CNN Image classifocationn python  Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import keras\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset imported succesfully\n"
     ]
    }
   ],
   "source": [
    "train = pd.read_csv('C:/Users/pc/Desktop/Fashion MNIST/fashion-mnist_train.csv')\n",
    "test = pd.read_csv('C:/Users/pc/Desktop/Fashion MNIST/fashion-mnist_test.csv')\n",
    "df = train.copy()\n",
    "test_df = test.copy()\n",
    "print(\"Dataset imported succesfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Head\n",
      "   label  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  \\\n",
      "0      2       0       0       0       0       0       0       0       0   \n",
      "1      9       0       0       0       0       0       0       0       0   \n",
      "2      6       0       0       0       0       0       0       0       5   \n",
      "3      0       0       0       0       1       2       0       0       0   \n",
      "4      3       0       0       0       0       0       0       0       0   \n",
      "\n",
      "   pixel9  ...  pixel775  pixel776  pixel777  pixel778  pixel779  pixel780  \\\n",
      "0       0  ...         0         0         0         0         0         0   \n",
      "1       0  ...         0         0         0         0         0         0   \n",
      "2       0  ...         0         0         0        30        43         0   \n",
      "3       0  ...         3         0         0         0         0         1   \n",
      "4       0  ...         0         0         0         0         0         0   \n",
      "\n",
      "   pixel781  pixel782  pixel783  pixel784  \n",
      "0         0         0         0         0  \n",
      "1         0         0         0         0  \n",
      "2         0         0         0         0  \n",
      "3         0         0         0         0  \n",
      "4         0         0         0         0  \n",
      "\n",
      "[5 rows x 785 columns]\n",
      "==============================\n"
     ]
    }
   ],
   "source": [
    "#view starting values of data set\n",
    "print(\"Dataset Head\")\n",
    "print(train.head())\n",
    "print(\"=\" * 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Features Details\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 60000 entries, 0 to 59999\n",
      "Columns: 785 entries, label to pixel784\n",
      "dtypes: int64(785)\n",
      "memory usage: 359.3 MB\n",
      "None\n",
      "==============================\n"
     ]
    }
   ],
   "source": [
    "# View How many samples and how many missing values for each feature\n",
    "print(\"Dataset Features Details\")\n",
    "print(train.info())\n",
    "print(\"=\" * 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring the dataset\n",
    "it is iportant to note that Label is the Target variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset Label Description\n",
    "0 T-shirt/top\n",
    "\n",
    "1 Trouser\n",
    "\n",
    "2 Pullover\n",
    "\n",
    "3 Dress\n",
    "\n",
    "4 Coat\n",
    "\n",
    "5 Sandal\n",
    "\n",
    "6 Shirt\n",
    "\n",
    "7 Sneaker\n",
    "\n",
    "8 Bag\n",
    "\n",
    "9 Ankle boot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examine Dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:  (60000, 785)\n",
      "Test:  (10000, 785)\n"
     ]
    }
   ],
   "source": [
    "print('Train: ', df.shape)\n",
    "print('Test: ', test_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, there are 60,000 Training Samples and 10,000 Test Samples.\n",
    "Each example is a 28x28 grayscale image, associated with a label from 10 classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 9, 6, 0, 3, 4, 5, 8, 7, 1], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.label.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking for Null Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " #Train DATASET\n",
    "df.isnull().any().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test DATASET\n",
    "test_df.isnull().any().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wonderful, So there are no Null Values in both Train and Test Set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mapping Classes lebels\n",
    "clothes = {0 : 'T-shirt/top',\n",
    "            1 : 'Trouser',\n",
    "            2 : 'Pullover',\n",
    "            3 : 'Dress',\n",
    "            4 : 'Coat',\n",
    "            5 : 'Sandal',\n",
    "            6 : 'Shirt',\n",
    "            7 : 'Sneaker',\n",
    "            8 : 'Bag',\n",
    "            9 : 'Ankle boot'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting Random Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(4, 4, figsize = (15,15))\n",
    "for row in axes:\n",
