Solar PV Image Classification Deep Learning Models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CS8982 Solar PV Detection Project - Final Deep Learning Model Presentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is a clean version of the notebook that was used to test a variety of different deep learning frameworks for the binary image classification problem of detecting solar panels from high resolution orthoimagery (HRO). The HRO images were obtained from the USGS Earth Explorer sampled from various places in the San Francisco Bay area with Contra Costa county. Each scene from the USGS is 1ft (or .3m) resolution per pixel, and standard 5,000x5,000 pixel space. Each of these scenes were split into 2500 tiles of 100x100 pixels space in order to be annotated with binary labels, either having a solar panel present or not. A total of ~27,000 images were randomly sampled and annotated, creating 688 positive labels, and over 26,000 negative labels.\n",
    "\n",
    "Three trial runs were performed with different splits between negative and positive labels:\n",
    "\n",
    "1) 10:1\n",
    "\n",
    "2) 100:3 (original rate of positive label discovery)\n",
    "\n",
    "3) 1:1\n",
    "\n",
    "Various deep learning models were trained as seen below using a basic Sequential model, as well as popular frameworks that are very deep, e.g. 20 to over 100 hidden layers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import imageio\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "# from keras import backend as K\n",
    "from tensorflow.python.client import device_lib\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten, AvgPool2D\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "import kerastuner as kt\n",
    "from kerastuner.tuners import RandomSearch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observe Tensorflow version and GPU is visible"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These two cells just validate the Tensorflow version installed, and that Tensorflow can see the CPU and GPU devices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.3.1'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[name: \"/device:CPU:0\"\n",
      "device_type: \"CPU\"\n",
      "memory_limit: 268435456\n",
      "locality {\n",
      "}\n",
      "incarnation: 5642190034637556104\n",
      ", name: \"/device:XLA_CPU:0\"\n",
      "device_type: \"XLA_CPU\"\n",
      "memory_limit: 17179869184\n",
      "locality {\n",
      "}\n",
      "incarnation: 16565787184366753094\n",
      "physical_device_desc: \"device: XLA_CPU device\"\n",
      ", name: \"/device:GPU:0\"\n",
      "device_type: \"GPU\"\n",
      "memory_limit: 4951408640\n",
      "locality {\n",
      "  bus_id: 1\n",
      "  links {\n",
      "  }\n",
      "}\n",
      "incarnation: 1103030964701983909\n",
      "physical_device_desc: \"device: 0, name: GeForce RTX 2060, pci bus id: 0000:01:00.0, compute capability: 7.5\"\n",
      ", name: \"/device:XLA_GPU:0\"\n",
      "device_type: \"XLA_GPU\"\n",
      "memory_limit: 17179869184\n",
      "locality {\n",
      "}\n",
      "incarnation: 16350516486517801030\n",
      "physical_device_desc: \"device: XLA_GPU device\"\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "print(device_lib.list_local_devices())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n"
     ]
    }
   ],
   "source": [
    "print(tf.config.list_physical_devices('GPU'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configure image data generators and flow directories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned above, three different splits between train and validation sets were experimented with. In the end, an equal 1:1 split between train and validation appeared to yield the best results. Below the flow from directory methods were pulled from the balanced dataset folders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\kenns\\miniconda3\\envs\\gpu_env\\lib\\site-packages\\keras_preprocessing\\image\\image_data_generator.py:337: UserWarning: This ImageDataGenerator specifies `zca_whitening`, which overrides setting of `featurewise_center`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n"
     ]
    }
   ],
   "source": [
    "train_gen = ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    zca_whitening=True,\n",
    "#     zca_epsilon=0.1, # default 0.000001\n",
    "#     featurewise_std_normalization=True,\n",
    "#     rotation_range=90,\n",
    "#     fill_mode=\"nearest\",\n",
    "#     width_shift_range=0.1,\n",
    "#     height_shift_range=0.1,\n",
    "#     zoom_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    vertical_flip=True,\n",
    "#     validation_split=0.2\n",
    ")\n",
    "valid_gen = ImageDataGenerator(rescale=1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1100 images belonging to 2 classes.\n",
      "Found 276 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_generator = train_gen.flow_from_directory(\n",
    "#     'images/original_set/train',\n",
    "    'images/balanced_set/train',\n",
    "    color_mode=\"rgb\",\n",
    "    target_size=(100,100),\n",
    "    batch_size=100,\n",
    "    class_mode='binary',\n",
    "    classes=['none','solar'],\n",
    "    shuffle=True)\n",
    "validation_generator = valid_gen.flow_from_directory(\n",
    "#     'images/original_set/valid',\n",
    "    'images/balanced_set/valid',\n",
    "    color_mode=\"rgb\",\n",
    "    target_size=(100,100),\n",
    "    batch_size=100,\n",
    "    class_mode='binary',\n",
    "    classes=['none','solar'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load ResNet101, VGG16 and Inception models from Keras applications API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we import just a few of the popular deep learning frameworks for image classification proplems. The performance from these can are considered a benchmark for many new algorithms being developed in the literature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "resnet101_model = tf.keras.applications.ResNet101(\n",
    "    include_top=False,\n",
    "    weights='imagenet', # None or other weights\n",
    "    input_tensor=None,\n",
    "    input_shape=(100, 100, 3),\n",
    "    pooling='max' # max or avg\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make it so the base layer weights from transfer learning are not trainable\n",
    "resnet101_model.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "vgg16_model = tf.keras.applications.VGG16(\n",
    "    include_top=False,\n",
    "    weights='imagenet', # None or other weights\n",
    "    input_tensor=None,\n",
    "    input_shape=(100, 100, 3),\n",
    "    pooling='max' # max or avg\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make it so the base layer weights from transfer learning are not trainable\n",
    "vgg16_model.trainable = False "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "incV3_model = tf.keras.applications.InceptionV3(\n",
    "    include_top=False,\n",
    "    weights='imagenet', # None or other weights\n",
    "    input_tensor=None,\n",
    "    input_shape=(100, 100, 3),\n",
    "    pooling='max' # max or avg\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make it so the base layer weights from transfer learning are not trainable\n",
    "incV3_model.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [(resnet101_model, 'resnet'), (vgg16_model, 'vgg'), (incV3_model, 'inception')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit and evaluate each model with the balanced image dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training history for each of the three deep learning frameworks will be fit on the balanced dataset, and saved to a dictionary so that they can be evaluated in a side by side comparison after."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(core):\n",
    "    output_layer = layers.Dense(1, activation='sigmoid')\n",
    "    concat_model = tf.keras.Sequential([core, output_layer])\n",
    "    concat_model.compile(loss='binary_crossentropy', optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), metrics=['accuracy'])\n",
    "    print(concat_model.summary())\n",
    "    return concat_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_history = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training and evaluating resnet model\n",
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "resnet101 (Functional)       (None, 2048)              42658176  \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 2049      \n",
      "=================================================================\n",
      "Total params: 42,660,225\n",
      "Trainable params: 2,049\n",
      "Non-trainable params: 42,658,176\n",
      "_________________________________________________________________\n",
      "None\n",
      "Epoch 1/30\n",
      "11/11 [==============================] - 4s 376ms/step - loss: 1.2088 - accuracy: 0.5027 - val_loss: 0.6950 - val_accuracy: 0.4855\n",
      "Epoch 2/30\n",
      "11/11 [==============================] - 2s 168ms/step - loss: 0.7768 - accuracy: 0.4945 - val_loss: 0.7716 - val_accuracy: 0.5000\n",
      "Epoch 3/30\n",
      "11/11 [==============================] - 2s 203ms/step - loss: 0.6810 - accuracy: 0.5436 - val_loss: 0.6224 - val_accuracy: 0.7246\n",
      "Epoch 4/30\n",
      "11/11 [==============================] - 2s 165ms/step - loss: 0.6110 - accuracy: 0.6991 - val_loss: 0.5866 - val_accuracy: 0.7971\n",
      "Epoch 5/30\n",
      "11/11 [==============================] - 2s 177ms/step - loss: 0.5781 - accuracy: 0.7691 - val_loss: 0.5611 - val_accuracy: 0.7826\n",
      "Epoch 6/30\n",
      "11/11 [==============================] - 2s 180ms/step - loss: 0.5659 - accuracy: 0.7436 - val_loss: 0.5485 - val_accuracy: 0.7935\n",
      "Epoch 7/30\n",
      "11/11 [==============================] - 2s 166ms/step - loss: 0.5479 - accuracy: 0.7418 - val_loss: 0.5473 - val_accuracy: 0.8043\n",
      "Epoch 8/30\n",
      "11/11 [==============================] - 2s 167ms/step - loss: 0.5322 - accuracy: 0.7800 - val_loss: 0.5211 - val_accuracy: 0.7464\n",
      "Epoch 9/30\n",
      "11/11 [==============================] - 2s 168ms/step - loss: 0.5259 - accuracy: 0.7618 - val_loss: 0.5179 - val_accuracy: 0.7935\n",
      "Epoch 10/30\n",
      "11/11 [==============================] - 2s 212ms/step - loss: 0.5177 - accuracy: 0.7591 - val_loss: 0.5301 - val_accuracy: 0.7971\n",
      "Epoch 11/30\n",
      "11/11 [==============================] - 2s 168ms/step - loss: 0.5326 - accuracy: 0.7455 - val_loss: 0.5279 - val_accuracy: 0.8007\n",
      "Epoch 12/30\n",
      "11/11 [==============================] - 2s 192ms/step - loss: 0.5169 - accuracy: 0.7545 - val_loss: 0.5155 - val_accuracy: 0.7862\n",
      "Epoch 13/30\n",
      "11/11 [==============================] - 2s 188ms/step - loss: 0.5134 - accuracy: 0.7627 - val_loss: 0.4992 - val_accuracy: 0.7319\n",
      "Epoch 14/30\n",
      "11/11 [==============================] - 2s 204ms/step - loss: 0.5191 - accuracy: 0.7673 - val_loss: 0.5286 - val_accuracy: 0.6920\n",
      "Epoch 15/30\n",
      "11/11 [==============================] - 2s 207ms/step - loss: 0.5138 - accuracy: 0.7755 - val_loss: 0.5066 - val_accuracy: 0.7029\n",
      "Epoch 16/30\n",
      "11/11 [==============================] - 2s 182ms/step - loss: 0.5172 - accuracy: 0.7609 - val_loss: 0.4953 - val_accuracy: 0.7862\n",
      "Epoch 17/30\n",
      "11/11 [==============================] - 2s 171ms/step - loss: 0.5076 - accuracy: 0.7627 - val_loss: 0.5119 - val_accuracy: 0.7971\n",
