leenet_for_mnist_digit_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9d637b8b",
   "metadata": {},
   "source": [
    "###  Lenet-5 For MNIST hand written digit dataset\n",
    "\n",
    "<hr>\n",
    "\n",
    "#### Challenge - Build a DNN Model for MNIST Dataset Provided below. Expected Test accuracy is 97 %. The model build should have the number of Neural Network parameters smaller than 1 Lakh\n",
    "\n",
    "Data Set URL - https://www.kaggle.com/datasets/oddrationale/mnist-in-csv\n",
    "\n",
    "<hr>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3e3d6fd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.10.0\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np                                           # For Feature scalling , Accuracy Calculation, Confusion_matrix\n",
    "import pandas as pd                                          # For Loading Datset  \n",
    "import matplotlib.pyplot as plt                              # For Image Visulization\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import Sequential                              # Sequential Api Has been used for Building Model\n",
    "from tensorflow.keras.layers import Reshape,MaxPooling2D,Flatten     # For Model Layers\n",
    "from tensorflow.keras.layers import BatchNormalization,Conv2D,Dense  # For Model Layers\n",
    "from tensorflow.keras.initializers import HeNormal, GlorotNormal     # For Layers Weight initialization\n",
    "from tensorflow.keras.optimizers import Adam                         # For Grediant Descent Optimization.\n",
    "from tensorflow.keras.callbacks import EarlyStopping,ModelCheckpoint\n",
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68d59446",
   "metadata": {},
   "source": [
    "#### Settingup Dataset Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ed7dfc6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traing and test file found on path .\\data\n"
     ]
    }
   ],
   "source": [
    "dataset_dir = os.path.join(\".\", 'data')                      #Dataset Directory Path\n",
    "\n",
    "training_file = os.path.join(dataset_dir, 'mnist_train.csv') #Training Dataset Path\n",
    "testing_file = os.path.join(dataset_dir, 'mnist_test.csv')   #Test Dataset Path \n",
    "\n",
    "if os.path.isfile(training_file) is False:                   #Validating the Training dataset Path\n",
    "    raise FileNotFoundError(f'training file {training_file} not Valid')\n",
    "\n",
    "if os.path.isfile(testing_file) is False:                    #Validating the Test dataset Path\n",
    "    raise FileNotFoundError(f'testing file {testing_file} not Valid')\n",
    "\n",
    "print(f'Traing and test file found on path {dataset_dir}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d373b10",
   "metadata": {},
   "source": [
    "#### Loading Taring and Testing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "38427abc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Dataset Shape 60000 X 785\n",
      "Test Dataset Shape 10000 X 785\n"
     ]
    }
   ],
   "source": [
    "training_df = pd.read_csv(training_file)\n",
    "\n",
    "traindata_size, train_feature = training_df.shape\n",
    "\n",
    "print(f'Training Dataset Shape {traindata_size} X {train_feature}')\n",
    "\n",
    "test_df = pd.read_csv(testing_file)\n",
    "\n",
    "testdata_size, test_feature = test_df.shape\n",
    "\n",
    "print(f'Test Dataset Shape {testdata_size} X {test_feature}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03e61d68",
   "metadata": {},
   "source": [
    "#### Training and Test datasets target values counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "61ea1381",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (axs1, axs2) = plt.subplots(nrows=1, ncols=2, figsize=(18, 5))\n",
    "\n",
    "label_counts1 = training_df['label'].value_counts().sort_index().to_dict()\n",
    "axs1.bar(label_counts1.keys(), label_counts1.values())\n",
    "axs1.set_xlabel('Label')\n",
    "axs1.xaxis.set_ticks(list(label_counts1.keys()))\n",
    "axs1.set_title('Traing-set label counts')\n",