    "    for axe in row:\n",
    "        index = np.random.randint(60000)\n",
    "        img = df.drop('label', axis=1).values[index].reshape(28,28)\n",
    "        cloths = df['label'][index]\n",
    "        axe.imshow(img, cmap='gray')\n",
    "        axe.set_title(clothes[cloths])\n",
    "        axe.set_axis_off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the Distribution of labels to anaylze if there are any skewed classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9    6000\n",
       "8    6000\n",
       "7    6000\n",
       "6    6000\n",
       "5    6000\n",
       "4    6000\n",
       "3    6000\n",
       "2    6000\n",
       "1    6000\n",
       "0    6000\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pc\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n",
      "C:\\Users\\pc\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:3672: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1dd85682d68>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 324x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x='label', data=df, kind='count', size=3, aspect= 1.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It beautiful that all classes are equally Distributed.There is no anomalies in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting Random Seeds for Reproducibilty.\n",
    "seed = 66\n",
    "np.random.seed(seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split the data into train/validation/test datasets\n",
    "Now we are gonna split the training data into Train and Validation Set. \n",
    "\n",
    "Train set is used for Training the model and Validation set is used for Evaluating our Model's Performance on the Dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = train.iloc[:,1:]\n",
    "Y = train.iloc[:,0]\n",
    "x_train, x_validate, y_train, y_validate = train_test_split(x_train, y_train, test_size = 0.2, random_state = 12345)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(43200, 28, 28, 1)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6000, 28, 28, 1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# * unpack the tuple\n",
    "x_train = x_train.reshape(x_train.shape[0], *(28, 28, 1))\n",
    "x_test = x_test.reshape(x_test.shape[0], *(28, 28, 1))\n",
    "x_validate = x_validate.reshape(x_validate.shape[0], *(28, 28, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first parameter in reshape indicates the number of examples.\n",
    " We pass it as -1, which means that it is an unknown dimension and we want numpy to figure it out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(43200, 28, 28, 1)\n",
      "(43200, 28, 28, 1)\n",
      "(10800, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)\n",
    "print(x_train.shape)\n",
    "print(x_validate.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building the Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import TensorBoard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\pc\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 28, 28, 64)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 14, 14, 32)        8224      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 7, 7, 32)          0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 7, 7, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 1568)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 256)               401664    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                2570      \n",
      "=================================================================\n",
      "Total params: 412,778\n",
      "Trainable params: 412,778\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.Sequential()\n",
    "# Must define the input shape in the first layer of the neural network\n",
    "model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=2, padding='same', activation='relu', input_shape=(28,28,1))) \n",
    "model.add(tf.keras.layers.MaxPooling2D(pool_size=2))\n",
    "model.add(tf.keras.layers.Dropout(0.3))\n",
    "model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=2, padding='same', activation='relu'))\n",
    "model.add(tf.keras.layers.MaxPooling2D(pool_size=2))\n",
    "model.add(tf.keras.layers.Dropout(0.3))\n",
    "model.add(tf.keras.layers.Flatten())\n",
    "model.add(tf.keras.layers.Dense(256, activation='relu'))\n",
    "model.add(tf.keras.layers.Dropout(0.5))\n",
    "model.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
    "# Take a look at the model summary\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss ='sparse_categorical_crossentropy', optimizer='adam' ,metrics =['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 43200 samples, validate on 10800 samples\n",
      "Epoch 1/150\n",
