JNotebooks/tutorial09_fully_connected_neural_networks_revisited.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fully Connected Neural Networks Revisited\n",
    "\n",
    "The main learning goal of this tutorial is to show that  Fully Connected Neural Networks are global in nature (i.e., fully connected ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load dataset, explore it  and split it into train, validation and test sets\n",
    "\n",
    "- [Load MNIST Keras](https://keras.io/api/datasets/mnist/#load_data-function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Development set\n",
      "Images:  (60000, 28, 28)\n",
      "Labels shape: (60000,)\n",
      "\n",
      "Number of classes: 10\n",
      "\n",
      "Classes: [0 1 2 3 4 5 6 7 8 9]\n",
      "\n",
      "Test set\n",
      "Images:  (10000, 28, 28)\n",
      "Labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Loading the data using the Keras function\n",
    "(X_dev, Y_dev), (X_test, Y_test) = tf.keras.datasets.mnist.load_data() # The data comes already split \n",
    "                                                                        # in dev and test sets\n",
    "print(\"Development set\")\n",
    "print(\"Images: \",X_dev.shape)\n",
    "print(\"Labels shape:\",Y_dev.shape)\n",
    "print(\"\\nNumber of classes:\",np.unique(Y_dev).size)\n",
    "print(\"\\nClasses:\",np.unique(Y_dev))\n",
    "print(\"\\nTest set\")\n",
    "print(\"Images: \",X_test.shape)\n",
    "print(\"Labels shape: \",Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.hist(Y_dev, bins = range(11))\n",
    "plt.xlabel(\"Labels\")\n",
    "plt.ylabel(\"Label Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x1296 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Disaplying some samples from the development set\n",
    "sample_indexes = np.random.choice(np.arange(X_dev.shape[0], dtype = int),size = 30, replace = False)\n",
    "plt.figure(figsize = (24,18))\n",
    "for (ii,jj) in enumerate(sample_indexes):\n",
    "    plt.subplot(5,6,ii+1)\n",
    "    plt.imshow(X_dev[jj], cmap = \"gray\")\n",
    "    plt.title(\"Label: %d\" %Y_dev[jj])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Randomly shffling samples equally across samples\n",
    "r,c = np.indices( (X_dev.shape[1:]) )\n",
    "np.random.shuffle(r)\n",
    "np.random.shuffle(c)\n",
    "X_dev = X_dev[:,r,c]\n",
    "X_test = X_test[:,r,c]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x1296 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Disaplying some samples from the development set after shuffling\n",
    "plt.figure(figsize = (24,18))\n",
    "for (ii,jj) in enumerate(sample_indexes):\n",
    "    plt.subplot(5,6,ii+1)\n",
    "    plt.imshow(X_dev[jj], cmap = \"gray\")\n",
    "    plt.title(\"Label: %d\" %Y_dev[jj])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train set\n",
      "Images:  (45000, 28, 28)\n",
      "Labels shape:  (45000,)\n",
      "\n",
      "Validation set\n",
      "Images:  (15000, 28, 28)\n",
      "Labels shape:  (15000,)\n"
     ]
    }
   ],
   "source": [
    "#The number of classes across samples looks balanced\n",
    "# Let's shuffle the samples and split them\n",
    "indexes = np.arange(X_dev.shape[0], dtype = int)\n",
    "np.random.shuffle(indexes)\n",
    "X_dev = X_dev[indexes]\n",
    "Y_dev = Y_dev[indexes]\n",
    "\n",
    "nsplit = int(0.75*X_dev.shape[0]) # Train/validation split\n",
    "\n",
    "# Train and validation split\n",
    "X_train = X_dev[:nsplit]\n",
    "Y_train = Y_dev[:nsplit]\n",
    "X_val = X_dev[nsplit:]\n",
    "Y_val = Y_dev[nsplit:]\n",
    "\n",
    "print(\"\\nTrain set\")\n",
    "print(\"Images: \",X_train.shape)\n",
    "print(\"Labels shape: \",Y_train.shape)\n",
    "print(\"\\nValidation set\")\n",
    "print(\"Images: \",X_val.shape)\n",
    "print(\"Labels shape: \",Y_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 255 33.31474044784581 78.5647702708273\n",
      "0 255 33.329464455782315 78.57564752163664\n"
     ]
    }
   ],
   "source": [
    "print(X_train.min(),X_train.max(),X_train.mean(),X_train.std())\n",
    "print(X_val.min(),X_val.max(),X_val.mean(),X_val.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Scaling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "norm_type = 0 # 0 -> min-max; 1-> standardization\n",
    "\n",
    "if norm_type == 0:\n",
    "    X_train = X_train/255\n",
    "    X_val = X_val/255\n",
    "    X_test = X_test/255\n",
    "elif norm_type == 1:\n",
    "    train_mean, train_std = X_train.mean(),X_train.std() \n",
    "    X_train = (X_train - train_mean)/train_std\n",
    "    X_val = (X_val - train_mean)/train_std\n",