    "axs1.set_ylabel('Count')\n",
    "\n",
    "\n",
    "label_counts2 = test_df['label'].value_counts().sort_index().to_dict()\n",
    "axs2.bar(label_counts2.keys(), label_counts2.values())\n",
    "axs2.set_xlabel('Label')\n",
    "axs2.xaxis.set_ticks(list(label_counts2.keys()))\n",
    "axs2.set_title('Test-set label counts')\n",
    "axs2.set_ylabel('Count')\n",
    "\n",
    "del label_counts1, label_counts2, fig, axs1, axs2\n",
    "#del test_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b562921",
   "metadata": {},
   "source": [
    "#### Dataset Visulization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0162a9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def dispaly_images(sample, ncols = 12):\n",
    "    count,_ = sample.shape                   # Total Number image to plot\n",
    "    nrows,x = divmod(count,ncols)            # Number of rows needed with with column = ncols, \n",
    "    nrows = nrows if x == 0 else nrows +1    # such that nrows x ncols >= count \n",
    "    fig = plt.figure(figsize = (18, 2*nrows))  # Creating grid type figure to house ncols times nrows images\n",
    "    for s in range(0,count):\n",
    "        lbl,img = sample[s,:][0],sample[s,:][1:] # Spliting each row of sample into target and image data \n",
    "        fig.add_subplot(nrows, ncols, s+1)       \n",
    "        plt.imshow(img.reshape(28,28), cmap=plt.get_cmap('gray'))  # Resizing image data into 28X28 image \n",
    "        plt.axis('off')\n",
    "        plt.title(lbl)\n",
    "    plt.show()\n",
    "\n",
    "dispaly_images( training_df.sample(36).values)        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33ce401d",
   "metadata": {},
   "source": [
    "####  Feature Scalling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aa90bdea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_x shape (60000, 784), train_y shape (60000,)\n",
      "train_x shape (10000, 784), test_y shape (10000,)\n"
     ]
    }
   ],
   "source": [
    "train_x, train_y = training_df.values[:, 1:], training_df.values[:, 0]\n",
    "test_x, test_y = test_df.values[:, 1:], test_df.values[:, 0]\n",
    "\n",
    "# Minmax Normalization X= (x-Xmin)/(Xmax-xmin) Xmax=255, Xmin=0\n",
    "train_x = train_x/255\n",
    "# Minmax Normalization X= (x-Xmin)/(Xmax-xmin) Xmax=255, Xmin=0\n",
    "test_x = test_x/255\n",
    "\n",
    "print(f'train_x shape {train_x.shape}, train_y shape {train_y.shape}')\n",
    "print(f'train_x shape {test_x.shape}, test_y shape {test_y.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "710ef16c",
   "metadata": {},
   "source": [
    "#### Building Model with Keras Layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1089aacd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model():\n",
    "    model = Sequential()\n",
    "    model.add(Reshape((28, 28,1), input_shape=(784,)))      # input Shape (28,28,1)\n",
    "    model.add(Conv2D(6, 5, activation='relu'))              # output size 6 X (28-5+1) X (28-5+1) = 6 X 24 X 24\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2) ))              # output size 6 X (24/2) X (24/2) = 6 X 12 X 12\n",
    "    model.add(Conv2D(16, 5, activation='relu'))             # output size 16 X (12-5+1) X (12-5+1) = 16 X 8 X 8         \n",
    "    model.add(MaxPooling2D(pool_size=(2, 2) ))              # output size 16 X (8/2) X (8/2) = 16 X 4 X 4\n",
    "    model.add(Flatten())                                    # output size 256\n",
    "    model.add(Dense(120,activation='relu'))                 # output size 120\n",
    "    model.add(Dense(84,activation='relu'))                  # output size 84 \n",
    "    model.add(Dense(10,activation='softmax'))               # output size 10\n",
    "    return model          \n",
    "\n",
    "def build_final_model():\n",
    "    model = Sequential()\n",
    "    model.add(Reshape((28, 28,1), input_shape=(784,)))      # input Shape (28,28,1)\n",
    "    model.add(Conv2D(6, 5, activation='tanh',\n",
