024)           4096      ['conv2d_209[0][0]']          \n","\n"," atchNormalization)                                                                               \n","\n","                                                                                                  \n","\n"," add_11 (Add)                (None, 6, 6, 1024)           0         ['block13_pool[0][0]',        \n","\n","                                                                     'batch_normalization_206[0][0\n","\n","                                                                    ]']                           \n","\n","                                                                                                  \n","\n"," block14_sepconv1 (Separabl  (None, 6, 6, 1536)           1582080   ['add_11[0][0]']              \n","\n"," eConv2D)                                                                                         \n","\n","                                                                                                  \n","\n"," block14_sepconv1_bn (Batch  (None, 6, 6, 1536)           6144      ['block14_sepconv1[0][0]']    \n","\n"," Normalization)                                                                                   \n","\n","                                                                                                  \n","\n"," block14_sepconv1_act (Acti  (None, 6, 6, 1536)           0         ['block14_sepconv1_bn[0][0]'] \n","\n"," vation)                                                                                          \n","\n","                                                                                                  \n","\n"," block14_sepconv2 (Separabl  (None, 6, 6, 2048)           3159552   ['block14_sepconv1_act[0][0]']\n","\n"," eConv2D)                                                                                         \n","\n","                                                                                                  \n","\n"," block14_sepconv2_bn (Batch  (None, 6, 6, 2048)           8192      ['block14_sepconv2[0][0]']    \n","\n"," Normalization)                                                                                   \n","\n","                                                                                                  \n","\n"," block14_sepconv2_act (Acti  (None, 6, 6, 2048)           0         ['block14_sepconv2_bn[0][0]'] \n","\n"," vation)                                                                                          \n","\n","                                                                                                  \n","\n"," global_average_pooling2d_1  (None, 2048)                 0         ['block14_sepconv2_act[0][0]']\n","\n","  (GlobalAveragePooling2D)                                                                        \n","\n","                                                                                                  \n","\n"," dense_9 (Dense)             (None, 128)                  262272    ['global_average_pooling2d_1[0\n","\n","                                                                    ][0]']                        \n","\n","                                                                                                  \n","\n"," dropout_5 (Dropout)         (None, 128)                  0         ['dense_9[0][0]']             \n","\n","                                                                                                  \n","\n"," dense_10 (Dense)            (None, 64)                   8256      ['dropout_5[0][0]']           \n","\n","                                                                                                  \n","\n"," dropout_6 (Dropout)         (None, 64)                   0         ['dense_10[0][0]']            \n","\n","                                                                                                  \n","\n"," dense_11 (Dense)            (None, 1)                    65        ['dropout_6[0][0]']           \n","\n","                                                                                                  \n","\n","==================================================================================================\n","\n","Total params: 21132073 (80.61 MB)\n","\n","Trainable params: 21077545 (80.40 MB)\n","\n","Non-trainable params: 54528 (213.00 KB)\n","\n","__________________________________________________________________________________________________\n"]}],"source":["xception_model = create_model(xception_base_model)\n","xception_model.summary()"]},{"cell_type":"code","execution_count":40,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Unfreezing number of layers in base model =  0\n","\n","Epoch 1/15\n"]},{"name":"stderr","output_type":"stream","text":["2023-06-28 01:41:57.950908: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] PluggableGraphOptimizer failed: INVALID_ARGUMENT: Unparseable tensorflow.GraphDef