Monkey Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "465710c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "\n",
    "import numpy as np\n",
    "np.random.seed(1)\n",
    "\n",
    "import os\n",
    "import pandas as pd\n",
    "import itertools\n",
    "import random\n",
    "from pathlib import Path\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential, load_model\n",
    "from keras.layers import Conv2D, MaxPooling2D, Activation, Dropout, Flatten, Dense\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cca2f629",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get dataset directories\n",
    "train_dir = Path('./data/training/training/')\n",
    "val_dir = Path('./data/validation/validation/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1af47054",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get class labels\n",
    "labels = pd.read_csv(\"./data/monkey_labels.txt\").rename(columns=lambda x: x.strip())\n",
    "labels = labels['Common Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "78868eb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1097 images belonging to 10 classes.\n",
      "Found 272 images belonging to 10 classes.\n"
     ]
    }
   ],
   "source": [
    "#Hyper Parameters\n",
    "LR = 1e-3\n",
    "height=150\n",
    "width=150\n",
    "batch_size = 64\n",
    "epochs = 200\n",
    "\n",
    "#Constants\n",
    "seed=1337\n",
    "channels=3\n",
    "num_classes = 10\n",
    "\n",
    "# Training generator. This creates batches of augmented training image data.\n",
    "# This augmentation includes rotating, shifting, and flipping images to reduce overfitting.\n",
    "train_datagen = ImageDataGenerator(\n",
    "        rescale=1./255,\n",
    "        rotation_range=40,\n",
    "        width_shift_range=0.2,\n",
    "        height_shift_range=0.2,\n",
    "        shear_range=0.2,\n",
    "        zoom_range=0.2,\n",
    "        horizontal_flip=True,\n",
    "        fill_mode='nearest'\n",
    ")\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_dir, \n",
    "    target_size=(height,width),\n",
    "    batch_size=batch_size,\n",
    "    seed=seed,\n",
    "    shuffle=True,\n",
    "    class_mode='categorical'\n",
    ")\n",
    "\n",
    "# Test generator\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "validation_generator = test_datagen.flow_from_directory(\n",
    "    val_dir, \n",
    "    target_size=(height,width), \n",
    "    batch_size=batch_size,\n",
    "    seed=seed,\n",
    "    shuffle=False,\n",
    "    class_mode='categorical'\n",
    ")\n",
    "\n",
    "train_num = train_generator.samples\n",
    "validation_num = validation_generator.samples "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "940a3f92",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create the model architecture. This consists of a set of Convolution,\n",
    "#ReLU, MaxPooling, DropOut(Training only), and Fully-Connected layers.\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, (3, 3), input_shape=(height, width, channels)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(32, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(64, (3, 3), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Conv2D(64, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(512))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(num_classes))\n",
    "model.add(Activation('softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "88159d91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 148, 148, 32)      896       \n",
      "                                                                 \n",
      " activation (Activation)     (None, 148, 148, 32)      0         \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2  (None, 74, 74, 32)        0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 72, 72, 32)        9248      \n",
      "                                                                 \n",
      " activation_1 (Activation)   (None, 72, 72, 32)        0         \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPoolin  (None, 36, 36, 32)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 36, 36, 64)        18496     \n",
      "                                                                 \n",
      " activation_2 (Activation)   (None, 36, 36, 64)        0         \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 34, 34, 64)        36928     \n",
      "                                                                 \n",
      " activation_3 (Activation)   (None, 34, 34, 64)        0         \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPoolin  (None, 17, 17, 64)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 17, 17, 64)        0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 18496)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 512)               9470464   \n",
      "                                                                 \n",
      " activation_4 (Activation)   (None, 512)               0         \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 512)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                5130      \n",
      "                                                                 \n",
      " activation_5 (Activation)   (None, 10)                0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 9541162 (36.40 MB)\n",
      "Trainable params: 9541162 (36.40 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Compile model\n",
    "model.compile(optimizer=Adam(learning_rate=LR),\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['acc'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "005da48f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#Create callbacks for saving the model and concelling training if no progress is being made\n",
    "filepath=str(os.getcwd()+\"/model.h5f\")\n",
    "checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')\n",
    "stopper = EarlyStopping(monitor='val_acc', patience=15)\n",
    "callbacks_list = [checkpoint, stopper]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "08a582fe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 2.2410 - acc: 0.1772\n",
      "Epoch 1: val_acc improved from -inf to 0.26562, saving model to C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17/17 [==============================] - 34s 2s/step - loss: 2.2410 - acc: 0.1772 - val_loss: 2.0581 - val_acc: 0.2656\n",
      "Epoch 2/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.9669 - acc: 0.2914\n",
      "Epoch 2: val_acc improved from 0.26562 to 0.37891, saving model to C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17/17 [==============================] - 32s 2s/step - loss: 1.9669 - acc: 0.2914 - val_loss: 1.7739 - val_acc: 0.3789\n",
      "Epoch 3/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.8759 - acc: 0.3214\n",
      "Epoch 3: val_acc improved from 0.37891 to 0.48047, saving model to C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17/17 [==============================] - 32s 2s/step - loss: 1.8759 - acc: 0.3214 - val_loss: 1.5820 - val_acc: 0.4805\n",
      "Epoch 4/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.6704 - acc: 0.4114\n",
      "Epoch 4: val_acc improved from 0.48047 to 0.53516, saving model to C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17/17 [==============================] - 32s 2s/step - loss: 1.6704 - acc: 0.4114 - val_loss: 1.4248 - val_acc: 0.5352\n",
      "Epoch 5/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.5562 - acc: 0.4540\n",
      "Epoch 5: val_acc did not improve from 0.53516\n",
      "17/17 [==============================] - 31s 2s/step - loss: 1.5562 - acc: 0.4540 - val_loss: 1.3454 - val_acc: 0.5312\n",
      "Epoch 6/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.5425 - acc: 0.4395\n",
      "Epoch 6: val_acc improved from 0.53516 to 0.57422, saving model to C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17/17 [==============================] - 33s 2s/step - loss: 1.5425 - acc: 0.4395 - val_loss: 1.3324 - val_acc: 0.5742\n",
      "Epoch 7/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.4581 - acc: 0.4705\n",
      "Epoch 7: val_acc did not improve from 0.57422\n",
      "17/17 [==============================] - 33s 2s/step - loss: 1.4581 - acc: 0.4705 - val_loss: 1.3825 - val_acc: 0.4766\n",
      "Epoch 8/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.4211 - acc: 0.5073\n",
      "Epoch 8: val_acc did not improve from 0.57422\n",