    "                     kernel_initializer=GlorotNormal()))    # output size 6 X (28-5+1) X (28-5+1) = 6 X 24 X 24\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2) ))              # output size 6 X (24/2) X (24/2) = 6 X 12 X 12\n",
    "    model.add(Conv2D(16, 5, activation='tanh',\n",
    "                    kernel_initializer=GlorotNormal()))     # output size 16 X (12-5+1) X (12-5+1) = 16 X 8 X 8         \n",
    "    model.add(BatchNormalization())\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2) ))              # output size 16 X (8/2) X (8/2) = 16 X 4 X 4\n",
    "    model.add(Flatten())                                    # output size 256\n",
    "    model.add(Dense(120,activation='tanh',\n",
    "                   kernel_initializer=GlorotNormal()))      # output size 120\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dense(84,activation='tanh',\n",
    "                   kernel_initializer=GlorotNormal()))      # output size 84 \n",
    "    model.add(Dense(10,activation='softmax'))               # output size 10\n",
    "    return model          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9aff18de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " reshape (Reshape)           (None, 28, 28, 1)         0         \n",
      "                                                                 \n",
      " conv2d (Conv2D)             (None, 24, 24, 6)         156       \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 24, 24, 6)        24        \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 12, 12, 6)        0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 8, 8, 16)          2416      \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 8, 8, 16)         64        \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 4, 4, 16)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 256)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 120)               30840     \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 120)              480       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 84)                10164     \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 10)                850       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 44,994\n",
      "Trainable params: 44,710\n",
      "Non-trainable params: 284\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = build_final_model()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e583374",
   "metadata": {},
   "source": [
    "## We can see from the Model summary ,Total number  parameter count is  44,994 which less than 1 Lakh"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4870b332",
   "metadata": {},
   "source": [
    "#### Setting up model optimizer and loss and callbacks For Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "89c110b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "750/750 [==============================] - ETA: 0s - loss: 0.2729 - accuracy: 0.9202\n",
      "Epoch 1: val_accuracy improved from -inf to 0.96633, saving model to .\\model\\coding_challenge_model.h5\n",
      "750/750 [==============================] - 13s 14ms/step - loss: 0.2729 - accuracy: 0.9202 - val_loss: 0.1137 - val_accuracy: 0.9663\n",
      "Epoch 2/30\n",
      "749/750 [============================>.] - ETA: 0s - loss: 0.0913 - accuracy: 0.9724\n",
      "Epoch 2: val_accuracy improved from 0.96633 to 0.97367, saving model to .\\model\\coding_challenge_model.h5\n",
      "750/750 [==============================] - 10s 13ms/step - loss: 0.0914 - accuracy: 0.9724 - val_loss: 0.0830 - val_accuracy: 0.9737\n",
      "Epoch 3/30\n",
      "750/750 [==============================] - ETA: 0s - loss: 0.0608 - accuracy: 0.9815\n",
      "Epoch 3: val_accuracy improved from 0.97367 to 0.97858, saving model to .\\model\\coding_challenge_model.h5\n",
      "750/750 [==============================] - 10s 13ms/step - loss: 0.0608 - accuracy: 0.9815 - val_loss: 0.0709 - val_accuracy: 0.9786\n",
      "Epoch 4/30\n",
      "750/750 [==============================] - ETA: 0s - loss: 0.0448 - accuracy: 0.9861\n",
