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    "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
    "\n",
    "# <!-- TITLE --> [GTSRB4] - CNN with GTSRB dataset - Data augmentation \n",
    "<!-- DESC --> Episode 4 : Improving the results with data augmentation\n",
    "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
    "\n",
    "## Objectives :\n",
    "  - Trying to improve training by **enhancing the data**\n",
    "  - Using Keras' **data augmentation utilities**, finding their limits...\n",
    "  \n",
    "The German Traffic Sign Recognition Benchmark (GTSRB) is a dataset with more than 50,000 photos of road signs from about 40 classes.  \n",
    "The final aim is to recognise them !  \n",
    "\n",
    "Description is available there : http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset\n",
    "\n",
    "\n",
    "## What we're going to do :\n",
    " - Increase and improve the training dataset\n",
    " - Identify the limits of these tools\n",
    "\n",
    "## Step 1 - Import and init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/markdown": [
       "**FIDLE 2020 - Practical Work Module**"
      ],
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     "text": [
      "Version              : 0.6.1 DEV\n",
      "Notebook id          : GTS4\n",
      "Run time             : Thursday 17 December 2020, 18:21:16\n",
      "TensorFlow version   : 2.1.0\n",
      "Keras version        : 2.2.4-tf\n",
      "Datasets dir         : /gpfswork/rech/mlh/uja62cb/datasets\n",
      "Running mode         : full\n",
      "Update keras cache   : False\n",
      "Save figs            : True\n",
      "Path figs            : ./run/figs\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.callbacks import TensorBoard\n",
    "\n",
    "import numpy as np\n",
    "import h5py\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import os, sys, time, random\n",
    "\n",
    "from importlib import reload\n",
    "\n",
    "sys.path.append('..')\n",
    "import fidle.pwk as pwk\n",
    "\n",
    "datasets_dir = pwk.init('GTSRB4')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2 - Dataset loader\n",
    "Dataset is one of the saved dataset: RGB25, RGB35, L25, L35, etc.  \n",
    "First of all, we're going to use a smart dataset : **set-24x24-L**  \n",
    "(with a GPU, it only takes 35'' compared to more than 5' with a CPU !)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_dataset(dataset_dir, name):\n",
    "    '''Reads h5 dataset from dataset_dir\n",
    "    Args:\n",
    "        dataset_dir : datasets dir\n",
    "        name        : dataset name, without .h5\n",
    "    Returns:    x_train,y_train,x_test,y_test data'''\n",
    "    # ---- Read dataset\n",
    "    filename=f'{dataset_dir}/GTSRB/enhanced/{name}.h5'\n",
    "    with  h5py.File(filename,'r') as f:\n",
    "        x_train = f['x_train'][:]\n",
    "        y_train = f['y_train'][:]\n",
    "        x_test  = f['x_test'][:]\n",
    "        y_test  = f['y_test'][:]\n",
    "\n",
    "    # ---- done\n",
    "    print('Dataset \"{}\" is loaded. ({:.1f} Mo)\\n'.format(name,os.path.getsize(filename)/(1024*1024)))\n",
    "    return x_train,y_train,x_test,y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3 - Models\n",
    "We will now build a model and train it...\n",
    "\n",
    "This is my model ;-) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A basic model\n",
    "#\n",
    "def get_model_v1(lx,ly,lz):\n",
    "    \n",
    "    model = keras.models.Sequential()\n",
    "    \n",
    "    model.add( keras.layers.Conv2D(96, (3,3), activation='relu', input_shape=(lx,ly,lz)))\n",
    "    model.add( keras.layers.MaxPooling2D((2, 2)))\n",
    "    model.add( keras.layers.Dropout(0.2))\n",
    "\n",
    "    model.add( keras.layers.Conv2D(192, (3, 3), activation='relu'))\n",
    "    model.add( keras.layers.MaxPooling2D((2, 2)))\n",
    "    model.add( keras.layers.Dropout(0.2))\n",
    "\n",
    "    model.add( keras.layers.Flatten()) \n",
    "    model.add( keras.layers.Dense(1500, activation='relu'))\n",
    "    model.add( keras.layers.Dropout(0.5))\n",
    "\n",
    "    model.add( keras.layers.Dense(43, activation='softmax'))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4 - Callbacks  \n",
    "We prepare 2 kind callbacks :  TensorBoard and Model backup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pwk.mkdir('./run/models')\n",
    "pwk.mkdir('./run/logs')\n",