proto\n","\n","2023-06-28 01:41:57.990418: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] PluggableGraphOptimizer failed: INVALID_ARGUMENT: Unparseable tensorflow.GraphDef proto\n"]},{"name":"stdout","output_type":"stream","text":["223/223 [==============================] - ETA: 0s - loss: 0.4715 - accuracy: 0.7923"]},{"name":"stderr","output_type":"stream","text":["2023-06-28 01:42:54.833314: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] PluggableGraphOptimizer failed: INVALID_ARGUMENT: Unparseable tensorflow.GraphDef proto\n","\n","2023-06-28 01:42:54.869082: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] PluggableGraphOptimizer failed: INVALID_ARGUMENT: Unparseable tensorflow.GraphDef proto\n"]},{"name":"stdout","output_type":"stream","text":["223/223 [==============================] - 86s 375ms/step - loss: 0.4715 - accuracy: 0.7923 - val_loss: 0.5935 - val_accuracy: 0.7557\n","\n","Epoch 2/15\n","\n","223/223 [==============================] - 96s 429ms/step - loss: 0.4159 - accuracy: 0.8254 - val_loss: 0.5658 - val_accuracy: 0.7571\n","\n","Epoch 3/15\n","\n","223/223 [==============================] - 114s 512ms/step - loss: 0.3900 - accuracy: 0.8419 - val_loss: 0.5290 - val_accuracy: 0.7520\n","\n","Epoch 4/15\n","\n","223/223 [==============================] - 101s 455ms/step - loss: 0.3843 - accuracy: 0.8403 - val_loss: 0.5254 - val_accuracy: 0.7689\n","\n","Epoch 5/15\n","\n","223/223 [==============================] - 103s 463ms/step - loss: 0.3726 - accuracy: 0.8451 - val_loss: 0.5544 - val_accuracy: 0.7436\n","\n","Epoch 6/15\n","\n","223/223 [==============================] - 102s 458ms/step - loss: 0.3603 - accuracy: 0.8530 - val_loss: 0.5221 - val_accuracy: 0.7596\n","\n","Epoch 7/15\n","\n","223/223 [==============================] - 102s 459ms/step - loss: 0.3584 - accuracy: 0.8545 - val_loss: 0.5218 - val_accuracy: 0.7577\n","\n","Epoch 8/15\n","\n","223/223 [==============================] - 102s 458ms/step - loss: 0.3433 - accuracy: 0.8575 - val_loss: 0.5580 - val_accuracy: 0.7771\n","\n","Epoch 9/15\n","\n","223/223 [==============================] - 101s 455ms/step - loss: 0.3364 - accuracy: 0.8631 - val_loss: 0.5262 - val_accuracy: 0.7647\n","\n","Epoch 10/15\n","\n","223/223 [==============================] - 101s 454ms/step - loss: 0.3318 - accuracy: 0.8678 - val_loss: 0.5256 - val_accuracy: 0.7748\n","\n","Epoch 11/15\n","\n","223/223 [==============================] - 104s 466ms/step - loss: 0.3349 - accuracy: 0.8637 - val_loss: 0.5258 - val_accuracy: 0.7791\n","\n","Epoch 12/15\n","\n","223/223 [==============================] - 101s 454ms/step - loss: 0.3241 - accuracy: 0.8720 - val_loss: 0.5370 - val_accuracy: 0.7819\n","\n","Epoch 13/15\n","\n","223/223 [==============================] - 102s 456ms/step - loss: 0.3291 - accuracy: 0.8673 - val_loss: 0.5452 - val_accuracy: 0.7647\n","\n","Epoch 14/15\n","\n","223/223 [==============================] - 101s 451ms/step - loss: 0.3161 - accuracy: 0.8756 - val_loss: 0.5471 - val_accuracy: 0.7675\n","\n","Epoch 15/15\n","\n","223/223 [==============================] - 102s 458ms/step - loss: 0.3159 - accuracy: 0.8724 - val_loss: 0.5331 - val_accuracy: 0.7833\n"]}],"source":["history = fit_model(xception_model, xception_base_model, epochs = 15)"]},{"cell_type":"code","execution_count":41,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 500x800 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plot_history(history)"]},{"cell_type":"markdown","metadata":{},"source":["#### Approach 6: CNN with Attention\n","\n","this file was run on KAGGLE for GPU-based NN boosting"]},{"cell_type":"markdown","metadata":{},"source":["HEM: normal\n","\n","ALL: cancer"]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2024-05-17T10:23:52.427860Z","iopub.status.busy":"2024-05-17T10:23:52.427245Z","iopub.status.idle":"2024-05-17T10:24:05.908894Z","shell.execute_reply":"2024-05-17T10:24:05.907914Z","shell.execute_reply.started":"2024-05-17T10:23:52.427830Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["2024-05-17 10:23:54.693312: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n","2024-05-17 10:23:54.693456: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n","2024-05-17 10:23:54.849698: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"]}],"source":["# importing libraries and modules\n","\n","import os\n","import shutil\n","import tensorflow as tf\n","from tensorflow.keras.models import Sequential\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import keras\n","from keras.layers import Input, InputLayer, Conv2D, MaxPooling2D, Flatten, ELU, Dense, BatchNormalization, Activation\n","from keras.callbacks import ModelCheckpoint\n","from keras.optimizers import Nadam\n","from keras.models import Model\n","from keras.layers import Multiply\n","from keras.regularizers import l2"]},{"cell_type":"code","execution_count":3,"metadata":{"execution":{"iopub.execute_input":"2024-05-17T10:24:05.910999Z","iopub.status.busy":"2024-05-17T10:24:05.910475Z","iopub.status.idle":"2024-05-17T10:25:47.318935Z","shell.execute_reply":"2024-05-17T10:25:47.317899Z","shell.execute_reply.started":"2024-05-17T10:24:05.910965Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Processing /kaggle/input/leukemia-classification/C-NMC_Leukemia/training_data/fold_0...