      "Epoch 4: val_accuracy did not improve from 0.97858\n",
      "750/750 [==============================] - 10s 13ms/step - loss: 0.0448 - accuracy: 0.9861 - val_loss: 0.0654 - val_accuracy: 0.9784\n",
      "Epoch 5/30\n",
      "750/750 [==============================] - ETA: 0s - loss: 0.0371 - accuracy: 0.9883\n",
      "Epoch 5: val_accuracy improved from 0.97858 to 0.98058, saving model to .\\model\\coding_challenge_model.h5\n",
      "750/750 [==============================] - 10s 13ms/step - loss: 0.0371 - accuracy: 0.9883 - val_loss: 0.0654 - val_accuracy: 0.9806\n",
      "Epoch 6/30\n",
      "749/750 [============================>.] - ETA: 0s - loss: 0.0301 - accuracy: 0.9907\n",
      "Epoch 6: val_accuracy improved from 0.98058 to 0.98208, saving model to .\\model\\coding_challenge_model.h5\n",
      "750/750 [==============================] - 10s 13ms/step - loss: 0.0301 - accuracy: 0.9907 - val_loss: 0.0607 - val_accuracy: 0.9821\n",
      "Epoch 7/30\n",
      "749/750 [============================>.] - ETA: 0s - loss: 0.0244 - accuracy: 0.9924\n",
      "Epoch 7: val_accuracy did not improve from 0.98208\n",
      "750/750 [==============================] - 9s 13ms/step - loss: 0.0245 - accuracy: 0.9924 - val_loss: 0.0614 - val_accuracy: 0.9814\n",
      "Epoch 8/30\n",
      "748/750 [============================>.] - ETA: 0s - loss: 0.0200 - accuracy: 0.9940\n",
      "Epoch 8: val_accuracy improved from 0.98208 to 0.98317, saving model to .\\model\\coding_challenge_model.h5\n",
      "750/750 [==============================] - 9s 12ms/step - loss: 0.0201 - accuracy: 0.9940 - val_loss: 0.0568 - val_accuracy: 0.9832\n",
      "Epoch 9/30\n",
      "748/750 [============================>.] - ETA: 0s - loss: 0.0172 - accuracy: 0.9944\n",
      "Epoch 9: val_accuracy did not improve from 0.98317\n",
      "750/750 [==============================] - 9s 12ms/step - loss: 0.0172 - accuracy: 0.9944 - val_loss: 0.0704 - val_accuracy: 0.9792\n",
      "Epoch 10/30\n",
      "748/750 [============================>.] - ETA: 0s - loss: 0.0160 - accuracy: 0.9947\n",
      "Epoch 10: val_accuracy improved from 0.98317 to 0.98325, saving model to .\\model\\coding_challenge_model.h5\n",
      "750/750 [==============================] - 9s 12ms/step - loss: 0.0159 - accuracy: 0.9947 - val_loss: 0.0574 - val_accuracy: 0.9833\n",
      "Epoch 11/30\n",
      "747/750 [============================>.] - ETA: 0s - loss: 0.0141 - accuracy: 0.9953\n",
      "Epoch 11: val_accuracy improved from 0.98325 to 0.98467, saving model to .\\model\\coding_challenge_model.h5\n",
      "750/750 [==============================] - 10s 13ms/step - loss: 0.0141 - accuracy: 0.9954 - val_loss: 0.0548 - val_accuracy: 0.9847\n",
      "Epoch 12/30\n",
      "748/750 [============================>.] - ETA: 0s - loss: 0.0096 - accuracy: 0.9973\n",
      "Epoch 12: val_accuracy did not improve from 0.98467\n",
      "750/750 [==============================] - 10s 13ms/step - loss: 0.0096 - accuracy: 0.9973 - val_loss: 0.0681 - val_accuracy: 0.9817\n",
      "Epoch 13/30\n",
      "750/750 [==============================] - ETA: 0s - loss: 0.0116 - accuracy: 0.9965\n",
      "Epoch 13: val_accuracy did not improve from 0.98467\n",
      "750/750 [==============================] - 9s 13ms/step - loss: 0.0116 - accuracy: 0.9965 - val_loss: 0.0646 - val_accuracy: 0.9820\n",
      "Epoch 14/30\n",
      "749/750 [============================>.] - ETA: 0s - loss: 0.0104 - accuracy: 0.9964\n",
      "Epoch 14: val_accuracy improved from 0.98467 to 0.98508, saving model to .\\model\\coding_challenge_model.h5\n",
      "750/750 [==============================] - 9s 12ms/step - loss: 0.0104 - accuracy: 0.9964 - val_loss: 0.0610 - val_accuracy: 0.9851\n",
      "Epoch 15/30\n",
      "747/750 [============================>.] - ETA: 0s - loss: 0.0076 - accuracy: 0.9977\n",
      "Epoch 15: val_accuracy did not improve from 0.98508\n",
      "750/750 [==============================] - 10s 13ms/step - loss: 0.0075 - accuracy: 0.9977 - val_loss: 0.0601 - val_accuracy: 0.9839\n",