    "\n",
    "# ---- Callback tensorboard\n",
    "log_dir = \"./run/logs/tb_\" + pwk.tag_now()\n",
    "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
    "\n",
    "# ---- Callback ModelCheckpoint - Save best model\n",
    "save_dir = \"./run/models/best-model.h5\"\n",
    "bestmodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, monitor='accuracy', save_best_only=True)\n",
    "\n",
    "# ---- Callback ModelCheckpoint - Save model each epochs\n",
    "save_dir = \"./run/models/model-{epoch:04d}.h5\"\n",
    "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5 - Load and prepare dataset\n",
    "### 5.1 - Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset \"set-24x24-L\" is loaded. (228.8 Mo)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "x_train,y_train,x_test,y_test = read_dataset(datasets_dir,'set-24x24-L')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 - Data augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "datagen = keras.preprocessing.image.ImageDataGenerator(featurewise_center=False,\n",
    "                             featurewise_std_normalization=False,\n",
    "                             width_shift_range=0.1,\n",
    "                             height_shift_range=0.1,\n",
    "                             zoom_range=0.2,\n",
    "                             shear_range=0.1,\n",
    "                             rotation_range=10.)\n",
    "datagen.fit(x_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6 - Train the model\n",
    "**Get the shape of my data :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Images of the dataset have this folowing shape :  (24, 24, 1)\n"
     ]
    }
   ],
   "source": [
    "(n,lx,ly,lz) = x_train.shape\n",
    "print(\"Images of the dataset have this folowing shape : \",(lx,ly,lz))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Get and compile a model, with the data shape :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = get_model_v1(lx,ly,lz)\n",
    "\n",
    "# model.summary()\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Train it :**  \n",
    "Note : La courbe d'apprentissage est visible en temps réel avec Tensorboard (See logs in ./run/logs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:sample_weight modes were coerced from\n",
      "  ...\n",
      "    to  \n",
      "  ['...']\n",
      "Train for 612 steps, validate on 12630 samples\n",
      "Epoch 1/10\n",
      "  1/612 [..............................] - ETA: 32:06 - loss: 3.7490 - accuracy: 0.0000e+00WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.197418). Check your callbacks.\n",
      "612/612 [==============================] - 11s 19ms/step - loss: 2.1350 - accuracy: 0.4047 - val_loss: 0.8193 - val_accuracy: 0.7628\n",
      "Epoch 2/10\n",
      "612/612 [==============================] - 8s 14ms/step - loss: 0.9029 - accuracy: 0.7225 - val_loss: 0.4106 - val_accuracy: 0.8832\n",
      "Epoch 3/10\n",
      "612/612 [==============================] - 8s 13ms/step - loss: 0.6049 - accuracy: 0.8151 - val_loss: 0.3155 - val_accuracy: 0.9124\n",
      "Epoch 4/10\n",
      "612/612 [==============================] - 8s 14ms/step - loss: 0.4579 - accuracy: 0.8573 - val_loss: 0.2479 - val_accuracy: 0.9331\n",
      "Epoch 5/10\n",
      "612/612 [==============================] - 8s 14ms/step - loss: 0.3652 - accuracy: 0.8873 - val_loss: 0.2529 - val_accuracy: 0.9295\n",
      "Epoch 6/10\n",
      "612/612 [==============================] - 8s 14ms/step - loss: 0.3122 - accuracy: 0.9031 - val_loss: 0.2002 - val_accuracy: 0.9483\n",
      "Epoch 7/10\n",
      "612/612 [==============================] - 8s 14ms/step - loss: 0.2673 - accuracy: 0.9142 - val_loss: 0.1842 - val_accuracy: 0.9471\n",
      "Epoch 8/10\n",
      "612/612 [==============================] - 8s 14ms/step - loss: 0.2355 - accuracy: 0.9259 - val_loss: 0.1583 - val_accuracy: 0.9554\n",
      "Epoch 9/10\n",
      "612/612 [==============================] - 8s 14ms/step - loss: 0.2159 - accuracy: 0.9312 - val_loss: 0.1779 - val_accuracy: 0.9531\n",
      "Epoch 10/10\n",
      "612/612 [==============================] - 8s 14ms/step - loss: 0.1935 - accuracy: 0.9385 - val_loss: 0.1789 - val_accuracy: 0.9544\n",
      "CPU times: user 1min 42s, sys: 4.84 s, total: 1min 47s\n",
      "Wall time: 1min 26s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "batch_size = 64\n",
    "epochs     = 10\n",
    "\n",
    "# ---- Shuffle train data\n",