      "43200/43200 [==============================] - 76s 2ms/sample - loss: 4.3887 - acc: 0.4623 - val_loss: 0.8466 - val_acc: 0.6875\n",
      "Epoch 2/150\n",
      "43200/43200 [==============================] - 73s 2ms/sample - loss: 0.9502 - acc: 0.6544 - val_loss: 0.6472 - val_acc: 0.7543\n",
      "Epoch 3/150\n",
      "43200/43200 [==============================] - 73s 2ms/sample - loss: 0.7803 - acc: 0.7049 - val_loss: 0.5486 - val_acc: 0.7912\n",
      "Epoch 4/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.6893 - acc: 0.7388 - val_loss: 0.5318 - val_acc: 0.7856\n",
      "Epoch 5/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.6426 - acc: 0.7534 - val_loss: 0.4876 - val_acc: 0.8058\n",
      "Epoch 6/150\n",
      "43200/43200 [==============================] - 72s 2ms/sample - loss: 0.6032 - acc: 0.7686 - val_loss: 0.4773 - val_acc: 0.8037\n",
      "Epoch 7/150\n",
      "43200/43200 [==============================] - 72s 2ms/sample - loss: 0.5719 - acc: 0.7796 - val_loss: 0.4438 - val_acc: 0.8319\n",
      "Epoch 8/150\n",
      "43200/43200 [==============================] - 72s 2ms/sample - loss: 0.5530 - acc: 0.7882 - val_loss: 0.4262 - val_acc: 0.8419\n",
      "Epoch 9/150\n",
      "43200/43200 [==============================] - 72s 2ms/sample - loss: 0.5355 - acc: 0.7971 - val_loss: 0.4175 - val_acc: 0.8485\n",
      "Epoch 10/150\n",
      "43200/43200 [==============================] - 72s 2ms/sample - loss: 0.5166 - acc: 0.8053 - val_loss: 0.4020 - val_acc: 0.8494\n",
      "Epoch 11/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.5024 - acc: 0.8104 - val_loss: 0.3837 - val_acc: 0.8569\n",
      "Epoch 12/150\n",
      "43200/43200 [==============================] - 70s 2ms/sample - loss: 0.4818 - acc: 0.8186 - val_loss: 0.3787 - val_acc: 0.8648\n",
      "Epoch 13/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.4718 - acc: 0.8245 - val_loss: 0.3624 - val_acc: 0.8648\n",
      "Epoch 14/150\n",
      "43200/43200 [==============================] - 72s 2ms/sample - loss: 0.4584 - acc: 0.8309 - val_loss: 0.3587 - val_acc: 0.8645\n",
      "Epoch 15/150\n",
      "43200/43200 [==============================] - 70s 2ms/sample - loss: 0.4484 - acc: 0.8326 - val_loss: 0.3505 - val_acc: 0.8716\n",
      "Epoch 16/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.4395 - acc: 0.8363 - val_loss: 0.3400 - val_acc: 0.8751\n",
      "Epoch 17/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.4243 - acc: 0.8438 - val_loss: 0.3330 - val_acc: 0.8764\n",
      "Epoch 18/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.4207 - acc: 0.8444 - val_loss: 0.3300 - val_acc: 0.8788\n",
      "Epoch 19/150\n",
      "43200/43200 [==============================] - 74s 2ms/sample - loss: 0.4071 - acc: 0.8506 - val_loss: 0.3266 - val_acc: 0.8781\n",
      "Epoch 20/150\n",
      "43200/43200 [==============================] - 72s 2ms/sample - loss: 0.4028 - acc: 0.8515 - val_loss: 0.3222 - val_acc: 0.8799\n",
      "Epoch 21/150\n",
      "43200/43200 [==============================] - 76s 2ms/sample - loss: 0.3949 - acc: 0.8556 - val_loss: 0.3164 - val_acc: 0.8843\n",
      "Epoch 22/150\n",
      "43200/43200 [==============================] - 74s 2ms/sample - loss: 0.3861 - acc: 0.8562 - val_loss: 0.3123 - val_acc: 0.8816\n",
      "Epoch 23/150\n",
      "43200/43200 [==============================] - 75s 2ms/sample - loss: 0.3811 - acc: 0.8602 - val_loss: 0.3126 - val_acc: 0.8831\n",
      "Epoch 24/150\n",
      "43200/43200 [==============================] - 75s 2ms/sample - loss: 0.3785 - acc: 0.8612 - val_loss: 0.3035 - val_acc: 0.8895\n",
      "Epoch 25/150\n",
      "43200/43200 [==============================] - 74s 2ms/sample - loss: 0.3728 - acc: 0.8604 - val_loss: 0.3070 - val_acc: 0.8833\n",
      "Epoch 26/150\n",
      "43200/43200 [==============================] - 75s 2ms/sample - loss: 0.3639 - acc: 0.8648 - val_loss: 0.2972 - val_acc: 0.8915\n",
      "Epoch 27/150\n",
      "43200/43200 [==============================] - 73s 2ms/sample - loss: 0.3582 - acc: 0.8682 - val_loss: 0.2934 - val_acc: 0.8919\n",
      "Epoch 28/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.3564 - acc: 0.8686 - val_loss: 0.2949 - val_acc: 0.8894\n",
      "Epoch 29/150\n",
      "43200/43200 [==============================] - 72s 2ms/sample - loss: 0.3521 - acc: 0.8720 - val_loss: 0.2916 - val_acc: 0.8946\n",
      "Epoch 30/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.3504 - acc: 0.8738 - val_loss: 0.2851 - val_acc: 0.8932\n",
      "Epoch 31/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.3415 - acc: 0.8733 - val_loss: 0.2797 - val_acc: 0.8974\n",
      "Epoch 32/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.3392 - acc: 0.8761 - val_loss: 0.2863 - val_acc: 0.8931\n",