      "Epoch 16/30\n",
      "747/750 [============================>.] - ETA: 0s - loss: 0.0103 - accuracy: 0.9969\n",
      "Epoch 16: val_accuracy did not improve from 0.98508\n",
      "750/750 [==============================] - 9s 12ms/step - loss: 0.0102 - accuracy: 0.9969 - val_loss: 0.0704 - val_accuracy: 0.9827\n",
      "Epoch 17/30\n",
      "750/750 [==============================] - ETA: 0s - loss: 0.0078 - accuracy: 0.9977\n",
      "Epoch 17: val_accuracy did not improve from 0.98508\n",
      "750/750 [==============================] - 9s 13ms/step - loss: 0.0078 - accuracy: 0.9977 - val_loss: 0.0603 - val_accuracy: 0.9835\n",
      "Epoch 18/30\n",
      "746/750 [============================>.] - ETA: 0s - loss: 0.0076 - accuracy: 0.9974\n",
      "Epoch 18: val_accuracy improved from 0.98508 to 0.98700, saving model to .\\model\\coding_challenge_model.h5\n",
      "750/750 [==============================] - 9s 13ms/step - loss: 0.0078 - accuracy: 0.9974 - val_loss: 0.0512 - val_accuracy: 0.9870\n",
      "Epoch 19/30\n",
      "747/750 [============================>.] - ETA: 0s - loss: 0.0063 - accuracy: 0.9980\n",
      "Epoch 19: val_accuracy improved from 0.98700 to 0.98767, saving model to .\\model\\coding_challenge_model.h5\n",
      "750/750 [==============================] - 9s 13ms/step - loss: 0.0063 - accuracy: 0.9980 - val_loss: 0.0508 - val_accuracy: 0.9877\n",
      "Epoch 20/30\n",
      "746/750 [============================>.] - ETA: 0s - loss: 0.0064 - accuracy: 0.9979\n",
      "Epoch 20: val_accuracy did not improve from 0.98767\n",
      "750/750 [==============================] - 9s 12ms/step - loss: 0.0064 - accuracy: 0.9979 - val_loss: 0.0678 - val_accuracy: 0.9818\n",
      "Epoch 21/30\n",
      "746/750 [============================>.] - ETA: 0s - loss: 0.0063 - accuracy: 0.9980\n",
      "Epoch 21: val_accuracy did not improve from 0.98767\n",
      "750/750 [==============================] - 10s 13ms/step - loss: 0.0064 - accuracy: 0.9980 - val_loss: 0.0577 - val_accuracy: 0.9863\n",
      "Epoch 22/30\n",
      "750/750 [==============================] - ETA: 0s - loss: 0.0047 - accuracy: 0.9984\n",
      "Epoch 22: val_accuracy did not improve from 0.98767\n",
      "750/750 [==============================] - 10s 13ms/step - loss: 0.0047 - accuracy: 0.9984 - val_loss: 0.0621 - val_accuracy: 0.9852\n",
      "Epoch 23/30\n",
      "747/750 [============================>.] - ETA: 0s - loss: 0.0073 - accuracy: 0.9974\n",
      "Epoch 23: val_accuracy did not improve from 0.98767\n",
      "750/750 [==============================] - 10s 13ms/step - loss: 0.0073 - accuracy: 0.9974 - val_loss: 0.0659 - val_accuracy: 0.9831\n",
      "Epoch 24/30\n",
      "746/750 [============================>.] - ETA: 0s - loss: 0.0055 - accuracy: 0.9983\n",
      "Epoch 24: val_accuracy did not improve from 0.98767\n",
      "750/750 [==============================] - 9s 12ms/step - loss: 0.0055 - accuracy: 0.9983 - val_loss: 0.0650 - val_accuracy: 0.9838\n",
      "Epoch 25/30\n",
      "747/750 [============================>.] - ETA: 0s - loss: 0.0050 - accuracy: 0.9983\n",
      "Epoch 25: val_accuracy did not improve from 0.98767\n",
      "750/750 [==============================] - 10s 13ms/step - loss: 0.0050 - accuracy: 0.9983 - val_loss: 0.0660 - val_accuracy: 0.9838\n",
      "Epoch 26/30\n",
      "748/750 [============================>.] - ETA: 0s - loss: 0.0046 - accuracy: 0.9984\n",
      "Epoch 26: val_accuracy did not improve from 0.98767\n",
      "750/750 [==============================] - 10s 14ms/step - loss: 0.0046 - accuracy: 0.9984 - val_loss: 0.0714 - val_accuracy: 0.9837\n",
      "Epoch 27/30\n",
      "746/750 [============================>.] - ETA: 0s - loss: 0.0069 - accuracy: 0.9978\n",
      "Epoch 27: val_accuracy did not improve from 0.98767\n",
      "750/750 [==============================] - 10s 13ms/step - loss: 0.0069 - accuracy: 0.9978 - val_loss: 0.0628 - val_accuracy: 0.9847\n",