\n","Checking /kaggle/input/leukemia-classification/C-NMC_Leukemia/training_data/fold_0/hem...\n","Checking /kaggle/input/leukemia-classification/C-NMC_Leukemia/training_data/fold_0/all...\n","Processing /kaggle/input/leukemia-classification/C-NMC_Leukemia/training_data/fold_1...\n","Checking /kaggle/input/leukemia-classification/C-NMC_Leukemia/training_data/fold_1/hem...\n","Checking /kaggle/input/leukemia-classification/C-NMC_Leukemia/training_data/fold_1/all...\n","Processing /kaggle/input/leukemia-classification/C-NMC_Leukemia/training_data/fold_2...\n","Checking /kaggle/input/leukemia-classification/C-NMC_Leukemia/training_data/fold_2/hem...\n","Checking /kaggle/input/leukemia-classification/C-NMC_Leukemia/training_data/fold_2/all...\n"]}],"source":["# moving down training hierarchy to read files and merge under common sections\n","\n","def combine_folders(input_folder_path, output_folder_path): # combining folds into a single folder with 2 subfolders hem and all\n","    target_folders = {\n","        'hem': os.path.join(output_folder_path, 'hem'),\n","        'all': os.path.join(output_folder_path, 'all')\n","    }\n","\n","    for folder in target_folders.values():\n","        os.makedirs(folder, exist_ok=True)\n","\n","    for fold in ['fold_0', 'fold_1', 'fold_2']: # going over patient folders\n","        fold_path = os.path.join(input_folder_path, fold)\n","        print(f\"Processing {fold_path}...\")\n","        for category in ['hem', 'all']: # checking against categories\n","            category_path = os.path.join(fold_path, category)\n","            print(f\"Checking {category_path}...\")\n","            if os.path.exists(category_path):\n","                for item in os.listdir(category_path):\n","                    source = os.path.join(category_path, item)\n","                    destination = os.path.join(target_folders[category], item)\n","                    shutil.copy2(source, destination)\n","            else:\n","                print(f\"Directory {category_path} does not exist.\")\n","\n","    for category, folder in target_folders.items():\n","        num_files = len(os.listdir(folder))\n","\n","input_folder_path = '/kaggle/input/leukemia-classification/C-NMC_Leukemia/training_data'\n","output_folder_path = '/kaggle/working/combined_dataset'\n","combine_folders(input_folder_path, output_folder_path)"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2024-05-17T10:26:07.480006Z","iopub.status.busy":"2024-05-17T10:26:07.479266Z","iopub.status.idle":"2024-05-17T10:26:11.210806Z","shell.execute_reply":"2024-05-17T10:26:11.210024Z","shell.execute_reply.started":"2024-05-17T10:26:07.479973Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Found 10661 files belonging to 2 classes.\n","Using 7463 files for training.\n","Found 10661 files belonging to 2 classes.\n","Using 3198 files for validation.\n"]}],"source":["# file generation\n","\n","batch_size = 32 # takes 32 files at a time for generation\n","img_height = 100 # size of images\n","img_width = 100\n","\n","train_ds = tf.keras.utils.image_dataset_from_directory( # splitting training daat into train and val \n","    output_folder_path,\n","    validation_split=0.3,\n","    subset=\"training\",\n","    seed=123,\n","    image_size=(img_height, img_width),\n","    batch_size=batch_size\n",")\n","\n","val_ds = tf.keras.utils.image_dataset_from_directory(\n","    output_folder_path,\n","    validation_split=0.3, # validation split from training\n","    subset=\"validation\",\n","    seed=123,\n","    image_size=(img_height, img_width),\n","    batch_size=batch_size\n",")"]},{"cell_type":"code","execution_count":115,"metadata":{"execution":{"iopub.execute_input":"2024-05-17T11:35:15.991139Z","iopub.status.busy":"2024-05-17T11:35:15.990722Z","iopub.status.idle":"2024-05-17T11:35:16.109384Z","shell.execute_reply":"2024-05-17T11:35:16.108657Z","shell.execute_reply.started":"2024-05-17T11:35:15.991107Z"},"trusted":true},"outputs":[{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_67\"</span>\n","</pre>\n"],"text/plain":["\u001b[1mModel: \"functional_67\"\u001b[0m\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)        </span>┃<span style=\"font-weight: bold\"> Output Shape      </span>┃<span style=\"font-weight: bold\">    Param # </span>┃<span style=\"font-weight: bold\"> Connected to      </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n","│ input_layer_33      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                 │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)                │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ conv2d_130 