      "Epoch 33/150\n",
      "43200/43200 [==============================] - 72s 2ms/sample - loss: 0.3310 - acc: 0.8769 - val_loss: 0.2834 - val_acc: 0.8935\n",
      "Epoch 34/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.3307 - acc: 0.8785 - val_loss: 0.2802 - val_acc: 0.8941\n",
      "Epoch 35/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.3264 - acc: 0.8797 - val_loss: 0.2753 - val_acc: 0.8994\n",
      "Epoch 36/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.3233 - acc: 0.8799 - val_loss: 0.2784 - val_acc: 0.8963\n",
      "Epoch 37/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.3224 - acc: 0.8821 - val_loss: 0.2714 - val_acc: 0.9002\n",
      "Epoch 38/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.3216 - acc: 0.8827 - val_loss: 0.2753 - val_acc: 0.8992\n",
      "Epoch 39/150\n",
      "43200/43200 [==============================] - 74s 2ms/sample - loss: 0.3158 - acc: 0.8828 - val_loss: 0.2680 - val_acc: 0.9019\n",
      "Epoch 40/150\n",
      "43200/43200 [==============================] - 70s 2ms/sample - loss: 0.3134 - acc: 0.8833 - val_loss: 0.2727 - val_acc: 0.9006\n",
      "Epoch 41/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.3097 - acc: 0.8855 - val_loss: 0.2676 - val_acc: 0.9025\n",
      "Epoch 42/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.3079 - acc: 0.8861 - val_loss: 0.2826 - val_acc: 0.8946\n",
      "Epoch 43/150\n",
      "43200/43200 [==============================] - 70s 2ms/sample - loss: 0.2972 - acc: 0.8890 - val_loss: 0.2708 - val_acc: 0.9012\n",
      "Epoch 44/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.3034 - acc: 0.8864 - val_loss: 0.2678 - val_acc: 0.9000\n",
      "Epoch 45/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.2953 - acc: 0.8921 - val_loss: 0.2659 - val_acc: 0.9010\n",
      "Epoch 46/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.2957 - acc: 0.8918 - val_loss: 0.2601 - val_acc: 0.9039\n",
      "Epoch 47/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.2949 - acc: 0.8915 - val_loss: 0.2656 - val_acc: 0.9026\n",
      "Epoch 48/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.2866 - acc: 0.8933 - val_loss: 0.2624 - val_acc: 0.9041\n",
      "Epoch 49/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.2876 - acc: 0.8936 - val_loss: 0.2599 - val_acc: 0.9055\n",
      "Epoch 50/150\n",
      "43200/43200 [==============================] - 72s 2ms/sample - loss: 0.2870 - acc: 0.8949 - val_loss: 0.2611 - val_acc: 0.9044\n",
      "Epoch 51/150\n",
      "43200/43200 [==============================] - 74s 2ms/sample - loss: 0.2834 - acc: 0.8961 - val_loss: 0.2577 - val_acc: 0.9072\n",
      "Epoch 52/150\n",
      "43200/43200 [==============================] - 78s 2ms/sample - loss: 0.2808 - acc: 0.8983 - val_loss: 0.2586 - val_acc: 0.9075\n",
      "Epoch 53/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.2788 - acc: 0.8973 - val_loss: 0.2649 - val_acc: 0.9025\n",
      "Epoch 54/150\n",
      "43200/43200 [==============================] - 73s 2ms/sample - loss: 0.2760 - acc: 0.8990 - val_loss: 0.2566 - val_acc: 0.9081\n",
      "Epoch 55/150\n",
      "43200/43200 [==============================] - 77s 2ms/sample - loss: 0.2766 - acc: 0.8974 - val_loss: 0.2571 - val_acc: 0.9069\n",
      "Epoch 56/150\n",
      "43200/43200 [==============================] - 76s 2ms/sample - loss: 0.2708 - acc: 0.8992 - val_loss: 0.2572 - val_acc: 0.9101\n",
      "Epoch 57/150\n",
      "43200/43200 [==============================] - 75s 2ms/sample - loss: 0.2696 - acc: 0.9001 - val_loss: 0.2508 - val_acc: 0.9128\n",
      "Epoch 58/150\n",
      "43200/43200 [==============================] - 85s 2ms/sample - loss: 0.2650 - acc: 0.9021 - val_loss: 0.2561 - val_acc: 0.9081\n",
      "Epoch 59/150\n",
      "43200/43200 [==============================] - 82s 2ms/sample - loss: 0.2669 - acc: 0.8999 - val_loss: 0.2553 - val_acc: 0.9078\n",
      "Epoch 60/150\n",
      "43200/43200 [==============================] - 78s 2ms/sample - loss: 0.2653 - acc: 0.9026 - val_loss: 0.2564 - val_acc: 0.9070\n",
      "Epoch 61/150\n",
      "43200/43200 [==============================] - 79s 2ms/sample - loss: 0.2616 - acc: 0.9036 - val_loss: 0.2512 - val_acc: 0.9107\n",
      "Epoch 62/150\n",
      "43200/43200 [==============================] - 79s 2ms/sample - loss: 0.2618 - acc: 0.9027 - val_loss: 0.2499 - val_acc: 0.9121\n",
      "Epoch 63/150\n",
      "43200/43200 [==============================] - 78s 2ms/sample - loss: 0.2606 - acc: 0.9022 - val_loss: 0.2535 - val_acc: 0.9092\n",
      "Epoch 64/150\n",
      "43200/43200 [==============================] - 76s 2ms/sample - loss: 0.2568 - acc: 0.9033 - val_loss: 0.2504 - val_acc: 0.9073\n",
      "Epoch 65/150\n",