      "Epoch 18/30\n",
      "11/11 [==============================] - 2s 170ms/step - loss: 0.5006 - accuracy: 0.7591 - val_loss: 0.4918 - val_accuracy: 0.7391\n",
      "Epoch 19/30\n",
      "11/11 [==============================] - 2s 171ms/step - loss: 0.4973 - accuracy: 0.7700 - val_loss: 0.4896 - val_accuracy: 0.7645\n",
      "Epoch 20/30\n",
      "11/11 [==============================] - 2s 169ms/step - loss: 0.4999 - accuracy: 0.7591 - val_loss: 0.4915 - val_accuracy: 0.7319\n",
      "Epoch 21/30\n",
      "11/11 [==============================] - 2s 171ms/step - loss: 0.4952 - accuracy: 0.7764 - val_loss: 0.4895 - val_accuracy: 0.7826\n",
      "Epoch 22/30\n",
      "11/11 [==============================] - 2s 187ms/step - loss: 0.4922 - accuracy: 0.7727 - val_loss: 0.4918 - val_accuracy: 0.7899\n",
      "Epoch 23/30\n",
      "11/11 [==============================] - 2s 196ms/step - loss: 0.4902 - accuracy: 0.7800 - val_loss: 0.4864 - val_accuracy: 0.7536\n",
      "Epoch 24/30\n",
      "11/11 [==============================] - 2s 199ms/step - loss: 0.4947 - accuracy: 0.7709 - val_loss: 0.4986 - val_accuracy: 0.7935\n",
      "Epoch 25/30\n",
      "11/11 [==============================] - 2s 198ms/step - loss: 0.5087 - accuracy: 0.7645 - val_loss: 0.4947 - val_accuracy: 0.7971\n",
      "Epoch 26/30\n",
      "11/11 [==============================] - 2s 198ms/step - loss: 0.5227 - accuracy: 0.7655 - val_loss: 0.4848 - val_accuracy: 0.7536\n",
      "Epoch 27/30\n",
      "11/11 [==============================] - 2s 201ms/step - loss: 0.4992 - accuracy: 0.7864 - val_loss: 0.4915 - val_accuracy: 0.7246\n",
      "Epoch 28/30\n",
      "11/11 [==============================] - 2s 202ms/step - loss: 0.4889 - accuracy: 0.7773 - val_loss: 0.5283 - val_accuracy: 0.6957\n",
      "Epoch 29/30\n",
      "11/11 [==============================] - 2s 197ms/step - loss: 0.5033 - accuracy: 0.7745 - val_loss: 0.4998 - val_accuracy: 0.7101\n",
      "Epoch 30/30\n",
      "11/11 [==============================] - 2s 198ms/step - loss: 0.5007 - accuracy: 0.7609 - val_loss: 0.4851 - val_accuracy: 0.7935\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Training and evaluating vgg model\n",
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "vgg16 (Functional)           (None, 512)               14714688  \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 513       \n",
      "=================================================================\n",
      "Total params: 14,715,201\n",
      "Trainable params: 513\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n",
      "None\n",
      "Epoch 1/30\n",
      "11/11 [==============================] - 2s 157ms/step - loss: 0.7020 - accuracy: 0.5118 - val_loss: 0.6376 - val_accuracy: 0.6486\n",
      "Epoch 2/30\n",
      "11/11 [==============================] - 1s 128ms/step - loss: 0.6278 - accuracy: 0.7336 - val_loss: 0.5766 - val_accuracy: 0.7899\n",
      "Epoch 3/30\n",
      "11/11 [==============================] - 2s 137ms/step - loss: 0.5685 - accuracy: 0.7882 - val_loss: 0.5323 - val_accuracy: 0.7899\n",
      "Epoch 4/30\n",
      "11/11 [==============================] - 1s 130ms/step - loss: 0.5306 - accuracy: 0.8164 - val_loss: 0.5025 - val_accuracy: 0.7935\n",
      "Epoch 5/30\n",
      "11/11 [==============================] - 2s 137ms/step - loss: 0.5126 - accuracy: 0.7982 - val_loss: 0.4807 - val_accuracy: 0.7971\n",
      "Epoch 6/30\n",
      "11/11 [==============================] - 1s 133ms/step - loss: 0.4926 - accuracy: 0.8027 - val_loss: 0.4676 - val_accuracy: 0.8007\n",
      "Epoch 7/30\n",
      "11/11 [==============================] - 2s 156ms/step - loss: 0.4788 - accuracy: 0.8109 - val_loss: 0.4552 - val_accuracy: 0.8043\n",
      "Epoch 8/30\n",
      "11/11 [==============================] - 2s 157ms/step - loss: 0.4660 - accuracy: 0.7991 - val_loss: 0.4463 - val_accuracy: 0.8007\n",
      "Epoch 9/30\n",
      "11/11 [==============================] - 2s 166ms/step - loss: 0.4576 - accuracy: 0.8145 - val_loss: 0.4408 - val_accuracy: 0.8043\n",
      "Epoch 10/30\n",
      "11/11 [==============================] - 2s 140ms/step - loss: 0.4504 - accuracy: 0.8218 - val_loss: 0.4336 - val_accuracy: 0.8007\n",
      "Epoch 11/30\n",
      "11/11 [==============================] - 2s 153ms/step - loss: 0.4437 - accuracy: 0.8264 - val_loss: 0.4291 - val_accuracy: 0.7971\n",
      "Epoch 12/30\n",
      "11/11 [==============================] - 2s 160ms/step - loss: 0.4377 - accuracy: 0.8200 - val_loss: 0.4249 - val_accuracy: 0.8007\n",
      "Epoch 13/30\n",
      "11/11 [==============================] - 2s 139ms/step - loss: 0.4346 - accuracy: 0.8355 - val_loss: 0.4215 - val_accuracy: 0.8043\n",
      "Epoch 14/30\n",
      "11/11 [==============================] - 1s 135ms/step - loss: 0.4264 - accuracy: 0.8255 - val_loss: 0.4167 - val_accuracy: 0.8007\n",
      "Epoch 15/30\n",
      "11/11 [==============================] - 2s 141ms/step - loss: 0.4259 - accuracy: 0.8182 - val_loss: 0.4151 - val_accuracy: 0.8043\n",
      "Epoch 16/30\n",
      "11/11 [==============================] - 2s 140ms/step - loss: 0.4183 - accuracy: 0.8255 - val_loss: 0.4126 - val_accuracy: 0.8043\n",
      "Epoch 17/30\n",
      "11/11 [==============================] - 1s 136ms/step - loss: 0.4141 - accuracy: 0.8409 - val_loss: 0.4102 - val_accuracy: 0.8043\n",
      "Epoch 18/30\n",
      "11/11 [==============================] - 2s 153ms/step - loss: 0.4183 - accuracy: 0.8236 - val_loss: 0.4067 - val_accuracy: 0.8043\n",
      "Epoch 19/30\n",
      "11/11 [==============================] - 2s 150ms/step - loss: 0.4117 - accuracy: 0.8300 - val_loss: 0.4063 - val_accuracy: 0.8043\n",
      "Epoch 20/30\n",
      "11/11 [==============================] - 2s 139ms/step - loss: 0.4113 - accuracy: 0.8291 - val_loss: 0.4033 - val_accuracy: 0.8043\n",
      "Epoch 21/30\n",
      "11/11 [==============================] - 2s 143ms/step - loss: 0.4114 - accuracy: 0.8309 - val_loss: 0.4013 - val_accuracy: 0.8043\n",
      "Epoch 22/30\n",
      "11/11 [==============================] - 2s 148ms/step - loss: 0.4029 - accuracy: 0.8273 - val_loss: 0.3999 - val_accuracy: 0.8043\n",
      "Epoch 23/30\n",
      "11/11 [==============================] - 2s 138ms/step - loss: 0.3974 - accuracy: 0.8391 - val_loss: 0.3991 - val_accuracy: 0.8152\n",
      "Epoch 24/30\n",
      "11/11 [==============================] - 2s 137ms/step - loss: 0.4043 - accuracy: 0.8291 - val_loss: 0.3975 - val_accuracy: 0.8116\n",
      "Epoch 25/30\n",
      "11/11 [==============================] - 2s 173ms/step - loss: 0.3953 - accuracy: 0.8282 - val_loss: 0.3958 - val_accuracy: 0.8152\n",
      "Epoch 26/30\n",
      "11/11 [==============================] - 2s 173ms/step - loss: 0.3955 - accuracy: 0.8400 - val_loss: 0.3953 - val_accuracy: 0.8225\n",
      "Epoch 27/30\n",
      "11/11 [==============================] - 2s 166ms/step - loss: 0.3975 - accuracy: 0.8345 - val_loss: 0.3932 - val_accuracy: 0.8152\n",
      "Epoch 28/30\n",
      "11/11 [==============================] - 2s 167ms/step - loss: 0.3972 - accuracy: 0.8382 - val_loss: 0.3952 - val_accuracy: 0.8152\n",
      "Epoch 29/30\n",
      "11/11 [==============================] - 2s 178ms/step - loss: 0.3791 - accuracy: 0.8400 - val_loss: 0.3920 - val_accuracy: 0.8188\n",
      "Epoch 30/30\n",
      "11/11 [==============================] - 2s 149ms/step - loss: 0.3837 - accuracy: 0.8436 - val_loss: 0.3916 - val_accuracy: 0.8188\n",
      "----------------------------------------------------------------------------------------------------\n",
      "Training and evaluating inception model\n",
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "inception_v3 (Functional)    (None, 2048)              21802784  \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 1)                 2049      \n",
      "=================================================================\n",
      "Total params: 21,804,833\n",
      "Trainable params: 2,049\n",
      "Non-trainable params: 21,802,784\n",
      "_________________________________________________________________\n",
      "None\n",
      "Epoch 1/30\n",
      "11/11 [==============================] - 3s 268ms/step - loss: 0.6937 - accuracy: 0.6136 - val_loss: 0.6000 - val_accuracy: 0.7138\n",
      "Epoch 2/30\n",
      "11/11 [==============================] - 2s 147ms/step - loss: 0.5665 - accuracy: 0.7355 - val_loss: 0.5500 - val_accuracy: 0.7500\n",
      "Epoch 3/30\n",
      "11/11 [==============================] - 1s 136ms/step - loss: 0.5064 - accuracy: 0.7745 - val_loss: 0.5320 - val_accuracy: 0.7717\n",
      "Epoch 4/30\n",
      "11/11 [==============================] - 2s 144ms/step - loss: 0.4709 - accuracy: 0.7982 - val_loss: 0.5278 - val_accuracy: 0.7681\n",
      "Epoch 5/30\n",
      "11/11 [==============================] - 2s 145ms/step - loss: 0.4627 - accuracy: 0.7964 - val_loss: 0.5134 - val_accuracy: 0.7790\n",
      "Epoch 6/30\n",
      "11/11 [==============================] - 1s 128ms/step - loss: 0.4501 - accuracy: 0.8091 - val_loss: 0.5045 - val_accuracy: 0.7899\n",
      "Epoch 7/30\n",
      "11/11 [==============================] - 1s 131ms/step - loss: 0.4467 - accuracy: 0.7991 - val_loss: 0.4968 - val_accuracy: 0.7862\n",
      "Epoch 8/30\n",
      "11/11 [==============================] - 2s 138ms/step - loss: 0.4303 - accuracy: 0.8082 - val_loss: 0.4992 - val_accuracy: 0.7717\n",
      "Epoch 9/30\n",
      "11/11 [==============================] - 2s 162ms/step - loss: 0.4321 - accuracy: 0.8118 - val_loss: 0.4895 - val_accuracy: 0.7790\n",
      "Epoch 10/30\n",
      "11/11 [==============================] - 1s 133ms/step - loss: 0.4086 - accuracy: 0.8218 - val_loss: 0.4846 - val_accuracy: 0.7826\n",
      "Epoch 11/30\n",
      "11/11 [==============================] - 1s 129ms/step - loss: 0.4167 - accuracy: 0.8227 - val_loss: 0.4828 - val_accuracy: 0.7971\n",
      "Epoch 12/30\n",
      "11/11 [==============================] - 2s 140ms/step - loss: 0.4181 - accuracy: 0.8109 - val_loss: 0.4733 - val_accuracy: 0.7862\n",
      "Epoch 13/30\n",
      "11/11 [==============================] - 2s 173ms/step - loss: 0.4019 - accuracy: 0.8209 - val_loss: 0.4665 - val_accuracy: 0.8007\n",
      "Epoch 14/30\n",
      "11/11 [==============================] - 2s 138ms/step - loss: 0.4125 - accuracy: 0.8245 - val_loss: 0.4623 - val_accuracy: 0.7971\n",
      "Epoch 15/30\n",
      "11/11 [==============================] - 1s 129ms/step - loss: 0.3935 - accuracy: 0.8191 - val_loss: 0.4677 - val_accuracy: 0.7971\n",
      "Epoch 16/30\n",
      "11/11 [==============================] - 1s 131ms/step - loss: 0.3783 - accuracy: 0.8391 - val_loss: 0.4769 - val_accuracy: 0.7971\n",
      "Epoch 17/30\n",
      "11/11 [==============================] - 1s 124ms/step - loss: 0.3985 - accuracy: 0.8191 - val_loss: 0.4708 - val_accuracy: 0.8043\n",
      "Epoch 18/30\n",
      "11/11 [==============================] - 2s 152ms/step - loss: 0.3895 - accuracy: 0.8309 - val_loss: 0.4574 - val_accuracy: 0.7971\n",
      "Epoch 19/30\n",
      "11/11 [==============================] - 2s 143ms/step - loss: 0.3814 - accuracy: 0.8291 - val_loss: 0.4577 - val_accuracy: 0.8007\n",
      "Epoch 20/30\n",
      "11/11 [==============================] - 1s 126ms/step - loss: 0.3800 - accuracy: 0.8273 - val_loss: 0.4580 - val_accuracy: 0.8116\n",
      "Epoch 21/30\n",
      "11/11 [==============================] - 1s 132ms/step - loss: 0.3826 - accuracy: 0.8264 - val_loss: 0.4413 - val_accuracy: 0.8043\n",