      "17/17 [==============================] - 32s 2s/step - loss: 1.4211 - acc: 0.5073 - val_loss: 1.5496 - val_acc: 0.4648\n",
      "Epoch 9/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.4978 - acc: 0.4647\n",
      "Epoch 9: val_acc improved from 0.57422 to 0.58203, saving model to C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17/17 [==============================] - 34s 2s/step - loss: 1.4978 - acc: 0.4647 - val_loss: 1.2844 - val_acc: 0.5820\n",
      "Epoch 10/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.3621 - acc: 0.4947\n",
      "Epoch 10: val_acc did not improve from 0.58203\n",
      "17/17 [==============================] - 32s 2s/step - loss: 1.3621 - acc: 0.4947 - val_loss: 1.2761 - val_acc: 0.5625\n",
      "Epoch 11/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.3252 - acc: 0.5198\n",
      "Epoch 11: val_acc did not improve from 0.58203\n",
      "17/17 [==============================] - 31s 2s/step - loss: 1.3252 - acc: 0.5198 - val_loss: 1.2812 - val_acc: 0.5508\n",
      "Epoch 12/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.3409 - acc: 0.4985\n",
      "Epoch 12: val_acc did not improve from 0.58203\n",
      "17/17 [==============================] - 31s 2s/step - loss: 1.3409 - acc: 0.4985 - val_loss: 1.2373 - val_acc: 0.5703\n",
      "Epoch 13/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.2944 - acc: 0.5266\n",
      "Epoch 13: val_acc improved from 0.58203 to 0.58984, saving model to C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17/17 [==============================] - 32s 2s/step - loss: 1.2944 - acc: 0.5266 - val_loss: 1.2049 - val_acc: 0.5898\n",
      "Epoch 14/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.3534 - acc: 0.5063\n",
      "Epoch 14: val_acc improved from 0.58984 to 0.60156, saving model to C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17/17 [==============================] - 33s 2s/step - loss: 1.3534 - acc: 0.5063 - val_loss: 1.1421 - val_acc: 0.6016\n",
      "Epoch 15/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.2539 - acc: 0.5440\n",
      "Epoch 15: val_acc did not improve from 0.60156\n",
      "17/17 [==============================] - 33s 2s/step - loss: 1.2539 - acc: 0.5440 - val_loss: 1.2458 - val_acc: 0.5898\n",
      "Epoch 16/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.2525 - acc: 0.5363\n",
      "Epoch 16: val_acc did not improve from 0.60156\n",
      "17/17 [==============================] - 32s 2s/step - loss: 1.2525 - acc: 0.5363 - val_loss: 1.2218 - val_acc: 0.5820\n",
      "Epoch 17/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.2139 - acc: 0.5566\n",
      "Epoch 17: val_acc did not improve from 0.60156\n",
      "17/17 [==============================] - 33s 2s/step - loss: 1.2139 - acc: 0.5566 - val_loss: 1.3726 - val_acc: 0.5195\n",
      "Epoch 18/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.1906 - acc: 0.5547\n",
      "Epoch 18: val_acc did not improve from 0.60156\n",
      "17/17 [==============================] - 32s 2s/step - loss: 1.1906 - acc: 0.5547 - val_loss: 1.3603 - val_acc: 0.5312\n",
      "Epoch 19/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.2125 - acc: 0.5508\n",
      "Epoch 19: val_acc did not improve from 0.60156\n",
      "17/17 [==============================] - 32s 2s/step - loss: 1.2125 - acc: 0.5508 - val_loss: 1.2178 - val_acc: 0.5977\n",
      "Epoch 20/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.1182 - acc: 0.6128\n",
      "Epoch 20: val_acc improved from 0.60156 to 0.63281, saving model to C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17/17 [==============================] - 33s 2s/step - loss: 1.1182 - acc: 0.6128 - val_loss: 1.0735 - val_acc: 0.6328\n",
      "Epoch 21/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.1750 - acc: 0.5721\n",
      "Epoch 21: val_acc did not improve from 0.63281\n",
      "17/17 [==============================] - 31s 2s/step - loss: 1.1750 - acc: 0.5721 - val_loss: 1.1158 - val_acc: 0.6250\n",
      "Epoch 22/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.1336 - acc: 0.5973\n",
      "Epoch 22: val_acc did not improve from 0.63281\n",
      "17/17 [==============================] - 31s 2s/step - loss: 1.1336 - acc: 0.5973 - val_loss: 1.3579 - val_acc: 0.5508\n",
      "Epoch 23/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.1854 - acc: 0.5770\n",
      "Epoch 23: val_acc did not improve from 0.63281\n",
      "17/17 [==============================] - 31s 2s/step - loss: 1.1854 - acc: 0.5770 - val_loss: 1.3868 - val_acc: 0.5352\n",
      "Epoch 24/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.1420 - acc: 0.5886\n",
      "Epoch 24: val_acc did not improve from 0.63281\n",
      "17/17 [==============================] - 31s 2s/step - loss: 1.1420 - acc: 0.5886 - val_loss: 1.2150 - val_acc: 0.5977\n",
      "Epoch 25/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.0694 - acc: 0.6196\n",
      "Epoch 25: val_acc did not improve from 0.63281\n",
      "17/17 [==============================] - 31s 2s/step - loss: 1.0694 - acc: 0.6196 - val_loss: 1.1293 - val_acc: 0.6211\n",
      "Epoch 26/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.0506 - acc: 0.6428\n",
      "Epoch 26: val_acc did not improve from 0.63281\n",
      "17/17 [==============================] - 31s 2s/step - loss: 1.0506 - acc: 0.6428 - val_loss: 1.1294 - val_acc: 0.6172\n",
      "Epoch 27/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.0217 - acc: 0.6268\n",
      "Epoch 27: val_acc did not improve from 0.63281\n",
      "17/17 [==============================] - 32s 2s/step - loss: 1.0217 - acc: 0.6268 - val_loss: 1.1923 - val_acc: 0.6211\n",
      "Epoch 28/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 0.9452 - acc: 0.6544\n",
      "Epoch 28: val_acc did not improve from 0.63281\n",
      "17/17 [==============================] - 31s 2s/step - loss: 0.9452 - acc: 0.6544 - val_loss: 1.1239 - val_acc: 0.6328\n",
      "Epoch 29/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 0.9771 - acc: 0.6350\n",
      "Epoch 29: val_acc did not improve from 0.63281\n",
      "17/17 [==============================] - 31s 2s/step - loss: 0.9771 - acc: 0.6350 - val_loss: 1.1675 - val_acc: 0.6172\n",
      "Epoch 30/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 0.9273 - acc: 0.6738\n",
      "Epoch 30: val_acc did not improve from 0.63281\n",
      "17/17 [==============================] - 30s 2s/step - loss: 0.9273 - acc: 0.6738 - val_loss: 1.1330 - val_acc: 0.6016\n",
      "Epoch 31/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.0201 - acc: 0.6244\n",
      "Epoch 31: val_acc improved from 0.63281 to 0.71094, saving model to C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17/17 [==============================] - 32s 2s/step - loss: 1.0201 - acc: 0.6244 - val_loss: 0.9004 - val_acc: 0.7109\n",
      "Epoch 32/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 0.9002 - acc: 0.6660\n",
      "Epoch 32: val_acc did not improve from 0.71094\n",
      "17/17 [==============================] - 30s 2s/step - loss: 0.9002 - acc: 0.6660 - val_loss: 1.0539 - val_acc: 0.6680\n",
      "Epoch 33/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 0.9965 - acc: 0.6399\n",
      "Epoch 33: val_acc did not improve from 0.71094\n",
      "17/17 [==============================] - 31s 2s/step - loss: 0.9965 - acc: 0.6399 - val_loss: 1.3444 - val_acc: 0.5508\n",
      "Epoch 34/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 0.9896 - acc: 0.6350\n",
      "Epoch 34: val_acc did not improve from 0.71094\n",
      "17/17 [==============================] - 31s 2s/step - loss: 0.9896 - acc: 0.6350 - val_loss: 1.0470 - val_acc: 0.6484\n",
      "Epoch 35/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 1.0145 - acc: 0.6370\n",
      "Epoch 35: val_acc did not improve from 0.71094\n",
      "17/17 [==============================] - 31s 2s/step - loss: 1.0145 - acc: 0.6370 - val_loss: 1.1045 - val_acc: 0.6523\n",
      "Epoch 36/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 0.8672 - acc: 0.7028\n",
      "Epoch 36: val_acc did not improve from 0.71094\n",
      "17/17 [==============================] - 31s 2s/step - loss: 0.8672 - acc: 0.7028 - val_loss: 1.0263 - val_acc: 0.6836\n",
      "Epoch 37/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 0.9072 - acc: 0.6805\n",