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │ input_layer_33[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n","│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>,  │        <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ conv2d_130[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ max_pooling2d_97    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalizat… │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ conv2d_131 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │ max_pooling2d_97… │\n","│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ conv2d_131[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ max_pooling2d_98    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalizat… │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ conv2d_132 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>,    │      <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │ max_pooling2d_98… │\n","│                     │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ batch_normalizatio… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">25</span>,    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │ conv2d_132[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalizatio…</span> │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ max_pooling2d_99    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ batch_normalizat… │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)      │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ conv2d_133 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>) │         <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span> │ max_pooling2d_99… │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ multiply_33         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>,    │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ max_pooling2d_99… │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Multiply</span>)          │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)               │            │ conv2d_133[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ flatten_33          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5408</span>)      │          <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ multiply_33[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)           │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_66 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │    <span style=\"color: #00af00; text-decoration-color: #00af00\">173,088</span> │ flatten_33[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_67 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span> │ dense_66[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n","└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n","</pre>\n"],"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m   Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to     \u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n","│ input_layer_33      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m100\u001b[0m,  │          \u001b[38;5;34m0\u001b[0m │ -                 │\n","│ (\u001b[38;5;33mInputLayer\u001b[0m)        │ \u001b[38;5;34m3\u001b[0m)                │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ conv2d_130 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m100\u001b[0m,  │        \u001b[38;5;34m896\u001b[0m │ input_layer_33[\u001b[38;5;34m0\u001b[0m… │\n","│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m100\u001b[0m,  │        \u001b[38;5;34m128\u001b[0m │ conv2d_130[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n","│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ max_pooling2d_97    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m50\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n","│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ conv2d_131 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m50\u001b[0m,    │      \u001b[38;5;34m9,248\u001b[0m │ max_pooling2d_97… │\n","│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m, \u001b[38;5;34m50\u001b[0m,    │        \u001b[38;5;34m128\u001b[0m │ conv2d_131[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n","│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ max_pooling2d_98    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n","│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ conv2d_132 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m,    │      \u001b[38;5;34m9,248\u001b[0m │ max_pooling2d_98… │\n","│                     │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ batch_normalizatio… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m, \u001b[38;5;34m25\u001b[0m,    │        \u001b[38;5;34m128\u001b[0m │ conv2d_132[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n","│ (\u001b[38;5;33mBatchNormalizatio…\u001b[0m │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ max_pooling2d_99    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ batch_normalizat… │\n","│ (\u001b[38;5;33mMaxPooling2D\u001b[0m)      │ \u001b[38;5;34m32\u001b[0m)               │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ conv2d_133 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m1\u001b[0m) │         \u001b[38;5;34m33\u001b[0m │ max_pooling2d_99… │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ multiply_33         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m,    │          \u001b[38;5;34m0\u001b[0m │ max_pooling2d_99… │\n","│ (\u001b[38;5;33mMultiply\u001b[0m)          │ \u001b[38;5;34m32\u001b[0m)               │            │ conv2d_133[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ flatten_33          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5408\u001b[0m)      │          \u001b[38;5;34m0\u001b[0m │ multiply_33[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n","│ (\u001b[38;5;33mFlatten\u001b[0m)           │                   │            │                   │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_66 (\u001b[38;5;33mDense\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │    \u001b[38;5;34m173,088\u001b[0m │ flatten_33[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n","├─────────────────────┼───────────────────┼────────────┼───────────────────┤\n","│ dense_67 (\u001b[38;5;33mDense\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)         │         \u001b[38;5;34m33\u001b[0m │ dense_66[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n","└─────────────────────┴───────────────────┴────────────┴───────────────────┘\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">192,930</span> (753.63 KB)\n","</pre>\n"],"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m192,930\u001b[0m (753.63 KB)\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">192,738</span> (752.88 KB)\n","</pre>\n"],"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m192,738\u001b[0m (752.88 KB)\n"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">192</span> (768.00 B)\n","</pre>\n"],"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m192\u001b[0m (768.00 B)\n"]},"metadata":{},"output_type":"display_data"}],"source":["# modelling\n","\n","def attention_mechanism(inputs): # Attention mechanism\n","\n","    att_weights = Conv2D(1, kernel_size=(1, 1), activation='sigmoid')(inputs)\n","\n","    attended_output = Multiply()([inputs, att_weights])\n","    \n","    return attended_output\n","\n","# MOdel Checkpoint focused on val_accuracy to store best weights of max val_accuracy\n","model_checkpoint = ModelCheckpoint('model.keras', monitor='val_accuracy', save_best_only=True, verbose=1, mode='max')\n","\n","input_dim = Input(shape=(100, 100, 3)) # input layer\n","\n","# Convolutional layers with BatchNormalization and MaxPooling\n","layer_1 = Conv2D(32, 3, activation=ELU(), padding='same')(input_dim)\n","x = BatchNormalization()(layer_1)\n","x = MaxPooling2D(pool_size=2, padding='same')(x)\n","\n","layer_2 = Conv2D(32, 3, activation=ELU(), padding='same')(x)\n","layer_2 = BatchNormalization()(layer_2)\n","x = MaxPooling2D(pool_size=2, padding='same')(layer_2)\n","\n","layer_2 = Conv2D(32, 3, activation='relu', padding='same')(x)\n","layer_2 = BatchNormalization()(layer_2)\n","x = MaxPooling2D(pool_size=2, padding='same')(layer_2)\n","\n","# Applying attention mechanism\n","x = attention_mechanism(x)\n","\n","# Flatten layer to reshape data\n","x = Flatten()(x)\n","\n","# Dense layers\n","layer_4 = Dense(32, activation='relu')(x)\n","\n","layer_5 = Dense(1, activation='sigmoid')(layer_4)\n","\n","model = Model(inputs=input_dim, outputs=layer_5) # setting model layers\n","model.summary() # summary of model\n","\n","model.compile(optimizer=Nadam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy']) # compiling the model"]},{"cell_type":"code","execution_count":116,"metadata":{"execution":{"iopub.execute_input":"2024-05-17T11:35:16.224505Z","iopub.status.busy":"2024-05-17T11:35:16.224212Z","iopub.status.idle":"2024-05-17T11:36:24.186534Z","shell.execute_reply":"2024-05-17T11:36:24.185671Z","shell.execute_reply.started":"2024-05-17T11:35:16.224480Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/12\n","\u001b[1m 12/234\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3s\u001b[0m 16ms/step - accuracy: 0.7340 - loss: 0.6387"]},{"name":"stderr","output_type":"stream","text":["W0000 00:00:1715945721.501733     114 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n"]},{"name":"stdout","output_type":"stream","text":["\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.7917 - loss: 0.4856"]},{"name":"stderr","output_type":"stream","text":["W0000 00:00:1715945726.868314     112 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","W0000 00:00:1715945727.473353     115 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n"]},{"name":"stdout","output_type":"stream","text":["\n","Epoch 1: val_accuracy improved from -inf to 0.81238, saving model to model.keras\n","\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 