      "Epoch 22/30\n",
      "11/11 [==============================] - 1s 128ms/step - loss: 0.3631 - accuracy: 0.8427 - val_loss: 0.4427 - val_accuracy: 0.8043\n",
      "Epoch 23/30\n",
      "11/11 [==============================] - 1s 126ms/step - loss: 0.3784 - accuracy: 0.8418 - val_loss: 0.4430 - val_accuracy: 0.8152\n",
      "Epoch 24/30\n",
      "11/11 [==============================] - 1s 129ms/step - loss: 0.3743 - accuracy: 0.8400 - val_loss: 0.4569 - val_accuracy: 0.8188\n",
      "Epoch 25/30\n",
      "11/11 [==============================] - 2s 149ms/step - loss: 0.3763 - accuracy: 0.8318 - val_loss: 0.4628 - val_accuracy: 0.8007\n",
      "Epoch 26/30\n",
      "11/11 [==============================] - 2s 140ms/step - loss: 0.3761 - accuracy: 0.8282 - val_loss: 0.4485 - val_accuracy: 0.8080\n",
      "Epoch 27/30\n",
      "11/11 [==============================] - 2s 142ms/step - loss: 0.3528 - accuracy: 0.8527 - val_loss: 0.4510 - val_accuracy: 0.8152\n",
      "Epoch 28/30\n",
      "11/11 [==============================] - 2s 157ms/step - loss: 0.3624 - accuracy: 0.8382 - val_loss: 0.4469 - val_accuracy: 0.8188\n",
      "Epoch 29/30\n",
      "11/11 [==============================] - 1s 133ms/step - loss: 0.3535 - accuracy: 0.8491 - val_loss: 0.4448 - val_accuracy: 0.8043\n",
      "Epoch 30/30\n",
      "11/11 [==============================] - 2s 143ms/step - loss: 0.3525 - accuracy: 0.8418 - val_loss: 0.4490 - val_accuracy: 0.8188\n",
      "----------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for model in models:\n",
    "    print(f'Training and evaluating {model[1]} model')\n",
    "    \n",
    "    # grab a model from the model hashmap\n",
    "    this_model = build_model(model[0])\n",
    "    \n",
    "    # fit the model using the train and validation image data generators\n",
    "    history = this_model.fit(\n",
    "        train_generator, \n",
    "        epochs=30,\n",
    "        validation_data=validation_generator,\n",
    "        batch_size=550, \n",
    "        verbose=1)\n",
    "    \n",
    "    # store the current model history in hashmap for plotting accuracy and loss\n",
    "    model_history[model[1]] = history\n",
    "    \n",
    "    # predict the probability distribution of the data\n",
    "#     predictions=this_model.predict_generator(validation_generator, steps=28, verbose=1)\n",
    "\n",
    "    # get the class with highest probability for each sample\n",
    "#     y_pred = np.argmax(predictions, axis=-1)\n",
    "\n",
    "    # get the classification report\n",
    "#     print(classification_report(test_labels, y_pred))\n",
    "    \n",
    "    print('-'*100)\n",
    "    \n",
    "# code to fetch the predictions using the image data generator obtained from\n",
    "# https://stackoverflow.com/questions/59909304/classification-report-using-prediction-generator-method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate model performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the charts below, we can see that the VGG16 and InceptionV3 models are performing 5% better than the ResNet101 model on average once the first two models converge around epoch 7 or 10. After that they hardly diverge. All of the models performance tends to saturate around that same point in the training process. The InceptionV3 validation results are  lower than its training results and stay that way after epoch 2, so perhaps some signs of overfitting. Looking at Loss results for binary crossentropy, we see a slightly different story. Again ResNet101 performs slightly worse with a gap in .1-.2 loss, while VGG16 and InceptionV3 form a tight convergence after the second epoch. However, observing their validation sets, the gap in loss for InceptionV3 between training and validation sets is much wider, again reinforcing the idea that the model is overfitting around epoch 2.  Therefore, VGG16 appears to be the most performant model on this dataset without any further tuning of hyperparameters, as it's training and validation loss stay divergent througha ll epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_model_accuracy(model_history):\n",
    "    legend_list = []\n",
    "    fig = plt.figure(figsize=(10,8))\n",
    "    for key, history in model_history.items():\n",
    "        plt.plot(history.history['accuracy'])\n",
    "        plt.plot(history.history['val_accuracy'], ':')\n",
    "        train_label = key + '_train'\n",
    "        legend_list.append(train_label)\n",
    "        test_label = key + '_valid'\n",
    "        legend_list.append(test_label)\n",
    "    plt.title('model accuracy')\n",
    "    plt.ylabel('accuracy')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(legend_list, loc='lower right')\n",
    "    plt.ylim(.4, 1)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_accuracy(model_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_model_loss(model_history):\n",
    "    legend_list = []\n",
    "    fig = plt.figure(figsize=(10,8))\n",
    "    for key, history in model_history.items():\n",
    "        plt.plot(history.history['loss'])\n",
    "        plt.plot(history.history['val_loss'], ':')\n",
    "        train_label = key + '_train'\n",
    "        legend_list.append(train_label)\n",
    "        test_label = key + '_valid'\n",
    "        legend_list.append(test_label)\n",
    "    plt.title('model loss')\n",
    "    plt.ylabel('loss')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(legend_list, loc='lower right')\n",
    "#     plt.ylim(0.2, 1)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_loss(model_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Manual method of constructing train and test datasets for Keras Tuner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will build up the train and validation datasets manually so that we can use Keras Tuner functionality to tune the hyperparameters of a simpler sequential neural network. Keras provides a lot of flexibility whe we construct our own deep learning framework, and this enables easy tuning of hyperparamters across the network, such as batch size, the width of hidden layers, the learning rate, dropout rate, etc. Seeing that above the VGG16 model worked the best of the three frameworks tested, I will rebuild the architecture from scratch so that the weights of the full network can be trained, and the hypertuner can reasonably update each parameter to optimize the module.\n",
    "\n",
    "Due to memory issues, this section was not very fruitful in the end.  The above pre-trained models still performed as good if not better than what tuned models I could yield below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Build shuffled train and validation sets for X and Y from image folders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "none_train_path = r'C:\\Users\\kenns\\automatic_solar_pv_detection\\images\\balanced_set\\train\\none'\n",
    "solar_train_path = r'C:\\Users\\kenns\\automatic_solar_pv_detection\\images\\balanced_set\\train\\solar'\n",
    "none_valid_path = r'C:\\Users\\kenns\\automatic_solar_pv_detection\\images\\balanced_set\\valid\\none'\n",
    "solar_valid_path = r'C:\\Users\\kenns\\automatic_solar_pv_detection\\images\\balanced_set\\valid\\solar'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "none_train_dir = np.array(os.listdir(none_train_path))\n",
    "solar_train_dir = np.array(os.listdir(solar_train_path))\n",
    "none_valid_dir = np.array(os.listdir(none_valid_path))\n",
    "solar_valid_dir = np.array(os.listdir(solar_valid_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_images(img_dir, path):\n",
    "    imgs = []\n",
    "    for tif in tqdm(img_dir):\n",
    "        img = imageio.imread(os.path.join(path, tif))\n",
    "        imgs.append(img[:, :, 0:3]) # [0:3] -> R,G,B, [1:4] -> G,B,NIR, just [4] -> NIR\n",
    "    return np.array (imgs) / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/questions/4601373/better-way-to-shuffle-two-numpy-arrays-in-unison\n",
    "# needed a way to shuffle the two train and validation sets for X and Y in unison\n",
    "def unison_shuffled_copies(a, b):\n",
    "    assert len(a) == len(b)\n",
    "    p = np.random.permutation(len(a))\n",
    "    return a[p], b[p]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 550/550 [00:04<00:00, 112.40it/s]\n"
     ]
    }
   ],
   "source": [
    "none_train = get_images(none_train_dir, none_train_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 138/138 [00:01<00:00, 91.33it/s]\n"
     ]
    }
   ],
   "source": [
    "none_valid = get_images(none_valid_dir, none_valid_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 550/550 [00:07<00:00, 74.86it/s]\n"
     ]
    }
   ],
   "source": [
    "solar_train = get_images(solar_train_dir, solar_train_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 138/138 [00:01<00:00, 70.41it/s]\n"
     ]
    }
   ],
   "source": [
    "solar_valid = get_images(solar_valid_dir, solar_valid_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# concatenate training arrays for solar and none, and build Y array with 1s and 0s for labels\n",
    "X_train = np.concatenate((solar_train, none_train), axis=0)\n",
    "Y_train = np.array([1]*len(solar_train) + [0]*len(none_train), dtype='float32').reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# concatenate validation arrays for solar and none, and build Y array with 1s and 0s for labels\n",
    "X_test = np.concatenate((solar_valid, none_valid), axis=0)\n",
    "Y_test = np.array([1]*len(solar_valid) + [0]*len(none_valid), dtype='float32').reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shuffle the X and Y arrays in unison so that labels align with 3D matrix of scaled RGB values for each image \n",
    "X_train, Y_train = unison_shuffled_copies(X_train, Y_train)\n",
    "X_test, Y_test = unison_shuffled_copies(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X training set:  (1100, 100, 100, 3)\n",
      "Y training set:  (1100, 1)\n",
      "X validation set:  (276, 100, 100, 3)\n",
      "Y validation set:  (276, 1)\n"
     ]
    }
   ],
   "source": [
    "# confirm the shape of the X and Y objects prior to training the model\n",
    "print('X training set: ', X_train.shape)\n",
    "print('Y training set: ', Y_train.shape)\n",
    "print('X validation set: ', X_test.shape)\n",
    "print('Y validation set: ', Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Build a vanilla network with a few hidden convolutional layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I attempt to use a simpler deep neural network that sort of emulates the modules found in the beginning of Inception or throughout the VGG framework. This means stacking multiple convolutional layers followed by max pooling, with an average pooling layer at the end, before flattening, passing through a fully connected layer, and the sigmoid classification layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMAGE_INPUT_SHAPE = X_train.shape[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_simple_nn():\n",
    "    inputs = tf.keras.Input(shape=IMAGE_INPUT_SHAPE)\n",
    "    x = inputs\n",
    "    \n",
    "    # first stage\n",
    "    x = Conv2D(filters = 64, \n",
    "            kernel_size =(5,5),\n",
    "            activation='relu',\n",
    "            padding='same')(x)\n",
    "    x = Conv2D(filters = 128, \n",
    "            kernel_size =(3,3),\n",
    "            activation='relu',\n",
    "            padding='same')(x)\n",