    "#x_train,y_train=pwk.shuffle_np_dataset(x_train,y_train)\n",
    "\n",
    "# ---- Train\n",
    "#\n",
    "history = model.fit(  datagen.flow(x_train, y_train, batch_size=batch_size),\n",
    "                      steps_per_epoch = int(x_train.shape[0]/batch_size),\n",
    "                      epochs=epochs,\n",
    "                      verbose=1,\n",
    "                      validation_data=(x_test, y_test),\n",
    "                      callbacks=[tensorboard_callback, bestmodel_callback, savemodel_callback] )\n",
    "\n",
    "model.save('./run/models/last-model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Evaluate it :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max validation accuracy is : 0.9554\n"
     ]
    }
   ],
   "source": [
    "max_val_accuracy = max(history.history[\"val_accuracy\"])\n",
    "print(\"Max validation accuracy is : {:.4f}\".format(max_val_accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss      : 0.1789\n",
      "Test accuracy  : 0.9544\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "\n",
    "print('Test loss      : {:5.4f}'.format(score[0]))\n",
    "print('Test accuracy  : {:5.4f}'.format(score[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7 - History\n",
    "The return of model.fit() returns us the learning history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"comment\">Saved: ./run/figs/GTS4-01-history_0</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zdljHRe72lZIDEekee8padhpcv6pzPe8HDnVuDYwMtAoUFulbvMQNv7XsqWmgtLKOsuo6yirrneemi33w4l9HaVU9dQ2R1S0oreqZIlNKDkR6mrW97x6y3wdbNjhDCYN9BXZu7fi4lFQoOijQV2Cs89w/p/vjFYlAfaOPsqp6yqvrmy76pcGLfmUdZYH15dX1lFXV4++mEgEGqKrtmVl0lRz0IllZWW0WORKXBMfgf7MUVi2FVcuc5eQUp7k8JS3wnBqyHHgduj24T+jyXtsDx3ZHTfnWKvc4SUCwr8C6bzpXeS9/UKCfQKC/wLCRoNLb0sOstVTXNbZoui+tqqc8pGm/tKqO8sBzVV33XZCTExPIyUwhNzOVnMwUcjJTyclMJTfkdXB7/4xkEnuo1oL+V0rUNTY29t25FqyFXdsCycAy57lk1977NdQ7DyqiH0NCQnOisFfyEC4ZabUuXDLSUN/yFsH2TR3HkZQMww9s7jQ4aqwzFE9kH1lrqW/0U1nbQFVtA5V1jc5zbQOVtc2vq+oam/cJrq9roKq2kQZf9w1ZzUpLIicjlZysVHIyUsgNPGeHXPSDyUBGalLY+Wvc1kf/gnfB5Sd237kfezPs6l/+8pcMHz4cj8cDwB133IExhoULF1JaWkpDQwN33XUXp59+eodvUVlZyemnnx72uKeffpp7770XYwwTJ07kmWeeYceOHfz4xz9m7dq1APzxj3+koKCAU045heXLlwNw7733UllZyR133MGMGTOYPn06H3zwAaeddhoHHXQQd911F/X19eTn5/Pss88yePBgKisrueaaa/j0008xxnD77bdTVlbG8uXLuf/++wF49NFHWbFiBffdd98+/2i7nbVOmd5gIrBqWeeG3nUnv9+ps18b4cQ5+yp3gNNZMNgqsP8oFaCRsGL94t5agjEh3+qdb/HZId/2c0O+4WdnpJCSFP99ZJQcxLBZs2Zx3XXXNSUHL730Em+++SbXX389/fv3p7i4mMMPP5zTTjutw8wzLS2NefPm7XXc119/zd13380HH3zAgAEDKCkpAeDaa6/lmGOOYd68efh8PiorKyktbb8KXVlZGQsWLACciZ8+/vhjjDE89thj3HPPPcyZM4c777yT7Oxsli1b1rRfSkoKEydO5J577iE5OZknn3ySP//5z/v64+se1sL2zc23Cb5Z5pS4bU9qOhw4HkZPgIMmwPADnLHt9bVOPfy6WqejXl1tYF3o61b7tDgmzPF1td1XUz5UYqIzO9yosc21BfIGdv/7Skypb/Sxo6yGPU0X8Ni8uIeTmpwYtum+dRKQk5lKv/RkEmLw2313UnIQw6ZMmcLOnTvZunUru3btIjc3l6FDh3L99dezcOFCEhIS2LJlCzt27GDIkCHtnstay0033bTXce+88w7nnHMOAwYMACAvLw+Ad955h6effhqAxMREsrOzO0wOzj///KbXmzdv5vzzz2fbtm3U19czYsQIAObPn88LL7zQtF9urjMk57jjjuOf//wnY8eOpaGhgQkTJkT40+om1sLWDSEtA8s7LtWbnuEkAwdNgNETnYto6972SclOc334STL3TWPj3olEW8nEXslGq/XB/bCw36jmjoPDD3RuO0ivV1PfyNaSaraVVrG1tJqtJc3PxRW1uDU7T1KCISs9mazUZDLTkslKSwo8J5OZmuQ8B9Y3vU519slMSyYtOf6/3XcnJQed1UbTf3c755xzeOWVV9i+fTuzZs3i2WefZdeuXXz22WckJydTVFREbW3Hs7G1dZy1ttP3u5KSkvCHTGbT+n0zM5sr0l1zzTXccMMNnHbaabz33nvccccdAG2+3+WXX85vfvMbxowZw+zZszsVT7fw+51kIHiLYNUy2FPe/jEZWXDgOCcROGgi7D/SmaTFLUlJzqOrFQKlz6msbXAu+iXVbA1JAraVVlNS2T1D5/bl4p6VlkxKUkJM3qvvLZQcxLhZs2ZxxRVXUFxczIIFC3jppZcYNGgQycnJvPvuu2zYsKFT5ykvLw973MyZMznzzDO5/vrryc/Pp6SkhLy8PGbOnMkf//hHrrvuOnw+H1VVVQwePJidO3eye/dusrKy+Oc//8mJJ4bvi1FeXk5hYSEAf/nLX5rWn3DCCTz88MM88MADgHNbITc3l8MOO4xNmzbx+eefs3Tp0n35kUXG74ct651k4JulsHo5VHbQSTAjK5AITHBuFQwb4W4yINIBay3l1fXN3/xbtQRU1HSiKmUYCQYG9k8nJzNVF/deRslBjBs3bhx79uyhsLCQoUOHctFFF3HqqadyyCGHMHnyZMaMGdOp87R13Lhx47j55ps55phjSExMZMqUKTz11FP87//+L1deeSWPP/44iYmJ/PGPf+SII47gtttu47DDDmPEiBHtvvcdd9zBueeeS2FhIYcffjjr1q0D4JZbbuGqq65i/PjxJCYmcvvtt3PWWWcBcN5557FkyZKmWw3dwu+DTWudvgKrArcJqjsY/pnV30kEgrcJCot649StEuestZRU1jU1+28JfPMPLld3cTheYoJhaE4GBXkZDM3NpCAvg4LA8+CcDJIT9X+hNzK2m4o1xBOPx2MBvF5vi/UrVqxg7NixrsTUF51yyilcf/31zJw5s819Iv6d+HywaU2gZWCZ0zJQU9X+Mf2ymxOB0ROjW7dfZB/4/Jbiipq97v1vK61ma2l1xNX2glKSEhia61z0h4Zc/AtzMxmYndZjY+ulx7XZdKOWA3FdWVkZ06ZNY9KkSe0mBp3i8zmT9wRbBlZ/1fGQvv65IcnABCcZUHOnuKTR52dHeU2Li3/weUdZTZd7+aenJDZd9FsnAfn90vpcb3xpn5KDXmbZsmVcfPHFLdalpqbyySefuBRRx3Jycli1alXXDm5shA2rnGTgm6VO+d6OKvVl5zUnAqMnwuBhSgakR1XWNrCttJrtpdVsK6t2Xgeed5TVdLn8blZacotm/9DnnMwU3eOXTlNy0MtMmDCBJUuWuB1G96qtgSUfwaIFsHJJx5P55A5oeZtgUIGSAelWjT4/O8tr2FYWSAACF//tZTVsK62msrZrHQABcjNTnVsAoRf/vEyG5mbQP11FpyQ6lBx0IJKhftKN/H5sTZUz098Ns9pPCPIGNbcKHDTBmeVPv0OJIhuYea/pW3+gBSD4vKu8Bv8+dOca0D+Ngty9WwCG5maSkao/29L99K+sHWlpaezevZv8/HwlCG6w1ukvULUHW7WH3VU1pG3dsHdiMGCwU18gmBAMaL8glEhnNPj87Cyr2avZP5gAdLX3P0BqUgJDcjMYmpPhPOdmMCTHeQzNzSBVBXrEZa4nB8aYBOCnwH8DRcAu4CXgNmttB93KwRgzGPg1cDIwGNgOzANut9aW7Utsw4YNY/PmzezaFWbiHOk+DQ3NFfpsoPOVtaTt3sGwt192lguLYNoMOPRo5zaBSISCY/+DF/0WCUBZDbvKa/ap+t+AfmktE4Cc9KZEIDczVV84JKa5nhwA9wPX4lzQ5wBjA8tTjDHHW2vb7JprjBkEfAIUAH8GlgPjgZ8ARxtjvmut7fLsM8nJyU1lf6UbWQub18Gi92DxAmdK43AGDIFjT4XDZjjJgUgH6ht9bC+rYXvwwt+qD0BNfdeG/gGkJSc2feMfmpvRIhEYkpPeKybfkb7L1eTAGDMOuAaYa609O2T9OuBBYBbwXDunuAkYDlxorX0+5PgPA8fdANzVDaFLNOzc6iQEi96DrRvD75OdC4ce47QSjBitvgMSlt9atpZU8e32CtZur2DNjgrW79xD8Z6OS4u3xQADs9MZkpO+V7P/0NwMsjPU+196L7dbDi7A+T/4QKv1jwK/BX5A+8nBsUAN8EKr9S8CTwCzUXIQW8p2O6MMFr0H69sYvpiRBVOPdBKC0RNUmlhaqG/0sX7nHicR2FHBt9vLWbdjD7VdKACUkZLU/K0/pBVgaE4GA7PT9O1f+iy3k4NDAT+wKHSltbbWGLMksL09qUCtbVXm0VrrN8bUACONMQOstcXRDFoiVFkBn/3HSQhWLXNuI7SWkgqTD3cSgnFTIVlDsgQqaupZu72iKRFYs72CjcWVna4DkGCcb//N9/2bE4GhORn0S0/Wt3+RMNxODgqAYmttuHFpW4DpxpgUa219G8d/BYw2xky21jYN7jfGTAaCBfr3B8ImB8aYK4Erf/KTn3T5A0gbamvgy4/hk3fhq8+cyoWtJSbB+KlOQjD5CGcKY+mTrLXsKK9pSgTW7HCSgZ3lHRS0CpGXlcrIwf0ZNaQ/owb3Z+Tg/gzNzSBJtf9FIuZ2cpABtDVgvTZkn7aSgweAM4CXjDHX4XRIHBdY3wAkB44Py1r7CPBIcG4F2UcN9bD8M6eF4MuPw9ciMAZGT4Jpx8DBR0JWvx4PU9zV6POzsbiSNYEkYM32ctbuqKCytnNDAw1QmJfJyEASMGqI88jLUnIpEi1uJwfVwKA2tqWF7BOWtfZ9Y8wsnM6LrwdW+4DHcFoVzgQ6mH9X9onfByu/dBKCzz9se4bDkWOcFoJDjoKc/J6MUFxUXdfo3A7YURFoFShnw67KTs8PkJyYwIhB/VokAiMG9VchIJFu5vb/sK3Ad4wxqWFuLRTi3HJoq9UAAGvty8aYucAEoB/wjbV2pzFmEdAIfNsdgfdp1sLaFfDJe/Dp+1BRGn6/YC2Cacc4VQql1wpOF+x0EKwItAqUs7Wk8yOJs9KSm1oBRg3uzwFDshmWn6nbAiIucDs5WAycAEwD3g+uNMakAZOBhZ05ibXWB4T2ORgCTAEW7EudAwkRSS2CaYGhh8NUI6I38vmdYYNrdlS0uDVQVtVuHt/C4Ox0Rg7uzwFD+je1CgzKTlfnQJEY4XZy8CJOrYLrCEkOgCtw+go8G1xhjBkFJFtrV7Z3wkDFxQeBRODuaAfc53S2FsEhRzsJwcgxqkXQy2wrreaLdcWs2V7Omh0VEQ0bTDCG4QOzWiQCIwf31wRBIjHO1eTAWrvMGPMH4OrArYE3aK6QuICWNQ7exil41HTlMcZk4QyDnAesA7JxaidMBW621r7bE5+j1ynbDYsXOgnBum/C75ORBQd/16lWOHqiahH0MmVVdSz8ehvvLN/Cis2dq0KenpLIyMAogVFDnNsCwwdmqVaASBxyu+UAnFaD9cCVOPMjFAMP4cyt0FGvpXpgKXAhMBSn8+Ji4ERr7b+7K+BeqWpPcy2Cb5aqFkEfVNvg4+NvdvD28i18tmYXvnamFWw9bPCAIdkMzcsgQa1GIr2C68lBoL/AnMCjvf2KwqyrxymxLPti01q47yZnOuTWVIugV/P5LUvWF/POsi18sHJ72LkGEhMMU0cOYPz+eU0JgYYNivRuricH4rKGenj0dy0TA9Ui6NWstXy7vYJ3lm3hva+2UlIZvtTI2GE5HDe+kKO/M5SczNQejlJE3KTkoK+b+xRs3eC8TkmFMy91pkFWLYJeZ3tpNe8s38I7y7awaXf42dCH5WVy3IRCjh1fQEFeZg9HKCKxQslBX7ZiCbw1t3n5vCthxsnuxSNRV1Fdz4Kvt/Hu8i18tSl8PYqczBRmjCvguAmFHDQ0W8MJRUTJQZ9VXQlPhnTzGH8oHHOSe/FI1NQ1+Ph41Q7eWb6VT7/dSWOYjoVpyYl8d8wQjptQyJQR+SQmqNCQiDRTctBXPfsHKNnlvM7qD5dep/oEcczntyzdsJu3l23hgxXbqa7fe56CBGOYOmoAx40vZProwaSl6L+/iISnvw590aIFzmyJQRdfqz4Gcchay9odFbwd6Fi4e0/4joWjC3KYOaGAY8YVqGOhiHSKkoO+prQY/vpQ8/L042Hqke7FIxHbUVbNu8u38vayLWwsDj/RVUFeBseNL+S48YUU5qtjoYhERslBX+L3w5P3Nc+cmD8IZv3E3ZikUypq6vnPiu28vWwLyzeWhN0nOyPYsbCA0QU56lgoIl2m5KAvee+f8PXnzmtj4Ec3Qoa+Vcaq+kYfn6zeyTvLtrBodfiOhalJCUwfM4Tjxhdy8MgBmsFQRKJCyUFfsW0TvPxY8/IJZztzIkhM8VunY+G7y7by/optVNWF61gIU0YOZOb4AqaPGUK6OhaKSJTpr0pf0NgIj6/e6UsAACAASURBVN3jVEMEKCyCM37oakjS0todTsXCd7/aSnFFbdh9DhqazXETCjlm3FCVLxaRbqXkoC/453OwYbXzOikZLv+FJk2KATvLa3h3+VbeXb6FdTv3hN1nSE46x01wOhbuNyCrhyMUkb5KyUFvt2YFvP5C8/KZl8B+I92Lp4+rqm1g4YptvLNsC8s2lBBu3sP+6ckcE6hYOLZQHQtFpOcpOejNamvg8d9DcObrgybA9850N6Y+qrK2gbkfr2PeonVUh+lHkJKUwBEHDea4CYUcMmqgOhaKiKuUHPRmLz8KO7c6r9Mz4LIbISHR3Zj6mKraBuZ9so65n6zbq3NhgoHJIwIVC8cMJjM12aUoRURaUnLQWy39BBa80bx8oQfyB7sXTx9TVdfA3z5Zz9xP1lJZ2zIpGJaXyUlT92fGuALy+6ljoYjEHiUHvdGeMnjqgeblqUfC4TPdi6cPqapr4O+L1vPqx+uorG1osW1YXiYXHX0gx4wrIDFB/QhEJHYpOehtrIWnH4SKwPS82XnO3Anq1Natqusa+fvi9bz68Vr21LRMCgryMrjoqAM5dnyBZj8Ukbig5KC3+fAt+OLD5uXZNzizLkq3qK5r5B+L1/NKmKRgaK6TFBw3QUmBiMQXJQe9ya7t8PyfmpePPQXGH+JePL1YTX0j/1i8gVc+WkNFmKTgwqMOYOaEQiUFIhKXlBz0Fn4fPHEv1FY7y4ML4ZzL3Y2pF6qtb+S1Tzfw8kdrKa+ub7FtSE46Fx51IDMnFGoooojENSUHvcW/58Lq5c7rhAS47BeQqp7w0VLb4OO1T9fz8od7JwWDc9K58MgDOH7iMCUFItIrKDnoDTathb/9pXn5lAth5Gj34ulFaht8vP7ZBl76cA1lVS2TgkHZ6Vxw5AF8b9IwkpUUiEgvouQg3jXUO5Mq+QJj6UeMhpNmuRtTL1DX4OP1zzfy8odrKKmsa7FtYP80LjjyAE6YvJ+SAhHplZQcxLu5T8GW9c7rlFS47OeQpF9rV9U1+Hjj8428FCYpGBBMCiYNIyVJlSZFpPfSVSSerVgCb81tXj73ChgyzL144lh9o49/fb6RFz4IkxT0S2PWkaP4r8n7KSkQkT5ByUG8qq6EJ+c0L48/BGac7F48caq+0cebX2zixQ/WULyntsW2/H6pnP/dA/j+FCUFItK3KDmIV895oWSX8zqzH1x6vaogRqC+0ce/l2zmhQ++pbiiZVKQl5XK+d8dxUkH76+kQET6JCUH8WjxQvj4neblH/4UcvLdiyeONPj8/HvJJl74z7fsCpMUnDfdSQpSk5UUiEjfpeQg3pQWwzMPNi8fcbwzsZK0q8Hn560vN/P8f75lZ3lNi225mamcN30kJ08drqRARAQlB/HFWnjqPqe/AUDeILjgJ+7GFOMafX7eWrqZ59//lh2tkoKczBTOPWIUpxwynDQlBSIiTZQcxJN3X4OvPndeGwOX3QgZme7GFKMafX7mL93Mc//5lh1lLZOC7IwUzp0+klOnDictRf8FRERa01/GeLFtE7zyePPyCWfB6InuxROjfH4/85du4fn/fMu20uoW27IzUjjniJGcdoiSAhGR9ugvZDxobITH74H6wPj7wiI44xJXQ4o1Pr+fd5Zt5dn3V++VFPRPT+acI0Zx2qHDSVdSICLSIf2ljAevPw/rVzuvk5Lh8l9Acoq7McWIYFLw3H9Ws7WkZVLQLz2Zcw4fyWmHFpGRqn/qIiKdpb+YsW7NCic5CDrjh7DfSPfiiSHvr9jGk+98w5aSqhbrs9KSOfvwEZw+rYjM1GSXohMRiV9KDmJZXS08/nvw+53lgyY4fQ2Efyxezx/e/KrFuqy0JM4+fKSSAhGRfaTkIJa99Cjs3Oq8TsuAH/0MEjTkbuHX2/CGJAaZqUmcdfhIzpxWRGaakgIRkX2l5CBWLV0EC15vXr7gJzBgiHvxxIgl64u5529LsIHl0QU53H3hNPqlKykQEYkWTUYfi/aUw1P3Ny8f/F2Yfrx78cSINdvL+fWLn9Hgc26zDMvP5M4LDlViICISZa4nB8aYBGPM9caYlcaYWmPMJmPMHGNMp6r7GGOyjDE3GWOWGWP2GGOKjTEfGmMuNSYOZyKyFp7+X6godZb758LF1/b5SZW2lVZzy/OLqa5vBJwZE39z4TSyMzRqQ0Qk2lxPDoD7gfuAr4FrgJeBa4HXjDHtxhfY/i/gTmAx8DPgLiAReBL4bfeF3U0+nA9ffNi8fOn10C/bvXhiQFlVHTc99wkllU6dh8zUJO6+YBqDczJcjkxEpHdytc+BMWYcTkIw11p7dsj6dcCDwCzguXZOcRhwJPCAtfb6kOO9wErgv4FfdkPo3aN4Ozz/x+blY06GidPciycGVNc1cuvzi5tqGCQnJvDr8w9hxOD+LkcmItJ7ud1ycAFggAdarX8UqAZ+0MHxwSvE1tCV1tp6oBio2uuIWOX3weP3Qm2gkM+gAjjvCndjclmDz8+dr3zGqm3lACQYuOmsKUwYrumpRUS6k9ujFQ4F/MCi0JXW2lpjzJLA9vYsAsqAXxhj1gOfAOnApcBU4MdRjrf7/N9cWL3ceZ2Q4FRBTE1zNyYX+a1lzj++5PO1xU3rrjlpAtPHaMSGiEh3c7vloAAottbWhdm2BRhgjGmzx5m1thQ4DSgBXgI24NxOuAo421r7aHtvboy50hjzaVeDj5pNa2HeX5qXT74ARo5xLx6XWWt59K0VvLu8uUHo4mMO4qSD93cxKhGRvsPt5CADCJcYANSG7NOeSmA5cC9wFnA58C3wnDHme+0daK19xFp7SOfD7QYN9fDYPeBzeuFTdJCTHPRhr3y0lrmfrGtaPnnq/lx01AEuRiQi0re4fVuhGhjUxra0kH3CMsZMAD4ErrfW/ilk/fM4CcOjxphR1lpflOKNvnl/gS3rndcpqXD5zyHJ7V+Le976cjOPvb2yafm7Y4Zw1YnjicdRqSIi8crtloOtOLcOUsNsK8S55VDfzvHX4yQRL4eutNZWA68Dw4Gi6ITaDVZ+CW/NbV4+93IYsp978bhs0eqd3Pfa0qblicPz+NWZk0lMUGIgItKT3E4OFgdiaDFezxiTBkwGOuoPUBh4DjfhQFKr59hSXQVP3OsUPQIYNxVmnOJuTC5auaWUu179HH/g5zFiUD9uP+8QUpI0l4SISE9zOzl4EbDAda3WX4HT1+DZ4ApjzChjTOteel8Hni8NXWmMyQFOB0qBNVGMN3qe90LJLud1Zj+YfUOfrYK4sbiSW59fTF2Dc/dncHY6d184jSxNoiQi4gpXv1Vba5cZY/4AXG2MmQu8AYzFqZC4gJYFkN7GuU0QegV9APgh8NtA/4MPgDyc5GIocJW1trHbP0ikPl0IH73dvHzxtZDTN8fuF1fUcvNzi6ioaQAgOyOF31w0jfx+fXcYp4iI22Khyf06YD1wJXAyTvGih4DbrLX+9g601m4wxkwDbgNm4lRUrAGWAD+z1s5t73hXlO2GZx5qXj5iJhxylHvxuKiytoGbn1vEzvIaAFKTE/mfWYcyLD/L5chERPo215ODwEiCOYFHe/sVtbF+DXBJ9CPrBtbCk/dB1R5nOW8QXOBxNyaX1DX4uP3FT1m/y/lZJCYYbj3nYMYU5rgcmYiIuN3noG9575/w1WfOa2PgRz+DjE5NPtmr+Px+fjvvC5ZvLGla97NTJ3LoAW2NahURkZ6k5KCnbN8ELz/WvPy9s2DMJPficYm1lof/9RUffrOjad0Vx49l5sRhLkYlIiKhlBz0hMZGeOz3UB8oBllYBGfGx52QaHtmwWre+Hxj0/I5R4zknCNGuhiRiIi0puSgJ7z+PKxf5bxOTHImVUpuc8qIXuu1Tzfw7Purm5ZnTijkspl9dw4JEZFYpeSgu61d6SQHQWdcAvv1vW/K76/Yxh/+tbxp+ZBRA7nh1Ikk9NHaDiIisUzJQXeqq4XHfw/+wIjMA8fDf53lbkwu+HL9bn43bwmBWpAcVJDNLeccTFKi/vmJiMQi/XXuTi8/Bju2OK/TMuCyGyGhb5UDXrO9gjte+pQGn5MgDcvL5M5Zh5Ke4vooWhERaYOSg+6ybLEzdDHogh/DgCHuxeOC7aXV3PL8IqrrnCKVeVmp3H3RNHIyw82zJSIisULJQXfYUw5P3de8PGU6TP+ee/G4oKyqjpueW0RJpTNCIyM1ibsvnMaQnAyXIxMRkY4oOYg2a+GZB6G81Fnunws/vLZPTapUU9/IrS8sZktJFQDJiQn8+vxDGDm4v8uRiYhIZyg5iLaP3obPP2hevvQ66Nd3SgI3+Pzc+crnrNpaDjizZP3qzMlMHN43J5YSEYlHSg6iye+DN15sXj7mJJh4mHvx9DC/tdz/2lI+W7Orad3VJ43nyLFDXYxKREQi1enkwOPxTOnOQHqFhET41RxnlsVBBXDuFW5H1KMem7+Ct5dtaVr+wdEHcsrU4S5GJCIiXRHJeLLPPB7PYuDPwAter7e6m2KKb1n94b9vgsoKSEt3O5oe8/JHa3j143VNyycdvD8/OPpAFyMSEZGuiuS2whvAwcCjwFaPx/OQx+OZ0D1hxTljoF+221H0mPlLN/PY/JVNy98dPZirvz8e04c6YYqI9CadTg68Xu8pQBFwJ1ABXAUs8Xg8H3g8nh96PJ607glRYtnib3dy32tLm5Yn7J/Hr86aQmKCEgMRkXhlrLUd79WKx+NJAE4G/hv4L5wkowx4GnjE6/WuiGaQ3c3j8VgAr9frdihxZeWWUn7xzCfUNfgAGDGoH/decgRZackuRyYiIp3Q5re4LiUHoTweTyFwOfBjYFBg9fvAw16v95V9OnkPUXIQuU3Fldzw1IdU1DQAMDg7nftnTye/nxqQRETiRJvJQTSGMo4DJgL5gTfaDRwFvOjxeD7zeDxFUXgPiSG799Ry83OLmhKD/unJ3H3hNCUGIiK9RJdmv/F4PIOAHwFX4PRDAHgb8AL/AIYDP8e57eAFTtrXQCU2VNY2cPNzi9hRXgNAanIid15wKPsNyHI5MhERiZaIkgOPxzMT54J/OpAMlAIPAH/0er3fhuy6ztndkwqcF6VYxWX1jT7uePFT1u3cA0BiguHWcw5mTGGuy5GJiEg0dTo58Hg8q4GROLcOPsVpEXjB6/XWtnPYaiBznyKUmODzW347bwnLNpY0rbv+lIkcesCgdo4SEZF4FEnLQSHwFOD1er2fdfKYZ4GPIg1KYou1lof/tZwPVm5vWnf5zDF8b9IwF6MSEZHuEklyUOD1essiObnX690EbIosJIk1zy5czRufb2xaPuvwEZxzxEgXIxIRke4USRGkiBID6R1e/2wDzyxc3bR83PgCrjh+rKofioj0YpH0OfgxzgiEo7xe79Yw2wuBhcBvvF7v49ELUdzywcrtPPyv5U3LU0cO4IbTJpGgxEBEpFeLpM7BhcC2cIkBgNfr3QJsBn4QjcDEXcs27Ob/zf0Cf6BG1kFDs7n13KkkJ2qWbxGR3i6Sv/SjgS872GcpMKbr4UgsWLejgttf/JQGnx+AwrxM7rzgUNJTulQWQ0RE4kwkyUE2zvwJ7akANOg9jm0vq+am5xZRVdcIQF5WKr+5cBo5makuRyYiIj0lkuRgG06Z5PZMBHZ1PRxxU3l1PTc/u4iSyjoAMlKTuOuCQxmSm+FyZCIi0pMiSQ7eBU70eDxHhtvo8XiOAr6PU0ZZ4kxtfSO3Pr+YzSVVACQnJnDHeYcwaki2y5GJiEhPi+Qm8u+A84H5Ho/HC7wJbMEpjvR94CdAXWA/iSM+v5+7Xv2cb7Y6d40M8MszJjOpKN/dwERExBWR1Dn4BmeehDrgOuBfOB0Q/wX8FKgFzvV6vSu6IU7pRgu+2sbib5vvBl31/fEc9Z2hLkYkIiJuimhcmtfrfR1nfoWfA6/i3EJ4FbgRGOX1et+IeoTS7RZ9u7Pp9RnTijj1kOEuRiMiIm6LeGya1+vdDczphljEBdZalqzb3bR8/ETNlyAi0tepok0ft2FXJaVVzuiEfunJjBrS3+WIRETEbV2qauPxeIbhdEQMO/jd6/Uu3JegpOd8sa646fXkonyVRhYRkciSA4/HcwJwPx1XQUzsckTSo0KTgykjBrgYiYiIxIpO31bweDyHAf8EcoCHcUa8LQQeBVYGll8D/if6YUp3aPT5WbahpGl5spIDEREhsj4HN+EMVzzU6/X+NLDuXa/X+2NgPHAncDzwSnRDlO7yzdYyquudMsmDs9MpUCVEEREhsuTgCOAfrWZlTADwer3W6/XeDqwAfh1JAMaYBGPM9caYlcaYWmPMJmPMHGNMZieOvcMYY9t5NEQSS18TOkph8oh8jPobiIgIkfU5yAY2hizXA60v4B/gTO0cifuBa4F5OEMkxwaWpxhjjrfW+ts5di7wbZj1E3FqMbwWYSx9ypL16m8gIiJ7iyQ52EnLGRd3AqNa7ZMMpHf2hMaYccA1wFxr7dkh69cBDwKzgOfaOt5auxSnSmPr8/458PLxzsbS19TWN/L1ptKm5clFSg5ERMQRyW2FVbRMBj4GvufxeA4C8Hg8Q4CzgdURnPMCnI6MD7Ra/yhQDfwggnMBYIzJwEkqtuDM/yBhLN9USqPfAlA0sB+5WZqSWUREHJEkB28Cx3g8nrzA8v/itBJ84fF4FuOMWBjI3hf69hwK+IFFoSuttbXAksD2SJ0H9AeetNb6unB8n9BiCONItRqIiEizSJKDPwNHAw0AXq/3A+BcYB3OaIVtwE+8Xu/TEZyzACi21taF2bYFGGCMSYngfACXARZ4oqMdjTFXGmM+jfD8vcKSVsWPREREgoy11r03N2YNkGyt3T/MtqeBi4Fca21ZJ883GqcF421r7fGdjcPj8VgAr9fb2UPiWnl1PefNeQuABGN49ecnkJHapWKZIiISv9ocohZJEaQnPB7P9dGJp0k1bZRgBtJC9umsywLPj3U5oj7gy/XNQxjHFOYoMRARkRYiua1wITAoyu+/FefWQbgEoRDnlkN9Z05kjEkCfgiU4AyLlDaoZLKIiLQnkuRgPdFPDhYHYpgWutIYkwZMBiLpD3AqMBh4po0+DBLQMjlQfwMREWkpkuTgOeD7Ho8nt8M9O+9FnM6D17VafwWQATwbXGGMGWWMaW/Cp+AtBdU2aMf2smq2lTp3alKTExkzLJq/ThER6Q0iSQ7+H843+Xc9Hs8pHo9n8L6+ubV2GfAH4CxjzFxjzOXGmDnAfcACWhZAehunPPNejDEFwInAosA5pQ2hoxQm7J9HcmIk/wRERKQviKQnWm3g2QB/B/B4POH2s16vN5LzXodzy+JK4GSgGHgIuK2D0smhLsWZJlodETvwRch8CupvICIi4URyEX8f5xZAVAUKFc0JPNrbr6idbb8BfhPdyHofa22r+RTU30BERPbW6eTA6/XO6MY4pAes37mHsipn8Ed2RgojBvd3OSIREYlFuuHch4SOUphUlE+CpmgWEZEwlBz0IV+sV38DERHpWKdvK3g8nts6uav1er13djEe6SaNPj/LNig5EBGRjkXSIfGOdrYFOyqawGslBzHmm61l1NQ7k1QOzklnaG6GyxGJiEisiiQ5OLaN9Tk4UytfC7wO/Glfg5Lo+2KtSiaLiEjnRDJaYUE7m//u8XheBBYBL+xzVBJ1LfobFCk5EBGRtkWtQ6LX612GUxzppmidU6Kjpr6RFZtLm5Ynq76BiIi0I9qjFTYC46N8TtlHyzeW4PM73UJGDOpHTmZbs2SLiIhEPzk4DKiJ8jllH2mKZhERiUQkQxn3b+cc++HMpHgk8FIU4pIo0nwKIiISiUhGK6yn/bkVDLAauHFfApLoKquqY+2OCgASEwwThue5HJGIiMS6SJKDpwmfHPiBUpyRCn/3er110QhMomNJyCiFMYU5pKdE8isXEZG+KJKhjJd2YxzSTZaov4GIiERIcyv0cuqMKCIikYqkQ+Io4LvA616vd3eY7QOAk4D/eL3etdELUbpqW2k128ucwSNpyYmMLsxxOSIREYkHkbQc/AqYA1S0sb0cuBf4+b4GJdER2mowcXgeyYlqKBIRkY5FcrWYAcz3er0N4TYG1r8FHBeFuCQKQvsbTNYtBRER6aRIkoNCnOGM7dkIFHQ5Gokav7UtRiqov4GIiHRWJMlBPdC/g3360X4tBOkh63bsoby6HoDsjBSKBvVzOSIREYkXkSQHy4GTPR5PcriNHo8nBTgF+Doagcm+WbI+5JZCUT4JxrgYjYiIxJNIkoO/AvsDL3k8niGhGwLLL+GUUX46euFJV7UYwjhStxRERKTzIimX9whwNnA68D2Px7MU2ILTF2EikAHMB/4U7SAlMg0+P8s2lDQtTylSciAiIp3X6ZYDr9frx6lj8FugATgcJ1k4HKc/wm+AkwP7iYtWbimjtsEHwNDcDIbkZrgckYiIxJOICu0Hhive5PF4bgHGADlAGbBSSUHsaDGEsSjfxUhERCQedWkWnkAioI6HMUolk0VEZF+ofHIvU13XyMotZU3Lk9RyICIiEVL55F5m2cbd+PxOqYlRg/uTk5nqckQiIhJvVD65l1myrrlRZ/IItRqIiEjkVD65l1F/AxER2Vcqn9yLlFXVsW7nHgCSEgzj989zOSIREYlHKp/ci4TeUhgzLJf0lC4NRhERkT5O5ZN7Ed1SEBGRaFD55F7CWtsqOVBnRBER6RqVT+4ltpVWs6O8BoD0lERGF+S4HJGIiMSrSG4r4PV6G7xe701APjAeODLwPMDr9d4C+Dwez+nRD1M6smR9c3+DCcPzSUqM6FcrIiLSJCrlkz0ez3CPx3M5MBsYCiRGJzzprM/Xqr+BiIhER5e7s3s8nkSc/gdXAsfjtEJYnH4H0oP81vLl+pDkQCWTRURkH0ScHHg8npHA5cClwODA6mLgz8DjXq93Q9Sik05Zu72CihqncGVOZgpFg/q5HJGIiMSzTiUHHo8nCTgTp5XgWJxWgnpgLk6nxL97vd7buhKAMSYB+Cnw30ARsAtnWORt1tqqTp4jD7gJOAMYBuzBqctwm7X2/a7EFU++WB86RfMAjDEuRiMiIvGu3eTA4/EcCFwBXAIMAAzwOfAU8JzX6y3xeDz7OjrhfuBaYB7OxE5jA8tTjDHHW2vbPb8xZjjwHpAFPA6sArJxhlcW7mNscSG0+JGGMIqIyL7qqOXgG5x+BDtxLuJPer3er6L15saYccA1wFxr7dkh69cBDwKzgOc6OM1fcT7HRGvttmjFFi8afH6WbSxpWlZnRBER2VedGe9mgTeAV6KZGARcgNMa8UCr9Y8C1cAP2jvYGHM0znDKe6y124wxycaYjCjHGNNWbi6lrsEHwNDcDAbn9KmPLyIi3aCj5OBWYAPOEMUPPB7P1x6P5xcej2dolN7/UMAPLApdaa2tBZYEtrfnpMDzRmPMa0ANUGWMWWWMaTex6C0+V8lkERGJsnaTA6/Xe7fX6x0FfB+nT8AonAqJGz0ez+sej+e8fXz/AqDYWlsXZtsWYIAxJqWd40cHnh8F8nD6RlyG01nyGWPM7H2ML+a17G+g5EBERPZdp8roeb3ef3u93nNwJla6Cac14fvA8zi3HSZ7PJ6pXXj/DCBcYgBQG7JPW4Jj9vYAx1prn7XWPgEcBZQBvwmMhgjLGHOlMebTCGOOGVV1DazcUgY492Ymqb6BiIhEQaTlk3d6vd7fer3eA4DvAa/gzLNwCLDI4/F84fF4rorglNVAahvb0kL2aUtN4Pl5a219cKW1thT4BzCE5taFvVhrH7HWHtL5cGPLsg0l+K0FYNSQ/mRntNfIIiIi0jldLsDv9Xrf9nq95+PUFfgFzhDCSTijDDprK86tg3AJQiHOLYf6MNuCNgeet4fZFhy5kBtBPHFFUzSLiEh32OfZebxeb7HX673X6/WOBY7DudXQWYsDMUwLXWmMSQMmAx01+Qc7Mg4Lsy24bmcE8cSV0P4Gk5UciIhIlER16j6v1/ue1+uNZJTAizh9Fq5rtf4KnL4GzwZXGGNGGWPGtNrvbzj9DX5gjMkK2XcoTrXE1dbabyOIJ26UVNayftceAJISDOP367UNJCIi0sO6PPFSNFhrlxlj/gBcbYyZi1NPIVghcQEtCyC9DQzH6XsXPL7UGHMjzrwOHxtjngBSgJ8Enq/ukQ/igtBWg+/sl0taiqu/ShER6UVi4YpyHbAeZ96Gk3EmcXoIZ16EDkszW2sfMcYU4/R7uBOnbsJHwIXW2g+6K2i3LWk1n4KIiEi0uJ4cWGt9OHMqzOlgv6J2ts3FmQSqT7DW8kVofYORSg5ERCR6otrnQHrG1tJqdpY7ozgzUpIYXZDtckQiItKbKDmIQ6FDGCcOzyMxQb9GERGJHl1V4tCSkORAQxhFRCTalBzEGb+1LFmv+RRERKT7KDmIM2u2V7CnpgGAvKxUhg/M6uAIERGRyCg5iDOh/Q0mF+VjjGlnbxERkcgpOYgz6m8gIiLdTclBHKlv9LF8Y0nTsvobiIhId1ByEEdWbC6jrtEpGlmYl8mg7HSXIxIRkd5IyUEcaXlLId/FSEREpDdTchBHQjsj6paCiIh0FyUHcaKqtoFvtpYDzrSUk4rUciAiIt1DyUGcWLqhBL+1ABwwNJv+6SkuRyQiIr2VkoM40XKKZrUaiIhI91FyECc+XxvS30BTNIuISDdSchAHdu+pZWNxJQDJiQmM2y/P5YhERKQ3U3IQB0KHMI4dlkNacqKL0YiISG+n5CAOfKFZGEVEpAcpOYhx1toWLQdKDkREpLspOYhxW0qq2FVRC0BGahIHFWS7HJGIiPR2Sg5i3Bfrmm8pTByeT2KCfmUiItK9dKWJcaElkw/WfAoiItIDlBzEMJ/f8mVIZ8TJ6m8gIiI9QMlBDFuzvZzK2gYA8rJS2X9AlssRiYhIX6DkIIaF9jeYMmIAxhgXoxERkb5CLzooEQAAEmhJREFUyUEM0xTNIiLiBiUHMaq+0cdXm0qalierM6KIiPQQJQcx6utNpdQ3+gEYlpfJwP7pLkckIiJ9hZKDGNXiloJmYRQRkR6k5CBGLQkdwlikWwoiItJzlBzEoMraBlZtLQPAAJOK1HIgIiI9R8lBDFq6YTd+67w+cGg2/dKT3Q1IRET6FCUHMUhDGEVExE1KDmLQknUqmSwiIu5RchBjiitq2VhcCUByYgLj9st1OSIREelrlBzEmCXrm28pjNsvl9TkRBejERGRvkjJQYwJ7W+gWwoiIuIGJQcxxFrbor+BOiOKiIgblBzEkM27qyjeUwtAZmoSBw7NdjkiERHpi5QcxJDQWwqTivJJTNAUzSIi0vNcTw6MMQnGmOuNMSuNMbXGmE3GmDnGmMxOHm/beFR2d+zRtkT9DUREJAYkuR0AcD9wLTAPmAOMDSxPMcYcb631d+Ic7wOPtFrXENUou5nPb/lyg/obiIiI+1xNDowx44BrgLnW2rND1q8DHgRmAc914lRrrbV/7Z4oe8a328uprG0EIL9fKvvld6rhREREJOrcvq1wAc7cQg+0Wv8oUA38oLMnMsakGGOyohhbj/pibcuSycaov4GIiLjD7eTgUMAPLApdaa2tBZYEtnfGOTjJxB5jzE5jzEPGmLjq6v9FSPGjyZqFUUREXOR2n4MCoNhaWxdm2xZgujEmxVpb3845FgEvA98C/YGTgKuBY4wx0621Md8xsa7Bx1cbS5uW1d9ARETc5HbLQQYQLjEAqA3Zp03W2sOstfdaa/9mrX3aWjsLuBmYAPy0vWONMVcaYz6NNOho+3pzKQ0+p9/lfvmZDOif5nJEIiLSl7mdHFQDqW1sSwvZJ1K/B+qBk9vbyVr7iLX2kC6cP6pC+xtoCKOIiLjN7eRgKzDAGBMuQSjEueXQ3i2FsKy1DcFz72N8PSK0v8HBSg5ERMRlbicHiwMxTAtdaYxJAyYDXWryDxw/DNixrwF2tz01DXy7rRyABAMTi/JdjkhERPo6t5ODFwELXNdq/RU4fQ2eDa4wxowyxowJ3ckY09aV9E6czpavRS/U7rF0w2781nl94NAcstKS3Q1IRET6PFdHK1hrlxlj/gBcbYyZC7xBc4XEBbQsgPQ2MBynLkLQLcaYw4F3gY1AFs5ohWOBT4CHuv1D7KOWUzSr1UBERNzn9lBGcFoN1gNX4nQgLMa5qN/WidLJ7wHfAS4B8gEfsBpntMJ9gXoJMS00OVB/A/n/27v/YLnK+o7j729+E0JCQlICARIp5bdCUkBqKZbqUKu12KEU0ShSkaEHccg40g5YCOBU25Efw4+DJUNbRaBghwhYZcRaqIIKKVxBkJ/mQiAEEgLB/A7J0z/OucnuZW+SC7t79m7er5k75+5zzp773Z079372eZ7zHEnqBJWHg5TSRop7Kly6jeNmNGi7Hbi9NZW13tI31vDCq6sAGDViGAfvPbHiiiRJqn7OwQ6tZ+GWGy0dsvckRo0YXmE1kiQVDAcVqh1SmOl8A0lShzAcVCSl1G8yovMNJEmdwXBQkUXLVrJ8ZbFy9LgxI9hv6pC6T5QkqYsZDipS22tw2PTdGD7MWzRLkjqD4aAiD9dMRpy5r0MKkqTOYTiowMZNm3jkuS3h4PAZhgNJUucwHFTg6ZdWsGrdmwBMHj+GvXbbueKKJEnawnBQgbohhRmTiXC+gSSpcxgOKuD6BpKkTmY4aLO1Gzby+KLXNj92fQNJUqcxHLTZY4uWs2FjcT+pfSaPY7ddxlRckSRJ9QwHbVZ7P4WZ9hpIkjqQ4aDN6pdMdr6BJKnzGA7a6I0163nmpRUADItiZURJkjqN4aCNHul9lVR+v/+eu7LzmJGV1iNJUiOGgzaqv4TR+QaSpM5kOGij2smIzjeQJHUqw0GbvLJiDS8sXwXA6BHDOHiviRVXJElSY4aDNunp3TKkcMg+kxg1YniF1UiSNDDDQZs8/BvnG0iShgbDQRuklOjpdfEjSdLQYDhog+eWrmT5ynUA7LLTSPbdfXzFFUmSNDDDQRvUzjc4bPpuDB/mLZolSZ3LcNAGtfMNvAujJKnTGQ5abOOmTTzy/PLNj2cZDiRJHc5w0GJPLV7B6nVvAjBl/Bj2nDS24ookSdo6w0GL9V8yOcL5BpKkzmY4aDHvpyBJGmoMBy20dsNGfv3C65sfez8FSdJQYDhooceeX86GjZsAmD5lHJPGjam4IkmSts1w0EIOKUiShiLDQQsZDiRJQ5HhoEXeWL2eZ5e8AcCwCN49fVLFFUmStH0MBy3yy95XSeX3B0ybwM6jR1ZajyRJ28tw0CIP1Q4pzHBIQZI0dBgOWqT2Zksz9zUcSJKGDsNBC7z8+moWL18NwOgRwzhw2q4VVyRJ0vYzHLRAT++rm78/dPpujBoxvMJqJEkaHMNBC9RdwjjDVRElSUNL5eEgIoZFxJyIeCIi1kbEooi4NCJ2fhvnGhsRCyMiRcTVrah3W1JK9Czc0nPg+gaSpKGm8nAAXA5cBjwOnA18B/gCcGdEDLa+i4FK/xs/t3Qlr61aB8D4nUay79TxVZYjSdKgjajyh0fEIRSB4LaU0ok17QuBK4GPAzdt57lmAecA5wKXNr/a7VN7CeNhMyYzzFs0S5KGmKp7Dk4BAriiX/s8YDUwe3tOEhHDy+fcBdzWzAIHq6duyWTnG0iShp5Kew6AI4FNwAO1jSmltRHRU+7fHnOAA4ETt3Vgq2V/eghH7787Dy9cxqx9p1RdjiRJg1Z1z8GewLKU0roG+14EJkfEqK2dICLeBVwEXJxS6h3MD4+IMyJiwWCesy1TJ47lw7P24fwTZ7HHxLHNPLUkSW1RdTgYCzQKBgBra47ZmmuBhRSTGgclpXRdSumIwT5PkqRuVvWwwmrgdwbYN6bmmIYiYjZwPHBsSmlDk2uTJGmHVHXPwWKKoYPRDfZNoxhyWN/oieVzLgO+DyyJiP0iYj9gennIhLLNtYslSRqEqsPBg2UNR9U2RsQY4HBga/MBdgKmAB8Bnq75uqfcP7t8fHpTK5YkqctVPaxwC3AexfoEP6lp/xzFXIMb+xoi4neBkSmlJ8qmVcBJDc45BcgpLmu8Hnik+WVLktS9Kg0HKaVHI+Ia4PMRcRvFEMFBFCsk3kv9Akj/TTFkEOVzNwD/2f+cETGj/PbZlNJb9kuSpK2ruucAil6DXuAMiiGCZcBVwAUppU0V1iVJ0g6p8nCQUtpIsdzxVpc8TinN2M7z9VL2LkiSpMGrekKiJEnqMIYDSZJUx3AgSZLqVD7noJNkWVZ1CZIktUvK87zhHD17DiRJUp1IKVVdQ1eKiAXe1Kn1fJ/bw/e5fXyv28P3eevsOZAkSXUMB5IkqY7hoHWuq7qAHYTvc3v4PreP73V7+D5vhXMOJElSHXsOJElSHcOBJEmqYzhokogYFhFzIuKJiFgbEYsi4tKI2Lnq2rpJROwfERdHxM8jYmlE/DYieiLifN/r1oqIsRGxMCJSRFxddT3dJCImRcTXI+KZ8u/H0oj4n4j4o6pr6xYRMS4izouIR8u/G8si4v6I+ExEeLO+flwhsXkuB74AzKe4w+RB5eOZEfFBbz/dNH8DnAXcAdwIbACOA74C/HVEHJ1SWlNhfd3sYmBy1UV0m4iYDtwDjAOuB54CJgDvAaZVV1n3iIhhwA+A9wHfBK4CxgKnAP9G8ff67yorsAM5IbEJIuIQ4FFgfkrpxJr2s4ErgU+mlG6qqr5uEhFHAE+nlFb0a/8KcD5wdkrJT7VNFhGzgAeAcynC7zUppc9XW1V3iIifADOAo1JKL1VcTleKiD8A7geuSCnNqWkfBTwBTEop7VpVfZ3IYYXmOAUI4Ip+7fOA1cDstlfUpVJKC/oHg9It5fbQdtazI4iI4RS/y3cBt1VcTleJiGOBY4B/Tim9FBEjI2Js