    "    X_test = (X_test - train_mean)/train_std\n",
    "else:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. One hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labels:\n",
      "[4 3 1 5 6]\n",
      "\n",
      "One hot encoded labels:\n",
      "[[0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "Y_train_oh = tf.keras.utils.to_categorical(Y_train)\n",
    "Y_val_oh = tf.keras.utils.to_categorical(Y_val)\n",
    "Y_test_oh = tf.keras.utils.to_categorical(Y_test)\n",
    "\n",
    "print(\"Labels:\")\n",
    "print(Y_train[:5])\n",
    "print()\n",
    "print(\"One hot encoded labels:\")\n",
    "print(Y_train_oh[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Define your model, cost function, optimizer, learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_model(ishape = (28,28),k = 10, lr = 1e-4):\n",
    "    model_input = tf.keras.layers.Input(shape = ishape)\n",
    "    model_input_flatten = tf.keras.layers.Flatten()(model_input)\n",
    "    out = tf.keras.layers.Dense(k,activation = 'softmax')(model_input_flatten)\n",
    "    model = tf.keras.models.Model(inputs = model_input, outputs = out)\n",
    "    model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=lr), loss='categorical_crossentropy', metrics = [\"accuracy\"])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 28, 28)]          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 10)                7850      \n",
      "=================================================================\n",
      "Total params: 7,850\n",
      "Trainable params: 7,850\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model = my_model()\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Define your callbacks (save your model, patience, etc.)\n",
    "\n",
    "- [Keras callbacks](https://keras.io/api/callbacks/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = \"best_model_mnist.h5\"\n",
    "early_stop = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience = 20)\n",
    "\n",
    "monitor = tf.keras.callbacks.ModelCheckpoint(model_name, monitor='val_loss',\\\n",
    "                                             verbose=0,save_best_only=True,\\\n",
    "                                             save_weights_only=True,\\\n",
    "                                             mode='min')\n",
    "# Learning rate schedule\n",
    "def scheduler(epoch, lr):\n",
    "    if epoch%10 == 0:\n",
    "        lr = lr/2\n",
    "    return lr\n",
    "\n",
    "lr_schedule = tf.keras.callbacks.LearningRateScheduler(scheduler,verbose = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Train your model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 1.5571 - accuracy: 0.6139 - val_loss: 1.0945 - val_accuracy: 0.7905 - lr: 5.0000e-05\n",
      "Epoch 2/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.9019 - accuracy: 0.8190 - val_loss: 0.7520 - val_accuracy: 0.8407 - lr: 5.0000e-05\n",
      "Epoch 3/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.6730 - accuracy: 0.8511 - val_loss: 0.6036 - val_accuracy: 0.8617 - lr: 5.0000e-05\n",
      "Epoch 4/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.5615 - accuracy: 0.8672 - val_loss: 0.5226 - val_accuracy: 0.8735 - lr: 5.0000e-05\n",
      "Epoch 5/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.4969 - accuracy: 0.8781 - val_loss: 0.4727 - val_accuracy: 0.8805 - lr: 5.0000e-05\n",
      "Epoch 6/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.4555 - accuracy: 0.8843 - val_loss: 0.4396 - val_accuracy: 0.8867 - lr: 5.0000e-05\n",
      "Epoch 7/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.4269 - accuracy: 0.8904 - val_loss: 0.4165 - val_accuracy: 0.8889 - lr: 5.0000e-05\n",
      "Epoch 8/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.4061 - accuracy: 0.8937 - val_loss: 0.3987 - val_accuracy: 0.8928 - lr: 5.0000e-05\n",
      "Epoch 9/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3901 - accuracy: 0.8966 - val_loss: 0.3850 - val_accuracy: 0.8965 - lr: 5.0000e-05\n",
      "Epoch 10/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3775 - accuracy: 0.8991 - val_loss: 0.3740 - val_accuracy: 0.8985 - lr: 5.0000e-05\n",
      "Epoch 11/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3692 - accuracy: 0.9010 - val_loss: 0.3692 - val_accuracy: 0.9001 - lr: 2.5000e-05\n",
      "Epoch 12/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3645 - accuracy: 0.9020 - val_loss: 0.3650 - val_accuracy: 0.9003 - lr: 2.5000e-05\n",
      "Epoch 13/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3602 - accuracy: 0.9028 - val_loss: 0.3609 - val_accuracy: 0.9008 - lr: 2.5000e-05\n",