      "Epoch 37: val_acc improved from 0.71094 to 0.75781, saving model to C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\\assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: C:\\Users\\Joe\\Documents\\Imperial AI\\Capstone\\CapstoneProject\\model.h5f\\assets\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17/17 [==============================] - 33s 2s/step - loss: 0.9072 - acc: 0.6805 - val_loss: 0.8013 - val_acc: 0.7578\n",
      "Epoch 38/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 0.8444 - acc: 0.6941\n",
      "Epoch 38: val_acc did not improve from 0.75781\n",
      "17/17 [==============================] - 32s 2s/step - loss: 0.8444 - acc: 0.6941 - val_loss: 1.0841 - val_acc: 0.6641\n",
      "Epoch 39/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 0.8443 - acc: 0.6999\n",
      "Epoch 39: val_acc did not improve from 0.75781\n",
      "17/17 [==============================] - 32s 2s/step - loss: 0.8443 - acc: 0.6999 - val_loss: 0.9340 - val_acc: 0.7109\n",
      "Epoch 40/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 0.9754 - acc: 0.6292\n",
      "Epoch 40: val_acc did not improve from 0.75781\n",
      "17/17 [==============================] - 32s 2s/step - loss: 0.9754 - acc: 0.6292 - val_loss: 1.2109 - val_acc: 0.6133\n",
      "Epoch 41/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 0.9186 - acc: 0.6902\n",
      "Epoch 41: val_acc did not improve from 0.75781\n",
      "17/17 [==============================] - 32s 2s/step - loss: 0.9186 - acc: 0.6902 - val_loss: 0.8589 - val_acc: 0.7227\n",
      "Epoch 42/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 0.7723 - acc: 0.7106\n",
      "Epoch 42: val_acc did not improve from 0.75781\n",
      "17/17 [==============================] - 33s 2s/step - loss: 0.7723 - acc: 0.7106 - val_loss: 1.0951 - val_acc: 0.6328\n",
      "Epoch 43/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 0.8270 - acc: 0.7028\n",
      "Epoch 43: val_acc did not improve from 0.75781\n",
      "17/17 [==============================] - 32s 2s/step - loss: 0.8270 - acc: 0.7028 - val_loss: 0.9272 - val_acc: 0.6953\n",
      "Epoch 44/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 0.7616 - acc: 0.7348\n",
      "Epoch 44: val_acc did not improve from 0.75781\n",
      "17/17 [==============================] - 32s 2s/step - loss: 0.7616 - acc: 0.7348 - val_loss: 1.0212 - val_acc: 0.6680\n",
      "Epoch 45/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 0.6612 - acc: 0.7583\n",
      "Epoch 45: val_acc did not improve from 0.75781\n",
      "17/17 [==============================] - 33s 2s/step - loss: 0.6612 - acc: 0.7583 - val_loss: 0.8327 - val_acc: 0.7383\n",
      "Epoch 46/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 0.6686 - acc: 0.7561\n",
      "Epoch 46: val_acc did not improve from 0.75781\n",
      "17/17 [==============================] - 31s 2s/step - loss: 0.6686 - acc: 0.7561 - val_loss: 1.1195 - val_acc: 0.6523\n",
      "Epoch 47/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 0.6861 - acc: 0.7590\n",
      "Epoch 47: val_acc did not improve from 0.75781\n",
      "17/17 [==============================] - 31s 2s/step - loss: 0.6861 - acc: 0.7590 - val_loss: 1.0042 - val_acc: 0.7031\n",
      "Epoch 48/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 0.6695 - acc: 0.7599\n",
      "Epoch 48: val_acc did not improve from 0.75781\n",
      "17/17 [==============================] - 31s 2s/step - loss: 0.6695 - acc: 0.7599 - val_loss: 0.8630 - val_acc: 0.7422\n",
      "Epoch 49/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 0.7365 - acc: 0.7222\n",
      "Epoch 49: val_acc did not improve from 0.75781\n",
      "17/17 [==============================] - 31s 2s/step - loss: 0.7365 - acc: 0.7222 - val_loss: 0.9567 - val_acc: 0.7070\n",
      "Epoch 50/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 0.6333 - acc: 0.7667\n",
      "Epoch 50: val_acc did not improve from 0.75781\n",
      "17/17 [==============================] - 31s 2s/step - loss: 0.6333 - acc: 0.7667 - val_loss: 1.0499 - val_acc: 0.6719\n",
      "Epoch 51/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 0.6481 - acc: 0.7638\n",
      "Epoch 51: val_acc did not improve from 0.75781\n",
      "17/17 [==============================] - 31s 2s/step - loss: 0.6481 - acc: 0.7638 - val_loss: 1.1740 - val_acc: 0.6562\n",
      "Epoch 52/200\n",
      "17/17 [==============================] - ETA: 0s - loss: 0.6969 - acc: 0.7386\n",
      "Epoch 52: val_acc did not improve from 0.75781\n",
      "17/17 [==============================] - 31s 2s/step - loss: 0.6969 - acc: 0.7386 - val_loss: 0.8998 - val_acc: 0.7188\n"
     ]
    }
   ],
   "source": [
    "#Train model\n",
    "history = model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch= train_num // batch_size,\n",
    "    epochs=epochs,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps= validation_num // batch_size,\n",
    "    callbacks=callbacks_list, \n",
    "    verbose = 1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "20bba4f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9ezcCAgLwwgsvoEqVKnj11Vfx1ltvISkpCb169UJwcDAOHjyIq1ev4s033wQA9OvXD3379sWwYcPw+OOP48yZM/jggw8MPSv2ttuev9NevXph/vz5GDp0KI4cOYJ27dpBr9dj+/btiIqKwlNPPWU4tkaNGnjooYewevVq3HfffVnyhagIcG3+LBVl1mbT1KlTx+LxMTExqnnz5iogIECVLVtWDR48WP37779ZZqpYm03TpUuXLOe0Nosg82yazO209j6xsbHqscceU4GBgap48eLq8ccfV6tWrcoyu8OSlJQUNWbMGFWxYkXl5+enGjVqpFauXJllBoo2m+bDDz/Mcg4AauLEiWb7vvrqK1W9enXl4+OjatSooebNm2dxVostvXv3VgBUy5YtLT7/yy+/qAYNGig/Pz9VsWJF9fLLL6vVq1dbvJbZzaZRSqmDBw+qDh06KD8/P1WqVCk1aNAg9dNPP2U5n3Zc8eLFVcmSJdUTTzyhYmNjLV6H8ePHqwoVKigPDw+z82T+G1BKqWvXrqmhQ4eq0NBQ5eXlpcLDw9X48eNVcnKy2XEA1PDhw7NcD2uzVjJ77733VJUqVZSvr6+KiopSX375pcW/q4yMDDV9+nRVt25d5ePjo4KDg1Xz5s3VL7/8YnbcwoUL1b333qv8/PxUYGCguueee8z+bej1evXBBx+oqlWrKj8/P9W4cWO1fv16q/8OfvjhhyxttvfvVCmZsfbGG28Y/v5Kly6t7r//fhUTE5PlvAsWLFAA1JIlS7K9blT46JTKVG2GiJzq3XffxWuvvYbY2FinJxgSFRaPP/44/v77b5w+fRre3t6ubg7lMw7TEDnRZ599BkCGENLS0rB+/Xp88skn6Nu3LwMRokxSUlLw77//YseOHVixYgWmTZvGQKSIYjBC5EQBAQGYPn06Tp8+jZSUFFSuXBmvvPIKXnvtNVc3jcjtxMXFoUWLFggKCsKQIUPwwgsvuLpJ5CIcpiEiIiKX4tReIiIicikGI0RERORSDEaIiIjIpQpEAqter8eFCxdQvHhxLpxERERUQCilcOvWrWzXKSoQwciFCxdsrrdARERE7uvs2bM2yxsUiGBEK9l89uxZBAUFubg1REREZI+EhASEhYVZXHrBVIEIRrShmaCgIAYjREREBUx2KRZMYCUiIiKXYjBCRERELsVghIiIiFyqQOSM2EMphfT0dGRkZLi6KVQAeXp6wsvLi1PHiYhcoFAEI6mpqYiLi8OdO3dc3RQqwAICAhAaGgofHx9XN4WIqEgp8MGIXq/HqVOn4OnpiQoVKsDHx4efbskhSimkpqbiypUrOHXqFKpXr26zOA8RETlXgQ9GUlNTodfrERYWhoCAAFc3hwoof39/eHt748yZM0hNTYWfn5+rm0REVGQUmo9//CRLucW/ISIi1+D/vkRERORSDEaIiIjIpRiMFCJt27bFyJEj7T7+9OnT0Ol02LNnT561iYiIKDsFPoG1IMputs+AAQOwYMECh8+7fPlyeHt72318WFgY4uLiUKZMGYffi4iIyFkYjLhAXFycYXvp0qV44403cOTIEcM+f39/s+PT0tLsCjJKlSrlUDs8PT1Rvnx5h15DRERu4s4dYNo0oEQJoGFDoH59oIAuJlv4hmmUAhITXfOllF1NLF++vOErODgYOp3O8H1ycjJKlCiB77//Hm3btoWfnx+++eYbXLt2Db169UKlSpUQEBCAevXq4bvvvjM7b+ZhmipVquDdd9/FM888g+LFi6Ny5cqYM2eO4fnMwzQbN26ETqfDunXr0LhxYwQEBKBFixZmgRIAvP322yhXrhyKFy+OwYMHY9y4cWjYsKHVnzcjIwODBg1CREQE/P39UbNmTXz88cdZjps3bx7q1KkDX19fhIaGYsSIEYbnbt68ieeeew4hISHw8/ND3bp18euvv9p1vYmICqWpU4HXXwdeeAFo1QoIDgYiI4EePYC33wZ+/RU4d87ue5MrFb6ekTt3gMBA17z37dtAsWJOOdUrr7yCqVOnYv78+fD19UVycjKio6PxyiuvICgoCL/99hv69euHqlWromnTplbPM3XqVLz11lt49dVX8eOPP+L5559H69atUatWLauvmTBhAqZOnYqyZcti6NCheOaZZ7Bt2zYAwOLFi/HOO+9g5syZaNmyJZYsWYKpU6ciIiLC6vn0ej0qVaqE77//HmXKlEFMTAyee+45hIaG4sknnwQAzJo1C6NGjcJ7772HTp06IT4+3vCeer0enTp1wq1bt/DNN9+gWrVqOHjwIDw9PXNyaYmICj6lgMWLZTs6Grh4ETh/HjhxQr6WLTMeW7Ei8N13ErC4K1UAxMfHKwAqPj4+y3NJSUnq4MGDKikpSXbcvq2U/Jry/+v2bYd/tvnz56vg4GDD96dOnVIA1IwZM7J9befOndXo0aMN37dp00a9+OKLhu/Dw8NV3759Dd/r9XpVrlw5NWvWLLP32r17t1JKqQ0bNigA6s8//zS85rffflMADNe3adOmavjw4WbtaNmypWrQoIG9P7JSSqlhw4apxx9/3PB9hQoV1IQJEyweu2bNGuXh4aGOHDni0Hs4KsvfEhGRu/r3X7nv+Pkppd0br1xR6s8/lfroI6X69lWqbl2lPD3luPBwpRIT872Ztu7fpgpfz0hAgPRQuOq9naRx48Zm32dkZOC9997D0qVLcf78eaSkpCAlJQXFsumJqV+/vmFbGw66fPmy3a8JDQ0FAFy+fBmVK1fGkSNHMGzYMLPjmzRpgvXr19s85+zZs/HVV1/hzJkzSEpKQmpqqmFo5/Lly7hw4QLat29v8bV79uxBpUqVUKNGDZvvQUTkFm7eBGJj5dHaV3w80K4dYDIc7RBtmL5LF2OeSJkyQPv28qVJSADq1QPOnAHeeguYMiVn75fHCl8wotM5bajElTIHGVOnTsX06dMxY8YM1KtXD8WKFcPIkSORmppq8zyZE191Oh30er3dr9Fm/pi+JvNsIJXNeOT333+Pl156CVOnTkXz5s1RvHhxfPjhh9i+fTuArAm7mWX3PBHZT68Hvv0WaN0aqFzZ1a1xMxs2AB98AISGSu5F9eryFRlpefhfKQk69uwx/zp92r73W7EC6NwZqFrVsXbq9cCSJbLdu7ftY4OCgE8/Bbp3Bz76COjbF6hTx7H3yweFLxgppLZs2YLu3bujb9++ACQ4OHbsGKKiovK1HTVr1sSOHTvQr18/w75//vnH5mu2bNmCFi1amPWonDhxwrBdvHhxVKlSBevWrUO7du2yvL5+/fo4d+4cjh49yt4RolxauhTo1w/o1g34+WdXt8aNXL0KPPUUYK3nuHx588Bk3z4JPG7etHx82bJAqVIy08X0KzhYHlesAHbuBL74Anj/fcfaGhMDnD0rgUbnztkf//DDEoz89BPw/PPApk3ywd2NMBgpICIjI7Fs2TLExMSgZMmSmDZtGi5evJjvwcgLL7yAZ599Fo0bN0aLFi2wdOlS/Pfff6hqI7KPjIzEwoULsWbNGkRERGDRokXYuXOnWdLrpEmTMHToUJQrV86QrLpt2za88MILaNOmDVq3bo3HH38c06ZNQ2RkJA4fPgydToeHHnooP35sokJjyxZ53LXLte1wOy++KIFIVBTQqxdw7Jh8HT8ugcrFi/KlXUCNt7f0NDRsaPyqXx8oWdL2+9WpIwHC3LnAm28CjizOqQ3RPPqo/a/75BNg7Vpp/9dfAwMH2v9++YDBSAHx+uuv49SpU3jwwQcREBCA5557Do888gji4+PztR19+vTByZMnMWbMGCQnJ+PJJ5/EwIEDsWPHDquvGTp0KPbs2YOePXtCp9OhV69eGDZsGFavXm04ZsCAAUhOTsb06dMxZswYlClTBj169DA8v2zZMowZMwa9evVCYmIiIiMj8d577+Xpz0pUGGn/VC9ckA/1JUq4sjVu4uefZezKw0Nu1Pfea/78zZsSlGgByq1bQN26EnhERQE+Po6/Z5cuQFiY9HD8+KMMn9gjLQ34/nvZ7tXL/verXBmYNAkYOxYYM0a6xkqXdrjZeUWnshvwdwMJCQkIDg5GfHw8gjIVdElOTsapU6cQERHBZd9dpEOHDihfvjwWLVrk6qbkCv+WqLBLTpae/bQ0+X7bNqBFC9e2yeVu3JBeirg4uVE7OmSSG2+/LXVCWrSQX4Y9fv8d6NRJhoEuXAC8HOhTSEsDGjUC9u8HBg8GvvwyZ+12gK37t6nCV/SM8tSdO3cwbdo0HDhwAIcPH8bEiRPx559/YsCAAa5uGhFlY+9eYyACAAcPuq4tbmPUKAlEataUnoP8NHiwBBMxMfLLsYc2RPPkk44FIoAMKc2eLdtffWV/AJQPGIyQQ3Q6HVatWoVWrVohOjoav/zyC5YtW4YHHnjA1U0jomzs3Gn+/YEDrmmH21i9GliwQJI5580D8nvmXvnywGOPyfasWdkfn5Qkia+AY0M0plq2BAYNku2hQ82jUxdiMEIO8ff3x59//onr168jMTER//77Lx7T/jERkVvT8kXCwuSxSPeMxMcDzz0n2y++6Lrxquefl8dvvpGaILasWiX5KpUrA82b5/w9339f8kX27wdmzMj5eZyIwQgRURGh9Yz07y+PRbpn5OWXZd2WatWAd95xXTvatJEk2MRECUhs0YZonnpKkm1zqnRpqTkCyNDUmTM5P5eTMBghIioC4uOBw4dlW0vxOn9e9hc5f/5pTN6cO9ep1bPttWCBlP2ATifDJYAM1VibUxIfLwvfATkfojE1YIBUvrtzR3qGXIzBCBFREaDVFalSRWp3Vagg3x865LImucbt25I4CgDDh0vPRD7buRN4+mng8celgCv695eAaP9+YOtWyy9auRJISZFelAYNct8InQ6YOVOSYH/66W5k5DoMRoiIigAtX0QroVG7tjwWuaGaceNkWKJKFSBTraI7d6QifF4XvJgzRx4zMoDPPoMUe9F6O6wlsmpDNL16Oa96ap06UnMEAF54wXXruoHBCBFRkaDlizRpIo/a8iRFKol10ybg889l+6uvsqw38+qrwP33S92zvHLrljGuACQwuX0bxkTWH3/MWpL+yhUZWgKcM0Rj6vXXJTA7exb48EPnntsBDEaIiIoAaz0jRSYYuXPHOKX12WfNV7a9a80aedy8Oe+a8d13kqtasyZQo4akgixYACA6WiLFtDSZZmzqhx+kG6VxY1kbx5kCAqR7ZsQIqbniIgxGCrC2bdti5MiRhu+rVKmCGdlM09LpdFi5cmWu39tZ5yGivBcXJxNHPDzkngcUwWGaDz8ETpwAKlWy2ANw/boxwdfe+mM5oQ3RPPecMW/0449lIV5D78js2RJ8aL79Vh6d3Sui6dJFVvYNDs6b89uBwYgLdOvWzWqRsL/++gs6nQ7//vuvw+fduXMnntPmzTvJpEmT0LBhwyz74+Li0KlTJ6e+FxHlDW2IJirKODKhBSNnz2Zf3qLAS0gw1tOYOtXiTdd0ea0DB4D0dOc3499/JZHYx0dyVvv3l3SR48fvTpTp2VMW2DtzRsq+A5Lhum2b5In07On8RrkJBiMuMGjQIKxfvx5nLMztnjdvHho2bIhGjRo5fN6yZcsiIJ+mqJUvXx6+vr758l5ElDuZ80UAWd2+fHnZ1noECq3PP5fF7qKiAJMFOE399ZdxOyVF1sNzNm028WOPAWXKSGCofX6cPh1SAVZbTVdLZF2yRB7btAEqVnR+o9xEoQtGlJLxOFd82ZuB3bVrV5QrVw4LFiww23/nzh0sXboUgwYNwrVr19CrVy9UqlQJAQEBqFevHr4zzXqyIPMwzbFjx9C6dWv4+fmhdu3aWLt2bZbXvPLKK6hRowYCAgJQtWpVvP7660i7Wx54wYIFePPNN7F3717odDrodDpDmzMP0+zbtw/3338//P39Ubp0aTz33HO4bZKZPXDgQDzyyCP46KOPEBoaitKlS2P48OGG97LkxIkT6N69O0JCQhAYGIh7770Xf2pJXHelpKRg7NixCAsLg6+vL6pXr465c+canj9w4AC6dOmCoKAgFC9eHK1atcKJEydsXkeiwiZzvohGS2It1EM1iYnAtGmy/eqrVouF/f23+ff//efcZty+DSxeLNumHdgjRgCensDGjcCePTDWHFm1Cjh92nwWTSFW6IKRO3ck2nTF15079rXRy8sL/fv3x4IFC2C6aPIPP/yA1NRU9OnTB8nJyYiOjsavv/6K/fv347nnnkO/fv2wfft2u95Dr9fjscceg6enJ/7++2/Mnj0br7zySpbjihcvjgULFuDgwYP4+OOP8eWXX2L69OkAgJ49e2L06NGoU6cO4uLiEBcXh54Wugnv3LmDhx56CCVLlsTOnTvxww8/4M8//8SIESPMjtuwYQNOnDiBDRs24Ouvv8aCBQuyBGSmbt++jc6dO+PPP//E7t278eCDD6Jbt26IjY01HNO/f38sWbIEn3zyCQ4dOoTZs2cj8G4/9Pnz5w3B2Pr167Fr1y4888wzSM+L/lciN6WU5Z4RoIgksc6ZA1y9ClStKpVLLdDrAe2/Vi1gc3YwsnSpzKSJjATatjXuDwsDnnhCtqdPh2S1tm8vv7hRoyRC8fKSoiSFmSoA4uPjFQAVHx+f5bmkpCR18OBBlZSUpJRS6vZtpeS3mP9ft2/b/zMdOnRIAVDr16837GvdurXq1auX1dd07txZjR492vB9mzZt1Isvvmj4Pjw8XE2fPl0ppdSaNWuUp6enOnv2rOH51atXKwBqxYoVVt/jgw8+UNHR0YbvJ06cqBo0aJDlONPzzJkzR5UsWVLdNrkAv/32m/Lw8FAXL15USik1YMAAFR4ertLT0w3HPPHEE6pnz55W22JJ7dq11aeffqqUUurIkSMKgFq7dq3FY8ePH68iIiJUamqqXefO/LdEVBgcOyb/P/n4KJWSYv7crFnyXOfOrmlbnktKUio0VH7IL7+0etiBA3JIQIBSM2bIdpcuzm1K06Zy3vffz/rc9u3ynLe3UhcuKKWWLTO/uTi7MfnI1v3blIPrD7u/gADX1W1xJF2jVq1aaNGiBebNm4d27drhxIkT2LJlC/744w8AQEZGBt577z0sXboU58+fR0pKClJSUlCsWDG7zn/o0CFUrlwZlSpVMuxrbmFhpR9//BEzZszA8ePHcfv2baSnpyMoKMj+H+TuezVo0MCsbS1btoRer8eRI0cQEhICAKhTpw48PT0Nx4SGhmLfvn1Wz5uYmIg333wTv/76Ky5cuID09HQkJSUZekb27NkDT09PtLFSQXHPnj1o1aoVvL29Hfp5iAoTrVfknnskcdKUOwzT/PWX9CpbmGmbe/PmyVSisDDjgjwWaEM0994LaOl6zuwZ2btXel68vY0pIaaaNJF1+mJipCjqWxMflhK5Fy7IAb17O68xbqrQDdPodECxYq75crQo3qBBg7Bs2TIkJCRg/vz5CA8PR/u7/yKnTp2K6dOnY+zYsVi/fj327NmDBx98EKmpqXadW1lIYNFlauDff/+Np556Cp06dcKvv/6K3bt3Y8KECXa/h+l7ZT63pffMHBTodDro9Xqr53355ZexbNkyvPPOO9iyZQv27NmDevXqGdrnn81y39k9T1QUWMsXAYzDNGfOuOZDXHo68OCDwEMPSV0vp0pNldVpAeCVV7JGYia05NVmzYB69WT77Fngxg3nNEVLXO3eHShXzvIxL70kj7NnA0lpXlILBZCk1ocfdk5D3FihC0YKkieffBKenp749ttv8fXXX+Ppp5823Ly3bNmC7t27o2/fvmjQoAGqVq2KYw6kd9euXRuxsbG4oEXWkGnDprZt24bw8HBMmDABjRs3RvXq1bPM8PHx8UGG6Xx3K++1Z88eJCYmmp3bw8MDNWrUsLvNmW3ZsgUDBw7Eo48+inr16qF8+fI4ffq04fl69epBr9dj06ZNFl9fv359bNmyxWaSLFFhZy1fBJDFW7Wboytm1Jw5I3kU6enOz9HAN9/ItNjy5YFnnrF5qNYz0ry5TLUND5fvbXTc2u3OHeNivLYqLzzyiLzv1at3jx8+XJJL3nwzS6VYZ9uxQ66BK/+rZDDiQoGBgejZsydeffVVXLhwAQNN+u8iIyOxdu1axMTE4NChQxgyZAguXrxo97kfeOAB1KxZE/3798fevXuxZcsWTJgwweyYyMhIxMbGYsmSJThx4gQ++eQTrFixwuyYKlWq4NSpU9izZw+uXr2KlJSULO/Vp08f+Pn5YcCAAdi/fz82bNiAF154Af369TMM0eREZGQkli9fjj179mDv3r3o3bu3WU9KlSpVMGDAADzzzDNYuXIlTp06hY0bN+L7778HAIwYMQIJCQl46qmn8M8//+DYsWNYtGgRjhw5kuM2ERUk6elS2wKw3DMCuHaoxvTz1f79Tjxxejrw7ruyPWaM9C5YER9v/NmbNZPH+vXl0RnFz374Qd4jIsL2UJSXF/C//8n2jBmAKlNWFsp5+eXcNyIbb70lgdinn+b5W1nFYMTFBg0ahBs3buCBBx5A5cqVDftff/11NGrUCA8++CDatm2L8uXL45FHHrH7vB4eHlixYgVSUlLQpEkTDB48GO+8847ZMd27d8dLL72EESNGoGHDhoiJicHrr79udszjjz+Ohx56CO3atUPZsmUtTi8OCAjAmjVrcP36ddx7773o0aMH2rdvj88++8yxi5HJ9OnTUbJkSbRo0QLdunXDgw8+mKX+yqxZs9CjRw8MGzYMtWrVwrPPPmvooSldujTWr1+P27dvo02bNoiOjsaXX37JHBIqMg4cAJKSgKAgmaRhiStn1Bw/btx2ajCydKlUWy1d2jhV1oqdOyVLNCIC0D47acGIM3prtIqrgwdbnVVsMGiQdIIcPAjcTR/McxkZwJYtsu2CBYyN8iWdNpccmU1DlFP8W6LCZs4cmYxx//3Wj/n8czmma9f8a5fmxReNE0aaNnXSSTMylIqKkpO+8062h0+eLIeaTmRculT2NWmSu6bs3y/n8fS8O0vGDto1efDB3L23vXbtkvcLClLKZLKj09g7m4Y9I0REhZStfBGNuwzTHDhwd32W3Fq+HDh0SJI/hg/P9nDTfBFNgwbyuH+/+RIxjtISVx9+GAgNte81//ufTIZYsyZ/equ0lLtWraT4mqswGCEisuLKFSlItXGjq1uSM7Zm0mi0YZrTp6VYqb02bpRrk5tZMKbDNLdvS75pFkoBX30lWZ3ZZVgqBbz9tmz/73/ZLvymlDEY0fJFAClM5ucnyacnT2b/c1iSnAwsXCjbjiwZVrWqzLoBjMvp5CXtb9ulQzRgMEJEZNXXXwM//igfsO1d7sFd3LljzMOw1TNStqx8KQU4ktv94otybWwUUbYpPR04dUq2S5eWR4t5IzNmyDTXfv2AmjWldoi1oOS33yTrNDDQmA1qw7Fjslqvn5+xNwSQHoK6dWU7p0msy5bJ1ODKlYEOHRx7rTbNd9EimV2TV/R6Y76IaVVYV2AwQkRkhTaMcPBg3i4rnxd275YhhvLls19fTesdsXeo5uhRY3LnoUM5a9/ZsxJT+Poab9ZZgpGYGGDsWNkOCpLoZdAgCUrmzjUPSpSSaSEAMGyYMcKxQat2EB2dtQxJbpNYTRNXHR3+aNVKiq8lJwNffJGz97fHf/9JwFS8uBTFc6VCE4yogvaxhdwO/4YoM9NhhEWLXNeOnDDNF8muIKOjM2p++MG4ndNgRAv0qlUz3vjN6npcuQI8+aR0oTz1lFRSnTpVCqOcOiV3+Ro1jEHJn3/KuJS/v6zpYgdL+SIarackJ8HI4cPA5s0yeyabEicW6XTG3pHPPpP6bXlByxe57z6ZWuxKOQpGZs6ciYiICPj5+SE6OhpbtH4eCwYOHGhY8dX0q46WNZVL2jTNO/auUkdkhfY3xKm/pDFNsPz229wlM+abO3eAtDS78kU02n/H9gYjP/5o3D58OGdDWFqgFxlprHpq6BnJyAD69gXOn5dekDlzZL2NUaMkEJk2Tebhnj5tDEpefFFe+9xzxjm62TCtvJpZbnpGvvpKHrt0yb5Xyponn5Sk14sXZaZyXnCXfBEAcDgWWrp0KUaOHImZM2eiZcuW+OKLL9CpUyccPHjQrE6G5uOPP8Z7771n+D49PR0NGjTAE9oyhbnk6emJEiVK4PLlywCk5oW10uREliilcOfOHVy+fBklSpQwWz+Hiq6kJBlKACQF4eJFYN06oGNH17bLprg4oFYtoHZt7LyyDYCHzXwRjSPDNMePy0Kynp4ShNy8CVy6JMNBjjANRrT8jMOHpZPD+923pdCGv79EPsWLG18YECDdBkOGyBjG++9LUALIWMuYMXa9/+3bxp4YS8GIFiCdOgUkJMgokT3S0yXXCHAscTUzHx9gxAhgwgRZzbdvX8eXHLFFr5feG8D1+SJADoKRadOmYdCgQRg8eDAAYMaMGVizZg1mzZqFKVOmZDk+ODgYwSYZzStXrsSNGzfw9NNPW30PbVE4TUJCgs02lb/7r0ALSIhyokSJEoa/JSItuTIoCOjTB5g1SyZ0uHUwsmYNkJCA638fwfG7Hd+NG2f/Mi0YOXlSgjBbyzppQzT333MdJ2O9cOJyEA4dcjwY0XqdqleXJM/AQAkQji+MQdSbb8qTs2cbI5XMMgcl8+bJYngmi4PasnOn3JDDwiz3XpQuLfvPn5egpWVL+36uv/6SpNOSJWXNndwYMkQmB+3eLYGDM3sw9u+X5N1ixYyLA7qSQ8FIamoqdu3ahXHjxpnt79ixI2JiYuw6x9y5c/HAAw8gXCv+b8GUKVPwpvbHaAedTofQ0FCUK1eO65BQjnh7e7NHhMxoN8vISJnIMWuWlLCYNUv+A3dLW7cCAP6BRCCRFZNQqlT2C0aWKyc332vXZEZNw4ZWDjx1Cj984gcgFE/88wp+QnecQFccPgy0a+dYU017Rjw8ZKho+3Zg/0tzEaWUzKCxsdKugRaUaEkWdrI0pTezBg0kGPnvP/uDkZ9/lscuXXKfh1G6tFyCL76Q3hFnBiPaEM1998lqwq7m0KW6evUqMjIysqw3EhISYte6KXFxcVi9ejW+/fZbm8eNHz8eo0wSkBISEhAWFpbt+T09PXlDISKnML1ZNmsmiZYnTgArV0pPiVu6G4zsKNsFuALce+MPIKFdtmMMOp30jmzZIkM1ZsHI7dsyT3XBApzYGIvdOAFPpOMRrMRR1MBv6OpwEmtGhrF+R2SkPNatrcf27R7YdyscTzRsCHzyiWMndZCWL2IpeVVTvz6wapVjeSNaMKLVCsmtkSMlGPn5Z/mb1K5XbmnJq+6QLwLkMIE1c06GsrGEvKkFCxagRIkS2a6x4uvri6CgILMvIqL8pAUj1avLzbpvX/leW4HV7Vy+bCgUsrOhLD/f5M4GY2JnNsxm1Cgld6unn5bxl4EDgY0b8SMk169tnSsou+JLREGiEEeDkbNnZYaIj48MkwBAvdNyF9/vdY/kifj52X2+K1fkpvr55/Ydb63YWWaOJrEePizTnn18gAcftO812alVC+jUSdrsrPhMrzcGI+6QLwI4GIyUKVMGnp6eWXpBLl++nO3qrEopzJs3D/369YNP5gndRERuxrRnBDAGI3/8IQmbbmfbNgCAqlMXO/YFAACa6P6RqmTLl2f7csOMmv16YMAAuUstWCBlWatXB95+Gz/Uk+HzJ14IBdq2RS0cBgAcPujYNCPt2latercGx8qVqLtBlozdX7addEM54LvvJKfipZfMZ0BZc/KkBDA+PrbzJUyDEXtK1Wu9Iu3amefc5pY2AjVvniQM59bBgzIkFxBgX05RfnAoGPHx8UF0dDTWrl1rtn/t2rVo0aKFzddu2rQJx48fx6BBgxxvJRFRPjPNGdEemzWTm5KFxatd7+4Qzfl7uuLiRbnJNxx9d836556TmTY2GGbUrD0vRVU8PWXa7LZtwJEjONlrAnbt84WHB/DoowBKlEBUDblDn7vgiVu37G+qWaB34gQwcCDqQub1Hr8YiKQk+88FGPMf0tKAl1/O/nitV+See6TomjU1akjAcvu2ccKOLVow8vDD2R/riAcekDzexETjtOHc0K5Xy5bukS8C5GCYZtSoUfjqq68wb948HDp0CC+99BJiY2Mx9O4yzePHj0d/C0lHc+fORdOmTVHXWmY0EZGbSEkxrpNSvbpxv1sP1dyt97SjTCcAMjU14J0Jcse9dk0ql9ooCFIn+BwA4ERSBSQXKy2l1b/8EmjRAtDpsGyZHNemjSS8AkDJlrURAukpP3zY/qYaZtJEKqB3byA+HuWaR6JMGQWlHBv2MZ2iCgA//QRs2GD7NfbkiwByo9Z6jLIbqrlyRQrGAkC3braPdZROJ7kjAPDppzJ9ODfcbYgGyEEw0rNnT8yYMQOTJ09Gw4YNsXnzZqxatcowOyYuLg6xmVY7io+Px7Jly9grQkQFwqlTct8ODDTeeAGgZ0+ZIbFrV84rj+aJxETg338BADtTpXTovfdCPtZ/8418/F+9WqbKWrJ7N0K6NUFJXIcenjg6b2uWpAdtSq9ZiahmzYxDNQ4EI4aekRJXpGqqtzd03y9F3bqSe2hWiTUbBw4Yhxy0uh4vvWS7QJ09+SIae/NGfvtN/mYaNTLmwThTnz6yhlBsrF2jblYp5V7FzjQ5SmAdNmwYTp8+jZSUFOzatQutW7c2PLdgwQJszLTEZXBwMO7cuYNnn302V40lIsoPpkM0prn5ZcpIMiHgZr0j27fL3TcsDDsOS10nQ7Gz2rWlMBgAjB4tGZamVq8GWrWC7mIcagecBgAcyKhldsjp01KXw8MDeOwxkyeaNzcmsR6wI6niLkMwcuMf2WjWDKhUKWslVjton/JbtgTeeUcW6t271/oCfnfuGNcZyq5nBLA/GPnpJ3l09hCNxs8PeP552Z4+PefnOXhQ6qD4+9tXnTe/FJq1aYiInMV0Jk1m/frJ4+LF9iU15ou7QzT6+1rjn7v3d7MbzQsvSOJBUpKMNWn1mObMkTGFxESgfXvUeVLGJDKXhdeGaFq3zlRpvXZtRPnIHN1DO+1LGtHrJU0EACJP/iEb998PwFjfzJFgxPRTfpkywBtvyPcTJsBiHsuuXTLMERpqXw+GFozYWigxKUkSm4G8C0YAWf/Px0d6drTeHUdpwVuLFlkXB3QlBiNERJlknkljqmtXKdtx5owhZ9T17jZkb5XuSEiQT71my395eADz5wMlSkgXx9tvy916yBDpURkwAFi1CrUbSjZn5mBEG6Lp0SPT+3p6olaUdB0dPmhfZHbunOTkeHsrVN5xd5GbuxXTHA1GtBnIgDH/YcQI+b1dugRYKAputh6NPeXVtQXzTpyQRFZL1q+XHpdKlWwUjHOCkBBJsQFy3juiBW/ulC8CMBghIsoi80waU/7+xpuyWwzVpKcb7rAf7O4AAOjc2UL1z0qVpHwsAEyeDLz7rmxPnCiBio+PxTVqYmNlFEinAx5/POvbR7UqAwA4fqk47CmArQV6ERVT4XXpvIw/3E3e0AKoc+fsm8JqacjBxwf46CPZnjYt6ywYWyv1WlK2rJRaUcr62j2ms2jyemk0LZF12TJjkrW9TIM3d8oXARiMEBFlYWuYBjAO1Xz/PZCcnD9tsmrPHiAxEYeKN8HSNZIv8vrrVo596injR2svLylcMWmS4Q6qBQPHj0vvBWBcobdVK8vrz1TqEIViuI105WW4brYYep2K3a1X1aKFYX5tcLBx6MSe3hFrQw4PPyydLSkpgOnqJUrZXqnXGlt5I3o98Msvsu2sqqu2NGggo1oZGTKzxhGHD0ttPD8/2LWAYn5iMEJEZCI1VYZgAOult1u3lptmfLzMonCpu0M0bxd/H0rp8MgjxqEFi2bPBt58U+a/ZlqwNDRUAoKMDGOeqxaMZBmiuUvXrKlxRs2uxGyba5jWm3I32si0qI0jSazWhhx0OukV0emApUuNU25jY2X1ZS8vIDo6+/NrbOWN7NolJVyKF8+/3gatCNqXX1ofOrJEC96aN7ddX8UVGIwQEZk4dUo+7QYEWF+J1sPDuD6Ny4dqtm7FYdTEkjiZ1aglcFpVvLgcdN99WZ7S1qgBZAjk7FnpSbA2RAMAKFcOUYFSo+TQugvZNtfQMxInSbeZgxF780ayG3Jo2BB45hnZfukl+Z1qvSINGsjv115acGepZ0SbRfPQQ/l3g+/cWQqyxcfLCJu93DVfBGAwQkRFlV4vWYB3y6hrTJNXbY3/awXQfvtN6ly4hFLAli14G69Brzzw8MNS4yw3DGXhDxpn0bRsCVSoYP01UdVSAQCH/s2+dKrh+ibukYgg0/xSe4MRe4Yc3n5basXs2CFVcx3NF9GYDtNkrhuXV1VXbfHwMC459PHHtmuqaNw5XwRgMEJERdV33wGjRkklM5M5utnli2jq1JFP32lpxtkm+e74cRy9HIzv0AuAHb0idjBNYrVY6MyCWo0DAQCHz9he3E6vN7m+OCa9M5nml5oGIzYKxho+5dsacihfHnj1VdkeN85YmdWRfBFAFqvz8pKeiLNnjftPnZICbZ6e0luRnwYMAEqWlFk+Ws6KLUePyhCVry/QtGnet89RDEaIqGj64gt5PH9eprveZWtab2ZaIuuiRU5um722bpVeEXiia1fH8iCs0XpGtmwx5lqYFTqzIOrBygCAw/GhUBnWp/heuCAJv166dITjTJYhGkBu/B4e0ttka0FCe0uav/QSEB4uM3S0YRZHe0Z8fICoKNk2HarRgoBWrYBSpRw7Z24VK2asODt0aPYVgbXgrVkzhxZEzjcMRoio6Dl0yFAoDACwcqVh09a03syeekpunDExshJsfjv26xEshiSvTJzonHNqPSOXL8tjixYyK9iWyM414IU03EZxnNtyyupxWqBXRXcGXsgwFDsz5e9v7JWyVhbekZLmfn7GArSATNWNiLD9Gku0vBHTJFZXDNGYGjdOeucuXZK4zlZJfndcj8YUgxEiKnrmzJFH7ePsihWGp+wdpgEkj6J9exlLcMVQzTtrm0APT3RufNlpS8FXrChF3TTZDdEAgHcxH1Tzl+TVw7+dsHqcoddJf1QSaRs1snhcdnkjR4/KDdjeIYcnn5SgCrC/2Flmmaf33rxpvMG7KhgpUQL4808JlLSA5MiRrMe563o0phiMEFHRkpQEfP21bM+cKUuzHjkCHD6MtDRjkSx7ekYAoK23JMAeWmalIlYeObH9Kr65JXfBie/5O+28pjNqABuzaDKJqii11w/9ddPqMYZpvTgm86OzVGYT2QUjpvki9gw56HRSUqV7d2D8+OyPtyRzMLJ6tdSbq10bqFYtZ+d0htKlJSCpX19yQtq1y7r80PHjMv3Yx8fxfJn8wmCEiIqWZcuAGzeAypWleEb79rJ/xQqcPi0zE/z9peZGtvR6ROz4HgBwctcN8+zGPPbO+NvIgBceKr4VTdoXd+q5tWCkWTP7V6CNqieBxaHD1rsdDD0jOG4xX0STXTCSk1khNWvKaJyj+SIaLRg5elTiWVcP0ZgqUwZYt05qtMTFyaXVAj/AGLw1bSp/2+6IwQgRFS1a4uqzz8o0iEceke9XrjRLXvWw53/HzZtR9ep2AMBJfRVgzBhnt9aiEyeAhRslSpjY4S+nn793b6BcOcd6EWq1KQcAOHytjNVKXMePyZCWvcHIgQNZFyM0HXLIz/yH8uXlpq/XS9Hb1atlvzsEI4AxIKlbVxKF27UzBn/uni8CMBghoqLk4EGpWOrpaayI1b279OPv2IHj/9wEYP8QDRYsQFVI5uoFVEDy9z/Jqml57N13gQzliY5Yg2ZP2Nl14YD27SUHwZEbbVQLyb85hCgYlg42oRRw/JhEFtWLX7JZJjYyUvJBEhOzri1jOuSQn1NUdTpjkz/7TKb5livnXtNky5aVgKR2bZkk1q6dBK7uXOxMw2CEiIoOLXG1WzdjFa/y5Q0D6cfWycpjdgUjt28DP/6IMriKQP8MKHjgDMKBF16AXSvG5dCpU8DChdLDMBFvWqyk6go1a8rjJZTHjQ27szwfFwfcSfaEJ9IR3jZCAkIrvLyMU2kzD9WYTlHN7yEHbahmyRJ57NbNzh60fFSunMTDUVEynbl5cwlMvL3dN18EYDBCREVFUhKwcKFsawUaNI8+CgA4vl9WvbNnJg2WLwcSE6GLjETV6vJf6cmge6T35bPPnNXqLN59F0hP1+EBrEWL8AvZz7vNJ0FBQMVgSWI9vOFilue1IYNwnIFP+1bZns9a3ogrZ4VowYg2dOQuQzSZhYRIQFKrFnDliuxr2tSxEvj5jcEIERU8a9bIKmG2SnRm9uOPkrgaHg507Gj+3N28kePXSgCws2dkwQJ5HDAAVatK0ubJrv+TfRMnytQGJzt92vi2E/GmVNtyI1HVpS75ob2pWX43xw6nA7g7k8ZCfZHMLAUjpiXNXTHkoAUjgMzieeCB/G+DvcqXl4qztWrJ9x06uLY92WEwQkQFy6lT8pH0ueeADz+0/3XaEI2WuGqqenWk166PU5BqWNkGI2fOyP/0Oh3Qvz+qVpXdJ0Oay1ort24Br7xif9vscPMm8NprMp20fclduA/b3GaIRqOVhT+UUCFLssfxLRKcRfqdN5Z5tcHS6r0nTsiQg6umqNaubfzT6dDBvXsaAAlItmyRac2jRrm6NbYxGCGigmXcOCBVFmbD+PHAH39k/5oDB4yJq08/bfGQM20HIB3e8PNIQcWK2ZxPG+5p1w6oXNkYjJzSGYdoFi401lPPgfh4WYRvzBigcWOpJ7F4sTw38c442XCzYESb3nsYtYyr0t11fHcCACCypoddiRZaz8jhw8YUHK1XpEkT1wQCfn7GXBZ3HaLJrEwZ+ZMPDHR1S2xjMEJEBUdMDPD993Iz69RJBu+feko+Mtvy5Zfy+PDDVpefPR7VDQBQTZ2AR4qN1WeVMgYjAwYAgDEYOQm5Uw4aJDtGjLBvSVXImi2rVwNjx8opSpUCunYFpk4Fdu2SH7V6dWDqi7FolfKnHKDdGd2E1pxDiMoSjBw7IwviVW9Rzq5zhYVJkda0NGMRL3eYFTJjBvDyy8Z1icg5GIwQUcGg18uqZ4Dc7FeskL76GzckATUx0fLrTCuuZk5cNXEcMjYTqY5KSUtrYmIkGzMw0FCeVFvr5OTJu6kSU6ZIre7du42BUDa6dJGVXz/8UNbt0+ulsuegQcA330g9taNHgVGVf5QXtGzpdlM5tPyEU4hAcsy/hv0qOQXHb5cHAER2rWXXuXS6rCv45qTYmbO1bw988IH1lYIpZ9zrL5mIyJolS4AdOyQImDxZ7gbLlsnA+L590hdtKaH1xx8l4cJS4qqJY8clCTUSx80WzstCyyDt0UOWTgVQpYrsun0buHoVUvDhrbdk54QJsgStDWlpwObNst2vn3S8xMZKzPPVV0CfPiaTZrZulUc3G6IB5FcRXDwDenji2J5E6e4BcGnVLiQiEB7IQJX7q9p9PtNg5NQpCci8vXNeRZXcF4MRInJ/SUmSKwJInkh5+ZSNChUkIPH2lpXqTJdn1ZhWXLXRk2BYIA/HpNZ3enrWg+7ckWEiABg40LDbzw+GPJNT2qK1Q4fK9Ivr1yUgseHECXm7YsWkE6dfPytl2JUyBiNuNpMGkN6MqDpyjQ+lRwL/Su/I8V9lOdnKxa7B18/+VepMk1i1XpF77zXEgFSIMBghovzz7bdyh/npJ8deN326fCwOCzMO1WhatAA+/VS2X30V+P1343MHDgDbtplXXLXCUAo+8JJ0b1hKPl25EkhIkK6QTMGAWd4IIJW7tGTWOXMk8cOKQ4fksVatbFaUPXpUCkf4+Vld8dbVatWSH8A0b+TYtssAgOqVUx06l9Yzsm+fe+SLUN5hMEJE+ePYMWDwYPmY+8QTwK+/2ve6ixclBwMA3nvPctnNIUOk50MpoFcvY2ShTed9+GGbK9+lpxuDiMiOd6OKFSuyHqjlngwYkKWXJUswAkjA0qePtGv4cCAlxeL7a8FItvmoWq9IkyZum7Sg/QyGGTVJScZA7x7HFvTTgpGTJ42TplyZL0J5h8EIEeU9vV56JpKSJOcjLU2SP+2ZlvvGG5KM0aSJzJyx5tNPJZng5k0pYnblinHWy5AhNt/i7Flpkq8vENb7bo/HypXmOSjnzxsTW/v3z3IOi8EIINmOgYHA9u2SS/LUU8B338nc3bsOyyiG/cGIGw7RaLLMqImJwXH93fotjYIcOlfZslLeXCmJSb28pCOMCh8GI0SU9z79VG6kgYEyw+Sxx6RWSPfuUjzMmv/+A+bOle1p02zPHvH1lWTV8uVleKZJEwlMqlTJtvyk9sm9alXAo9OD0vty+rS8v2bRIgmqWrUyRh4mrAYjFSpI0mtoqBRDW7pUlsUtWxZ48EFg1iwc+k+GL2qZTjRRSgKqnTslH+bDD41Lxbph8qpG+xmOoCYyzp4HvvkGxyD19avXsD9fRKP1jgCSL+Lu9TIoZxiMEFHeOnbMuBb9hx9KedPvvpMiGsnJ8qh94jelFDB6tAQATzwhU1mzY5rQqlUAzSZxVWsicLfyakCAcdaNNlSjlHGIxiRx1ZTVYASQXqBz56R3ZPx46T5ISwP++ANq2DAc3ivDN1FLJsoc3zp15K5brpwEVU8+KQVILl2SoMuNp5NEREiF1GT4IxaVoRZ9Y5w2be9qyCa0JFaAQzSFGYMRIjeVliZrrmmLchVIpsMz7dsbh0t8fOTTfseOMkOlc2e5UZtavVqGRXx8JFfExI0bMrRiUYsWxsRRLy+rFVdNGWbSaAvk3V04zzDFd8cOGUvx95cpvRZowcjZs8YCsWY8PCSwePdd+cUePgy8/z7ON3oYt1EcXkhD5A/vAqtWyfN37sjrKlSQn6l3b0nQXbMGCA7O9mdyFS8voEYN2T6EKFzJKIlbCIJOpwz1WBxh2jPC5NXCy8vVDSAiy154QWal1q8v66498ojb1bjK3iefGIdn5s41nyri5yc3+y5dZKjmwQeBdeuA6GiJxEaPluNefNFsWGTlSumcSEyUWbZa3GDmuedk/mfJkjYTVzWGBEvtk3vXrjIDZ+9emaur9Yo8/rgsT2tBSIjEKklJUiMk216AmjWBsWNx6B4AHYFqIYnwfnYcULmyDC1VqSLbbpqoakutWpKnfAhRCIbkxlSurIOfn+Pn0oIRT0/mixRmBe2/NqIi4cIFY6rEf//JPTA6OmtOpVs7dkw+yQPARx9J0bHM/P2BX36RHIj4eOkp2btXZsEcPiwLa9yt0ZGaKot9PfqoHJqeLqMXVmcJ9+kjPS52NhUwCSBKlwZat5btJUvkCzCUf7dEpzOvxGovw0ya5iWkUNqzz0qOS/XqBTIQAcxn1ORmiAaQPJFnn5UJVcUdm4xDBQiDESI39PnncrNt1kzuxYGBwJ49ciOOjpaaXG4dlGRkyPCINjxjoww7ihWTFeGaNpUCYQ88IF1BgFRaDQ5GbKzEBtOny+5Ro2TUIj1d0kl+/jl3TdWCB8MwDSBdUQDwzjsyLlSpkiyMZ4PWgWMofGYHu2fSFCCGGTWe9XIdjHh6Smz68stOahy5JQYjRG7mzh1g9mzZfvll4O23JRdz/HjjZJTu3eUT46+/umlQ8sknUmzM0vCMJUFBUqwsOloKjl27Jne0Z5/Fb78B99wjKSUlSkjv0NSpMnLy1FMyotOjh3Sw5MS5c9Lr4u2dqeqpFoxoa970729cP94Km0msVpgWPCsstJ/lsF8DHK8oWac5DUaoaGAwQuRmFi2SDoKICAk6ABk1ePdd+cT9yivSmbBrF9Ctm3xYT7KxyGy+O3o0++EZS0qUkLojDRsC3t5I++hjvDLBC127yvW4916pLq5dEy8vuVZPPmksW2JvHTVTptN6zWKNypXNq5xaqC2SWW6CkcLUM1KzpsSf1xL98ZeXDHeZ9ToRZcJghMiN6PWyRDkA/O9/WT+IlykjE0tOnZKZnv7+smZHboYpnCojQ2bPJCfLcIut4RlLSpUC/vkH5/+Kxf1TOuCDD2T3Cy8AW7Ygy2wMLy9g8WIZqtECklWrHHtLLV/E4s3yscfksVkzucNmw9Fg5MYNma0LFK6ekYAAYwx65ow8smeEbGEwQuRG1qyRHILixW0vpVK2rKwJp81a3bkzf9pny/XrwBuddmLetho4Wawe1JdfZT88Y0IpqVX2yeeeaPhQeWzdKqM3P/wgoz7Wcjm1gKRHDxluefRRY20we2SZSWNq5EjgtdeAefPsOpejwYiWL1KpUuFLzswcXFmoE0dkwKm9RG5ES9AcPNjqDFIzTZoAM2e6PhhRChj4xG38sr4ZgGZAIlC5jdSFaNdOHqtUyfqaQ4dkAbQNG6SH58oV4/MNG0ogYs8nam9vWYNPrweWL5eAZOVK4KGHsn+tzWCkWDGZ4WInrefm5k3p9ShZ0vbxhTFfRBMVZVyzMCzM8pJCRBoGI0RuYv9+YO1aqSXyv//Z95p775XHXbtkhCSb/Mo88+23wC/rA+GNVNwbfAw7EmsjNlaHhQuNy8NUqSJBSZ06Ejxt3Ahcvmx+Hn9/KbT64IPAiBFwqC6Ft7fMwO3ZUwqnPvKITPt98EHbr8syrTcXAgKkGv3Fi9I7Eh1t+/jCOJNGY/ozcYiGssNghMhNaLkijz6atRfBmpo1pXv/1i35lG1arTK/XLwI/G94OgAvvIHJeG1LTyRW1SEmRno8Nm6U4OP0aVmixZSfnwQfWg/KvfdKwdWc0gISrf7II49IPo21pWn0euDECdl2VoJl1ar2ByOFMXlVY9rbw2CEssNghMgNXL4MfPONbL/0kv2v8/SUG97GjVKxPL+DEaWAYcOA6/FeuAf/4pXHjgP16qEYJADQgoDbt2Wm78aN0htwzz0SfDRp4vy6Xj4+UplVqz/y8MMyy6Z9+6zHnj8PpKRI3knlys55/4gIICbGvryRwj5Mo+FMGsoOE1iJ3MDs2XJTvPdek5LXv/8uFUmzqaDVpIk8uiJv5PvvZUjEC2mYj6fhPWmCxeMCA2W4ZMoUOf6NN2Tx27wqMKoFJNpafN26AevXZz1OG6KJiJCAxBnsLXyWnGw8pjD2jJQpI18Ae0YoewxGiFwsJUWSUAHpFTFMQJk8WZJI3n3X5uu1vJEdO/KujZZcvgwMHy7bE/AOGjxR03yJVRfz9QV+/FGWvklKksBk40bzY7IskOcE9s6oOXZMholKlJB1bQqjIUOkt46r7VJ2GIwQudh330mtiUqVTBaEvX3b2NWxZImxCqgFWs/If//Jp+38MmKEFEqtj714Fe9Kd4eb8fUFli2TJWqSkiQw2bTJ+LzNmTQ5ZG8wYjpE48AM6ALl7beBffukfAyRLQxGiFxIKeN03hEjJAETgCQdpKfL9u3bcke1IiwMKFdODt+zJ0+ba/DjjzLt1lOXgfl4Gj5PPOKa7Fk7aAHJQw9Jqf3OnYHNm+U5Z86k0WjByJkzxl+hJYV5Jg2RoxiMELnQhg3SoxEQkKlYqTaeoM1ttVF0S6cz9o7kx1DN1auStAoA49W7aITdbtkrYsrPT3JVOnY0BiRbt+bNME2FCpKzkp4u695YU5hn0hA5KkfByMyZMxEREQE/Pz9ER0djy5YtNo9PSUnBhAkTEB4eDl9fX1SrVg3z7KxoSFSYab0iAwdmKpClBSMTJki0sWmTcQ6qBVreSH4ksf7vf1KcrE5QLF7D2zJtxU17RUz5+UkhtA4dZNSrUydZRgdwbs+Ih4ex+JmtoZrCPJOGyFEOByNLly7FyJEjMWHCBOzevRutWrVCp06dEBsba/U1Tz75JNatW4e5c+fiyJEj+O6771CL/wLJiU6flpLg+ZkzkVtHjxoXdjMrcmaaL9K3r7FqV+YiHSbyq2dk5UrJcfHwUJif8Dh8dWlu3ytiyt9f6o888IBc5tRUmR5t71p+9soub0SvB44ckW32jBABUA5q0qSJGjp0qNm+WrVqqXHjxlk8fvXq1So4OFhdu3bN7vdITk5W8fHxhq+zZ88qACo+Pt7R5lIRsHSpUsWLKwUo1aiRUsePu7pF9hk2TNrcpUumJ9askSfCw+X7pUvl+0qVlEpPt3iuK1fkEECpGzfypr3XrikVEiLvMa7mMtl48sm8ebM8lpioVPv28iPUrOn88w8fLuceP97y8ydPyvM+PlZ/pUSFQnx8vF33b4d6RlJTU7Fr1y507NjRbH/Hjh0RExNj8TU///wzGjdujA8++AAVK1ZEjRo1MGbMGCTZWPN8ypQpCA4ONnyFhYU50kwqIlJSJOmzZ0+pQOrhIUvMN2ok65O4sxs3jB0dWYqcaUM0bdvKY/fuMh3h3Dngzz8tnq9MGeOn8X/+cXJj7xo5Umb9RFVNxsQjvWX46PXX8+bN8lhAgBREmzgR+Pxz558/u54RbYimRg3XlfAncicOBSNXr15FRkYGQjJNig8JCcHFixctvubkyZPYunUr9u/fjxUrVmDGjBn48ccfMVwrUGDB+PHjER8fb/g6e/asI82kIuDkSSkjrt1Ixo+XlIqWLYGEBFlKfuRI6YZ3RwsXSiJl/frA/fdnejJzMOLrC/TpI9vz51s9p8W8kbQ0KT1av775KnQO2r4dWLRIAr55ld+EH1IKTK6INQEBwKRJliuz5paWM2Kt8Bln0hCZy1ECqy7TpHilVJZ9Gr1eD51Oh8WLF6NJkybo3Lkzpk2bhgULFljtHfH19UVQUJDZF5FmxQrp/di1SzoMfvtN6oJVqSKzU8aOleM+/liqfJ4549LmWrRunTz265epxoRpvogWjADA00/L44oVwPXrFs9pMW/k44+l9Oi+fZJ/kpGRo/auXi2PPR64iWYb35NGF6Bckfxmb88IgxEi4VAwUqZMGXh6embpBbl8+XKW3hJNaGgoKlasiODgYMO+qKgoKKVwzta8N6JMUlNlSOOxx4D4eKB5c6mr0bmz8Rhvb+D996ULvmRJuTHfcw/wyy8ua3YWer2UEQEkWDKzbZvMCa1SxXy1vHvuARo2lIvw7bcWz5ulZyQ2VsYhAOnS+OMP4J13ctRmrVBY+ytLZOPJJ2X5XbJI6xm5elV66jLjTBoicw4FIz4+PoiOjsbatWvN9q9duxYtDAtqmGvZsiUuXLiA27dvG/YdPXoUHh4eqFSpUg6aTEXRmTNA69bGlW1Hj5YbpLV0om7dgN27pbfgxg1ZLG3sWBm1cLUjR6Ryqb+/xBhmMg/RmHrmGXm0Mi2+USOJOc6fBy5cAPDiizIW1KqV8TWTJkmJeQckJwN//SXbbXZPL9C5IvklKMi4LouloRoO0xBl4mhm7JIlS5S3t7eaO3euOnjwoBo5cqQqVqyYOn36tFJKqXHjxql+/foZjr9165aqVKmS6tGjhzpw4IDatGmTql69uho8eLDTs3GpcNq6VamSJWX2QYkSSv30k/2vTUlR6sUXjTNNmjVTatu2PGuqXebMkba0bWvhyWbN5MkFC7I+d/WqTL8AlNq92+K569WTp1e+ul02vLyU2r9fnhw8WPaVLavUuXN2t3fjRnlZed/rSg8o1bOn3a8typo0keu2fLn5/suXZb9OJ7N6iAqzPJlNAwA9e/bEjBkzMHnyZDRs2BCbN2/GqlWrEH53on5cXJxZzZHAwECsXbsWN2/eROPGjdGnTx9069YNn3zyibPiKSrEzp4FHn1UejfuvVd6Ox5+2P7X+/hIb8qPP8qn1b//liTXhx6SbVfYulUeW7bM9IRpvoillcVKl5aZNYDVRFZD3shndxNHxowxDqd88okM9Vy5IlOQ7Owm2rRRAQDapvwuuWHsFbGLtbwRbYgmPFySaIkIjveMuAJ7RoqmpCSlGjeWT5ENG+b+U+SZM9I54OVl7Cl56CGltm93TnvtVbWqvPfq1Zme+P13eaJKFesvXrVKjilVSqnk5CxPz54tT3fAGjlP5ot2/LhSQUFy0OjR2Tf21i3VLuSAApSahSFKPfts9q8hpZRSr74ql3nYMPP9X3wh+zt1ck27iPJTnvWMEOUHpWT9k3/+kRkzK1bk/lNk5crAl19KzsYzz0h9h99/B5o2ldVc86o+h6m4OPmkrNNJAq4ZW/kimo4dgYoVZUaNhazcJiVl5beduBfqk0+zXrRq1Yy9KlOnSklVa/bvR0p0C/x1SbIx2468B5g92/rxZCa7nhHmixAZMRghiy5eNCbZucLs2XLP9PAAliwxn1iSW1WrAnPnSlAycKAEJatWyTBQt24yCzavbNsmj/XrAyYTzIQ9wYinJzBggGxnTmTV61F3xmD4IQk3URLHa3W1fI7HHgNGjZLtgQOzrnmjlJy7SRPsOBqMZPgjpFQqak4bIr8Qskt2wQhn0hAZ8X8WsqhDB0k10OpL5KeYGJkIAgBTpkhb8oLWSXD4MNC/v9xnf/0VaNdO0jfygpYvct99mZ6wVl/EkoED5XHNGpk6o1mwAN5/bcY9HnsBZLNOzXvvAS1ayBzpJ54wLupz+7YEO4MGAUlJ2BQ5GADQ5gEfWCklRFZowcjp0zKdW8OZNERZMRihLO7cAfbvl/9Ae/e2uVis0124INVT09KAHj2Al1/O+/eMjAS+/lpuEhUqyLRbrQfD2awmr27bJgXJIiKyX7WtenWZrqvXSylXQApa3L1Y97b0AZDNCr7e3sDSpTL/dPduif7275fuoUWLpAdmyhRsDO8PwHI+LdlWqRLg5SWlYS5ckH2JicYifAxGiIwYjFAWp08bt2/elNksiYl5/76pqfIh/eJF6ZWZPx/5+mm8enVJyQCMRb6c6fZtKdIGWOgZsWeIxpRpzRGlpIjK9etAgwZoMrgBADtW8K1USQqo6XTAnDlAdLREZBUrAhs2IHXUOMTE6BxqFhmZrgasDdVoK/WWKSOTo4hIMBihLLT/OKtUAUJCJIdi0CC55+WlkSNliCY4WBJWAwPz9v0s0XoAtNjAmbZvl86PypUtFGtzNBjp0QMoVgw4flzGsrSk1FmzcG8zWXlt9247Zu926GAs656aKnOed+8GWrXCzp1AUhJQtiw/xedU5rwRDtEQWcZgpAA5dkzyGRwsoOkw7T/ORo2kPoeXl/ToT5uWd+85bx4wa5Z8SF+8WHopXEGLBXbudH5vkNV8kVu3bNcXsSQwUGqFAMCECfL43HNA8+aIjARKlJA0kP377TjX66/L4j6zZ8tCP2XLAjDGR23a5G8PVWGSORjhTBoiyxiMFCDvvCM3CK0kel7R/uOsWlVunNr7jR1rXODNmXbuBJ5/XrYnTZJptq5SpYr0XKSnG9ePcRarwYgj+SKmtKEaQAKIKVMASCJu48ayO9uhGkDGE8aPB4aYz5bRhqqYL5Jz1oIRzqQhMsdgpIC4cwdYtky283rKrWkwAki9jwEDJF+yZ0/nroJ7+bLMNE1Nlcqqr73mvHPnlNY74sy8kfR04/ouuc4X0bRoAdSuLdtTp0pBlru0Sqw2k1htSEszJvEyXyTnOExDZB8GIwXETz8Zp5ueOiVj+XklczCi08kQSnS0zDR57DHnvH9amiz+eu4cUKOGTAxxhzIWeZE3snevDPsEB1tY7DanwYhOJwVS/vgD6NfP7CltBV+7ekYs+OcfCYBLlzbGO+Q402AkPR04elS+ZzBCZM4N/usneyxaZNxWSvJH8oJSxlVGtf9IAVlhdvlymQXw77/A0KG5T2gdO1Z6HwIDpRBoliJgLqLFBDt2yA3ZGbQhmhYtMgVct24ZS7/mZDwkPNxiIRatZ+TAgZzlvpjmi7hDgFhQaf+GLl2S/J20NCmKa221aaKiiv/NFACXLsmHX0BmXQLGsWdnu3xZbsA6Xdb0hcqVJZHVw0N6MT7/POfv8+23xlyUhQvd65NiRITMek1LMw6t5JY25OG0fJFsVKggX3q9BI+OYr6Ic5QoAZQsKdtaAcGaNRngEWXGfxIFwNKlcr9q0gR48EHZl1fBiDZEExYmK95mdv/9wIcfyvZLLwFbtjj+Hnv2AIOlsCdefVXqmLgTnc7YO+KMoRqlbCSvam/Qrl3u3yiTnOaNpKUZ28t8kdyLkKV98Ntv8uhOgTeRu2AwUgBoQzR9+xr/I8urJFYtGNH+A7XkpZeAp56SMfDu3aWEur2uXZPgIylJSlpMnpy79uYVrUfAGUmsp07JAnne3sZcDoOc5ovYIad5I7t2ydBOqVJA3bpOb1aRow3VaL1snElDlBWDETd3+LCkFHh6SgCgBSN53TNimi+SmU4HfPWVrDp744YsLvfKK9kX2MrIkPLyp0/L+b/9Vn4ud6TFBtu35z5ZV+tliI6W3BuD3OaLZCOnPSNaANa6NYcTnEH7t6StT8OeEaKs+F+Nm/vmG3l86CEpJaF9qjpyRG7uzmZPMAJI8c8NG4AXXpDvP/hAhnBM123L7LXXJPclIEAqrGpj6e6oWjXJz0lNBf7+O3fnyra+SNWqkpDjZFqtkZMnZekae+VhZ02RlPnfEoMRoqwYjLgxvV6qkQLGmZtVqgC+vkBKinPrfWjsDUYAaccnnwA//AAEBclNt2FDY7KtqR9/lIViAWDuXKB+fac1OU/odFam+N68CXTtKivd9ekj1U+//FLK4h47Jr+YTKwmr+bxXb9ECZkyDRg7YLKTnm4Mnpi86hym/5Y8PGRhRiIy5+XqBpB127bJkEbx4lIQDJBhjZo1gf/+k6Eae4IGRzgSjGh69JAg5IknJDn1oYfkHj1pkrT3wAHjqvejR8twU0HQtq0MJRnyRpQCnn7amIlorURrhQoSNUZH41p0Rxw82BWATOs1kw9dEPfeK7UtduyQ30t2/v1X6tmULOn+AWNBYfpvqVo1CeKJyBx7RtyYNkTTo4d5roE2VOPsvJHkZOMwi6NBTmSkJOhp9UfeflvKXxw5Ylz19/77jb0jBYHWM/D333JtMG2aFETx8ZEqcB9+CAwfLvXr69SR8SdA1ouPiQE+/RQxA78AANTyPYmyb/1PirVcvZrn+SIaR/NGtMCrVSvmizhL5crGa8khGiLL2DPippKTge+/l+2+fc2fy6sZNWfOSCBRrJhhrTSH+PnJPbp1a1mzbcMGaatS8h/ykiWy6F5BUb06EBoqM2G2f/kf2rzyijwxY4ZEXZkpJdOFTp+WIZtt27B1yT3ANaBlynrg00/lC5CaInmYL6IxnVGjVPYL3jFfxPm8veVXfPo0Z9IQWcPPPm5q1SpJT6hYMesH57yaUWNaeTU3q7T26iUf+uvVkxugr690COQkwHEls7yRCX9I8NCrl+VARHtBmTKSOdqrF/DZZ9hacxAA4L5hDaQXRasFryX8tG+fpz9Dw4Zy/S9fBt580/axzBfJOzVrymO9eq5tB5G7KkCfU4sWrbZInz5Zp7+aDtPY82nXXjnJF7GmZk0Z3pg9Wz6dR0fn/pyu0La1HkuWeGDjrWhMjIoC5syx+4InJxtHYu576V4g8m43xZUrwObNMoZluvJuHvD3l6Gxl16SYMTTE3j9dcvH7tkDJCRIWf4GDfK0WUXOhx9Kj+ETT7i6JUTuicGIG7p+3ZgjmXmIBpAZEjqd1Pi4cgUoV84572tPwTNHBAQAo0Y551yu0ubATAAj8DeaIXnxMvgFBtr92n/+kanBISGSuGhQtizw+ONOb6s1I0dKO155BXjjDclfmDAh63HaEE2rVu5b/6WgqlePvSJEtnCYxg398IMUEGvQwPJ/YP7+xoDBmUM1zuwZcUs//yyr89m7yuAff6Dm5/9DCC4iGf7Yccux7EPT+iLO6r3KqbFjgSlTZPu114zbprTkVeaLEFF+YzDihkzLv1uTFzNqCnUwEh8veRwffigXb8AA20HJuXNAnz7QQaFN1XMAHC8NrwUjLVvmsM1ONm4c8O67sv3qq8D77xufy8iQkSOA+SJElP8YjLiZkyelvohOJ6XTrXH2jBqlCnkwsmiRLEfs7y/V5BYutB6UpKUBTz4pU3AbNULbFyWBwpFF8/R6G8XOXGj8eJl2DUhwoi16uHev5IsEBUnSKxFRfmIw4ma0iqvt20vtLGucPaPm2jUpfQFIva5CRSmZcwxId8COHVJF1VpQ8sorUjQlOBj44Qe07eANQHZZKLBq0cGDMhsqIMD9bu4TJhgXKBw7Fpg61TxfpCBNvyaiwoHBiBtRyljozNYQDeD8YRqtV6RChUyLuRUGW7ZIdBAQAPTvL9N7fvlFKoFlDko6dwamT5fXff01ULUqatWSJOGkJPuLh2m9Is2aSZ0Jd/P661IhFwDGjJG1hQAO0RCRazAYySdffSXd41u2WP90vXOnlO729wcee8z2+bRg5OxZKd+dW4V6iGbmTHns3Vt6OzSNGxuDkm7dJChZvVqeGzMG6N4dgHm9EXvzRqwujudGJk6U2TUAcOmSPDJ5lYhcgcFIPvjvP+DZZ+XTaOvWsu7HAw9IcLJtm0y7BIy9Io88IuvR2FK6tLGI2JEjuW+jacGzQuXSJam4BgDPP2/5mMaNZabNP/9IrsgzzxgzPe+yuGieDe6WvGrNpEnGuiOlSgH33OPS5hBREcXR4XywapU8hobKrIXLl4F16+QLkNGDli2NBbK0FXqzExUldUYOHcp9UbFC2zMyd64kpDZtCjRqZPvY6Ghg6VKLT2k9BjExEjz6+Fg/zblzUvrbw0OGadyZTifF0O65R6r9Ml+EiFyBPSP5QOv5nzABuHhRVrH97DNZAK9MGZnksXatFDErV04WmLOHM2fUOLvgmVvIyAC+kIXqrPaK2Kl2bePvSgsaLbl0ybhCcYMGMjvF3el0spihtqgeEVF+4+egPBYfb0xm7NRJ/uOvXVu+hg+XNIWDB6X7f+dOKRdt76dTZyaxFsqekdWrgdhYGRd78slcnUrLG1m2TH5XLVpkPWbTJillEhcnvV3vvJOrtyQiKjLYM5LH1q2TD+g1ali+0Xt4AHXrAiNGyOSNrl3tP7ezpvempck9G3DTYOS//4CePeXREdp03qefdsoUIWtJrHq9pJjcf78EIlFRElh26pTrtyQiKhLYM5LHtCGahx5y/rm1YOT4cQkocjqFNDZWbqh+fkD58s5rn1Po9TLusXu3rLz377+SvZudU6eMF9/aKrsO0vJGtm0zXu+rVyXH5/ff5bl+/SQGKlbMKW9JRFQksGckDyllvEnlxafkSpVkOCAtzTjMkhOm+SIe7vYXsXixBCKARE39+0uAkp0vvpBfQIcOQPXqTmlKnToy4yQxEdi1S4KSe+6R37Gfn0zf/vprBiJERI5yt1tPoXLggMys8PPLm2JSHh7OyRtx23yRpCTj8rIDBgC+vjI1yXRRFUtSUmQWDZDrxFVTHh7G3+OoUbJ97pwMwW3fDgwa5PoF8YiICiIGI3lIGyVo2zbvqpo6Y0ZNntUYuXJFCqpolbUc9fHHUtUtLEzGPj7/XPa/9hqwYYP11y1bJuMnFStKMTMn0oZq/vpLcoF69ZLZNfXrO/VtiIiKFAYjeSgvh2g0bt0z8vLLksH71ltW63dYdeWKcZ37d9+VaO6ZZ6SHRK83TluxREtcfe45pxfO6NBBej98fYHZs2UUKbsCdUREZBuDkTxy65aUfgfyNhhxxoyaPAlGNm6UBArNc88Zu2DsMXmyLCPbqJFx+WKdTkq716snBT169QLS081ft2+flD/19AQGD871j5FZVJT8aPv2AUOGcFiGiMgZGIzkkQ0bJLG0alUgMjLv3sd0mEapnJ3D6QXPUlOBYcNke/BgKcqRkCBBRVpa9q8/elS6HQBZ4940qzYgAPjhByAwUObYarXMNdrrHnnE9rLHudC6tdNyYomICAxG8oyWL6IVOssrkZHSCXDrFnDhguOvv3FDvgAnBiNTp0pXTblyshzs4sWyQN3ffxuXirVl3Djp8ejSRYp3ZFazpjFB9b33gF9/le1bt4BFi2TbiYmrRESUtxiM5AGl8ra+iCkfH6BaNdnOyVCNNnJSrpx0NuTayZMyxAJIUFKyJFClCvDll7JvyhRg/Xrrr9+6FVixQnpDtHXtLXnySeCFF2S7f39ZDGbxYglIatSwHMQQEZFbYjCSB44cAc6ckUChXbu8f7/czKhxar6IUlJKNjlZfvA+fYzPPfGELF2sFNC3rySoWnr9mDGyPXiw1My35aOPZEGVGzfk/DNnyv7nn2cyBxFRAcJgJA9ovSJt2uRPASyHZ9T8+af0KiQmOjcYWb5cfngfH5nRkjkgmDFDIqe4OCnRnjnJ5YcfpGBHsWKylGx2fHyA77+XSmT//CNZpf7+MuOGiIgKDAYjeUCb0pvXQzQah2bUpKZKzfLPPgNmz3ZeMHLrFvDii7L9yiuS15FZQIBM8fX1BX77DfjkE+NzKSmSKwIAY8faX5c+PBz45hvj9089JUNDRERUYOQoGJk5cyYiIiLg5+eH6OhobNHmsFqwceNG6HS6LF+HnbHuvRu6c8e4kFp+LZSm9YzYdUlXrAAuXpTtRYucV/Bs4kTg/Hk50fjx1o+rVw+YNk22x441lnqfOVMSWEJDgdGjHXvvTp0kP6VWLQmEiIioQHE4GFm6dClGjhyJCRMmYPfu3WjVqhU6deqEWG3ZVyuOHDmCuLg4w1f1Qjo3csMG+ZAfHm4MEvKa9j5xcUB8fDYHf/aZcXvvXpw8nAIgl8HI7t1SLRWQKqnZlZt9/nmge3fppXnqKamy+tZb8txbb+VsbGvUKOkastQjQ0REbs3hYGTatGkYNGgQBg8ejKioKMyYMQNhYWGYpVW9tKJcuXIoX7684cvT09PqsSkpKUhISDD7KihMh2jyK4cyONhYUsNm78jevTJbxcsLaNkSGfDA6XNSoTTHwYheL8GFXi8zXOwZm9LpZGpupUpSUyQ6WpJQ69aVFXqJiKhIcSgYSU1Nxa5du9CxY0ez/R07dkRMTIzN195zzz0IDQ1F+/btscHWuiIApkyZguDgYMNXWFiYI810KdP6IvnJriRWbW2Xxx8HRo/GOVRCut4T3t4q5/XBvvxSkk6LFwemT7f/daVLS66Hh4dxZs2HH0rRFCIiKlIcCkauXr2KjIwMhISEmO0PCQnBRS0PIZPQ0FDMmTMHy5Ytw/Lly1GzZk20b98emzdvtvo+48ePR3x8vOHr7NmzjjTTZY4dA06cALy987/MRbZJrDduGBM9R4wAOnfGyeINAQBVyt3JWQxw6ZIx6fSddxyveNqmjbGC6oMPyhcRERU5OVpFTJdp/EEplWWfpmbNmqhpMo7fvHlznD17Fh999BFat25t8TW+vr7w9fXNSdNcShuiue++/F88LdtaI/PnA0lJQIMGQMuWgE6Hk9FPABuBquoEgBwsOztmDHDzpqwfo5V/d9TEibIUbqNGrA1CRFREOdQzUqZMGXh6embpBbl8+XKW3hJbmjVrhmPHjjny1gWCq4ZogGyGafR64xDNiBGGm/7Jym0BAFUv/w3cvu3YG+7cKT0tOp2sB5PT4RWdToKRoKCcvZ6IiAo8h4IRHx8fREdHY+3atWb7165dixYtWth9nt27dyM0NNSRt3Z7SUmymivgmmBE6xk5cUJm85j5/XcptVqihHEFXAAnUysCAKqmH5Upv47Qhlf69QPuvTdnjSYiIkIOhmlGjRqFfv36oXHjxmjevDnmzJmD2NhYDB06FIDke5w/fx4LFy4EAMyYMQNVqlRBnTp1kJqaim+++QbLli3DsmXLnPuTuNjmzRKQVKwI1KmT/+8fGiqdCwkJwPHjmdqgTecdNEgKj9116pT0kFTFSWDRfxJY2GPLFmDNGpmVM3Gik34CIiIqqhwORnr27Ilr165h8uTJiIuLQ926dbFq1SqEh4cDAOLi4sxqjqSmpmLMmDE4f/48/P39UadOHfz222/o3Lmz834KN5Bfq/Rao9PJUM2OHTJUYwhGjh+Xxul0WVayNVRfxUlg3T5Z9je7JFSlgAkTZHvQICfVkScioqJMp1TmBULcT0JCAoKDgxEfH48gN80tqFVLFsj78UeZOesKAwcCX38ti+ZqoygYNUqm3HbpAvz6q+HYW7eMaRo3mz6I4O1/yNRabaE6a9auBTp2lJLux49LrRAiIiIL7L1/c20aJzh1SgIRLy/ggQdc144sM2oSE4F582R7xAizY7Uy8KVLA8FPPybf3B1as8q0V+T55xmIEBGRUzAYcQJtSm+LFlIN1VWyzKhZvFjqw0dGSm+GCW2IJiICUjnVx0dWvd271/ob/PKLzKIJCDDWFyEiIsolBiNOoOWL5NcqvdaY9ozoM5QxcXX4cKl0asJstd6SJYFu3WTHokWWT67XG8d+XnwRcGAqNxERkS0MRnJJr5fF8QDXByNVq0r116Qk4OyyHdLTERBgcb0Xs2AEMM6kWbwYSE/PevIffgD++08STbLLKyEiInIAg5FcOndO6oV5ewP16rm2LV5egLYY8qHP/pSNfv2kvkgmWYKRTp0kgeTiRWDdOvOD09ONU3hHjwZKlXJ624mIqOhiMJJLR47IY7VqEgy4mjZU8+mWexCPIBmisSBLMOLjA/TsKduZh2oWL5YftHRpYORIp7eZiIiKNgYjuaQFIybL77jUc88B3h7pWIXOiPY7gN3pWbtr9Hrg9GnZNisT0r+/PK5YYSwPn5oKTJok26+8wrLtRETkdAxGcsndgpGObVOxtUQ3hOM0TiRXQvPmsnSMaTWZuDgpGe/pCYSFmby4SRMZ57lzB1i+XPbNmyeRS/nyVntZiIiIcoPBSC5pwYg2rdblfvwRTa7/jn/Ld0G3LnqkpEhJkD59pNAZYByiCQ/PNLSk0xkTWRctkkzYt96S7ydMMCslT0RE5CwMRnLJrXpG9Hrg/fcBAKWGPYWffvHAhx9KD8h33wGNG8sEmyz5Iqb69pXHdeuA116TEvFhYcCzz+bPz0BEREUOg5FcSEwEtGV43CIYWbHCOP12xAjodDILd9MmWcDv6FEZifniCzncYjASEQG0aiXjOtOmyb433pDy70RERHmAwUguHDsmj6VLy5dL6fXAm2/K9siRUsjsrpYtgT17pA5KcjLw11+yPyLCyrlMV++tVg0YMCAvWkxERASAwUiuuNUQzYoVMgYTFGRx+m2ZMsBvvwHvvmssxmq13U88YewJefNNKaJCRESUR9ygMkbB5TbBiI1eEVMeHsD48UC7dkBMDNC1q5XzlSgBfPONdP089VRetJiIiMiAwUguuE0wovWKBAfbVZSsWTP5sqlHD6c0jYiIKDscpskFtwhG9HpjUTIbvSJERETuisFIDinlJsHI8uXA/v1294oQERG5GwYjORQXJxXTPT1lwolTbN4MXLpk//GZc0UsLIhHRETk7hiM5JDWKxIRIWvM5dq6dUCbNkCDBlIrxB7sFSEiokKAwUgOHT4sj04rAz93rjxeuiRBiVYMxBr2ihARUSHBYCSHnJovkpAArFwp27VqATdvAg88AKxda/01y5axV4SIiAoFBiM55NRgZNkyWZSuZk3gn3+Ajh1l5dyuXWXabmamvSIvvcReESIiKtAYjOSQU4ORRYvksX9/oFgx4Oefpc5Haqo8LlhgfvyyZcCBA9Ir8uKLTmgAERGR6zAYyYHkZOD0adnOdTBy9iywcaNs9+kjj76+wJIlwDPPSC/I008DH38sz7FXhIiIChlWYM2B48elzkhwMFCuXC5PtnixnKxNGyA83Ljf0xP46it5k+nTJS/k5k3JKWGvCBERFSIMRnLAdIhGp8vFiZQCFi6UbdOVcjU6HTB1qlRVfeMNqbRarJg8N2oUe0WIiKhQ4DBNDjgtX+Tff4FDhwA/P+trweh0wOuvA598It8nJkoQ8r//5fLNiYiI3AODkRxwWjCiJa527y7DLra88IL0olSuDEybxl4RIiIqNDhMkwNOCUbS0oDvvpNtS0M0lvTrZ/+xREREBQR7RhyklJOqr/7xB3D5MlC2rNQVISIiKqIYjDjo8mUgPl5SOSIjc3EibYimd2/A29spbSMiIiqIGIw4SBuiqVJF8k5zJD4e+Okn2eawCxERFXEMRhzklHyRZcukclpUFNCokVPaRUREVFAxGHGQU4IRrbZI//65LFRCRERU8DEYcVCug5EzZ4BNmyQI0cq/ExERFWEMRhyU62Bk8WJ5bNsWCAtzRpOIiIgKNAYjDkhNBU6elO0cBSPZlX8nIiIqghiMOODkSSAjAwgMBCpUyMEJ/vlHulb8/YHHH3d6+4iIiAoiBiMO0Iqd1aiRw7xTrbbII48AQUHOahYREVGBxmDEAbnKF8lJ+XciIqIigMGIA7RgJEdl4H//Hbh6FQgJATp0cGq7iIiICjIGIw7IVc+Iafl3L65PSEREpGEw4oAcByO3bgE//yzbHKIhIiIyw2DETteuyRcAVK/u4Iv37gVSUoBKlYCGDZ3dNCIiogKNwYidtF6RsDCgWDEHX7x/vzzWr8/y70RERJkwGLFTrvJFtGCkXj2ntYeIiKiwyFEwMnPmTERERMDPzw/R0dHYsmWLXa/btm0bvLy80LAADlU4JRipW9dp7SEiIiosHA5Gli5dipEjR2LChAnYvXs3WrVqhU6dOiE2Ntbm6+Lj49G/f3+0b98+x411Ja3gmcPBiFLAvn2yzWCEiIgoC4eDkWnTpmHQoEEYPHgwoqKiMGPGDISFhWHWrFk2XzdkyBD07t0bzZs3z/Y9UlJSkJCQYPblajnuGbl4Ebh+HfDwyGGBEiIiosLNoWAkNTUVu3btQseOHc32d+zYETExMVZfN3/+fJw4cQITJ060632mTJmC4OBgw1eYi1e3TU8HTpyQbYeDEW2Ipnp1wM/Pqe0iIiIqDBwKRq5evYqMjAyEhISY7Q8JCcHFixctvubYsWMYN24cFi9eDC87i32NHz8e8fHxhq+zZ8860kynO3VKqrn7+8tsGocwX4SIiMimHJUC1WWanqqUyrIPADIyMtC7d2+8+eabqFGjht3n9/X1ha+vb06alie0IZoaNWS0xSEMRoiIiGxyKBgpU6YMPD09s/SCXL58OUtvCQDcunUL//zzD3bv3o0RI0YAAPR6PZRS8PLywh9//IH7778/F83PH5xJQ0RElHcc+pzv4+OD6OhorF271mz/2rVr0aJFiyzHBwUFYd++fdizZ4/ha+jQoahZsyb27NmDpk2b5q71+STHwYheDxw4INsMRoiIiCxyeJhm1KhR6NevHxo3bozmzZtjzpw5iI2NxdChQwFIvsf58+excOFCeHh4oG6mm3C5cuXg5+eXZb87y3EwcuYMkJgI+PoCkZFObxcREVFh4HAw0rNnT1y7dg2TJ09GXFwc6tati1WrViE8PBwAEBcXl23NkYImx8GIVl8kKoor9RIREVmhU0opVzciOwkJCQgODkZ8fDyCgoLy9b1v3gRKlpTt+HjAobd/911gwgSgb19g0aK8aB4REZHbsvf+zbVpsqH1ioSGOhiIAExeJSIisgODkWxwJg0REVHeYjCSjRwHI2lpxgVtGIwQERFZxWAkGzkORo4dk4AkMBCoXNnp7SIiIiosGIxk4+hReczxmjR16wIWqtMSERGRYDBig1LGBfIcLhOiTevlEA0REZFNDEZsuHgRuHNH1qOpUsXBFzN5lYiIyC4MRmzQekXCwgAfHwdfrAUj9eo5tU1ERESFDYMRG7RgpFo1B194547xxewZISIisonBiA05DkYOHZKEk7JlgXLlnN4uIiKiwoTBiA05DkaYL0JERGQ3BiM2MBghIiLKewxGbMjxtF4GI0RERHZjMGJFQgJw9apsO9wzwhojREREdmMwYoXWK1K2LFC8uAMvvHEDOH9etuvUcXq7iIiIChsGI1bkOF/kwAF5DAsDgoOd2iYiIqLCiMGIFUxeJSIiyh8MRqzIdTDCyqtERER2YTBiBXtGiIiI8geDEStyFIwoxWCEiIjIQQxGLEhNBc6elW2HgpFLl4Br12SZ31q18qRtREREhQ2DEQtOnwb0eqBYMSAkxIEXavVFIiMBf/+8aBoREVGhw2DEguPH5bFqVUCnc+CFHKIhIiJyGIMRC5i8SkRElH8YjFjAYISIiCj/MBixIEfBiF5vrL7KYISIiMhuDEYsyFEwcuYMkJgI+PgA1avnSbuIiIgKIwYjmej1wMmTsu1QMKIN0URFAV5eTm8XERFRYcVgJJMLF4CUFMDTE6hc2YEXMl+EiIgoRxiMZKIN0YSHA97eDrxQqzHCYISIiMghDEYy4UwaIiKi/MVgJJMcBSNpacDhw7LNYISIiMghDEYy0YKRyEgHXnTsmAQkgYEOJpoQERERg5FMctQzog3R1Kkji+QRERGR3XjnzCRXwQiHaIiIiBzGYMTEjRvyBcgieXZjMEJERJRjDEZMaL0i5csDxYo58EJtWm+9ek5vExERUWHHYMREjoZo/voLOH5cqqQ1aJAn7SIiIirMGIyYyFEw8tpr8jhwIFCmjLObREREVOgxGDFx/Lg82h2MrF8vX97ewOuv51m7iIiICjMGIyYc6hlRytgr8txzUj+eiIiIHMZgxIRDwcjq1ZIv4ucHTJiQp+0iIiIqzBiM3JWUBJw/L9vZBiOmvSIjRgChoXnaNiIiosKMwchdp07JY/HiduShrlgB7N4t5d9feSXP20ZERFSYMRi5y3SIRqezcWBGhjFZ9aWXOIOGiIgolxiM3GV3vsiSJcDBg0CJEsCoUXndLCIiokIvR8HIzJkzERERAT8/P0RHR2PLli1Wj926dStatmyJ0qVLw9/fH7Vq1cL06dNz3OC8YlcwkpYGTJok2y+/LAEJERER5YqXoy9YunQpRo4ciZkzZ6Jly5b44osv0KlTJxw8eBCVK1fOcnyxYsUwYsQI1K9fH8WKFcPWrVsxZMgQFCtWDM8995xTfghnsCsY+fprKUZStizwv//lS7uIiIgKO51SSjnygqZNm6JRo0aYNWuWYV9UVBQeeeQRTJkyxa5zPPbYYyhWrBgWLVpk8fmUlBSkpKQYvk9ISEBYWBji4+MRFBTkSHPtVrMmcPQo8OefQPv2FhsFVK8OnD0LTJsm+SJERERkVUJCAoKDg7O9fzs0TJOamopdu3ahY8eOZvs7duyImJgYu86xe/duxMTEoE2bNlaPmTJlCoKDgw1fYWFhjjTTYRkZxtk0VntGvvxSApEKFYChQ/O0PUREREWJQ8HI1atXkZGRgZCQELP9ISEhuHjxos3XVqpUCb6+vmjcuDGGDx+OwYMHWz12/PjxiI+PN3ydPXvWkWY67Nw5SQfx9gYsxj137gDvvCPbr78O+PvnaXuIiIiKEodzRgBAl2nuq1Iqy77MtmzZgtu3b+Pvv//GuHHjEBkZiV69elk81tfXF76+vjlpWo5o+SIREbL4bhaffw5cvAhUqQI880y+tYuIiKgocCgYKVOmDDw9PbP0gly+fDlLb0lmERERAIB69erh0qVLmDRpktVgJL/ZTF5NSADef1+2J04EfHzyrV1ERERFgUPDND4+PoiOjsbatWvN9q9duxYtWrSw+zxKKbMEVVezGYx8/DFw7ZpkuPbtm6/tIiIiKgocHqYZNWoU+vXrh8aNG6N58+aYM2cOYmNjMfRuUuf48eNx/vx5LFy4EADw+eefo3LlyqhVqxYAqTvy0Ucf4YUXXnDij5E7NoORJUvk8bXXAK8cjWoRERGRDQ7fXXv27Ilr165h8uTJiIuLQ926dbFq1SqEh4cDAOLi4hAbG2s4Xq/XY/z48Th16hS8vLxQrVo1vPfeexgyZIjzfopcOn5cHrMEI3q9MVJxoOeHiIiI7OdwnRFXsHeeck4oBQQHA7duAQcOALVrmzx57pxMr/HykmV92TNCRERktzypM1IYXb0qgQggs2nMnDwpj+HhDESIiIjySJEPRrRRmIoVLZQPsXv1PCIiIsopBiO24g3tyapV8609RERERQ2DEXuCEfaMEBER5RkGIwxGiIiIXIrBiK14Q0tgZTBCRESUZxiMWAtG4uOl8ipgYZoNEREROUuRDkYSE2X9O8BCMKJFKeXKAcWL52u7iIiIipIiHYxoozAlSgClSmV6kvkiRERE+aJIByPMFyEiInI9BiNgjREiIiJXYjACTuslIiJypSIdjJw+LY8MRoiIiFynSK/+9ssvsjBvloUEU1OBs2dlm8EIERFRnirSwYinpyzIm8WZM4BeDwQEACEh+d4uIiKioqRID9NYZZq8qtO5ti1ERESFHIMRS5gvQkRElG8YjFjCYISIiCjfMBixhAXPiIiI8g2DEUtY8IyIiCjfMBjJTCn2jBAREeUjBiOZXbwI3LkDeHhYmfdLREREzsRgJDNtiKZyZcDHx7VtISIiKgIYjGSmDdEwX4SIiChfMBjJjNN6iYiI8hWDkcwYjBAREeUrBiOZMRghIiLKVwxGMmPOCBERUb5iMGLq1i3g8mXZZs8IERFRvmAwYkrrFSldGggOdm1biIiIiggGI6aYL0JERJTvGIyY4po0RERE+Y7BiCmuSUNERJTvGIyY4jANERFRvmMwYorBCBERUb5jMKJJSwPOnJFtBiNERET5hsGI5uxZICMD8PUFQkNd3RoiIqIig8GIxnQmjQcvCxERUX7hXVfDfBEiIiKXYDCiYTBCRETkEgxGNCx4RkRE5BIMRjQseEZEROQSDEYAQCkO0xAREbkIgxEAuHIFuH0b0OmAKlVc3RoiIqIihcEIYOwVqVgR8PNzbVuIiIiKGAYjAPNFiIiIXChHwcjMmTMREREBPz8/REdHY8uWLVaPXb58OTp06ICyZcsiKCgIzZs3x5o1a3Lc4DzBfBEiIiKXcTgYWbp0KUaOHIkJEyZg9+7daNWqFTp16oTY2FiLx2/evBkdOnTAqlWrsGvXLrRr1w7dunXD7t27c914p2EwQkRE5DI6pZRy5AVNmzZFo0aNMGvWLMO+qKgoPPLII5gyZYpd56hTpw569uyJN954w+LzKSkpSElJMXyfkJCAsLAwxMfHIygoyJHm2ue++4Bt24DvvgOeesr55yciIiqCEhISEBwcnO3926GekdTUVOzatQsdO3Y029+xY0fExMTYdQ69Xo9bt26hVKlSVo+ZMmUKgoODDV9hYWGONNNx7BkhIiJyGYeCkatXryIjIwMhISFm+0NCQnDx4kW7zjF16lQkJibiySeftHrM+PHjER8fb/g6e/asI810zJ07gNZ2BiNERET5zisnL9LpdGbfK6Wy7LPku+++w6RJk/DTTz+hXLlyVo/z9fWFr69vTprmOG0mTYkSgI3eGiIiIsobDgUjZcqUgaenZ5ZekMuXL2fpLcls6dKlGDRoEH744Qc88MADjrc0r3CIhoiIyKUcGqbx8fFBdHQ01q5da7Z/7dq1aNGihdXXfffddxg4cCC+/fZbdOnSJWctzStcII+IiMilHB6mGTVqFPr164fGjRujefPmmDNnDmJjYzF06FAAku9x/vx5LFy4EIAEIv3798fHH3+MZs2aGXpV/P39ERwc7MQfJYdY8IyIiMilHA5GevbsiWvXrmHy5MmIi4tD3bp1sWrVKoSHhwMA4uLizGqOfPHFF0hPT8fw4cMxfPhww/4BAwZgwYIFuf8JcovDNERERC7lcJ0RV7B3nnKO1KgBHDsGrF8PtGvn3HMTEREVYXlSZ6TQycgATp+WbeaMEBERuUTRDkbOnQPS0gBvb6BSJVe3hoiIqEgq2sGIli8SEQF4erq2LUREREUUgxGAyatEREQuxGAEYL4IERGRCzEYAdgzQkRE5EI5Wpum0HjqKaBKFaBlS1e3hIiIqMgq2sHI44/LFxEREblM0R6mISIiIpdjMEJEREQuxWCEiIiIXIrBCBEREbkUgxEiIiJyKQYjRERE5FIMRoiIiMilGIwQERGRSzEYISIiIpdiMEJEREQuxWCEiIiIXIrBCBEREbkUgxEiIiJyqQKxaq9SCgCQkJDg4pYQERGRvbT7tnYft6ZABCO3bt0CAISFhbm4JUREROSoW7duITg42OrzOpVduOIG9Ho9Lly4gOLFi0On09n9uoSEBISFheHs2bMICgrKwxYWTby+eYvXN+/xGuctXt+85+7XWCmFW7duoUKFCvDwsJ4ZUiB6Rjw8PFCpUqUcvz4oKMgtf0mFBa9v3uL1zXu8xnmL1zfvufM1ttUjomECKxEREbkUgxEiIiJyqUIdjPj6+mLixInw9fV1dVMKJV7fvMXrm/d4jfMWr2/eKyzXuEAksBIREVHhVah7RoiIiMj9MRghIiIil2IwQkRERC7FYISIiIhcisEIERERuVShDUZmzpyJiIgI+Pn5ITo6Glu2bHF1kwqszZs3o1u3bqhQoQJ0Oh1Wrlxp9rxSCpMmTUKFChXg7++Ptm3b4sCBA65pbAE0ZcoU3HvvvShevDjKlSuHRx55BEeOHDE7htc452bNmoX69esbKlQ2b94cq1evNjzPa+tcU6ZMgU6nw8iRIw37eI1zZ9KkSdDpdGZf5cuXNzxfGK5voQxGli5dipEjR2LChAnYvXs3WrVqhU6dOiE2NtbVTSuQEhMT0aBBA3z22WcWn//ggw8wbdo0fPbZZ9i5cyfKly+PDh06GBY4JNs2bdqE4cOH4++//8batWuRnp6Ojh07IjEx0XAMr3HOVapUCe+99x7++ecf/PPPP7j//vvRvXt3w3/WvLbOs3PnTsyZMwf169c3289rnHt16tRBXFyc4Wvfvn2G5wrF9VWFUJMmTdTQoUPN9tWqVUuNGzfORS0qPACoFStWGL7X6/WqfPny6r333jPsS05OVsHBwWr27NkuaGHBd/nyZQVAbdq0SSnFa5wXSpYsqb766iteWye6deuWql69ulq7dq1q06aNevHFF5VS/Pt1hokTJ6oGDRpYfK6wXN9C1zOSmpqKXbt2oWPHjmb7O3bsiJiYGBe1qvA6deoULl68aHa9fX190aZNG17vHIqPjwcAlCpVCgCvsTNlZGRgyZIlSExMRPPmzXltnWj48OHo0qULHnjgAbP9vMbOcezYMVSoUAERERF46qmncPLkSQCF5/oWiFV7HXH16lVkZGQgJCTEbH9ISAguXrzoolYVXto1tXS9z5w544omFWhKKYwaNQr33Xcf6tatC4DX2Bn27duH5s2bIzk5GYGBgVixYgVq165t+M+a1zZ3lixZgn///Rc7d+7M8hz/fnOvadOmWLhwIWrUqIFLly7h7bffRosWLXDgwIFCc30LXTCi0el0Zt8rpbLsI+fh9XaOESNG4L///sPWrVuzPMdrnHM1a9bEnj17cPPmTSxbtgwDBgzApk2bDM/z2ubc2bNn8eKLL+KPP/6An5+f1eN4jXOuU6dOhu169eqhefPmqFatGr7++ms0a9YMQMG/voVumKZMmTLw9PTM0gty+fLlLJEj5Z6W0c3rnXsvvPACfv75Z2zYsAGVKlUy7Oc1zj0fHx9ERkaicePGmDJlCho0aICPP/6Y19YJdu3ahcuXLyM6OhpeXl7w8vLCpk2b8Mknn8DLy8twHXmNnadYsWKoV68ejh07Vmj+hgtdMOLj44Po6GisXbvWbP/atWvRokULF7Wq8IqIiED58uXNrndqaio2bdrE620npRRGjBiB5cuXY/369YiIiDB7ntfY+ZRSSElJ4bV1gvbt22Pfvn3Ys2eP4atx48bo06cP9uzZg6pVq/IaO1lKSgoOHTqE0NDQwvM37LLU2Ty0ZMkS5e3trebOnasOHjyoRo4cqYoVK6ZOnz7t6qYVSLdu3VK7d+9Wu3fvVgDUtGnT1O7du9WZM2eUUkq99957Kjg4WC1fvlzt27dP9erVS4WGhqqEhAQXt7xgeP7551VwcLDauHGjiouLM3zduXPHcAyvcc6NHz9ebd68WZ06dUr9999/6tVXX1UeHh7qjz/+UErx2uYF09k0SvEa59bo0aPVxo0b1cmTJ9Xff/+tunbtqooXL264pxWG61sogxGllPr8889VeHi48vHxUY0aNTJMkyTHbdiwQQHI8jVgwACllEwtmzhxoipfvrzy9fVVrVu3Vvv27XNtowsQS9cWgJo/f77hGF7jnHvmmWcM/xeULVtWtW/f3hCIKMVrmxcyByO8xrnTs2dPFRoaqry9vVWFChXUY489pg4cOGB4vjBcX51SSrmmT4aIiIioEOaMEBERUcHCYISIiIhcisEIERERuRSDESIiInIpBiNERETkUgxGiIiIyKUYjBAREZFLMRghIiIil2IwQkRERC7FYISIiIhcisEIERERudT/AdrPWMTQ4pWpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot learning history\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "epochs = range(1, len(acc) + 1)\n",
    "\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.plot(epochs, acc, 'red', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'blue', label='Validation acc')\n",
    "plt.legend()\n",
    "\n",
    "plt.figure()\n",
    "plt.title('Training and validation loss')\n",
    "plt.plot(epochs, loss, 'red', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'blue', label='Validation loss')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fde26a15",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, target_names,title='Confusion matrix',cmap=None,normalize=False):\n",
    "    accuracy = np.trace(cm) / float(np.sum(cm))\n",
    "    misclass = 1 - accuracy\n",
    "    if cmap is None:\n",
    "        cmap = plt.get_cmap('Blues')\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "\n",
    "    if target_names is not None:\n",
    "        tick_marks = np.arange(len(target_names))\n",
    "        plt.xticks(tick_marks, target_names, rotation=45)\n",
    "        plt.yticks(tick_marks, target_names)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float32') / cm.sum(axis=1)\n",
    "        cm = np.round(cm,2)\n",
    "        \n",
    "\n",
    "    thresh = cm.max() / 1.5 if normalize else cm.max() / 2\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        if normalize:\n",
    "            plt.text(j, i, \"{:0.2f}\".format(cm[i, j]),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "        else:\n",
    "            plt.text(j, i, \"{:,}\".format(cm[i, j]),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel(\"Predicted label\\naccuracy={:0.4f}\\n misclass={:0.4f}\".format(accuracy, misclass))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "33b52a16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 5s 1s/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_trained = load_model(filepath)\n",
    "# Predict the values from the validation dataset\n",
    "Y_pred = model_trained.predict(validation_generator, validation_num // batch_size+1)\n",
    "# Convert predictions classes to one hot vectors \n",
    "Y_pred_classes = np.argmax(Y_pred, axis = 1)\n",
    "# Convert validation observations to one hot vectors\n",
    "#Y_true = np.argmax(Y_val,axis = 1) \n",
    "# compute the confusion matrix\n",
    "confusion_mtx = confusion_matrix(y_true = validation_generator.classes,y_pred = Y_pred_classes)\n",
    "# plot the confusion matrix\n",
    "plot_confusion_matrix(confusion_mtx, normalize=True, target_names=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "65095858",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 precision    recall  f1-score   support\n",
      "\n",
      " mantled_howler                       0.68      0.73      0.70        26\n",
      " patas_monkey                         0.59      0.68      0.63        28\n",
      " bald_uakari                          0.93      0.93      0.93        27\n",
      " japanese_macaque                     0.91      0.70      0.79        30\n",
      " pygmy_marmoset                       0.72      0.69      0.71        26\n",
      " white_headed_capuchin                0.78      0.64      0.71        28\n",
      " silvery_marmoset                     0.78      0.81      0.79        26\n",
      " common_squirrel_monkey               0.73      0.68      0.70        28\n",
      " black_headed_night_monkey            0.76      0.96      0.85        27\n",
      " nilgiri_langur                       0.67      0.69      0.68        26\n",
      "\n",
      "                       accuracy                           0.75       272\n",
      "                      macro avg       0.76      0.75      0.75       272\n",
      "                   weighted avg       0.76      0.75      0.75       272\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(validation_generator.classes, Y_pred_classes,target_names=labels))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}