      "43200/43200 [==============================] - 74s 2ms/sample - loss: 0.2505 - acc: 0.9053 - val_loss: 0.2481 - val_acc: 0.9119\n",
      "Epoch 66/150\n",
      "43200/43200 [==============================] - 76s 2ms/sample - loss: 0.2579 - acc: 0.9052 - val_loss: 0.2516 - val_acc: 0.9131\n",
      "Epoch 67/150\n",
      "43200/43200 [==============================] - 76s 2ms/sample - loss: 0.2536 - acc: 0.9057 - val_loss: 0.2504 - val_acc: 0.9142\n",
      "Epoch 68/150\n",
      "43200/43200 [==============================] - 76s 2ms/sample - loss: 0.2520 - acc: 0.9078 - val_loss: 0.2526 - val_acc: 0.9108\n",
      "Epoch 69/150\n",
      "43200/43200 [==============================] - 78s 2ms/sample - loss: 0.2501 - acc: 0.9059 - val_loss: 0.2472 - val_acc: 0.9111\n",
      "Epoch 70/150\n",
      "43200/43200 [==============================] - 76s 2ms/sample - loss: 0.2481 - acc: 0.9080 - val_loss: 0.2469 - val_acc: 0.9132\n",
      "Epoch 71/150\n",
      "43200/43200 [==============================] - 80s 2ms/sample - loss: 0.2480 - acc: 0.9082 - val_loss: 0.2453 - val_acc: 0.9151\n",
      "Epoch 72/150\n",
      "43200/43200 [==============================] - 76s 2ms/sample - loss: 0.2467 - acc: 0.9079 - val_loss: 0.2451 - val_acc: 0.9122\n",
      "Epoch 73/150\n",
      "43200/43200 [==============================] - 81s 2ms/sample - loss: 0.2447 - acc: 0.9082 - val_loss: 0.2441 - val_acc: 0.9158\n",
      "Epoch 74/150\n",
      "43200/43200 [==============================] - 79s 2ms/sample - loss: 0.2400 - acc: 0.9114 - val_loss: 0.2469 - val_acc: 0.9138\n",
      "Epoch 75/150\n",
      "43200/43200 [==============================] - 79s 2ms/sample - loss: 0.2401 - acc: 0.9101 - val_loss: 0.2510 - val_acc: 0.9164\n",
      "Epoch 76/150\n",
      "43200/43200 [==============================] - 72s 2ms/sample - loss: 0.2322 - acc: 0.9122 - val_loss: 0.2395 - val_acc: 0.9148\n",
      "Epoch 77/150\n",
      "43200/43200 [==============================] - 74s 2ms/sample - loss: 0.2348 - acc: 0.9123 - val_loss: 0.2458 - val_acc: 0.9131\n",
      "Epoch 78/150\n",
      "43200/43200 [==============================] - 73s 2ms/sample - loss: 0.2435 - acc: 0.9099 - val_loss: 0.2447 - val_acc: 0.9135\n",
      "Epoch 79/150\n",
      "43200/43200 [==============================] - 73s 2ms/sample - loss: 0.2320 - acc: 0.9148 - val_loss: 0.2466 - val_acc: 0.9134\n",
      "Epoch 80/150\n",
      "43200/43200 [==============================] - 73s 2ms/sample - loss: 0.2281 - acc: 0.9134 - val_loss: 0.2444 - val_acc: 0.9172\n",
      "Epoch 81/150\n",
      "43200/43200 [==============================] - 76s 2ms/sample - loss: 0.2327 - acc: 0.9120 - val_loss: 0.2406 - val_acc: 0.9180\n",
      "Epoch 82/150\n",
      "43200/43200 [==============================] - 76s 2ms/sample - loss: 0.2282 - acc: 0.9139 - val_loss: 0.2472 - val_acc: 0.9184\n",
      "Epoch 83/150\n",
      "43200/43200 [==============================] - 76s 2ms/sample - loss: 0.2329 - acc: 0.9127 - val_loss: 0.2430 - val_acc: 0.9153\n",
      "Epoch 84/150\n",
      "43200/43200 [==============================] - 73s 2ms/sample - loss: 0.2275 - acc: 0.9139 - val_loss: 0.2527 - val_acc: 0.9144\n",
      "Epoch 85/150\n",
      "43200/43200 [==============================] - 75s 2ms/sample - loss: 0.2249 - acc: 0.9162 - val_loss: 0.2443 - val_acc: 0.9156\n",
      "Epoch 86/150\n",
      "43200/43200 [==============================] - 77s 2ms/sample - loss: 0.2266 - acc: 0.9158 - val_loss: 0.2436 - val_acc: 0.9175\n",
      "Epoch 87/150\n",
      "43200/43200 [==============================] - 82s 2ms/sample - loss: 0.2223 - acc: 0.9172 - val_loss: 0.2388 - val_acc: 0.9170\n",
      "Epoch 88/150\n",
      "43200/43200 [==============================] - 73s 2ms/sample - loss: 0.2163 - acc: 0.9188 - val_loss: 0.2455 - val_acc: 0.9169\n",
      "Epoch 89/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.2225 - acc: 0.9156 - val_loss: 0.2456 - val_acc: 0.9192\n",
      "Epoch 90/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.2147 - acc: 0.9180 - val_loss: 0.2446 - val_acc: 0.9175\n",
      "Epoch 91/150\n",
      "43200/43200 [==============================] - 72s 2ms/sample - loss: 0.2186 - acc: 0.9184 - val_loss: 0.2479 - val_acc: 0.9176\n",
      "Epoch 92/150\n",
      "43200/43200 [==============================] - 70s 2ms/sample - loss: 0.2182 - acc: 0.9164 - val_loss: 0.2446 - val_acc: 0.9197\n",
      "Epoch 93/150\n",
      "43200/43200 [==============================] - 70s 2ms/sample - loss: 0.2158 - acc: 0.9194 - val_loss: 0.2399 - val_acc: 0.9210\n",
      "Epoch 94/150\n",
      "43200/43200 [==============================] - 70s 2ms/sample - loss: 0.2161 - acc: 0.9208 - val_loss: 0.2481 - val_acc: 0.9178\n",
      "Epoch 95/150\n",