      "Epoch 14/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3563 - accuracy: 0.9035 - val_loss: 0.3572 - val_accuracy: 0.9017 - lr: 2.5000e-05\n",
      "Epoch 15/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3526 - accuracy: 0.9042 - val_loss: 0.3539 - val_accuracy: 0.9024 - lr: 2.5000e-05\n",
      "Epoch 16/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3493 - accuracy: 0.9049 - val_loss: 0.3508 - val_accuracy: 0.9027 - lr: 2.5000e-05\n",
      "Epoch 17/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3461 - accuracy: 0.9056 - val_loss: 0.3480 - val_accuracy: 0.9039 - lr: 2.5000e-05\n",
      "Epoch 18/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3432 - accuracy: 0.9063 - val_loss: 0.3451 - val_accuracy: 0.9049 - lr: 2.5000e-05\n",
      "Epoch 19/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3404 - accuracy: 0.9070 - val_loss: 0.3427 - val_accuracy: 0.9055 - lr: 2.5000e-05\n",
      "Epoch 20/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3379 - accuracy: 0.9076 - val_loss: 0.3403 - val_accuracy: 0.9059 - lr: 2.5000e-05\n",
      "Epoch 21/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3359 - accuracy: 0.9078 - val_loss: 0.3391 - val_accuracy: 0.9063 - lr: 1.2500e-05\n",
      "Epoch 22/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3347 - accuracy: 0.9081 - val_loss: 0.3379 - val_accuracy: 0.9072 - lr: 1.2500e-05\n",
      "Epoch 23/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3336 - accuracy: 0.9085 - val_loss: 0.3369 - val_accuracy: 0.9069 - lr: 1.2500e-05\n",
      "Epoch 24/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3325 - accuracy: 0.9086 - val_loss: 0.3358 - val_accuracy: 0.9071 - lr: 1.2500e-05\n",
      "Epoch 25/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3314 - accuracy: 0.9089 - val_loss: 0.3347 - val_accuracy: 0.9076 - lr: 1.2500e-05\n",
      "Epoch 26/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3303 - accuracy: 0.9090 - val_loss: 0.3338 - val_accuracy: 0.9080 - lr: 1.2500e-05\n",
      "Epoch 27/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3293 - accuracy: 0.9091 - val_loss: 0.3329 - val_accuracy: 0.9081 - lr: 1.2500e-05\n",
      "Epoch 28/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3283 - accuracy: 0.9094 - val_loss: 0.3320 - val_accuracy: 0.9085 - lr: 1.2500e-05\n",
      "Epoch 29/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3274 - accuracy: 0.9096 - val_loss: 0.3310 - val_accuracy: 0.9087 - lr: 1.2500e-05\n",
      "Epoch 30/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3264 - accuracy: 0.9100 - val_loss: 0.3301 - val_accuracy: 0.9090 - lr: 1.2500e-05\n",
      "Epoch 31/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3256 - accuracy: 0.9101 - val_loss: 0.3297 - val_accuracy: 0.9089 - lr: 6.2500e-06\n",
      "Epoch 32/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3252 - accuracy: 0.9103 - val_loss: 0.3293 - val_accuracy: 0.9090 - lr: 6.2500e-06\n",
      "Epoch 33/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3247 - accuracy: 0.9103 - val_loss: 0.3289 - val_accuracy: 0.9092 - lr: 6.2500e-06\n",
      "Epoch 34/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3243 - accuracy: 0.9104 - val_loss: 0.3284 - val_accuracy: 0.9095 - lr: 6.2500e-06\n",
      "Epoch 35/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3239 - accuracy: 0.9104 - val_loss: 0.3280 - val_accuracy: 0.9095 - lr: 6.2500e-06\n",
      "Epoch 36/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3234 - accuracy: 0.9107 - val_loss: 0.3276 - val_accuracy: 0.9098 - lr: 6.2500e-06\n",
      "Epoch 37/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3230 - accuracy: 0.9106 - val_loss: 0.3272 - val_accuracy: 0.9099 - lr: 6.2500e-06\n",
      "Epoch 38/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3226 - accuracy: 0.9108 - val_loss: 0.3268 - val_accuracy: 0.9103 - lr: 6.2500e-06\n",
      "Epoch 39/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3222 - accuracy: 0.9109 - val_loss: 0.3264 - val_accuracy: 0.9100 - lr: 6.2500e-06\n",
      "Epoch 40/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3218 - accuracy: 0.9111 - val_loss: 0.3260 - val_accuracy: 0.9101 - lr: 6.2500e-06\n",
      "Epoch 41/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3214 - accuracy: 0.9111 - val_loss: 0.3259 - val_accuracy: 0.9101 - lr: 3.1250e-06\n",
      "Epoch 42/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3212 - accuracy: 0.9112 - val_loss: 0.3257 - val_accuracy: 0.9101 - lr: 3.1250e-06\n",
      "Epoch 43/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3210 - accuracy: 0.9112 - val_loss: 0.3255 - val_accuracy: 0.9101 - lr: 3.1250e-06\n",