    "    x = Conv2D(filters = 256, \n",
    "            kernel_size =(1,1),\n",
    "            activation='relu',\n",
    "            padding='same')(x)\n",
    "    x = MaxPool2D(pool_size=(2,2), strides=(2,2))(x)\n",
    "    \n",
    "    # second stage\n",
    "    x = Conv2D(filters = 512, \n",
    "            kernel_size =(3,3),\n",
    "            activation='relu',\n",
    "            padding='same')(x)\n",
    "    x = Conv2D(filters = 512, \n",
    "            kernel_size =(3,3),\n",
    "            activation='relu',\n",
    "            padding='same')(x)\n",
    "    x = AvgPool2D(pool_size=(2,2), strides=(2,2))(x)\n",
    "    \n",
    "    x = Flatten()(x)\n",
    "    x = Dense(64, activation='relu')(x)\n",
    "    outputs = Dense(units=1, activation=\"sigmoid\")(x)\n",
    "    \n",
    "    model = tf.keras.Model(inputs, outputs)\n",
    "    \n",
    "    model.compile(\n",
    "        optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),\n",
    "        loss='binary_crossentropy', \n",
    "        metrics=['accuracy'])\n",
    "    print(model.summary())\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         [(None, 100, 100, 3)]     0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 100, 100, 64)      4864      \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 100, 100, 128)     73856     \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 100, 100, 256)     33024     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 50, 50, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 50, 50, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 50, 50, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "average_pooling2d (AveragePo (None, 25, 25, 512)       0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 320000)            0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 64)                20480064  \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 24,131,841\n",
      "Trainable params: 24,131,841\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "simple_model = build_simple_nn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      " 2/35 [>.............................] - ETA: 5s - loss: 4.1260 - accuracy: 0.5000WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0833s vs `on_train_batch_end` time: 0.1844s). Check your callbacks.\n",
      "35/35 [==============================] - 11s 323ms/step - loss: 0.9197 - accuracy: 0.5382 - val_loss: 0.6746 - val_accuracy: 0.5145\n",
      "Epoch 2/30\n",
      "35/35 [==============================] - 10s 280ms/step - loss: 0.6074 - accuracy: 0.6973 - val_loss: 0.4825 - val_accuracy: 0.7862\n",
      "Epoch 3/30\n",
      "35/35 [==============================] - 10s 282ms/step - loss: 0.4978 - accuracy: 0.7791 - val_loss: 0.4652 - val_accuracy: 0.7899\n",
      "Epoch 4/30\n",
      "35/35 [==============================] - 10s 283ms/step - loss: 0.4912 - accuracy: 0.7682 - val_loss: 0.5741 - val_accuracy: 0.6196\n",
      "Epoch 5/30\n",
      "35/35 [==============================] - 10s 285ms/step - loss: 0.4654 - accuracy: 0.7827 - val_loss: 0.6643 - val_accuracy: 0.6884\n",
      "Epoch 6/30\n",
      "35/35 [==============================] - 10s 283ms/step - loss: 0.6736 - accuracy: 0.5773 - val_loss: 0.6661 - val_accuracy: 0.4891\n",
      "Epoch 7/30\n",
      "35/35 [==============================] - 10s 287ms/step - loss: 0.5988 - accuracy: 0.7018 - val_loss: 0.5638 - val_accuracy: 0.7645\n",
      "Epoch 8/30\n",
      "35/35 [==============================] - 10s 287ms/step - loss: 0.4487 - accuracy: 0.8064 - val_loss: 0.4788 - val_accuracy: 0.7826\n",
      "Epoch 9/30\n",
      "35/35 [==============================] - 10s 288ms/step - loss: 0.4355 - accuracy: 0.8145 - val_loss: 0.4541 - val_accuracy: 0.8007\n",
      "Epoch 10/30\n",
      "35/35 [==============================] - 10s 288ms/step - loss: 0.3987 - accuracy: 0.8191 - val_loss: 0.4375 - val_accuracy: 0.8116\n",
      "Epoch 11/30\n",
      "35/35 [==============================] - 10s 289ms/step - loss: 0.3672 - accuracy: 0.8345 - val_loss: 0.4627 - val_accuracy: 0.7609\n",
      "Epoch 12/30\n",
      "35/35 [==============================] - 10s 290ms/step - loss: 0.3349 - accuracy: 0.8482 - val_loss: 0.4845 - val_accuracy: 0.7971\n",
      "Epoch 13/30\n",
      "35/35 [==============================] - 10s 291ms/step - loss: 0.3149 - accuracy: 0.8755 - val_loss: 0.4921 - val_accuracy: 0.7500\n",
      "Epoch 14/30\n",
      "35/35 [==============================] - 10s 291ms/step - loss: 0.3131 - accuracy: 0.8673 - val_loss: 0.5333 - val_accuracy: 0.7899\n",
      "Epoch 15/30\n",
      "35/35 [==============================] - 10s 291ms/step - loss: 0.2259 - accuracy: 0.9100 - val_loss: 0.5809 - val_accuracy: 0.7645\n",
      "Epoch 16/30\n",
      "35/35 [==============================] - 10s 291ms/step - loss: 0.1706 - accuracy: 0.9473 - val_loss: 0.8304 - val_accuracy: 0.7681\n",
      "Epoch 17/30\n",
      "35/35 [==============================] - 10s 292ms/step - loss: 0.1497 - accuracy: 0.9418 - val_loss: 0.9008 - val_accuracy: 0.7500\n",
      "Epoch 18/30\n",
      "35/35 [==============================] - 10s 293ms/step - loss: 0.1148 - accuracy: 0.9682 - val_loss: 1.1488 - val_accuracy: 0.7790\n",
      "Epoch 19/30\n",
      "35/35 [==============================] - 10s 293ms/step - loss: 0.1125 - accuracy: 0.9645 - val_loss: 1.1961 - val_accuracy: 0.7754\n",
      "Epoch 20/30\n",
      "35/35 [==============================] - 10s 293ms/step - loss: 0.0816 - accuracy: 0.9809 - val_loss: 1.7208 - val_accuracy: 0.7428\n",
      "Epoch 21/30\n",
      "35/35 [==============================] - 10s 293ms/step - loss: 0.0974 - accuracy: 0.9745 - val_loss: 1.0644 - val_accuracy: 0.7717\n",
      "Epoch 22/30\n",
      "35/35 [==============================] - 10s 294ms/step - loss: 0.0674 - accuracy: 0.9873 - val_loss: 1.5453 - val_accuracy: 0.7500\n",
      "Epoch 23/30\n",
      "35/35 [==============================] - 10s 294ms/step - loss: 0.0570 - accuracy: 0.9918 - val_loss: 1.7865 - val_accuracy: 0.7826\n",
      "Epoch 24/30\n",
      "35/35 [==============================] - 10s 295ms/step - loss: 0.0504 - accuracy: 0.9882 - val_loss: 2.6453 - val_accuracy: 0.7971\n",
      "Epoch 25/30\n",
      "35/35 [==============================] - 10s 294ms/step - loss: 0.1230 - accuracy: 0.9664 - val_loss: 2.1369 - val_accuracy: 0.7609\n",
      "Epoch 26/30\n",
      "35/35 [==============================] - 10s 296ms/step - loss: 0.1273 - accuracy: 0.9600 - val_loss: 1.6060 - val_accuracy: 0.7174\n",
      "Epoch 27/30\n",
      "35/35 [==============================] - 10s 294ms/step - loss: 0.1216 - accuracy: 0.9655 - val_loss: 1.4125 - val_accuracy: 0.7391\n",
      "Epoch 28/30\n",
      "35/35 [==============================] - 10s 296ms/step - loss: 0.0618 - accuracy: 0.9864 - val_loss: 2.0080 - val_accuracy: 0.7826\n",
      "Epoch 29/30\n",
      "35/35 [==============================] - 10s 296ms/step - loss: 0.0554 - accuracy: 0.9873 - val_loss: 1.7869 - val_accuracy: 0.7536\n",
      "Epoch 30/30\n",
      "35/35 [==============================] - 10s 295ms/step - loss: 0.0459 - accuracy: 0.9909 - val_loss: 2.6214 - val_accuracy: 0.7754\n"
     ]
    }
   ],
   "source": [
    "simple_history = simple_model.fit(\n",
    "    X_train, Y_train, \n",
    "    epochs=30, \n",
    "    validation_data=(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_accuracy({'simple_model' : simple_history})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lGTRr68zrFQrxqdD+hNC03VR0PsXtBAFRd6SIiEig5r4MT/QO/Xit7FUw+3lnDJrUTt5G2DALKsrcTlIjFWEiIiKB6nkunD/WuYsxlNZNh0/vgvwtoT1PY7RsMvz3LKgodjtJjdQdKSIiEqhWvZ1HqPW9FHpdAEkZoT9XY9PvCmjdF+JS3E5SIxVhIiIigdo8F1p0Cv2g7/jU0LbfmCW3ch4RQN2RIiIigSgvhpdGwQ8vNcz5Vk+Dr//ZMOdqTJa/D7vCe7mifVSEiYiIBMJ44eqJ0Puihjnfptkw58WIGGAeNirK4N2fw+K33U4SEGMj7M6LQYMG2blz57odQ0REJLTKi8EbCx5dLwmYzwe5ayEm0ZlGJAwYY+ZZa6tcbFRjwkRERAKxa7WzpFDmoIYpjKLjQ3+OxsbjgfSubqcImMprERGRQPzwErx2Yegmaa3KrGfgy3803Pki3ZZ5sPhdqKxwO0lAdCVMREQkEENvdeYJa8giLHsF7N3dcOeLdEsmwLxXoe8lbicJiMaEiYiIhCtrG7boi3TlxVCwzZlGJExUNyZM3ZEiIiI1sda5ypK3sWHPqwKsdqLjw6oAq4mKMBERkZoUbIeJN8Kqzxr2vNbChBtgxmMNe95I5PPBN/8Pti12O0nANCZMRESkJokZ8OvZkJDesOc1BjBAZA0dckXhdvjiIYhLhTb93E4TEBVhIiIiNfFGQcue7pz7kpfdOW+kadYW7tnqdopaUXekiIhITTbMgmXvuZshwm6kc0VMovOIECrCREREajLvFfj8XnfOXZwHzwyBuePcOX+kWPERfDfW7RS1ou5IERGRmox5HIp2unPuuBRnjFNyG3fOHyl+/AQ2znLmc4sQKsJERERqEpvkPNxgDFz8kjvnjiQXjHXmCYsg6o4UERGpTtle+PZJyP7R3RwVZVBZ7m6GcBdh622qCBMREalO3gaY+lfYvsS9DFvmwz/bwbrp7mUIZ8W74aPbYesCt5PUioowERGR6rTsCXdtgO5nu5chvSsMvgmS27qXIZzlb4Olk5yfEURrR4qIiEjjEIZrbbqydqQxpp0x5itjzHJjzDJjzG1VHGOMMU8ZY9YYYxYbYwaGKo+IiEidLJsMP4TBwHhrIXed5gurTpgVYDUJZXdkBXCHtbYXcAJwqzGm12HHnAV09T9uBp4LYR4REZHaWzbZmSfMbQvfgKf6Q85at5OEn1nPwFePuJ2i1kI2RYW1dhuwzf+8wBizAsgElh902PnA/6zTJzrbGJNqjGnjf6+IiIj7Lvufc4ek244ZAec8AQkt3E5SdzuWw/pv4PhbgtvuzhWwNye4bTaABpknzBjTERgAfH/Yrkxg00Hbm/2vqQgTEZHwEZPgdgJIbQ+DbnA7Rf0sehPmvwr9Lof41OC1e0FkzZS/T8jvjjTGJAETgd9ba/Pr2MbNxpi5xpi52dnZwQ0oIiJyNIXZMOVO2L7U7SSOvbmw4Tu3U9RNeTEMvx1+tzC4BVgEC2kRZoyJxinAxltrJ1VxyBag3UHbWf7XDmGtfcFaO8haOygjIyM0YUVERA6XvxkWvgmFO9xO4pj1NLx6TsTNDA/A7GfhiT4QFetsB+t32LUaxl8G2xYHp70GFLLuSGOMAV4GVlhrHz/KYR8AvzHGvAUcD+zReDAREQkbbQfA3ZvC547E/ldD51PAE4GrDrY7Hk78LcQkwsSbnAlWr5lY/3ZL9kD+EddvIkIo/ysOA64FlhhjFvpfuwdoD2CtfR6YApwNrAH2AteHMI+IiEjtGRM+Ux+kd3EekajjcOcB0OFEKC0Enw889eyUyxoEv/q2/vlcEMq7I2cC1X5r/XdFRs5y5yIi0rTMegYqiuGkO91OcsC2xVCwDbqd6XaSwFWUQlE2NMt0CtpIv8EgSLRskYiIyNFsX+ys2xhOZj4BH9/hdora2fwDPNEb1n5x4DVrYfU02Hj4xAm19OFt8Pl99WvDJRHYqSwiItJALnrB7QRHOvVeiI53O0XttOgEZz8GbQ9aGKeyHD6+HVr3g/bj69628TiPCKS1I0VERMQd2augeUeIinE7Sci4snakiIhIRNu1Bt68CrYvcTvJkZZMgJUfu50icJt+gOK8I1/P6OYUYL5KZ5B+E6MiTEREpCrFuyF3bfhMT3Gw756BuePcThGY8mIYd6aTuSr52+C5E2HJu7Vv+6fp8Nxw54paBNKYMBERkaq0Gwy31nPQeKhc9Q4kpLmdIjDGC9dMgJR2Ve9PagUte9ZtTUxvNDRrG7Ez8GtMmIiIiEiIaEyYiIhIbU25E6b+1e0UVSsvgS/+Bqs+dztJzTbMgm2Laj6ushwWvO78bk2EijAREZGqVJZDZYXbKaoWFQsLXoMtEdAz9Nlf4PN7az5u0xx4/1ZY/n7gbb98htN+hNKYMBERkaqc+2+3ExydMXD7MmdMVLi79BUoK6z5uI7D4IbPnDUmA5U1GDK61zma21SEiYiIRKJIKMAAmncI/Nj2Jzg/A11T8sx/1C1TmFB3pIiIyOE2fg//ORl2LHM7ydHlbYJ3flb/ZX9Cact8WPwOVJQF/p4fP4Wxg50pQqrTCOYVUxEmIiJyOGMgMR3iUtxOcnSxybB1ARTtdDvJ0S1511nb0eMN/D0pWc6jZE/1xy18Hf7VEfK31iuimzRFhYiIiIRGZTns2eSsHRlsG2Y5Rd5Zj4I3fEdXVTdFRfimFhERkcjmja57AVa827ljstuZVe/vcKLziGDqjhQRETnc6xc784SFu/XfOsv27F7vdpIj7dkCX/8T8jbW7f1f/h3eue7oY8PKi+ueLUyoCBMRETlcq97QorPbKWoWnwpJGVBW5HaSI+1Y6hRhe3Pr9v7hf4CbvoD45kfusxYe6x7Y/GNhTGPCREREJDRKCyEqrv5jtqx1bpbYx1cJs56CNsdC51Pr13aIaUyYiIhIoA7/Cz8SBDqvVkOLTap/G1/8DQq3w/ljD7zm8cLw2+vftsvC8L+YiIiIi1Z8CI92gV2r3U4SmB9egkc7Q0Wp20kOsNaZmmLNtOC0ZzyHzgtWkh+eXbC1pCJMRETkYM3aQvezIamV20kCk94N+l4aXkVJSR6sngo5a+vf1qn3wnlPH3ql7/vn4eHMiB+cr+5IERGRg2UNch6R4piTnEc4iW8Of1genFnt93UN5/7kjAVL7+qMA4tJguj4+rfvIl0JExEROVhludsJas9aKM5zO8WRgjVOrbIcxp0Fn9/nbGcNgqG/Dk7bLlIRJiIicrCxx8MHv3M7Re188Bt4YaTbKQ6Y+lf4+l/Ba88bDRe9AOf+29netRrKS4LXvktUhImIiBxs4HXQZZTbKWqn53lwwq+dK2LhIH8bFGwLbpudTobk1s7Yt2cGwXfPBLd9F2ieMBEREYkMRbtg8i8hIQ2G/x5a9nQ7UY2qmydMV8JERET2KS+O3G6ukvzwXL4omGKbOXOGdRweEQVYTVSEiYiI7PPVw/BIJhRmu52k9l4Z48zN5bYfXoJXz4WKsuC3HRUDN8+AgdcGv20XaIoKERGRfY69ElLaOesxRppT74XoBLdTgDcGohOdgikUwnFlgDrSmDARERGRENGYMBERkeqUFsLn90L+VreT1J3PB1sXQvYq9zJE2IUdt6kIExER2TQbZj8Heza7naR+Xj0PZj/r3vk3zobHe8OWee5liCAaEyYiItLlNLh9OSRHyHqRVfF44IrXoUUn9zLEJECHodAs070MEURFmIiING0VpRAVG9kF2D5uryHZ5li4+CV3M0QQdUeKiEjTVVkBzw2D6f/ndpLgKC2EJRMgZ60754/UOdZcoiJMRESarspS6D4aWvdzO0lwVJTCxBthxYfunPuf7WHW0w1/7gil7kgREWm6YhLhjL+7nSJ4EtPgV7MgvVvDn7uiFIbfDplVzsYgVVARJiIiTdPmeeDxQtv+bicJrla93TlvXDM45W53zh2h1B0pIiJ1s2czjD0BPo3Qv3i//Bu8cx34Kt1OElx5G+Hrf0HBjoY9b8EOZ4ydBExFmIiI1E3Bdshe4cxLVbzb7TS1d9mrcNn/nKthjUlhNnz9CGxf3LDnfftqeP2ihj1nhFN3pIiI1E3WIPjjaigvhrhUt9PUXlxK4+uKBGeaiD9vdLoHG9LQW511IyVguhImIiJ1l9QSmncAY9xOErjN8+CVc2D3ereThIY3quELMIDeF0KPMQ1/3gimIkxERGqvohSeHwHLJsOy99yZEqGuCnfA3hxISHM7SehsmAWTboHK8oY5357Nzlg0rR1ZKyrCRESk9op3Q1IriE501lz8/j9uJwpcj7OdaRxik91OEjoF22DddMjf0jDnm/0cPDO48d3kEGLGRljVOmjQIDt37ly3Y4iIyD6FOyG+OXij3U5Ss22LoXXfyOo+rQufz1lLsqHsXOncpNH7woY7Z4Qwxsyz1lY5eZquhImISO0d/A/4pJaRUYDlroMXToZvn3Q7Seg1ZAEG0LKHCrA6UBEmIiK19+wJ8OU/nOd7c+Grh2HrAncz1SQlC84fC/0udztJw5j3ijMPWqjtzYW1XzrrVkqtqAgTEZHaqayAzqdCRndn2xiY8Shsme9urpp4o6H/VdCsjdtJGkZ5sTN2L9SD89fPhNcuhF0/hvY8jZDGhImISP2Vl0B0nNspjm72c5CQDv0udTtJ41OSD1vnQ7sTwvs74BKNCRMRkeApyT9yKoJw/svXWlg6CVZ/7naSximuGXQaGd7fgTClIkxERGrnzSvh9YsPfW3dNzDp5oabl6o2jIEbP4dzHnc7ScP76HaYcENozzH/f7BrTWjP0UipCBMRkdrpf+WRg9vzt8L6b52JUMNJebEzsawxjXtesKNJbuvckBAqBTvgg9/qKmMdaUyYiIjUn7XhOffWt0/C7OfhV99CQgu30zQ+1jqz5UcnQGIjXoGgHqobE6YFvEVEJHB7tkBMgjM568HCsQADyDzOGYzf1AuwygpnTclgMwZS2wW/3SZC3ZEiIhK4L/8Ozwypeo3Ar/8JU+9v+EzV6TgcTn/I7RTusRaeHQrv3xqa9he8DiunhKbtJkBFmIiIBG7QDXDWP6u+8lW4Awq2N3ymqlSWO+tZluS7ncRdxkDfS6DnuaFpf9bTsPjt0LTdBGhMmIiIND5rvoDXL4Ir34LuZ7mdpvGqrIDSfHX3VkNjwkREpP52b4C9u6D1saEZXxRMXUbBL2dCy95uJwkPPh/MfxXiUqDPRcFr1xulAqwe1B0pIiKBWTgeXjoNKkqq3l+8G145B5ZMaNhch9vXw9O6b8MvZB3OFrwOKz4IXnsrP3bGCPoqg9dmE6Nvp4iIBGbQDXDVuxCbVPX+2JSqB+w3JGvh1XPhu7Hu5gg3Hg9c/S5c8t/gtbnpe1j0Fni8wWuziQnz68kiIhI2kls7j6PxeOD6jxsuT1XK90JSK4ht5m6OcLSv27Bkj3PDQn2nljj9ITj1r/XP1YSpCBMRkZrlb3WWJup2JsSnup3m6GIS4ZKX3U4Rvnw+ePkMp1D9WRC6JsN9bGCYU3ekiIjUbO2XMPlmKNxZ/XErPoLHeznL2TS0nLXO7O1ydB4PnHofnP5g/drZvgTevd75zKXOVMKKiEjNjr0S2hwLaV2qPy65DXQYBpWlDZPrYJ/fC1vmw+3LdIWmOj3PqX8bBTtg81zwRte/rSZM84SJiEjjsHOlc4dmh6FuJwl/1sIXDzlF1Cn3uJ2mUatunjB1R4qISPVKC+DLf8Cu1YG/x41pC1r2UAEWKGOcFQ4Kd7h/R2sTpiJMRESql/0jfPOYM1lrICb/yhn83ZDWfQOrpzXsOSPdeU/DuU/WfvH1ynJ4cRQsey8ksZqSkHWaG2PGAecAO621farYPxJ4H1jnf2mStbYJr7IqIhKmsgbBnzcFPv6n43DI6BbaTIeb9RTkbYKupzXseSPZvvm9dq93xni1Pz6w9xXvduaK03iwegvZmDBjzElAIfC/aoqwP1prazVCUGPCRETkCGVFkL8N0mu4cUAOZS28eCqUF8Ovv6v9VTGpkStjwqy1M4DcULUvIiINwFp4/1ZnQezaqCx3CqOGEpOoAqwujHG6Ja+ZqALMBW6PCRtqjFlkjPnEGKNVVkVEws3eHFj7tdNlFajyEngkC2Y9E6pUh1r1OXz3rNYwrKvWfSAl03leUVbz8eMvgyl3hjZTE+HmRCrzgQ7W2kJjzNnAe0DXqg40xtwM3AzQvn37BgsoItLkJabDH5Y5M60HKjoOTr4L2gU4xqi+Vn0Ca6bB0F83zPkaq/d/A3tz4co3qj8uo7szH5zUW0jnCTPGdAQ+qmpMWBXHrgcGWWt3VXecxoSJiMgRSvZAXIrbKSLb7OecNSVPutOZWV+CoroxYa5dCTPGtAZ2WGutMWYITtdojlt5RESkCpNugbb94YRf1e59Ph/kbYCUrIa5i04FWP0F8t+4skKrEQRRyEpdY8ybwHdAd2PMZmPMjcaYXxpjfuk/5BJgqTFmEfAUcIWNtOn7RUQaM2uhOBdKC2v/3hUfwFP9YefyoMc69DwfwUe3N+xNAI3dxu9h4VG6JL98CP7dr3bd03JUIStnrbVX1rD/GaCBRm2KiEitGQNXv1u397Y73pkINLltcDMdLvcn+OlriE4I7Xmaku+edpaA6nvZkVe9MgeBJ1rdlUGitSNFRCSyWavpFYKpMBui450JWaXetHakiIjU3nu3wuRf1nzc0RRmw9aFQYtzVCrAgispwynAfD5nAtx9ykucdUQlaFSEiYhI1VKyoFlm3d8/9T5484rg5Tncig/hlXOgcGfoztGUTboJ/ne+M/EuwNov4JF2DVNYNxG6xUFERKp2yt31e//xt8CxV4auu9BX6RQI8S2C37ZA/6uhKBs8/lIhvRuccg+kVzmlp9SBxoSJiMiRKsu1QLNIEGhMmIiI1M4XD8JTA+o3FYG1sOE72BGCaSp8Pqd9Cb2lk+Czvzj/Hfd1TUpQqAgTEZEjZQ2GPpfUfyqCN6+AOS8EJ9PBVn0Kj/eE7FXBb1sOtWOZM+/bc0Ph++fdTtOoaEyYiIgcqdf5zqM+jIGr3oHUEKz5m5AGHYeHpm051Ml/gmG3werPoe0At9M0KirCRETkUCX5TgEVm1z/ttqHaBHv9seHrm05VFSs8+h7idtJGh11R4qIyKEWvQX/bA8F2+vfVmE2LBgPRbvq39Y+FWXOgt0iEU5FmIiIHKrDUBj1V0hqVf+2ctfC+7+GzUG8q33jLPhXR1j/bfDaFHFBQEWYMeY2Y0wz43jZGDPfGHNGqMOJiIgLWveF4bcHZ26vNv3hN3Oh6+n1b2uf1PZw0p+gdZ/gtSnigkCvhN1grc0HzgCaA9cC/wxZKhERcUdpAWxbDJUVwWkvOs6Z3NPjDU57AC06ORPJxqUEr00RFwRahO3759DZwGvW2mUHvSYiIo3F+m/hPyNg85zgtblhFswO0tQGleWwdUHwikQRFwVahM0zxnyOU4R9ZoxJBuoxg5+IiISlrEFw8ctON2KwrP4cpt0fnMJp2yJ4YSSs/Kj+bYm4LKBli4wxHqA/8JO1Ns8Y0wLIstYuDnG+I2jZIhGRCFOyB7yxTtdkfRXvhjVfQOdTIUFrRkr4C8ayRUOBH/0F2DXAvYDuDxYRaUx8lc4SNYXZwW03LiU4BRhAfHNnvioVYNIIBFqEPQfsNcYcC9wBrAX+F7JUIiLS8LJXwoTrYe2XwW/72ydh6cT6teHzwZIJwS8SRVwSaBFWYZ1+y/OBZ6y1Y4EgTKUsIiJhI70b3Px1cKeT2GfRW7CmnsVd9kqYeCOsmRqcTCIuC3TZogJjzN04U1OM8I8Riw5dLBERaXDe6NCtDXjzdIiKqV8b6d2cdrRepDQSgV4JuxwoxZkvbDuQBTwaslQiItLw5rzo3H0YCvUtwAC8UdC2v8aDSaMRUBHmL7zGAynGmHOAEmutxoSJiDQWpQXwyZ+c6SRCYfd6eO9W2L60bu+3FmY+Uff3i4ShQJctugyYA1wKXAZ8b4zRcuoiIo1FbDLctR4G3RiiExinwNuzuW5vL9gGXzwEG78LbiwRFwU6T9gi4HRr7U7/dgYwzVp7bIjzHUHzhImIRCBr678WZUm+8zOuWf3ziDSQYMwT5tlXgPnl1OK9IiIS7r59qv5TSFQnGIuBxzVTASaNSqCF1KfGmM+MMT83xvwc+BiYErpYIiLSoBa+AWu/Cu05lkyA8Zc6V8Vq69O7YbWmppDGJaApKqy1dxpjLgaG+V96wVo7OXSxRESkQf36O6goDe05yvc6SxiVFTpj0AJVWgjL3oOkVqGZw0zEJQGNCQsnGhMmItIEWQu+CmcuM5EIUt2YsGqvhBljCoCqqjQDWGutOudFRCLdt09BSR6M+qvbSY7OGBVg0uhUW4RZa7U0kYhIY5e7tuHWY5x0MyRmwJn/CPw9E38B7YbAkF+ELpeICwJdtkhERBqrc59suHPFNqvdeDBfJRRlO+PIRBoZFWEiItJwxjxWu+M9XrjuvZBEEXGb5voSEWnK5rwIr10EFWVuJxFpclSEiYg0ZcbjDHgPxgLbgchdB08eC8s/COz48Zc5c4SJNELqjhQRacoG3+g8Gkpya2jTHxJaBHZ8WmdIyQppJBG3qAgTEWmqgrGeY21Fx8NlrwZ+/OhHQpdFxGXqjhQRaaqWTYJ/94PdGxr+3JXlNR9TXhz6HCIuUhEmItJUJWZA5nHQrG3Dnvf7F+DhTCjbW/1xb1/j3DQg0kipO1JEpKk65iTn0dDaHAtDb4XKUiDh6Mf1PLfBIom4QUWYiEhTVFkBlWUQU00RFCrtj3ceNTnu5yGPIuImdUeKiDRF2xbCI5mw9it3zu/zQfHuo+/P3wplRQ2XR8QFKsJERJqKvE2w7hvneeZx0P1saDvAnSz/PQvevf7o+z+5C54f0XB5RFyg7kgRkabig99C7k9w2yJnaoorxruXZcgvnIlij7r/Zti7q+HyiLhARZiISGO1eipMewCu/wTimsGZ/3AWz27oucGq0veS6vcfo6tg0vipO1JEpLGwFjZ+74ynAohvAXEpUJTtbLfqDant3ct3MGudnHtzj9yX/SPsWOYcI9KIqQgTEWksCrbDf0fDDy8721nHwfVTnKV/wk3hDni8Jyx+58h9M5+AV89r+EwiDUzdkSIikWzag1CyB855HJq1gavehfYnuJ2qZkmt4JwnoMPwI/edcg/0vyo8uk1FQkhFmIhIpNm2GNr0c577KsBXfmAdyK6nuZstUMbAoBuq3pfaPny6TUVCSN2RIiKRpGQPvDIGNs9zts/4G5z3dGReNSrJh/UznTnD9tm6AJa9F9jakiIRTkWYiEgkqShz7iz0eN1OUn9LJzoF5Z6NB15bMB7evxWIwKJSpJaMjbC7TwYNGmTnzp3rdgwREamvPVtg5wroMBRiEp3XKsshdx1kdHM3m0iQGGPmWWsHVbVPY8JERCLFhlmQkN54CpSUTOdxMG904/n9RGqg7kgRkUjxyZ9g0i/cThFc25fCuhnO800/wIxHnbFiIk2AijARkUhxzWQ4f6zbKYLry7/DlDud5xtnwfRHwRvjbiaRBqIxYSIi4p7sH50uyBadnO3SAmdpJZFGoroxYboSJiIS7jbMgjevPLAcUWOS0f1AAQYqwKRJ0cB8EZFwl78VctZCfHO3kwRfRSms+BBK8pwxYaP+euRgfZFGSkWYiEi463sJ9Lk4MidkrYnxwORfOksuVZTC2f/ndiKRBqMiTEQknO1a4yzA3RgLMHDGg/16trNMkTe68f6eIlXQmDARkXCVtxHGDoHZz7qdJLTSu0BUjAowaXJUhImIhKuEdDjrX9DzPLeThNbSifBACmyc7XYSkQal7kgRkXAVkwBDGtnkrFXJ6AntT3TulBRpQlSEiYiEo4VvONM19DzX7SSh16oX3PCJ2ylEGpy6I0VEwo21MHcczH/N7SQiEkK6EiYiEm6MgRs+g+LdbicRkRBSESYiEk58lc5PjxcS093NIiIhpe5IEZFwsmwyPDMI8ja5nUREQixkRZgxZpwxZqcxZulR9htjzFPGmDXGmMXGmIGhyiIiEjES06HtAGimpXtEGrtQXgl7BRhdzf6zgK7+x83AcyHMIiISGTqNhEvGgUcdFSKNXcj+L7fWzgByqznkfOB/1jEbSDXGtAlVHhGRsLfiQygvdjuFiDQQN/+plQkcPOhhs/81EZGmZ+cKePsaZ2oKEWkSIuLuSGPMzThdlrRv397lNCIiIZDRA34+BVr3dTuJiDQQN6+EbQHaHbSd5X/tCNbaF6y1g6y1gzIyMhoknIhIgzIGOg6DuGZuJxGRBuJmEfYBcJ3/LskTgD3W2m0u5hERcce0B2HmE26nEJEGFrLuSGPMm8BIIN0Ysxm4H4gGsNY+D0wBzgbWAHuB60OVRUQkbFkLuT9BfKrbSUSkgYWsCLPWXlnDfgvcGqrzi4hEBGPgslcPzJQvIk2GJqIREXFLWREU7XKee7zuZhGRBqciTETELXPHwRN9YM9mt5OIiAsiYooKEZFGqeuZ4KuAlCy3k4iIC1SEiYi4JaOb8xCRJkndkSIiDc3ng68egdx1bicRERepCBMRaWg7l8PMx2HzD24nEREXqQg7zIacIn735gI25BS5HUVEGqvWfeC2xdD7QreTiIiLVIQdJtrr4cPFW5m8oMoVlERE6mfffGDN2oA32t0sIuIqFWGHaZsaz4md05g0fwvOfLIiIkH0znXw4e/dTiEiYUBFWBUuHpjFxty9zN2w2+0oItKYWAsZPSCts9tJRCQMqAirwpm9W5MQ42XSfE2gKCJBkLcR1n7lLFE06j448bduJxKRMKAirAqJsVGM7tOajxZvo6Rc67mJSD19+Hv44LdQWeF2EhEJIyrCjuLigVkUlFQwdfkOt6OISCTaMAtK8p3nZz8K138CXs2PLSIHqAg7iqGd0mibEqcuSRGpvd3r4ZUx8N1YZzutM6S2czWSiIQfFWFH4fEYLhiQyYzVu9hZUOJ2HBEJdz4fbJnvPG/eES5/HYbd5mokEQlvKsKqcdHATCp9lg8WbnU7ioiEu+n/gnFnwu4NznaPMRCT4G4mEQlrKsKq0aVlMsdmpTBxviZuFZEqlORD4U7n+aAb4PyxkNre3UwiEjFUhNXg4uOyWLEtn+Vb892OIiLhpLICXjgZPrrd2U5uBf0uc6ahEBEJgIqwGpzTry3RXsPkBRqgLyIcuPLljYKRd8OIP7ibR0QiloqwGrRIjOGU7i2ZvGArFZU+t+OIiJt+mg5P9IF1M5ztfpdB5nHuZhKRiKUiLAAXH5fFrsJSvlmzy+0oIuKmdkNg8I2Q3t3tJCLSCKgIC8Ap3VuSmhDNJA3QF2ma8jZBaSFEx8PoR5zxXyIi9aQiLAAxUR7OO7Ytny/bTn5JudtxRKShfXwHvDDSWYBbRCRIVIQF6KKBWZRW+JiyeJvbUUSkoZ38J2fhbd35KCJBpCIsQMdmpdA5I1FdkiJNUdYg6HW+2ylEpJFRERYgYwwXDcxizvpcNubsdTuOiDSEPVvgi4egKMftJCLSCKkIq4ULBmRiDEzSnGEiTcO66TDraSgrdDuJiDRCKsJqITM1nqGd0pg0fwtWA3RFGr/+V8Hty6F5B7eTiEgjpCKsli4emMXG3L3M27Db7SgiEkoVZc7PpAx3c4hIo6UirJZG92lNfLSXifPVJSnSaJWXwNPHwff/cTuJiDRiKsJqKTE2irP6tOajxdsoKa90O46IhEJFMXQ9HVr2dDuJiDRiKsLq4OLjsigoqWDaih1uRxGRUIhvDuc8Dsec5HYSEWnEVITVwQmd0miTEqc5w0Qao60LIPtHt1OISBOgIqwOvB7DBQMymb4qm+yCUrfjiEgwfX4fvHE5+HxuJxGRRk5FWB1dPDCTSp/l/YW6GibSqFz6Clz8Enj0x6OIhJb+lKmjLi2TOTYrRV2SIo1NYrqzTJGISIipCKuHiwZmsXxbPiu25bsdRUTqa+dKeOMK2L3e7SQi0kSoCKuHc49tS5THMElzholEvtyfYMdSiEl2O4mINBEqwuqhRWIMp/RoyXsLt1JRqUG8IhGtx9lw2yJITHM7iYg0ESrC6unigVlkF5Qyc80ut6OISF3lbQRrweN1O4mINCEqwurplB4ZpCZEM1ED9EUiU8keePZE+OofbicRkSZGRVg9xUZ5ObdfWz5ftp38knK344hIbXlj4fQHoNcFbicRkSZGRVgQXHxcFqUVPj5Zss3tKCJSW9FxMPgmaN3H7SQi0sSoCAuCY7NS6JSRqC7JABSVVlCgK4YSLlZ9Bsvf1+z4IuIKFWGH8/ngs7/AN/8v4LcYY7h4YBZz1uWyKXdvCMNFNp/Pcs2Lsxj2zy94Z+4mrLVuR5Km7oeXYcajYIzbSUSkCVIRdjiPBwq2QeHOWr3tggGZGEPjn0E/dx08PxzWTHO2d66Ef/c9sL11AfyzA6z90tneMAseSIWfvuaDRVtJ2DqLCZ67+X8Tvua6cXNUtIq7rnwTrnxbRZiIuEJFWFUuehHO+let3pKZGs/QTmlMWrC58V3hmfcqzHrGee6NgZR2EJ3gbMckQodhEN/C2U5Ig36XQ3IbZzslC066k9KkLB797EdSMzLp2jKJP57Vm/kbdnPmv2fwyrfr8Pka2Wcm4c9X6UxJkZLpdhIRaaJUhFVl31xBOWth0w8Bv+2igVlsyNnLvA27QxTMBdbCT187D5/P+Qvryjehw4nO/tR2cOHzkDnQv90ezv4/aNnzwPapf+G/Kwxb8oq5+tyzMDdP59KTB/L57SdxXmYBD3y4nMv+8x1rswvd+A2lKdo4G54+DnYsczuJiDRhKsKOxlp45zqYcofzPACj+7QmPtrbOAbo5210umSNgQuehavedrpq6yC3qIyxX63h1B4tObFL+v52MleM45Gdt/LC6ERW7yzkrCe/YexXayjX6gMScgZadILmHd0OIiJNmIqwozEGLngOrno34PEiSbFRnNWnNR8t3kpJeWWIA4ZQRSmMOws++J2zHR1fr5nEn/piNUWlFdx9Vo9Dd/S/CjPqPs44+WSm/eFkTuvZkkc/+5ELxn7L0i176vELiNSg/fFw7SSnO11ExCUqwqrTph8kt3KeV5QG9JaLBmZRUFLBFytqN7A/rETFwtmPwhl/r3dT63YV8frsDVw+uD1dWx22MHJCCzjxt2AMGTaHZ9t9xfNX92dnQSnnj/2WRz9bGdnFrISndTOgTDeEiIj7VIQFYtLNMOGGgA4d2jmN1s3imDh/c4hDBVlpAbx9rTNvEjiLGad3qXez//fpSmKiPNx+etfqD1z8Dsx8gtGZpUy7/WQuHJDJ2K/WMuapb5i3IbfeOUQAKMyG1y/WEkUiEhZUhAWiTX9n4HkAEzp6PYYLBmQyfVU22QWBXT0LC54o2LPZeQTJvA25fLJ0Ozef1ImWyXHVHzzsNvjlTGjRiZSEaB67qDev3jCEknIflzz/HQ98sIyi0oqgZZMmKjEdrnsfjr/F7SQiIirCAjL01zDijoAHpl88MJNKn+WDRVtDHCwI1kxzulqj4+HGqTD4xqA0a63lHx+voGVyLDef1KnmNxgDLY5xni+ZAC+ewslt4bPbT+K6Ezrwyqz1nPnvGXyzOjso+aSJMsa5sze1vdtJRERUhNXK+pmw4PUaD+vaKpl+WSm8OOMnNuQUNUCwOtqxzOma+f4/zrY3KmhNf7J0O/M35vGH07uREFPLduObQ3JriE0mKTaKB8/vw7u/HEqM18O1L8/hTxMWsadYSx9JLc39L3z1iJYoEpGwoSKsNr5/3pm01FfzYPGHL+xLaUUllz7/Hat3FDRAuFrY95dQq95wxRtw/C+D2nxZhY9/fbqS7q2SuXRQu9o30GUUXP2us7ByeQlsXcjgji2YctsIfjWyMxPnb+H0x6eH3+cq4W3bItg0u85TrYiIBJv+NKqNc56Em78KaLqGPpkpvH3LUCxw+Quzw2fKhW2L4NnjIXuVs91jDETFBPUUr8/ewIacvfz57B54PfVcDuaLh2DcaCjYTly0l7tG9+C9Xw/DZ+GW1+dRqHFiEqhz/+1MOSMiEiZUhNVGYpozdspXCXmbajy8W6tk3r1lKPHRXq58cXZ4zKSfkA5xKeALTXfenuJynvpyNcO7pDOyW0b9GxxxB5z/jNM96dc3K4WnruzP+l1F3D1pSeNbJkqCy+eDohzneZD/wSEiUh8qwupi4o3w2gVQUVbjoR3TE3nnl0NJS4zh2pe/Z9aaXaHPd7jyYpj/mjPzf0qmMwC/Ve+QnOrZr9ewp7icu8/ugQnGosiJadD3Euf5tkXw1cNgLSd2TueOM7rz4aKtvDZ7Q/3PI43XnP/A4z1hyzy3k4iIHEJFWF0cdz2ccg94owM6PDM1nnduGUpW83h+/soPfLlyR4gDHmbuf+GD38DWBc52MIqjKmzevZf/frueCwdk0rttSvBPsPx9WPgGFDtXFH91cmdO7dGSv320nIWb8oJ/PolMxXnw5d8PfN97jIHT7oc2A1yNJSJyOBNpXTmDBg2yc+fOdTtGnewuKuO6cXNYuT2fJ68YwNl92zTMiX0+2LYAMo8L6Wl+/9YCPlm6na/+OJK2qfHBP4G1sDfHmevJ54OyAvJ88Yx5aiYAH/12OM0T1d3UJPl8UJzrfDdK8uGJ3jDyzzD0VreTiUgTZ4yZZ60dVNU+XQmrj8Xvwsd3BHx488QYxv/ieI7NSuU3b8xn4rwQzqqftwnevBKKdjl3g4W4AFu8OY/3Fm7lxuHHhKYAA+cKXmK683zWk/DccFJ9eTx3zUCyC0q5/Z2F+HyR9Y8KCZLXL4QJ1zvP45rB7UtVgIlI2FMRVh+718G2xVAW+FxgzeKi+d+NQ5wxTe8uCt14pj2bnTEweaEfL2Wt5eEpK0hLjOFXIzuH/HwAdBwBvc6DxAz6ZaVy37m9+PrHbMZ+taZhzi/u2rEcpv7VuToK0P8aGHDdge24EHSHi4gEmYqw+hh+O9zwGcQk1uptCTFRvPSzQZzWsyX3vbeUF2asDV6mfQVhh6Fw26KQXwED+HLlTmb/lMttp3UlOS6wcXL1ljUIzvyHc3WsYAfXbP07V/dJ4PFpq5i52oWbHyT0youd1R0Ati+GOS9Bjr/o7nep8wjReEcRkVBQEVYf3minq6+0ANZ9U6u3xkV7ee6a4zinXxsenrKSJ6auqv9UCzuWwZPHHliEOzpE3YIHqaj08fCUFXRKT+TKIS4tBbN1AWbV5/xlZAZdMpK47a0FbN9T4k4WCY3d6+H/9XCWtALofSHcsQLSa1gYXkQkjKkIC4ZP/+yMvyqp3YSs0V4PT14xgEuPy+LJL1bz8JQV9SvEUts73XQZ3eveRi29PXcTa7OLuOusHkR7Xfo6dR8Nty8lIasPz10zkAHl8/nd+B8or9TyNBFt0VvO3bAAqR2g/1XQsqezHRWrLkcRiXgqwoLhpD/BtZPq9JeC12P418X9+NnQDrz4zTrufW9p7QeXb54HleUQmwyX/head6x1jrooLK3giamrGNyxOWf0atUg5zyq2GQAuvjW85LnYfpueZP/+3Slu5mkdirKYOvCA9uL33YKMXC6GUc/ApkDXYkmIhIKIS3CjDGjjTE/GmPWGGP+XMX+nxtjso0xC/2Pm0KZJ2Sad4B2Q5znAawreTiPx/DAeb351cjOjP9+I398