      "Epoch 28/30\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "748/750 [============================>.] - ETA: 0s - loss: 0.0054 - accuracy: 0.9982\n",
      "Epoch 28: val_accuracy did not improve from 0.98767\n",
      "750/750 [==============================] - 10s 13ms/step - loss: 0.0054 - accuracy: 0.9982 - val_loss: 0.0682 - val_accuracy: 0.9852\n",
      "Epoch 29/30\n",
      "746/750 [============================>.] - ETA: 0s - loss: 0.0049 - accuracy: 0.9983\n",
      "Epoch 29: val_accuracy did not improve from 0.98767\n",
      "750/750 [==============================] - 10s 13ms/step - loss: 0.0049 - accuracy: 0.9983 - val_loss: 0.0775 - val_accuracy: 0.9831\n",
      "Epoch 29: early stopping\n"
     ]
    }
   ],
   "source": [
    "# model.compile(loss='sparse_categorical_crossentropy',optimizer='Adam',metrics=['accuracy'])\n",
    "\n",
    "saved_model_path = os.path.join('.','model','coding_challenge_model.h5')\n",
    "\n",
    "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10)\n",
    "mc = ModelCheckpoint(saved_model_path, monitor='val_accuracy', mode='max', verbose=1, save_best_only=True)\n",
    "\n",
    "model.compile(loss='sparse_categorical_crossentropy',\n",
    "              optimizer=Adam(learning_rate=0.0005),\n",
    "              metrics=['accuracy']\n",
    "             )\n",
    "\n",
    "history = model.fit(train_x, \n",
    "                    train_y, \n",
    "                    epochs=30,\n",
    "                    validation_split=0.2, \n",
    "                    batch_size=64,\n",
    "                    callbacks=[es,mc]\n",
    "                   )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e407ad3",
   "metadata": {},
   "source": [
    "#### Loss & Accuracy visualization  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "df853e81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_model_training(data):\n",
    "    fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(18, 5))\n",
    "    ax1.plot(data.history['loss'], label=\"Training\")\n",
    "    ax1.plot(data.history['val_loss'], label=\"validation\")\n",
    "    ax1.set_title('Training and validation Loss')\n",
    "    ax1.set_xlabel('Iterations')\n",
    "    ax1.set_ylabel('Loss')\n",
    "    ax1.legend()\n",
    "    ax1.grid()\n",
    "\n",
    "    ax2.plot(data.history['accuracy'], label=\"Training\")\n",
    "    ax2.plot(data.history['val_accuracy'], label=\"validation\")\n",
    "    ax2.axhline(y=0.97, color='g', linestyle='--')\n",
    "    ax2.set_title('Training and validation')\n",
    "    ax2.set_xlabel('Iterations')\n",
    "    ax2.set_ylabel('Accuracy')\n",
    "    ax2.grid()\n",
    "    ax2.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_model_training(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2212e989",
   "metadata": {},
   "source": [
    "#### Model Evaluation on Test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "37845762",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss 0.052936431020498276\n",
      "Test accuracy 0.9865999817848206\n",
      "[0.052936431020498276, 0.9865999817848206]\n"
     ]
    }
   ],
   "source": [
    "trained_model = load_model(saved_model_path)\n",
    "score = trained_model.evaluate(test_x, test_y, verbose=0)\n",
    "\n",
    "print(f'Test loss {score[0]}')\n",
    "print(f'Test accuracy {score[1]}')\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4389e89",
   "metadata": {},
   "source": [
    "## Accuracy achieved on Test Dataset is 98.6%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "48a0e897",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 3ms/step\n",
      "y_pred Shape (10000,)\n"
     ]
    }
   ],
   "source": [
    "y_prob = trained_model.predict(test_x)\n",
    "y_pred = y_prob.argmax(axis=1)\n",
    "\n",
    "print(f'y_pred Shape {y_pred.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7062db18",
   "metadata": {},
   "source": [
    "#### Accuracy score calculations [ Manually Recalculating Accuracy using numpy ] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fb6af309",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9866\n"