      "Epoch 44/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3208 - accuracy: 0.9113 - val_loss: 0.3253 - val_accuracy: 0.9103 - lr: 3.1250e-06\n",
      "Epoch 45/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3206 - accuracy: 0.9113 - val_loss: 0.3251 - val_accuracy: 0.9104 - lr: 3.1250e-06\n",
      "Epoch 46/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3204 - accuracy: 0.9115 - val_loss: 0.3249 - val_accuracy: 0.9105 - lr: 3.1250e-06\n",
      "Epoch 47/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3202 - accuracy: 0.9115 - val_loss: 0.3247 - val_accuracy: 0.9105 - lr: 3.1250e-06\n",
      "Epoch 48/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3200 - accuracy: 0.9114 - val_loss: 0.3245 - val_accuracy: 0.9105 - lr: 3.1250e-06\n",
      "Epoch 49/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3198 - accuracy: 0.9115 - val_loss: 0.3244 - val_accuracy: 0.9105 - lr: 3.1250e-06\n",
      "Epoch 50/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3196 - accuracy: 0.9115 - val_loss: 0.3242 - val_accuracy: 0.9105 - lr: 3.1250e-06\n",
      "Epoch 51/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3194 - accuracy: 0.9115 - val_loss: 0.3241 - val_accuracy: 0.9105 - lr: 1.5625e-06\n",
      "Epoch 52/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3193 - accuracy: 0.9116 - val_loss: 0.3240 - val_accuracy: 0.9107 - lr: 1.5625e-06\n",
      "Epoch 53/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3193 - accuracy: 0.9116 - val_loss: 0.3239 - val_accuracy: 0.9108 - lr: 1.5625e-06\n",
      "Epoch 54/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3192 - accuracy: 0.9116 - val_loss: 0.3238 - val_accuracy: 0.9107 - lr: 1.5625e-06\n",
      "Epoch 55/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3191 - accuracy: 0.9117 - val_loss: 0.3237 - val_accuracy: 0.9107 - lr: 1.5625e-06\n",
      "Epoch 56/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3190 - accuracy: 0.9117 - val_loss: 0.3236 - val_accuracy: 0.9107 - lr: 1.5625e-06\n",
      "Epoch 57/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3189 - accuracy: 0.9117 - val_loss: 0.3235 - val_accuracy: 0.9107 - lr: 1.5625e-06\n",
      "Epoch 58/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3188 - accuracy: 0.9117 - val_loss: 0.3235 - val_accuracy: 0.9107 - lr: 1.5625e-06\n",
      "Epoch 59/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3187 - accuracy: 0.9117 - val_loss: 0.3234 - val_accuracy: 0.9107 - lr: 1.5625e-06\n",
      "Epoch 60/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3186 - accuracy: 0.9118 - val_loss: 0.3233 - val_accuracy: 0.9107 - lr: 1.5625e-06\n",
      "Epoch 61/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3185 - accuracy: 0.9117 - val_loss: 0.3232 - val_accuracy: 0.9107 - lr: 7.8125e-07\n",
      "Epoch 62/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3185 - accuracy: 0.9117 - val_loss: 0.3232 - val_accuracy: 0.9107 - lr: 7.8125e-07\n",
      "Epoch 63/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3184 - accuracy: 0.9117 - val_loss: 0.3232 - val_accuracy: 0.9108 - lr: 7.8125e-07\n",
      "Epoch 64/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3184 - accuracy: 0.9117 - val_loss: 0.3231 - val_accuracy: 0.9108 - lr: 7.8125e-07\n",
      "Epoch 65/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3183 - accuracy: 0.9118 - val_loss: 0.3231 - val_accuracy: 0.9108 - lr: 7.8125e-07\n",
      "Epoch 66/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3183 - accuracy: 0.9118 - val_loss: 0.3230 - val_accuracy: 0.9108 - lr: 7.8125e-07\n",
      "Epoch 67/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3182 - accuracy: 0.9118 - val_loss: 0.3230 - val_accuracy: 0.9108 - lr: 7.8125e-07\n",
      "Epoch 68/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3182 - accuracy: 0.9118 - val_loss: 0.3229 - val_accuracy: 0.9108 - lr: 7.8125e-07\n",
      "Epoch 69/1000\n",
      "1407/1407 [==============================] - 2s 1ms/step - loss: 0.3181 - accuracy: 0.9118 - val_loss: 0.3229 - val_accuracy: 0.9108 - lr: 7.8125e-07\n",
      "Epoch 70/1000\n",
      "1160/1407 [=======================>......] - ETA: 0s - loss: 0.3214 - accuracy: 0.9110"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-9fb452c2046d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m model.fit(X_train,Y_train_oh,batch_size = 32, epochs = 1000, \\\n\u001b[0;32m----> 2\u001b[0;31m           verbose = 1, callbacks= [early_stop, monitor, lr_schedule],validation_data=(X_val,Y_val_oh))\n\u001b[0m",