33ms/step - accuracy: 0.7918 - loss: 0.4855 - val_accuracy: 0.8124 - val_loss: 0.4244\n","Epoch 2/12\n","\u001b[1m232/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.8392 - loss: 0.3878\n","Epoch 2: val_accuracy improved from 0.81238 to 0.82896, saving model to model.keras\n","\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.8392 - loss: 0.3879 - val_accuracy: 0.8290 - val_loss: 0.4083\n","Epoch 3/12\n","\u001b[1m233/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.8554 - loss: 0.3573\n","Epoch 3: val_accuracy did not improve from 0.82896\n","\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.8554 - loss: 0.3573 - val_accuracy: 0.8005 - val_loss: 0.4577\n","Epoch 4/12\n","\u001b[1m232/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.8670 - loss: 0.3259\n","Epoch 4: val_accuracy improved from 0.82896 to 0.84522, saving model to model.keras\n","\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.8670 - loss: 0.3259 - val_accuracy: 0.8452 - val_loss: 0.3770\n","Epoch 5/12\n","\u001b[1m229/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.8852 - loss: 0.2962\n","Epoch 5: val_accuracy improved from 0.84522 to 0.84897, saving model to model.keras\n","\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.8851 - loss: 0.2962 - val_accuracy: 0.8490 - val_loss: 0.3622\n","Epoch 6/12\n","\u001b[1m229/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.8961 - loss: 0.2734\n","Epoch 6: val_accuracy did not improve from 0.84897\n","\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.8961 - loss: 0.2734 - val_accuracy: 0.7942 - val_loss: 0.4653\n","Epoch 7/12\n","\u001b[1m233/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.9001 - loss: 0.2512\n","Epoch 7: val_accuracy did not improve from 0.84897\n","\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.9002 - loss: 0.2511 - val_accuracy: 0.8474 - val_loss: 0.3727\n","Epoch 8/12\n","\u001b[1m229/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.9138 - loss: 0.2255\n","Epoch 8: val_accuracy did not improve from 0.84897\n","\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.9138 - loss: 0.2253 - val_accuracy: 0.8390 - val_loss: 0.3986\n","Epoch 9/12\n","\u001b[1m233/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.9252 - loss: 0.1959\n","Epoch 9: val_accuracy improved from 0.84897 to 0.85553, saving model to model.keras\n","\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.9253 - loss: 0.1958 - val_accuracy: 0.8555 - val_loss: 0.3830\n","Epoch 10/12\n","\u001b[1m231/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.9394 - loss: 0.1654\n","Epoch 10: val_accuracy improved from 0.85553 to 0.85616, saving model to model.keras\n","\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.9393 - loss: 0.1655 - val_accuracy: 0.8562 - val_loss: 0.3792\n","Epoch 11/12\n","\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.9522 - loss: 0.1435\n","Epoch 11: val_accuracy did not improve from 0.85616\n","\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.9522 - loss: 0.1435 - val_accuracy: 0.8427 - val_loss: 0.4524\n","Epoch 12/12\n","\u001b[1m229/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.9585 - loss: 0.1239\n","Epoch 12: val_accuracy improved from 0.85616 to 0.85804, saving model to model.keras\n","\u001b[1m234/234\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.9585 - loss: 0.1239 - val_accuracy: 0.8580 - val_loss: 0.3903\n"]}],"source":["# training model\n","\n","history = model.fit(train_ds,validation_data=val_ds,epochs=12,batch_size = 200, callbacks=[model_checkpoint],verbose=1)"]},{"cell_type":"code","execution_count":117,"metadata":{"execution":{"iopub.execute_input":"2024-05-17T11:36:24.188433Z","iopub.status.busy":"2024-05-17T11:36:24.188136Z","iopub.status.idle":"2024-05-17T11:36:24.833408Z","shell.execute_reply":"2024-05-17T11:36:24.832447Z","shell.execute_reply.started":"2024-05-17T11:36:24.188399Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 500x800 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plot_history(history) # plotting history"]}],"metadata":{"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"datasetId":849724,"sourceId":1449674,"sourceType":"datasetVersion"}],"dockerImageVersionId":30699,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"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":"3.10.13"},"vscode":{"interpreter":{"hash":"f6becb90ea85e501e0c5dc0cf472a45ace99c50f8d1426b3da8c341d18623653"}}},"nbformat":4,"nbformat_minor":4}
diff --git a/Algorithms and Deep Learning Models/Leukaemia Classification using DL/README.md b/Algorithms and Deep Learning Models/Leukaemia Classification using DL/README.md
new file mode 100644
index 000000000..1c837e0d1
--- /dev/null
+++ b/Algorithms and Deep Learning Models/Leukaemia Classification using DL/README.md	
@@ -0,0 +1,84 @@
+# Leukaemia Classification using DL
+
+## PROJECT TITLE
+
+Leukaemia Classification using DL
+
+## 🎯 GOAL
+
+To classify normal from abnormal cell images of Leukaemia.