      "43200/43200 [==============================] - 71s 2ms/sample - loss: 0.2146 - acc: 0.9180 - val_loss: 0.2380 - val_acc: 0.9186\n",
      "Epoch 96/150\n",
      "43200/43200 [==============================] - 70s 2ms/sample - loss: 0.2160 - acc: 0.9184 - val_loss: 0.2400 - val_acc: 0.9200\n",
      "Epoch 97/150\n",
      "43200/43200 [==============================] - 70s 2ms/sample - loss: 0.2109 - acc: 0.9213 - val_loss: 0.2425 - val_acc: 0.9161\n",
      "Epoch 98/150\n",
      "43200/43200 [==============================] - 70s 2ms/sample - loss: 0.2119 - acc: 0.9207 - val_loss: 0.2384 - val_acc: 0.9184\n",
      "Epoch 99/150\n",
      "43200/43200 [==============================] - 75s 2ms/sample - loss: 0.2080 - acc: 0.9224 - val_loss: 0.2416 - val_acc: 0.9167\n",
      "Epoch 100/150\n",
      "43200/43200 [==============================] - 75s 2ms/sample - loss: 0.2090 - acc: 0.9231 - val_loss: 0.2424 - val_acc: 0.9165\n",
      "Epoch 101/150\n",
      "43200/43200 [==============================] - 72s 2ms/sample - loss: 0.2065 - acc: 0.9226 - val_loss: 0.2426 - val_acc: 0.9188\n",
      "Epoch 102/150\n",
      "43200/43200 [==============================] - 76s 2ms/sample - loss: 0.2090 - acc: 0.9211 - val_loss: 0.2365 - val_acc: 0.9196\n",
      "Epoch 103/150\n",
      "43200/43200 [==============================] - 74s 2ms/sample - loss: 0.2039 - acc: 0.9238 - val_loss: 0.2432 - val_acc: 0.9171\n",
      "Epoch 104/150\n",
      "43200/43200 [==============================] - 76s 2ms/sample - loss: 0.2062 - acc: 0.9232 - val_loss: 0.2422 - val_acc: 0.9194\n",
      "Epoch 105/150\n",
      "43200/43200 [==============================] - 80s 2ms/sample - loss: 0.2006 - acc: 0.9244 - val_loss: 0.2491 - val_acc: 0.9188\n",
      "Epoch 106/150\n",
      "43200/43200 [==============================] - 96s 2ms/sample - loss: 0.2019 - acc: 0.9251 - val_loss: 0.2374 - val_acc: 0.9211\n",
      "Epoch 107/150\n",
      "43200/43200 [==============================] - 89s 2ms/sample - loss: 0.2024 - acc: 0.9239 - val_loss: 0.2368 - val_acc: 0.9184\n",
      "Epoch 108/150\n",
      "43200/43200 [==============================] - 88s 2ms/sample - loss: 0.2014 - acc: 0.9234 - val_loss: 0.2389 - val_acc: 0.9206\n",
      "Epoch 109/150\n",
      "43200/43200 [==============================] - 90s 2ms/sample - loss: 0.1996 - acc: 0.9249 - val_loss: 0.2461 - val_acc: 0.9170\n",
      "Epoch 110/150\n",
      "43200/43200 [==============================] - 85s 2ms/sample - loss: 0.1999 - acc: 0.9246 - val_loss: 0.2430 - val_acc: 0.9192\n",
      "Epoch 111/150\n",
      "43200/43200 [==============================] - 87s 2ms/sample - loss: 0.1960 - acc: 0.9259 - val_loss: 0.2524 - val_acc: 0.9166\n",
      "Epoch 112/150\n",
      "43200/43200 [==============================] - 87s 2ms/sample - loss: 0.1954 - acc: 0.9267 - val_loss: 0.2382 - val_acc: 0.9209\n",
      "Epoch 113/150\n",
      "43200/43200 [==============================] - 89s 2ms/sample - loss: 0.1964 - acc: 0.9270 - val_loss: 0.2353 - val_acc: 0.9195\n",
      "Epoch 114/150\n",
      "43200/43200 [==============================] - 89s 2ms/sample - loss: 0.1921 - acc: 0.9274 - val_loss: 0.2415 - val_acc: 0.9195\n",
      "Epoch 115/150\n",
      "43200/43200 [==============================] - 81s 2ms/sample - loss: 0.1982 - acc: 0.9257 - val_loss: 0.2374 - val_acc: 0.9203\n",
      "Epoch 116/150\n",
      "43200/43200 [==============================] - 85s 2ms/sample - loss: 0.1923 - acc: 0.9262 - val_loss: 0.2551 - val_acc: 0.9148\n",
      "Epoch 117/150\n",
      "43200/43200 [==============================] - 85s 2ms/sample - loss: 0.1951 - acc: 0.9281 - val_loss: 0.2483 - val_acc: 0.9171\n",
      "Epoch 118/150\n",
      "43200/43200 [==============================] - 87s 2ms/sample - loss: 0.1912 - acc: 0.9285 - val_loss: 0.2417 - val_acc: 0.9184\n",
      "Epoch 119/150\n",
      "43200/43200 [==============================] - 89s 2ms/sample - loss: 0.1918 - acc: 0.9268 - val_loss: 0.2442 - val_acc: 0.9198\n",
      "Epoch 120/150\n",
      "43200/43200 [==============================] - 88s 2ms/sample - loss: 0.1921 - acc: 0.9261 - val_loss: 0.2489 - val_acc: 0.9185\n",
      "Epoch 121/150\n",
      "43200/43200 [==============================] - 84s 2ms/sample - loss: 0.1949 - acc: 0.9268 - val_loss: 0.2383 - val_acc: 0.9215\n",
      "Epoch 122/150\n",
      "43200/43200 [==============================] - 81s 2ms/sample - loss: 0.1902 - acc: 0.9294 - val_loss: 0.2370 - val_acc: 0.9194\n",
      "Epoch 123/150\n",
      "43200/43200 [==============================] - 81s 2ms/sample - loss: 0.1914 - acc: 0.9278 - val_loss: 0.2538 - val_acc: 0.9147\n",
      "Epoch 124/150\n",