      "\u001b[0;32m~/Documents/software/virtualenv/venv01/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     64\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/software/virtualenv/venv01/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m    846\u001b[0m                 batch_size=batch_size):\n\u001b[1;32m    847\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 848\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    849\u001b[0m               \u001b[0;31m# Catch OutOfRangeError for Datasets of unknown size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    850\u001b[0m               \u001b[0;31m# This blocks until the batch has finished executing.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/software/virtualenv/venv01/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    578\u001b[0m         \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 580\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/software/virtualenv/venv01/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    609\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    610\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 611\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    612\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    613\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/software/virtualenv/venv01/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2418\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2419\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2420\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2422\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/software/virtualenv/venv01/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   1663\u001b[0m          if isinstance(t, (ops.Tensor,\n\u001b[1;32m   1664\u001b[0m                            resource_variable_ops.BaseResourceVariable))),\n\u001b[0;32m-> 1665\u001b[0;31m         self.captured_inputs)\n\u001b[0m\u001b[1;32m   1666\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1667\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/software/virtualenv/venv01/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1744\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1745\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1746\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1747\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1748\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/software/virtualenv/venv01/lib/python3.6/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    596\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    599\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[0;32m~/Documents/software/virtualenv/venv01/lib/python3.6/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.fit(X_train,Y_train_oh,batch_size = 32, epochs = 1000, \\\n",
    "          verbose = 1, callbacks= [early_stop, monitor, lr_schedule],validation_data=(X_val,Y_val_oh))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Test your model on the test and extract relevant metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 841us/step - loss: 0.3091 - accuracy: 0.9157\n",
      "Categorical cross-entropy: 0.309135377407074\n",
      "Accuracy: 0.9157000184059143\n"
     ]
    }
   ],
   "source": [
    "model.load_weights(model_name)\n",
    "metrics = model.evaluate(X_test,Y_test_oh)\n",
    "print(\"Categorical cross-entropy:\", metrics[0])\n",
    "print(\"Accuracy:\", metrics[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "843\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x1296 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Ypred = model.predict(X_test).argmax(axis = 1)\n",
    "wrong_indexes = np.where(Ypred != Y_test)[0]\n",
    "print(wrong_indexes.size)\n",
    "\n",
    "# Disaplying some samples from the development set\n",
    "sample_indexes = np.random.choice(np.arange(wrong_indexes.shape[0], dtype = int),size = 30, replace = False)\n",
    "plt.figure(figsize = (24,18))\n",
    "for (ii,jj) in enumerate(sample_indexes):\n",
    "    plt.subplot(5,6,ii+1)\n",
    "    plt.imshow(X_test[wrong_indexes[jj]], cmap = \"gray\")\n",
    "    plt.title(\"Label: %d, predicted: %d\" %(Y_test[wrong_indexes[jj]],Ypred[wrong_indexes[jj]]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (Optional) Displaying the weights of the layers as images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x1296 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weights = model.get_weights()\n",
    "plt.figure(figsize = (24,18))\n",
    "for ii in range(10):\n",
    "    plt.subplot(2,5,ii+1)\n",
    "    plt.imshow(weights[0][:,ii].reshape(28,28), cmap = \"gray\")\n",
    "    plt.title(\"Weights neuron: %d\" %(ii))\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}