1XV1ofHldnFtY0ppPbAMWNX2ijqc4aA5jgQ2UXyy2iyltBboKfertfYqty9XWkV3mgMcCNhT0HwfLrfPR8SdwBpgVUQ8FRF+qGieB4DXgXMj4qSI2CciDoiIrwK/D8yttLoOZDhojj2BZSmldQ32vQhMLruv1ALlJ9sLgDcBh2+aKCLeBVwEXJxS6q24nG50QLmdB0wCTgU+C6wHboiI06oqrJuklF4D/gJYDtwKPEcxnHAWcGJKaV6F5XUkJyQ2x1igUTAAWFtzzPr2lLPDuQI4GjgvpfRk1cV0mWuBhcBlVRfSpXYpt78Fjiu7uYmI+cBvgH+MiG86obkpVgK/opjMfD9FGDsLuCkiTkgp3V1lcZ3GnoPmWA2MHmDfmJpj1GQRcQlFd/d1KaWvVl1PNym7tY8Hzkwpbai6ni7Vd2XNzX3BADZ/0r0DmMqW3gW9TRHxbopAcHdK6Usppfkppesp5nssAeaVPZAqGQ6aYzHF0EGjgDCNYsjBXoMmi4i5wJcpLkU6s9pqukv5u3wZ8H1gSUTsFxH7AdPLQyaUbc7wfmdeKLdLGuzru3JhYptq6WZzKD6ofae2MaW0Gvgvit/rGe0vq3MZDprjQYr38qjaxogYAxwOLKiiqG4WERcCFwLfAk5PXpPbbDsBU4CPAE/XfN1T7p9dPj69iuK6SN8k5r0a7Otre6VNtXSzvvUiGvUOjOi3FYaDZrkFSMA5/do/RzHX4Ma2V9TFIuICitnFNwCnOR7bEquAkxp8ZeX+u8rHd1RSXff4LsV8g9kRMa6vMSL2AD5GsabHM1UV10UeL7efqW0se75OAF4Dnm1zTR3NRZCaJCKuohj7nk/RFdu3QuJ9wJ/4D6w5IuIs4GrgeeAfKC4hrfWyE4taJyJmUExQdBGkJomIM4B/AR4D/hUYBfwtsAfw5ymlH1ZYXlcoV6F8iGKI5kaKv8uTKD7AzQDOSinllRXYgexGaZ5zgF7gDIqu2GUUS3ReYDBoqr41I/ahWAa1v3sBw4GGjJTSdRGxjGL1yUsoAu/PgE+klO6rtLgukVJ6LiKOorjk+QPAxykmg/YAX0wpubhXP/YcSJKkOs45kCRJdQwHkiSpjuFAkiTVMRxIkqQ6hgNJklTHcCBJkuoYDiRJUh0XQZLUNbIsm0txz43j8jy/p9pqpKHLcCBpsyzLtmdVNP/xSl3OcCCpkYu2sq+3XUVIqobhQNJb5Hk+t+oaJFXHcCDpbasd4wemU9yA7ECK2xB/Dzgvz/MlDZ73exR31fwAMIXiRmU/Ai7J8/zpBscPp7iD3qeAQynuXPgicA/wTwM8568obmZ0KLAW+CHwxTzPX3wnr1naEXi1gqRmmAN8A/glcAXwJHAacH+WZVNqD8yy7EhgATAbeBD4OvBz4JPAgizLjuh3/CjgLuBaYG/gJuBK4P+AvwT+sEE9GfBtiiGQa4BfAScDP8qybPQ7frVSl7PnQNJblD0CjazN8/xrDdr/DHhvnucP15zjcoqehK8Bny3bAvgWMB6Ynef5jTXHnwz8B/DtLMsOzvO871bnc4EPAncCJ+V5vq7mOaPLc/X3IeDIPM8frTn2JuAU4ATg1gFfvCR7DiQ1dOEAX38/wPE31AaD0lxgBfCJmk/r76MYdvhZbTAAyPP8FuCnwAHAMbB5OCED1gBn1gaD8jnr8jxf2qCeK2uDQWleuT1qgNcgqWTPgaS3yPM8BvmUexucY0WWZT3A+4GDgB5gVrn7xwOc58cUwWAm8L8UQWIC8Is8zxcPop4FDdoWlduJgziPtEOy50BSM7w8QHvfZMQJ/bYvDXB8X/uu/baDnUT4eoO2N8vt8EGeS9rhGA4kNcPuA7RPLbcr+m2nNjgWYI9+x/X9k5/29kuTNFiGA0nN8P7+DVmWTQAOp7iM8Ndlc9+8hD8e4Dx97Q+V2ycoAsJ7sizbsxmFSto2w4GkZvhUlmUz+7XNpRhGuLlmIuF9FJc5HlOuQ7BZ+fhY4CmKiYnkeb4RyIGdgG/0vwwxy7JR/S+VlPTOOSFR0lts5VJGgO/med7Tr+0HwH1Zlt1KMW/gmPKrl5orHPI8T1mWnQrcDdySZdntFL0DBwAfo1g86dM1lzFCsZTze4GPAk9lWfa98ri9geOBLwH//rZeqKSGDAeSGrlwK/t6Ka48qHU5MJ9iXYOTgZUU/7DPy/P8ldoD8zz/RbkQ0pcp1i/4KMUKiTdTrJD4ZL/j12dZ9iHgTODTwKlAAIvLn/nTwb88SVsTKW3PTdgk6a28RbLUnZxzIEmS6hgOJElSHcOBJEmq45wDSZJUx54DSZJUx3AgSZLqGA4kSVIdw4EkSapjOJAkSXUMB5Ikqc7/A/qv12bJlj8zAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div class=\"comment\">Saved: ./run/figs/GTS4-01-history_1</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pwk.plot_history(history, save_as='01-history')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 8 - Evaluate best model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.1 - Restore best model :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded.\n"
     ]
    }
   ],
   "source": [
    "loaded_model = tf.keras.models.load_model('./run/models/best-model.h5')\n",
    "# best_model.summary()\n",
    "print(\"Loaded.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.2 - Evaluate it :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss      : 0.1789\n",
      "Test accuracy  : 0.9544\n"
     ]
    }
   ],
   "source": [
    "score = loaded_model.evaluate(x_test, y_test, verbose=0)\n",
    "\n",
    "print('Test loss      : {:5.4f}'.format(score[0]))\n",
    "print('Test accuracy  : {:5.4f}'.format(score[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot confusion matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"comment\">Saved: ./run/figs/GTS4-02-confusion-matrix</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_sigmoid = model.predict(x_test)\n",
    "y_pred    = np.argmax(y_sigmoid, axis=-1)\n",
    "\n",
    "cmap = plt.get_cmap('Oranges')\n",
    "pwk.plot_confusion_matrix(y_test,y_pred,range(43), figsize=(16, 16),normalize=False, cmap=cmap, save_as='02-confusion-matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End time is : Thursday 17 December 2020, 18:23:00\n",
      "Duration is : 00:01:44 577ms\n",
      "This notebook ends here\n"
     ]
    }
   ],
   "source": [
    "pwk.end()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"todo\">\n",
    "     What you can do:\n",
    "    <ul>\n",
    "        <li>Try different datasets / models</li>\n",
    "        <li>Test different hyperparameters (epochs, batch size, optimization, etc.)</li>\n",
    "        <li>What's the best strategy?  How to compare?</li>\n",
    "    </ul>\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}