      "43200/43200 [==============================] - 84s 2ms/sample - loss: 0.1875 - acc: 0.9293 - val_loss: 0.2443 - val_acc: 0.9186\n",
      "Epoch 125/150\n",
      "43200/43200 [==============================] - 84s 2ms/sample - loss: 0.1923 - acc: 0.9262 - val_loss: 0.2494 - val_acc: 0.9130\n",
      "Epoch 126/150\n",
      "43200/43200 [==============================] - 85s 2ms/sample - loss: 0.1878 - acc: 0.9296 - val_loss: 0.2411 - val_acc: 0.9197\n",
      "Epoch 127/150\n",
      "43200/43200 [==============================] - 86s 2ms/sample - loss: 0.1851 - acc: 0.9294 - val_loss: 0.2441 - val_acc: 0.9182\n",
      "Epoch 128/150\n",
      "43200/43200 [==============================] - 82s 2ms/sample - loss: 0.1920 - acc: 0.9273 - val_loss: 0.2419 - val_acc: 0.9193\n",
      "Epoch 129/150\n",
      "43200/43200 [==============================] - 81s 2ms/sample - loss: 0.1881 - acc: 0.9293 - val_loss: 0.2430 - val_acc: 0.9193\n",
      "Epoch 130/150\n",
      "43200/43200 [==============================] - 81s 2ms/sample - loss: 0.1849 - acc: 0.9315 - val_loss: 0.2463 - val_acc: 0.9196\n",
      "Epoch 131/150\n",
      "43200/43200 [==============================] - 81s 2ms/sample - loss: 0.1817 - acc: 0.9322 - val_loss: 0.2459 - val_acc: 0.9203\n",
      "Epoch 132/150\n",
      "43200/43200 [==============================] - 84s 2ms/sample - loss: 0.1918 - acc: 0.9290 - val_loss: 0.2431 - val_acc: 0.9196\n",
      "Epoch 133/150\n",
      "43200/43200 [==============================] - 82s 2ms/sample - loss: 0.1854 - acc: 0.9300 - val_loss: 0.2430 - val_acc: 0.9190\n",
      "Epoch 134/150\n",
      "43200/43200 [==============================] - 82s 2ms/sample - loss: 0.1837 - acc: 0.9297 - val_loss: 0.2480 - val_acc: 0.9189\n",
      "Epoch 135/150\n",
      "43200/43200 [==============================] - 82s 2ms/sample - loss: 0.1828 - acc: 0.9319 - val_loss: 0.2496 - val_acc: 0.9174\n",
      "Epoch 136/150\n",
      "43200/43200 [==============================] - 83s 2ms/sample - loss: 0.1843 - acc: 0.9310 - val_loss: 0.2379 - val_acc: 0.9195\n",
      "Epoch 137/150\n",
      "43200/43200 [==============================] - 82s 2ms/sample - loss: 0.1781 - acc: 0.9325 - val_loss: 0.2404 - val_acc: 0.9198\n",
      "Epoch 138/150\n",
      "43200/43200 [==============================] - 81s 2ms/sample - loss: 0.1779 - acc: 0.9334 - val_loss: 0.2458 - val_acc: 0.9204\n",
      "Epoch 139/150\n",
      "43200/43200 [==============================] - 82s 2ms/sample - loss: 0.1778 - acc: 0.9325 - val_loss: 0.2497 - val_acc: 0.9168\n",
      "Epoch 140/150\n",
      "43200/43200 [==============================] - 81s 2ms/sample - loss: 0.1809 - acc: 0.9332 - val_loss: 0.2509 - val_acc: 0.9153\n",
      "Epoch 141/150\n",
      "43200/43200 [==============================] - 80s 2ms/sample - loss: 0.1818 - acc: 0.9307 - val_loss: 0.2417 - val_acc: 0.9196\n",
      "Epoch 142/150\n",
      "43200/43200 [==============================] - 81s 2ms/sample - loss: 0.1795 - acc: 0.9313 - val_loss: 0.2516 - val_acc: 0.9173\n",
      "Epoch 143/150\n",
      "43200/43200 [==============================] - 85s 2ms/sample - loss: 0.1842 - acc: 0.9306 - val_loss: 0.2524 - val_acc: 0.9173\n",
      "Epoch 144/150\n",
      "43200/43200 [==============================] - 82s 2ms/sample - loss: 0.1774 - acc: 0.9332 - val_loss: 0.2414 - val_acc: 0.9198\n",
      "Epoch 145/150\n",
      "43200/43200 [==============================] - 82s 2ms/sample - loss: 0.1781 - acc: 0.9331 - val_loss: 0.2417 - val_acc: 0.9219\n",
      "Epoch 146/150\n",
      "43200/43200 [==============================] - 82s 2ms/sample - loss: 0.1716 - acc: 0.9345 - val_loss: 0.2409 - val_acc: 0.9210\n",
      "Epoch 147/150\n",
      "43200/43200 [==============================] - 85s 2ms/sample - loss: 0.1703 - acc: 0.9355 - val_loss: 0.2544 - val_acc: 0.9165\n",
      "Epoch 148/150\n",
      "43200/43200 [==============================] - 82s 2ms/sample - loss: 0.1765 - acc: 0.9334 - val_loss: 0.2529 - val_acc: 0.9174\n",
      "Epoch 149/150\n",
      "43200/43200 [==============================] - 86s 2ms/sample - loss: 0.1759 - acc: 0.9341 - val_loss: 0.2432 - val_acc: 0.9204\n",
      "Epoch 150/150\n",
      "43200/43200 [==============================] - 83s 2ms/sample - loss: 0.1734 - acc: 0.9350 - val_loss: 0.2497 - val_acc: 0.9186\n"
     ]
    }
   ],
   "source": [
    "epochs = 150\n",
    "\n",
    "history = cnn_model.fit(x_train, y_train, batch_size = 512, epochs = epochs,\n",
    "                        verbose = 1, validation_data = (x_validate, y_validate))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vizualizing the Loss and Accuracy evolution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Loss evolution')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.plot(history.history['loss'], label='Loss')\n",