+
+## 🧵 DATASET
+
+The link for the dataset used in this project: https://www.kaggle.com/datasets/andrewmvd/leukemia-classification
+
+## 🧾 DESCRIPTION
+
+This project aims to identify whether the given medical image contains Leukaemia cells or not.
+
+## 🧮 WHAT I HAD DONE
+
+1. Data collection: From the link of the dataset given above. 
+
+2. Data preprocessing: Preprocessed the image in order to have all images in equal shape.
+
+3. Model selection: Chose three Image detection architecture VGG16, ResNet50 and Inception for Image detection. Created models for CNN and CNN with Attention mechanism.
+
+4. Comparative analysis: Compared the accuracy score of all the models.
+
+## 🚀 MODELS USED
+
+1. VGG16
+2. ResNet50
+3. Inception
+4. Xception
+5. CNN
+6. CNN with Attention
+
+## 📚 LIBRARIES NEEDED
+
+The following libraries are required to run this project:
+
+- numpy==1.24.3
+- pandas==1.5.0
+- matplotlib==3.6.0
+- tensorflow==2.6.0
+
+## 📋 EVALUATION METRICS
+
+The evaluation metrics used for assessing the models:
+
+- Accuracy 
+- Loss
+
+## 📈 RESULTS
+
+Results on Val dataset:
+
+| Model      | Accuracy | Loss    |
+|------------|----------|---------|
+| Inception    | 0.775     | 0.498   |
+| ResNet50    | 0.802     | 0.514    |
+| VGG16    | 0.77     | 0.536    |
+| CNN    | 0.784     | 0.506    |
+| Xception    | 0.783     | 0.533    |
+| CNN (Attention)    | 0.858     | 0.39    |
+
+## 📢 CONCLUSION
+
+Based on results we can draw following conclusions:
+
+1. Inception: The Inception model achieved an accuracy of 77.5% with a loss of 0.498. It demonstrates good performance in distinguishing between leukemia and non-leukemia samples.
+
+2. ResNet50: The ResNet50 model performed slightly better with an accuracy of 80.2% and a loss of 0.514. It shows improved capabilities compared to Inception in leukemia detection.
+
+3. VGG16: The VGG16 model achieved an accuracy of 77.0% with a loss of 0.536. It falls slightly behind ResNet50 in terms of accuracy and loss.
+
+4. CNN: The CNN model achieved an accuracy of 78.4% with a loss of 0.506. It demonstrates similar performance to ResNet50 and shows potential in leukemia detection.
+
+5. Xception: The Xception model achieved an accuracy of 78.3% with a loss of 0.533. It shows comparable performance to the other models in this task.
+
+6. CNN with Attention: The CNN with Attention mechanism model achieved an astonishingly high accuracy of 85.8% with a significantly lower loss of 0.39. It demonstrates exceptional abilities to generalize and classify with a simple and lightweight architecture.
+
+Overall, all the models performed relatively well in leukemia detection, with accuracies ranging from 77% to 85.8%, with CNN-Attention being the clear winner.  
+
diff --git a/Algorithms and Deep Learning Models/Leukaemia Classification using DL/Requirements.txt b/Algorithms and Deep Learning Models/Leukaemia Classification using DL/Requirements.txt
new file mode 100644
index 000000000..05eabaada
--- /dev/null
+++ b/Algorithms and Deep Learning Models/Leukaemia Classification using DL/Requirements.txt	
@@ -0,0 +1,5 @@
+numpy==1.24.3
+pandas==1.5.0
+matplotlib==3.6.0
+tensorflow==2.6.0
+Keras
\ No newline at end of file