dxEVgV7FyV0H486EGY/W+rz19cL0tewqLOOes3sGZ2LWYGjdFy57DTvoRl78Zh2fLdJErmHt4Cu/0+53lqgq9a8Jesk4uO59d3KJiDSAkBVhxhgvMBY4C+gFXGmM6VXFoW9ba/v7Hy+FKk+DmPMivDAyoJn0D2eM4a7RPfjjGd2YtGALv31zAWUVARRiLY5xlvU54de1z1sP2/eU8MI3P3FOvzYMaN+8Qc9do17ncde5/TkuM4E2ky5i95SH3E4kVdk4G57qDzn+G1MG/gwuexWi/GMZ45troL2INGqhvBI2BFhjrf3JWlsGvAWcH8LzuS+1PaR3g/K9dW7iN6d25b5zevHJ0u3c/NpcSsqPcmVt3isHFuE+9gqIT63zOevi8ak/4vPBXaN7NOh5AxUb5eXJK/qz3HTmmWWxR/8cpeGU7YXvnoWN3zvbqR2g+TEH7uht2QO6nQneKPcyiog0oFD+aZcJHLzK9Wbg+CqOu9gYcxKwCrjdWlvzytjhqtuZzqOebhx+DAkxXu6ZvITrxs3hgv6ZJMdF7X+kmL0c88Xfqex2NlHnPYnH07BXC1Zuz+fdeZu5cdgxtGuR0KDnro2sjOa0uuIZ/vzKDxS8v5T/6/mTs6P3he4Ga0pKC6FgO6R3AY8Xpv8TBt8E7Y+HZm3guvfcTigi4hq3/8n5IfCmtbbUGHML8Cpw6uEHGWNuBm4GaN/epWkQamPPZlj5MRx/S52buHJIexJivNw5YTFz1uX6X7WAU3C1M/ewbXYald9PISk2iuTYKJLjoknaX6xFkxQbRTP/dtvUeAa2b06HtIR6j996ZMpKkmOj+M2pXerVTkM4pUdLfntqF57+cjV/2P4fWsdb6Hm+M7WI1F9hNuzddeCuxZ++ht0b4LifOdv/Ox+MB26a6tzR+Jt5kJThWlwRkXASyiJsC9DuoO0s/2v7WWtzDtp8Cfi/qhqy1r4AvADO2pHBjRkCC8bDzCechYNTsurczPn9MzmjV2vyissoKC4jdfp95MdlsqLjtRSWVlBQUk5hSQX5JRX7twtKKsgtKmNDzl4KSpzXSg8aW5aWGMOA9s0Z2CGV49o3p19WKvEx3oAzfbM6m+mrsrl3TE9SEyJjncbfn9aNeRt2M2rDrUy6oS/dPR6nC2zPFsjo5na88JK3CTZ9D30udsZjLXvP+QfFxS86+7/+Fyx8HX6/xNme/k9YOhHuWu9sL5sMK6ccKMJOuQeiD7paqgJMRGS/UBZhPwBdjTHH4BRfVwBXHXyAMaaNtXabf/M8YEUI8zScYb9zxmmlZMHOFfDRH5zZ3TMHOnOJFWyHFp2cyV5rEB/jJT4mnjZJ0cBuWsan0OXYtrWKU1bhY92uIuZt2M38jc5j2oodAER5DD3bNOO4Ds0Z0D6Vge2bk9U8vsqrZZU+yz8+XkG7FvFcO7RDrTK4yesxPHXlAMY89Q23TFzHB7/NpNlXD8GC151VBRLTnbtaPYEXo43Glnkw/zVn+ofoeFg6AaY9AN3PhpgEKNgG2xZCZYUzVqtlT+h2lnNXozEw4FroPOpAe6f/Dc74x4HtLqMOP6OIiPiZes/SXl3jxpwN/BvwAuOstf8wxjwEzLXWfmCMeQSn+KoAcoFfWWurndxp0KBBdu7cuSHLHHRb5sNnf4FznnAGHi//AN65Fm7+GtoOgM1znVnAR9zhXCWoKANP1IHusooyqChxFiWurHAKhSDcMba7qIwFm3Y7hdmGPBZuyqPYP3i9ZXIsA9s357gOzhWz3m1TiIv28u7cTdw5YTFPXzmAc2tZCIaDH9bncsULszm9ZyueO78NZt0Mp1gGeOUcSG5z4IpP3kZIbts4BokX5TgFVkwCrP8WProdLn/duQq4/H344Ldw4zRnO38r7M2Flr3UZSsiEgTGmHnW2kFV7gtlERYKEVeEHS5/K6yf6XRVxiTCwjfh4zvg9qWQ0AJmPw9fPAi3L3O2v/gbbPgWfv5xSK/UVFT6WLm9wLlStmE38zfmsTHXucszxuuhd2YzNuXuJbN5Au/9+sTwmResll6c8RP/mLKCe8f05KYRnQ7smPlvZ7LdQdc7V3ke6wZdz4ALxjr7V0+DNv0gqaUruQNWVgRrvoDWfZyrrVvmwYunwhVvQo+zIftH50rXqfdCq97OFUDj0VQQIiIhoiIs3O3r2gHYMAtWfQqnPei89uU/nG7NfWNsGtDOghIWbMzzF2W7+Sm7iBeuG8RxHcJsXrBasNZyy2vz+HLlTt66+QQGdWxx5EG+SmecU0oWdDgRivPgXx3glHvh5Dudq5Ozn4We50Ja5wb/HQ5RXgLfPeMs1N75FCjaBY92hjP+Dif+1inK5o6DHuc4c8qJiEiDUhEmcpA9xeWc98xMSst9fPS74aQnxVb/hsoKZ1xUUktnLrjtS+D54XDJf6HPRc5ko1886Cxf1bqPMx9WWZEz1iwUV5im/Mm5ynXCL50C/v86OVfwRv3V2b91obN+aAMs4C4iItWrrghrBANeRGonJT6aZ68eyEXPzuKUR78mq0UCbVPiaJMaR9vUeNqmxNMmxXneqlkcMVFRkHXQ/z+t+8KdP0F0nLNdtAu2LwVfhbO9bjq8eQXc9IXzvm2LYcUHcPwvD9wEUFMXYEWpM6UDOGO2Kivgwuec7dy1B/YZ43Rdxxx0B2Lb/kH5nEREJLRUhEmT1LttCv/9+WA+XrKNbXtK2JJXzNwNu9lTXH7IccZARlIsbVLjaesvzNrs/+mhbaqXjKwheH43/8CbMnrA6H85qycAbF8M3/w/GHKzsz13HHz5N/jtfKco27oAdq12JpH1RsOHtzmzyt862zk+uY2zQPs+10w89JeJCd8Jc0VE5OjUHSlykKLSCrbtKWZrXskhP/cVatvySvbfRbpPbJSHEV3TObN3a07r2YrmiVXMn1ZR5hRYxjg3Zix/3ynUPB74/D74/nn4y3bn5otlk52F2YffrgHzIiIRTmPCRILEWsue4vIDRdqeEtbuLGTq8h1sySvG6zGc0KkFo3u35ozerWnVLK7mRstLIH+L+4P8RUQk6FSEiYSYtZZlW/P5dOl2Pl22nTU7CwEY0D6V0b1bM7pPazqkJbqcUkREGpqKMJEGtmZnAZ8t28GnS7ezZMseAHq0TmZ0H6cg694qOWLnWhMRkcCpCBNx0ebde/ls2Q4+W7qdHzbkYi10TEvgzD6tGd27NcdmpeLxqCATEWmMVISJhInsglKmLt/Bp8u2M2vNLip8ltbN4jizdytO69WKwR1bEBfdBNewFBFppFSEiYShPcXlfLnS6bKcviqbknIfsVEeju+Uxkld0xnRNYNurZLUbSkiEsFUhImEueKySmavy2HGqmy+Wb1r/8D+Vs1iGdE1gxFd0xneJZ20mmb3FxGRsKIZ80XCXHyMl1O6t+SU7s4C4Vvzipm5ehfTV2czbcUOJszbDECfzGaM6JrBSV0zOK5Dc2KiPG7GFhGRetCVMJEwV+mzLN2yh29WZzNj1S7mb9xNhc+SEOPlhH1dl90y6JSeqK5LEZEwo+5IkUakoKSc2T/l+rsus1mfsxeAzNR4RnRN5+RuGZzYJZ2U+GiXk4qIiIowkUZsY85evlmTzTerdvHtml0UlFbg9RgGtk/lpK4ZnNw9gz5tUzQNhoiIC1SEiTQR5ZU+Fm7KY/qP2Uxflb1/otgWiTGc1DWdk7plMKJrBhnJGuAvItIQVISJNFG7CkudAf6rspmxKpucojLAGeB/UtcMTu6WwcAOzYn2aoC/iEgoqAgTEXw+Z33L6at2MmPVLuZt3E2lz5IUG8WwLmmc3K0lJ3VLJ6t5gttRRUQaDRVhInKE/JJyZq1xrpJN/zGbrXtKAOickciQY9Lo0TqZbq2S6dE6meaJMS6nFRGJTCrCRKRa1lrWZhfytX8s2eLNe9hTXL5/f8vkWLq3TqZ7q2S6tXYKs64tk4mP0RJLIiLVUREmIrVirWVHfik/7ijgx+35/Li9kB935LN6RyGlFT4AjIEOLRL2F2fdWzeje+tkOqYlEKUxZiIigGbMF5FaMsbQOiWO1ilxnNwtY//rlT7Lhpwiftxe4C/QnJ9Tl+/A5//3XEyUhy4ZSXRumUR8tAevx0OUx+D1P6IO+ekhyuvfZ/yvew/dP7B9Kp0yklz6JEREQkdFmIgEzOsxdMpIolNGEmf1bbP/9ZLyStbsLDykOFu0KY+yCh+V1lLps1RU+pyfPrv/ZyBiozw8dH5vLhvUTisCiEijoiJMROotLtpLn8wU+mSm1Op9vkOKMqdIqzyoSCsqreDBD5dz18QlfL8ul79f0IeEGP2xJSKNg/40ExHXeDyGmP0z+Vc9yP/VG4bw9JerefKL1SzZvIfnrhlIl5bJDRdSRCRENHpWRMKa12P4/WndeO2G48ktKuPcp7/lvQVb3I4lIlJvKsJEJCIM75rOlNtG0Dczhd+/vZC7Jy2hpLzS7VgiInWmIkxEIkarZnG88Yvj+dXIzrw5ZyMXPTuL9buK3I4lIlInKsJEJKJEeT3cNboH434+iC15xZz79Ew+WbLN7VgiIrWmIkxEItKpPVrx8e+G06llEr8aP58HP1xGmX8iWRGRSKAiTEQiVlbzBN69ZSjXD+vIf79dz6X/+Y7Nu/e6HUtEJCAqwkQkosVEebj/3N48d/VAftpZyJinZvLFih1uxxIRqZGKMBFpFM7q24YPfzuczNR4bnx1Lv/8ZCUVleqeFJHwpSJMRBqNjumJTPr1iVw5pD3PT1/LVS9+z/Y9JW7HEhGpkoowEWlU4qK9PHJRX/59eX+Wbt3DmKe+4ZvV2W7HEhE5goowEWmULhiQyQe/GUaLxBiufXkOt7w2lyWb97gdS0RkPxVhItJodWmZzPu/GcbvTu3Cd2tzOPeZmVw3bg5z1uW6HU1EBGOtdTtDrQwaNMjOnTvX7RgiEmEKSsp5bfYGXv5mHTlFZQw5pgW/OaULI7qmY4ypuQERkTowxsyz1g6qcp+KMBFpSorLKnlzzkZemPET2/NL6JeVwq2ndOH0nq3weFSMiUhwqQgTETlMaUUlk+Zv4bmv17Ixdy/dWyXz61M6c06/tnhVjIlIkKgIExE5iopKHx8t3sbYr9awemchHdMS+NXIzlw4IIuYKA2bFZH6UREmIlIDn8/y+fIdjP1qDUu27KFtShw3n9SJK4a0Jy7a63Y8EYlQKsJERAJkrWX6qmzGfrWGH9bvJj0phhuHd+KaE9qTHBftdjwRiTAqwkRE6uD7n3J45qs1fLN6F83iohjTrw292jSjR5tm9GidrKKsCSsvL2fz5s2UlGhFBnHExcWRlZVFdPShfy6oCBMRqYdFm/J4fvpaZq3NYU9x+f7Xs5rH07NNM+fROpmebZrRvkWC7rJsAtatW0dycjJpaWma4kSw1pKTk0NBQQHHHHPMIfuqK8KiGiSdiEgEO7ZdKs9dcxzWWrbtKWHl9nxWbCtgxbZ8VmzL54sVO/D5/z2bEOOle+tkerRuRq82TmHWXVfNGp2SkhI6duyoAkwAMMaQlpZGdnbtlkhTESYiEiBjDG1T42mbGs+pPVrtf72kvJJVO/YVZc7Pjxdv5c05FfuPadcinh6tm9G7bTNO7pbBsVmpumIW4VSAycHq8n1QESYiUk9x0V76ZaXSLyt1/2v7rpqt2JbPyu0FLN+Wz0r/VbN/T1tNelIso3q05LRerRjeJZ34GN2BKdLUqAgTEQmBg6+ajep54KpZ3t4yvv4xm2krdjBlyTbenruJ2CgPw7ukc1qvVozq0ZKWzeJcTC6R7KabbuIPf/gDvXr1qndbSUlJFBYWBiFV3XXs2JG5c+eSnp5ep2Py8vJ44403+PWvf13rc5999tm88cYbpKam1vq9gVIRJiLSgFITYrhgQCYXDMikrMLHnHW5TFuxg2krdvDFyp0AHJuVwqierTitZyt6tklWt5cE7KWXXtr/3FqLz1p8Fgzg8Rg8Tey7lJeXx7PPPltlEVZRUUFU1NHLoClTpoQyGqAiTETENTFRHoZ3TWd413TuP7cXP+4o4IsVO5m6fAdPTFvF41NXkZkaz6ieLTmtZyuO79SC2Ch1W4abBz9cxvKt+UFts1fbZtxzdk/y9pb7CymLtewvqqy1FBYW8ZubrmXb1q34Kiv55e138tar47jj3r/Rq98Aju+eyWXXXs83X00jo2Urfvun+/j3w/ezfetm7n7wn5x21hgmvz2eaZ98SEF+Pju2b+Oiy67gj3/+C15jsEBuURleA0//+3EmT5pIeVkpF154IQ8++GCVudevX8/o0aM54YQTmDVrFoMHD+b666/n/vvvZ+fOnYwfP54hQ4aQm5vLDTfcwE8//URCQgIvvPAC/fr1IycnhyuvvJItW7YwdOhQDp7B4fXXX+epp56irKyM448/nmeffRavt/r/H/785z+zdu1a+vfvz+mnn86YMWO47777aN68OStXrmTVqlVccMEFbNq0iZKSEm677TZuvvlm4MAVtsLCQs466yyGDx/OrFmzyMzM5P333yc+Pr7e/521JoeISBgwxtCjdTNuPaUL7906jO/vGcW/Lu5LzzbNeGfuJq4bN4eBD03l1+PnMWn+ZpZu2cOqHQX8lF3Ipty9bNtTTHZBKXl7yygqraC0ohKfL7KmIJIDSsorWbW9gG17itmRX8KuwjJ27y2joKSCvWUVlFb4+OqLqbRs3YZPps9m6rc/cOaZo/F6DM3io0hPjqF4bxGjTz+N7+ctJC01hRefeISJH05h3OtvM/b/PUxslAdjYNH8eTz50mtMnvotH0yexPSZ37M9vwRrYfPuvbw5+SMWLlvJuMmfM2nqTOb8MJcZM2YcNfuaNWu44447WLlyJStXruSNN95g5syZPPbYYzz88MMA3H///QwYMIDFixfz8MMPc9111wHw4IMPMnz4cJYtW8aFF17Ixo0bAVixYgVvv/023377LQsXLsTr9TJ+/PgaP8d//vOfdO7cmYULF/Loo48CMH/+fJ588klWrVoFwLhx45g3bx5z587lqaeeIicn54h2Vq9eza233sqyZctITU1l4sSJtfsPehS6EiYiEoZaJsdx+eD2XD64PSXllXy7ZhfTVuzkixU7mLJke8DteAxEeT3EeD1EeQ1RHg/RXkOU15AYE8Xgji0Y3jWdoZ3TaKZpNOrk/nN7B62tkvJKNu8uZm9ZBfExXtqmxvuLpSq6EYcP5rGH/sLzjz7EOeecw4gRI4iN8pCRHEeblHhiYmK4/KLzMMZw3IBjiY2NpX16M7KGDWbLpo10SEukZXIco888g+N7dgTgyssuYfvqhVx29sl4DPRoncy4eTP5YebXXD1mJBWVlr1FhXw3fymDTxhW5Q0lxxxzDH379gWgd+/ejBo1CmMMffv2Zf369QDMnDlzfyFz6qmnkpOTQ35+PjNmzGDSpEkAjBkzhubNmwPwxRdfMG/ePAYPHgxAcXExLVu2rNNnPGTIkEPm8nrqqaeYPHkyAJs2bWL16tWkpaUd8Tv1798fgOOOO27/71FfKsJERMJcXLSXUT1bMapnK3y+Pizbms+WvGIqfD4qKi3llT7KKy0VPv/PSh8Vvn2v7zvmwH7nNR+5e8uZOH8zr83egMc486GN6JLO8K4ZDGifSrRXnSUNxeez7CwoJbuwFK+Bds0TSE2IrnY8YLdu3Zg/fz5Tpkzh3nvvZdSoUYfsj44+8H6Px0NsbOz+5xUVB6ZPOfwcxhwYOxYT5cVrDPfccze33HILlT5LTqGTc/XOAlLjo2nZLO6Q9VX3naem89aGtZaf/exnPPLII3V6/8ESExP3P//666+ZNm0a3333HQkJCYwcObLKVRAO/p28Xi/FxcX1zgEqwkREIorHY+iblULfrJSgtFdW4WPBxt3MXLOLb1bv4pmv1vDUl2tIjPFyfKc0hndJZ0TXdLq0TArqDQIl5ZVszN3Lhpy9bMgpYvfeMvq3a87xnVo0uStyhSXlbMkrobSikuYJMbRJiSMqgAJ469attGjRgmuuuYbU1NRDBuXXxtSpU8nNzSU+Pp733nuPcePGHbL/zDPP5L777uPqq68mKSmJ8oIcmnu9eBJT2VVYyp7iAlITYmjVLPYoZzjSiBEjGD9+PPfddx9ff/016enpNGvWjJNOOok33niDe++9l08++YTdu3cDMGrUKM4//3xuv/12WrZsSW5uLgUFBXTo0KHa8yQnJ1NQUHDU/Xv27KF58+YkJCSwcuVKZs+eHfDvEAwqwkREmrCYKA/Hd0rj+E5p3HFGd/bsLee7n3KYuSabmat38aX/js1WzWIZ3iWD4V3TGNYlnZbJNU+jsae4nI05e1mfU+QvuIpYn7OXjTl72Z5/6NUGjwGfBa/H0DczhWFd0hjWOZ2BHZofcpWlMamo9LFtTwm795YRE+XhmPTEWq2ssGTJEu688048Hg/R0dE899xz/PGPf6x1jiFDhnDxxRezefNmrrnmGgYNOnSFnTPOOIMVK1YwdOhQwJm64vXXX6dz69akJcWQXVBKTlEZecXlFOWXEMhIxAceeIAbbriBfv36kZCQwKuvvgo4Y8WuvPJKevfuzYknnkj79u0B6NWrF3//+98544wz8Pl8REdHM3bs2BqLsLS0NIYNG0afPn0466yzGDNmzCH7R48ezfPPP0/Pnj3p3r07J5xwQoCfWnBo7UgRETmqTbl7+XbNLr5Zs4tZa3axe6+zdmaP1skM75LOsK7pJMVG7b+itSFnLxv8BVfe3vJD2spIjqVjWgLtWyQ6P9MS6JDmPI+P8TJ/Qx6z1u7i2zW7WLR5D5U+S2yUh0Edm3Ni53RO7JxG38yUgK4ShdqKFSvo2bNnnd5rrWVPcTlb80qo9FnSk2NolRznygoKr7zyCnPnzuWZZ56pVztlFT52FpSwu6gcYyAtKYaMpNiw+G/VkKr6XmjtSBERqZN2LRK4Ykh7rhjSHp/PsmxrPt+syebbNbv43+wNvDRz3f5jPQYym8fToUUiZ/dtc6DgSk+gfYsEEmKq/ytnaOc0hnZ2rsgVlJTzw/pcvl2Tw7drdvHoZz8CkBwbxfGd0pwrZV3S6RrkbtJQK62oZMvuYgpLK0iIiSIzNb5RrJYQE+Uhq3kCGcmV7MwvJbuglNzCMtKTY0lPisHraVrFWKB0JUxEROqkuKySeRt2U+7z0TEtkczUeGKiQvOX7a7CUr5bm+O/UpbDxty9AKQnxXJiZ6coO7FzOu1aJITk/Ier7ZUwn3UGtO/ILwWgdUocaYkxEVVAHiwnJ+eIGwHAuYsxLS2NkvJKduSXsKe4nCiPISM5lrTE2JBd7aspT0Op7ZUwFWEiIhJxNuXu5bu1OXzrL8p2FTrFTVJsFOlJMaQnxZKRHEt6kv+RfOC1DP9r9bkCVZsibG9ZBZt3F1NSXkmzuGjahrBYDTd7yyrYkV9KQUk50V4PGcmxpMRF+2fvb3yLoKs7UkREGr12LRJo1yKBywa3w1rL6p2FzFqziw25e8kuKGVXYSmrdxby3U85R4xN2ycxxkv6QUXZvkItLSmWZnFRJMdFkRwXvf9nUmwUSbFReAO8mlPps+zILyGnsJQor4cOaYmkxDetOz8TYqI4Jj2KotIKtu8pYWteMVtxpnfYt5SS15jDfvpf9y+ztP91D862/1iv/5hILuRUhImISEQzxtCtVTLdWiVXub+swkdOUSm7CsrY5Z/jaldhqb9YK2NXQSlrswv5fl3p/hsPqpMUG8XTZ7XEu73goGKB/UWBxxiMgZzCMsorfaQlxtI6JbZJj4tKjI2iU0YiRWWVlJZX4rOWSh/+n3b/zwqfjzIfVFqLz/96dQwGr9cQ5Tnw8Ho9R90Ot6JNRZiIiDRqMVEe2qTE0yal5rX+yit97C4qI7+kgoKScgpKKigsPfB83yM2qpzYaA+VPkvlvsLBZ/ev8wjOJLvtWySRGKu/asEplvddTQyU76Bi7OCirdJaKiqdz77C5zyv8FmKy31UlFZQeZQluwzg9Xj8xZkhJT6a9KTA5zcLNn0zRERE/KK9Hlo2i6Nls+qPW7FiBR3SEqvct69wCLerLpHIYwweb+0/w/1X1vwrRThX2Q7brqz5SluoNd1royIiIiHgMYYo71HWewyxm266ieXLlwelraSkpKC0Ux8dO3Zk165dtT7GYwzRXg/xMV6S46JJTXDG+7VOiSOruTM/XeeWSVVOOrzv9966dSuXXHJJleccOXIkwbhJUFfCREREGom6Ll0kR2rbti0TJkwI6TlCeiXMGDPaGPOjMWaNMebPVeyPNca87d//vTGmYyjziIiIhMR/x8CC8c7zynJne9HbznbZXmd76URnu2SPs738A2e7KMfZ/vETZ7tgR0CnLCoqYsyYMRx77LH06dOHt99++5ArNElJSdx555307t2b0047jTlz5jBy5Eg6derEBx84537llVc4//zzGTlyJF27duXBBx+s8lyPPvoogwcPpl+/ftx///1HzbR+/Xp69OjBz3/+c7p168bVV1/NtGnTGDZsGF27dmXOnDkA5ObmcsEFF9CvXz9OOOEEFi9eDDjzfZ1xxhn07t2bm266iYOn0Xr99dcZMmQI/fv3dxYTr6ys8TP685//zNixY/dvP/DAAzz22GMUFhYyatQoBg4cSN++fXn//fer/F369OkDQHFxMVdccQU9e/bkwgsvDNoC3iErwowxXmAscBbQC7jSGNPrsMNuBHZba7sATwD/ClUeERGRxuTTTz+lbdu2LFq0iKVLlzJ69OhD9hcVFXHqqaeybNkykpOTuffee5k6dSqTJ0/mr3/96/7j5syZw8SJE1m8eDHvvvvuEd1sn3/+OatXr2bOnDksXLiQefPmMWPGjKPmWrNmDXfccQcrV65k5cqVvPHGG8ycOZPHHnuMhx9+GHDWiBwwYACLFy/m4Ycf5rrrrgPgwQcfZPjw4SxbtowLL7yQjRs3As4YvLfffptvv/2WhQsX4vV6GT9+fI2f0eWXX84777yzf/udd97h8ssvJy4ujsmTJzN//ny++uor7rjjDqqbN/W5554jISGBFStW8OCDDzJv3rwazx2IUHZHDgHWWGt/AjDGvAWcDxzcWX0+8ID/+QTgGWOMsZE2g6yIiDRt13984Lk3+tDtmIRDt+NSDt1OTDt0O7lVQKfs27cvd9xxB3fddRfnnHMOI0aMOGR/TEzM/sKsb9++xMbGEh0dTd++fVm/fv3+404//fT9s8pfdNFFzJw585BFvD///HM+//xzBgwYAEBhYSGrV6/mpJNOqjLXMcccQ9++fQHo3bs3o0aNwhhzyHlnzpzJxInOlcFTTz2VnJwc8vPzmTFjBpMmTQJgzJgxNG/eHHBmvp83bx6DBw8GnCtTLVu2rPEzGjBgADt37mTr1q1kZ2fTvHlz2rVrR3l5Offccw8zZszA4/GwZcsWduzYQevWratsZ8aMGfzud78DoF+/fvTr16/GcwcilEVYJrDpoO3NwPFHO8ZaW2GM2QOkAdWPwhMREWniunXrxvz585kyZQr33nvvEcv2REdH7785wOPxEBsbu/95RUXF/uMOv4Hg8G1rLXfffTe33HJLQLn2naem89aGtZaf/exnPPLII7V+76WXXsqECRPYvn07l19+OQDjx48nOzubefPmER0dTceOHSkpKalTtvqIiLsjjTE3G2PmGmPmZmdnux1HRETEdVu3biUhIYFrrrmGO++8k/nz59epnalTp5Kbm0txcTHvvfcew4YNO2T/mWeeybhx4ygsLARgy5Yt7Ny5s17ZR4wYsb878euvvyY9PZ1mzZpx0kkn8cYbbwDwySefsHv3bgBGjRrFhAkT9p83NzeXDRs2BHSuyy+/nLfeeosJEyZw6aWXArBnzx5atmxJdHQ0X331VY1tHZxr6dKl+8ew1Vcor4RtAdodtJ3lf62qYzYbY6KAFCDn8IastS8AL4CzdmRI0oqIiESQJUuWcOedd+LxeIiOjua5557jj3/8Y63bGTJkCBdffDGbN2/mmmuuOaQrEuCMM85gxYoVDB06FHAG/L/++usBdQcezQMPPMANN9xAv379SEhI4NVXXwWcsWJXXnklvXv35sQTT6R9+/YA9OrVi7///e+cccYZ+Hw+oqOjGTt2LB06dKjxXL1796agoIDMzEzatGkDwNVXX825555L3759GTRoED169Ki2jV/96ldcf/319OzZk549e3LcccfV+Xc/WMgW8PYXVauAUTjF1g/AVdbaZQcdcyvQ11r7S2PMFcBF1trLqmtXC3iLiIjbarOAdzh75ZVXmDt3Ls8884zbURqFsFnA2z/G6zfAZ4AXGGetXWaMeQiYa639AHgZeM0YswbIBa4IVR4RERGRcBKyK2GhoithIiLitsZyJayucnJyjrgRAJy7GPfdadkU84TNlTARERFpnNLS0li4cKHbMfYLtzyBioi7I0VERMJNpPUkSWjV5fugIkxERKSW4uLiyMnJUSEmgFOA5eTkEBd35ILg1VF3pIiISC1lZWWxefNmNHel7BMXF0dWVlat3qMiTEREpJaio6M55phj3I4hEU7dkSIiIiIuUBEmIiIi4gIVYSIiIiIuiLjJWo0x2UBgq3bWTzqwqwHO0xTpsw0dfbahpc83dPTZhpY+39Cp6bPtYK3NqGpHxBVhDcUYM/doM9xK/eizDR19tqGlzzd09NmGlj7f0KnPZ6vuSBEREREXqAgTERERcYGKsKN7we0AjZg+29DRZxta+nxDR59taOnzDZ06f7YaEyYiIiLiAl0JExEREXGBirDDGGNGG2N+NMasMcb82e08jY0xZr0xZokxZqExZq7beSKZMWacMWanMWbpQa+1MMZMNcas9v9s7mbGSHaUz/cBY8wW//d3oTHmbDczRipjTDtjzFfGmOXGmGXGmNv8r+v7W0/VfLb67gaBMSbOGDPHGLPI//k+6H/9GGPM9/7a4W1jTExA7ak78gBjjBdYBZwObAZ+AK601i53NVgjYoxZDwyy1mq+mnoyxpwEFAL/s9b28b/2f0Cutfaf/n9ENLfW3uVmzkh1lM/3AaDQWvuYm9kinTGmDdDGWjvfGJMMzAMuAH6Ovr/1Us1nexn67tabMcYAidbaQmNMNDATuA34AzDJWvuWMeZ5YJG19rma2tOVsEMNAdZYa3+y1pYBbwHnu5xJpErW2hlA7mEvnw+86n/+Ks4fvlIHR/l8JQistdustfP9zwuAFUAm+v7WWzWfrQSBdRT6N6P9DwucCkzwvx7wd1dF2KEygU0HbW9GX95gs8Dnxph5xpib3Q7TCLWy1m7zP98OtHIzTCP1G2PMYn93pbrL6skY0xEYAHyPvr9BddhnC/ruBoUxxmuMWQjsBKYCa4E8a22F/5CAawcVYdLQhltrBwJnAbf6u3wkBKwz1kDjDYLrOaAz0B/YBvw/V9NEOGNMEjAR+L21Nv/gffr+1k8Vn62+u0Fira201vYHsnB60HrUtS0VYYfaArQ7aDvL/5oEibV2i//nTmAyzhdYgmeHf0zIvrEhO13O06hYa3f4/wD2AS+i72+d+cfTTATGW2sn+V/W9zcIqvps9d0NPmttHvAVMBRINcZE+XcFXDuoCDvUD0BX/10OMcAVwAcuZ2o0jDGJ/oGiGGMSgTOApdW/S2rpA+Bn/uc/A953MUujs69A8LsQfX/rxD+4+WVghbX28YN26ftbT0f7bPXdDQ5jTIYxJtX/PB7nRr4VOMXYJf7DAv7u6u7Iw/hv2/034AXGWWv/4W6ixsMY0wnn6hdAFPCGPt+6M8a8CYwE0oEdwP3Ae8A7QHtgA3CZtVaDy+vgKJ/vSJzuHAusB245aAyTBMgYMxz4BlgC+Pwv34Mzdknf33qo5rO9En13680Y0w9n4L0X50LWO9bah/x/v70FtAAWANdYa0trbE9FmIiIiEjDU3ekiIiIiAtUhImIiIi4QEWYiIiIiAtUhImIiIi4QEWYiIiIiAtUhImIBMgYM9IY85HbOUSkcVARJiIiIuICFWEi0ugYY64xxswxxiw0xvzHv+BuoTHmCWPMMmPMF8aYDP+x/Y0xs/0LG0/et7CxMaaLMWaaMWaRMWa+Maazv/kkY8wEY8xKY8x4/wzlIiK1piJMRBoVY0xP4HJgmH+R3UrgaiARmGut7Q1Mx5kBH+B/wF3W2n44s4zve308MNZaeyxwIs6ixwADgN8DvYBOwLAQ/0oi0khF1XyIiEhEGQUcB/zgv0gVj7MQtA9423/M68AkY0wKkGqtne5//VXgXf8ap5nW2skA1toSAH97c6y1m/3bC4GOwMyQ/1Yi0uioCBORxsYAr1pr7z7kRWPuO+y4uq7ZdvB6cJXoz1ERqSN1R4pIY/MFcIkxpiWAMaaFMaYDzp93l/iPuQqYaa3dA+w2xozwv34tMN1aWwBsNsZc4G8j1hiT0JC/hIg0fvoXnIg0Ktba5caYe4HPjTEeoBy4FSgChvj37cQZNwbwM+B5f5H1E3C9//Vrgf8YYx7yt3FpA/4aItIEGGvrekVeRCRyGGMKrbVJbucQEdlH3ZEiIiIiLtCVMBEREREX6EqYiIiIiAtUhImIiIi4QEWYiIiIiAtUhImIiIi4QEWYiIiIiAtUhImIiIi44P8DB4vFmiehwwkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_loss({'simple_model' : simple_history})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, the model begins of overfit rather quickly after the fourth epoc.  The model quickly optimizes its weights in the first 4 epochs seeing a consistent drop and divergence in train and validation loss in tandem to concurrent increating in train and validation accuracy. Starting in the fifth epoch, the validation accuracy decreases and loss increases steeply. Prior to overfitting, this model achives performance similar to the API models above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Build VGG16 model within the hyperparamter space"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cells below are not needed to be ran. In fact, they will likely fail due to a memory error received from the hyperparamter space being too large. Below I attempted to use the Keras Tuner library to tune the hyperparamters of the VGG16 model. The first attempt failed because the hp.Int() method only picked one value to modify the filter size per trial. All of the layers ended up with the same filter size. In the second approach I tried to build up the VGG model dynamically by adding a each convolutional module with increasing filter size (ranging from 64 to 512 as in the original model). This crashes the TF kernel as well, so the hyperparameter tuning was largely unsuccessful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_hyper_vgg16_model_1(hp):\n",
    "    model = Sequential()\n",
    "    \n",
    "    # first stage two layers of 3x3x64\n",
    "    model.add(Conv2D(input_shape=IMAGE_INPUT_SHAPE,\n",
    "        filters=hp.Int('filters',\n",
    "            min_value=32,\n",
    "            max_value=96,\n",
    "            step=32),\n",
    "        activation='relu',\n",
    "        kernel_size=(3,3),\n",
    "        padding=\"same\"))\n",
    "    model.add(Conv2D(filters=hp.Int('filters',\n",
    "            min_value=32,\n",
    "            max_value=96,\n",
    "            step=32),\n",
    "        activation='relu',\n",
    "        kernel_size=(3,3),\n",
    "        padding=\"same\"))\n",
    "    model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\n",
    "    \n",
    "    # second stage two layers of 3x3x128\n",
    "    model.add(Conv2D(filters=hp.Int('filters',\n",
    "            min_value=96,\n",
    "            max_value=160,\n",
    "            step=32),\n",
    "        activation='relu',\n",
    "        kernel_size=(3,3),\n",
    "        padding=\"same\"))\n",
    "    model.add(Conv2D(input_shape=IMAGE_INPUT_SHAPE, filters=\n",
    "        hp.Int('filters',\n",
    "            min_value=96,\n",
    "            max_value=160,\n",
    "            step=32),\n",
    "        activation='relu',\n",
    "        kernel_size=(3,3),\n",
    "        padding=\"same\"))\n",
    "    model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\n",
    "    \n",
    "    # third stage three layers of 3x3x256\n",
    "    model.add(Conv2D(filters=hp.Int('filters',\n",
    "            min_value=192,\n",
    "            max_value=320,\n",
    "            step=64),\n",
    "        activation='relu',\n",
    "        kernel_size=(3,3),\n",
    "        padding=\"same\"))\n",
    "    model.add(Conv2D(input_shape=IMAGE_INPUT_SHAPE, filters=\n",
    "        hp.Int('filters',\n",
    "            min_value=192,\n",
    "            max_value=320,\n",
    "            step=64),\n",
    "        activation='relu',\n",
    "        kernel_size=(3,3),\n",
    "        padding=\"same\"))\n",
    "    model.add(Conv2D(input_shape=IMAGE_INPUT_SHAPE, filters=\n",
    "        hp.Int('filters',\n",
    "            min_value=192,\n",
    "            max_value=320,\n",
    "            step=64),\n",
    "        activation='relu',\n",
    "        kernel_size=(3,3),\n",
    "        padding=\"same\"))\n",
    "    model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\n",
    "    \n",
    "    # fourth stage three layers of 3x3x512\n",
    "    model.add(Conv2D(filters=hp.Int('filters',\n",
    "            min_value=384,\n",
    "            max_value=640,\n",
    "            step=128),\n",
    "        activation='relu',\n",
    "        kernel_size=(3,3),\n",
    "        padding=\"same\"))\n",
    "    model.add(Conv2D(input_shape=IMAGE_INPUT_SHAPE, filters=\n",
    "        hp.Int('filters',\n",
    "            min_value=384,\n",
    "            max_value=640,\n",
    "            step=128),\n",
    "        activation='relu',\n",
    "        kernel_size=(3,3),\n",
    "        padding=\"same\"))\n",
    "    model.add(Conv2D(input_shape=IMAGE_INPUT_SHAPE, filters=\n",
    "        hp.Int('filters',\n",
    "            min_value=384,\n",
    "            max_value=640,\n",
    "            step=128),\n",
    "        activation='relu',\n",
    "        kernel_size=(3,3),\n",
    "        padding=\"same\"))\n",
    "    model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\n",
    "    \n",
    "    # fifth stage three layers of 3x3x512\n",
    "    model.add(Conv2D(filters=hp.Int('filters',\n",
    "            min_value=384,\n",
    "            max_value=640,\n",
    "            step=128),\n",
    "        activation='relu',\n",
    "        kernel_size=(3,3),\n",
    "        padding=\"same\"))\n",
    "    model.add(Conv2D(input_shape=IMAGE_INPUT_SHAPE, filters=\n",
    "        hp.Int('filters',\n",
    "            min_value=384,\n",
    "            max_value=640,\n",
    "            step=128),\n",
    "        activation='relu',\n",
    "        kernel_size=(3,3),\n",
    "        padding=\"same\"))\n",
    "    model.add(Conv2D(input_shape=IMAGE_INPUT_SHAPE, filters=\n",
    "        hp.Int('filters',\n",
    "            min_value=384,\n",
    "            max_value=640,\n",
    "            step=128),\n",
    "        activation='relu',\n",
    "        kernel_size=(3,3),\n",
    "        padding=\"same\"))\n",
    "    model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))\n",
    "    \n",
    "    # sixth stage flatten two relu dense and classification header\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(units=4096,activation=\"relu\"))\n",
    "    model.add(Dense(units=4096,activation=\"relu\"))\n",
    "    model.add(Dense(units=1, activation=\"sigmoid\"))\n",
    "    \n",
    "    model.compile(\n",
    "        optimizer=tf.keras.optimizers.Adam(\n",
    "            hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])),\n",
    "        loss='binary_crossentropy', \n",
    "        metrics=['accuracy'])\n",
    "    print(model.summary())\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As can be seen below, the hyperparameter tuner only chose a single filter size value and applied it to all the layers, this doesn't work for our use-case even though the best model still got 81% validation accuracy, which is on par with the most performant model above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 100, 100, 32)      896       \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 100, 100, 32)      9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 50, 50, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 50, 50, 32)        9248      \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 50, 50, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 25, 25, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 25, 25, 32)        9248      \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 25, 25, 32)        9248      \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 25, 25, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 12, 12, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 12, 12, 32)        9248      \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 12, 12, 32)        9248      \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 12, 12, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 6, 6, 32)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 6, 6, 32)          9248      \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 6, 6, 32)          9248      \n",
      "_________________________________________________________________\n",
      "conv2d_12 (Conv2D)           (None, 6, 6, 32)          9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 3, 3, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 288)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 4096)              1183744   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 4096)              16781312  \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 4097      \n",