    "plt.plot(history.history['val_loss'], label='val_Loss')\n",
    "plt.legend()\n",
    "plt.title('Loss evolution')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title(\" Accuracy Evolution\")\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(['Accuracy', 'val_accuracy'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating the model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6000/6000 [==============================] - 4s 655us/sample - loss: 0.2320 - acc: 0.9265\n",
      "Test Accuracy : 0.927\n"
     ]
    }
   ],
   "source": [
    "evaluation = model.evaluate(x_test, y_test)\n",
    "print(f'Test Accuracy : {evaluation[1]:.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    " #get the predictions for the test data\n",
    "predicted_classes = model.predict_classes(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.4835845e-09, 1.3155711e-10, 4.3822768e-01, 1.9306255e-11,\n",
       "       5.6083643e-01, 4.3774853e-18, 9.3585829e-04, 4.4857516e-21,\n",
       "       4.8536983e-09, 3.6216379e-17], dtype=float32)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_img = x_test[0]\n",
    "prediction = model.predict(x_test)\n",
    "prediction[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(prediction[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-57-2f85bb3b7780>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mL\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mW\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m28\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m28\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"Prediction Class = {predicted_classes[i]:0.1f}\\n True Class = {y_test[i]:0.1f}\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m     \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'off'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    866\u001b[0m         \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    867\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 868\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    869\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    870\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   4373\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4374\u001b[0m             return self._engine.get_value(s, k,\n\u001b[1;32m-> 4375\u001b[1;33m                                           tz=getattr(series.dtype, 'tz', None))\n\u001b[0m\u001b[0;32m   4376\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4377\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mholds_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_boolean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 0"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "L = 5\n",
    "W = 5\n",
    "fig, axes = plt.subplots(L, W, figsize = (12,12))\n",
    "axes = axes.ravel()\n",
    "\n",
    "for i in np.arange(0, L * W):  \n",
    "    axes[i].imshow(x_test[i].reshape(28,28))\n",
    "    axes[i].set_title(f\"Prediction Class = {predicted_classes[i]:0.1f}\\n True Class = {y_test[i]:0.1f}\")\n",
    "    axes[i].axis('off')\n",
    "\n",
    "plt.subplots_adjust(wspace=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1dd86be1f98>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cmatrix = confusion_matrix(y_test, predicted_classes)\n",
    "plt.figure(figsize = (14,10))\n",
    "sns.heatmap(cmatrix, annot=True)\n",
    "# Sum the diagonal element to get the total true correct values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Precision and Recall "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Class 0       0.88      0.89      0.89       629\n",
      "     Class 1       1.00      0.99      0.99       580\n",
      "     Class 2       0.89      0.87      0.88       550\n",
      "     Class 3       0.91      0.93      0.92       612\n",
      "     Class 4       0.86      0.88      0.87       598\n",
      "     Class 5       0.99      0.98      0.99       613\n",
      "     Class 6       0.80      0.78      0.79       592\n",
      "     Class 7       0.97      0.99      0.98       594\n",
      "     Class 8       0.98      0.98      0.98       623\n",
      "     Class 9       0.98      0.97      0.98       609\n",
      "\n",
      "    accuracy                           0.93      6000\n",
      "   macro avg       0.93      0.93      0.93      6000\n",
      "weighted avg       0.93      0.93      0.93      6000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "num_classes = 10\n",
    "target_names = [f\"Class {i}\" for i in range(num_classes)]\n",
    "\n",
    "print(classification_report(y_test, predicted_classes, target_names = target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}