      "=================================================================\n",
      "Total params: 18,081,025\n",
      "Trainable params: 18,081,025\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "tuner = RandomSearch(\n",
    "    build_hyper_vgg16_model_1,\n",
    "    objective='val_accuracy',\n",
    "    max_trials=5,\n",
    "    executions_per_trial=3,\n",
    "    directory='log_dir',\n",
    "    project_name='solar_pv_class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Search space summary\n",
      "Default search space size: 2\n",
      "filters (Int)\n",
      "{'default': None, 'conditions': [], 'min_value': 32, 'max_value': 96, 'step': 32, 'sampling': None}\n",
      "learning_rate (Choice)\n",
      "{'default': 0.01, 'conditions': [], 'values': [0.01, 0.001, 0.0001], 'ordered': True}\n"
     ]
    }
   ],
   "source": [
    "tuner.search_space_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 5 Complete [00h 00m 50s]\n",
      "val_accuracy: 0.5\n",
      "\n",
      "Best val_accuracy So Far: 0.8128019173940023\n",
      "Total elapsed time: 00h 03m 20s\n",
      "INFO:tensorflow:Oracle triggered exit\n"
     ]
    }
   ],
   "source": [
    "tuner.search(X_train, Y_train, \n",
    "    epochs=5, \n",
    "    validation_data=(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 100, 100, 96)      2688      \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 100, 100, 96)      83040     \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 50, 50, 96)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 50, 50, 96)        83040     \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 50, 50, 96)        83040     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 25, 25, 96)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 25, 25, 96)        83040     \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 25, 25, 96)        83040     \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 25, 25, 96)        83040     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 12, 12, 96)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 12, 12, 96)        83040     \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 12, 12, 96)        83040     \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 12, 12, 96)        83040     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 6, 6, 96)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 6, 6, 96)          83040     \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 6, 6, 96)          83040     \n",
      "_________________________________________________________________\n",
      "conv2d_12 (Conv2D)           (None, 6, 6, 96)          83040     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 3, 3, 96)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 864)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 4096)              3543040   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 4096)              16781312  \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 4097      \n",
      "=================================================================\n",
      "Total params: 21,327,617\n",
      "Trainable params: 21,327,617\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<tensorflow.python.keras.engine.sequential.Sequential at 0x2481f1af5f8>]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuner.get_best_models(num_models=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because of the issue in imlementing the Keras Tuner library above on each convolutional layer of the VGG16 model, I need to dynamically build up the model, where each layer is at the scale respective to that stage in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_hyper_vgg16_model_2(hp):\n",
    "    inputs = tf.keras.Input(shape=IMAGE_INPUT_SHAPE)\n",
    "    x = inputs\n",
    "    for i in range(1, 6):\n",
    "        if i == 1:\n",
    "            x = Conv2D(\n",
    "                filters=hp.Int('filters_' + str(i),\n",
    "                    32, 96, step=32, default=64),\n",
    "                kernel_size=(3,3),\n",
    "                activation='relu',\n",
    "                padding='same')(x)\n",
    "        if i == 2:\n",
    "            x = Conv2D(\n",
    "                filters=hp.Int('filters_' + str(i),\n",
    "                    96, 160, step=32, default=128),\n",
    "                kernel_size=(3,3),\n",
    "                activation='relu',\n",
    "                padding='same')(x)\n",
    "        elif i == 3:\n",
    "            x = Conv2D(\n",
    "                filters=hp.Int('filters_' + str(i),\n",
    "                    192, 320, step=64, default=256),\n",
    "                kernel_size=(3,3),\n",
    "                activation='relu',\n",
    "                padding='same')(x)            \n",
    "        else:\n",
    "            for j in range(1, 3):\n",
    "                x = Conv2D(\n",
    "                    filters=hp.Int('filters_' + str(i),\n",
    "                        384, 640, step=128, default=512),\n",
    "                    kernel_size=(3,3),\n",
    "                    activation='relu',\n",
    "                    padding='same')(x)\n",
    "                \n",
    "        x = MaxPool2D(pool_size=(2,2), strides=(2,2))(x)\n",
    "        \n",
    "    x = Flatten()(x)\n",
    "    x = Dense(units=4096,activation=\"relu\")(x)\n",
    "    x = Dense(units=4096,activation=\"relu\")(x)\n",
    "    outputs = Dense(units=1, activation=\"sigmoid\")(x)\n",
    "    model = tf.keras.Model(inputs, outputs)\n",
    "    \n",
    "    model.compile(\n",
    "        optimizer=tf.keras.optimizers.Adam(\n",
    "            hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])),\n",
    "        loss='binary_crossentropy', \n",
    "        metrics=['accuracy'])\n",
    "    print(model.summary())\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 100, 100, 3)]     0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 100, 100, 64)      1792      \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 100, 100, 64)      36928     \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 100, 100, 64)      36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 50, 50, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 50, 50, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 25, 25, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 25, 25, 256)       295168    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 12, 12, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 12, 12, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 12, 12, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 6, 6, 512)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 6, 6, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 6, 6, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 3, 3, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 4608)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 4096)              18878464  \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 4096)              16781312  \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 4097      \n",
      "=================================================================\n",
      "Total params: 44,368,129\n",
      "Trainable params: 44,368,129\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "hyper_tuner = RandomSearch(\n",
    "    build_hyper_vgg16_model_2,\n",
    "    objective='val_accuracy',\n",
    "    max_trials=1,\n",
    "    executions_per_trial=3,\n",
    "    directory='log_dir_2',\n",
    "    project_name='solar_pv_class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Search space summary\n",
      "Default search space size: 6\n",
      "filters_1 (Int)\n",
      "{'default': 64, 'conditions': [], 'min_value': 32, 'max_value': 96, 'step': 32, 'sampling': None}\n",
      "filters_2 (Int)\n",
      "{'default': 128, 'conditions': [], 'min_value': 96, 'max_value': 160, 'step': 32, 'sampling': None}\n",
      "filters_3 (Int)\n",
      "{'default': 256, 'conditions': [], 'min_value': 192, 'max_value': 320, 'step': 64, 'sampling': None}\n",
      "filters_4 (Int)\n",
      "{'default': 512, 'conditions': [], 'min_value': 384, 'max_value': 640, 'step': 128, 'sampling': None}\n",
      "filters_5 (Int)\n",
      "{'default': 512, 'conditions': [], 'min_value': 384, 'max_value': 640, 'step': 128, 'sampling': None}\n",
      "learning_rate (Choice)\n",
      "{'default': 0.01, 'conditions': [], 'values': [0.01, 0.001, 0.0001], 'ordered': True}\n"
     ]
    }
   ],
   "source": [
    "hyper_tuner.search_space_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 1 Complete [00h 01m 57s]\n",
      "val_accuracy: 0.5\n",
      "\n",
      "Best val_accuracy So Far: 0.5\n",
      "Total elapsed time: 00h 01m 57s\n",
      "INFO:tensorflow:Oracle triggered exit\n"
     ]
    }
   ],
   "source": [
    "hyper_tuner.search(X_train, Y_train, \n",
    "    epochs=10, \n",
    "    validation_data=(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 100, 100, 3)]     0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 100, 100, 96)      2688      \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 100, 100, 96)      83040     \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 100, 100, 96)      83040     \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 50, 50, 96)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 50, 50, 128)       110720    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 25, 25, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 25, 25, 320)       368960    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 12, 12, 320)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 12, 12, 384)       1106304   \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 12, 12, 384)       1327488   \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 6, 6, 384)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 6, 6, 512)         1769984   \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 6, 6, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 3, 3, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 4608)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 4096)              18878464  \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 4096)              16781312  \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 4097      \n",
      "=================================================================\n",
      "Total params: 42,875,905\n",
      "Trainable params: 42,875,905\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<tensorflow.python.keras.engine.functional.Functional at 0x2aecc6d3a58>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hyper_tuner.get_best_models(num_models=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I keep running out of memory when attempting to run all hyperparater tuning options through the Keras Tuner. Perhaps this would be possible by using an iterative approach to manually building out the tuning infrastructure, but I don't believe this is worth my time. One other option would be to run this same script in a CoLab notebook or somewhere leveraging scalable computing resources."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appendix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of VGG, Inception and ResNet Architectures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](https://i.pinimg.com/originals/3a/01/b4/3a01b4e0f4daba9863b16a006d36f113.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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