     ]
    }
   ],
   "source": [
    "true_pred = np.sum(np.equal(test_y, y_pred))\n",
    "total_observation = 10000\n",
    "accuracy = true_pred / total_observation\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beca6304",
   "metadata": {},
   "source": [
    "## Accuracy achieved on Test Dataset is 98.6%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3c1ce1a",
   "metadata": {},
   "source": [
    "#### Calculating Confusion Matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bcc7f596",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 972    0    0    0    0    0    4    2    2    0]\n",
      " [   0 1127    1    1    0    1    2    1    1    1]\n",
      " [   1    2 1022    0    0    0    0    4    3    0]\n",
      " [   0    0    3  993    0    8    0    2    1    3]\n",
      " [   0    0    0    0  970    0    3    0    0    9]\n",
      " [   2    0    0    5    0  881    2    0    1    1]\n",
      " [   3    2    1    1    2    1  946    0    2    0]\n",
      " [   1    7    5    2    0    1    0 1008    1    3]\n",
      " [   3    0    2    1    0    1    0    2  964    1]\n",
      " [   2    2    0    4    8    3    0    3    4  983]]\n"
     ]
    }
   ],
   "source": [
    "from itertools import product  # For calculation of confusion Matrix\n",
    "\n",
    "\n",
    "def build_confusion_matrix(y_true, y_predict):\n",
    "    class_lbl = np.unique(np.concatenate((y_true, y_predict)))  # Collecting Unique Class labels\n",
    "    n_lbl = class_lbl.shape[0]                                  # Counting Unique Class labels   \n",
    "    temp = list(zip(y_true, y_predict))                         # Zipping Y-true and Y-pred\n",
    "    confu_mat = []                                              \n",
    "    for combinations  in product(class_lbl,repeat=2):          # Iterating over all Possible combinations class label ; NC2 \n",
    "        confu_mat.append(temp.count(combinations))             # Couting the combination on ziped list       \n",
    "    return np.asarray(confu_mat).reshape(n_lbl,n_lbl)          # Reshapping the array Matrix\n",
    "\n",
    "mat = build_confusion_matrix(test_y, y_pred)    \n",
    "print(mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ab5888b",
   "metadata": {},
   "source": [
    "#### Plotting the Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "18eee627",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_confusion_matix(confusion_matix):\n",
    "    fig, ax = plt.subplots(figsize=(6, 8))\n",
    "    ax.matshow(confusion_matix, cmap=plt.cm.Blues, alpha=0.3)\n",
    "    for i in range(confusion_matix.shape[0]):\n",
    "        for j in range(confusion_matix.shape[1]):\n",
    "            ax.text(x=j, y=i, s=confusion_matix[i, j],\n",
    "                    va='center', ha='center', size='xx-large')\n",
    "\n",
    "    plt.xlabel('Predictions', fontsize=18)\n",
    "    plt.ylabel('Actuals', fontsize=18)\n",
    "    ax.xaxis.set_ticks([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
    "    ax.yaxis.set_ticks([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
    "    plt.title('Confusion Matrix', fontsize=22)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_confusion_matix(mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0303ea1",
   "metadata": {},
   "source": [
    "#### Neagtive Prediction visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "998ac1c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(134, 785)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a new Column in test Dataframe with Predicted output\n",
    "test_df['prediction'] = y_pred\n",
    "\n",
    "test_df['FP_FN'] = test_df.apply(    # Creating Another Column For FP and FN indicators\n",
    "    lambda row: 1 if row['label'] != row['prediction'] else 0,\n",
    "    axis=1)\n",
    "\n",
    "# Selecting all the False Prediction in a sample dataframe\n",
    "sample_fp_fn = test_df[test_df['FP_FN'] == 1].copy()\n",
    "\n",
    "# Replaceing the True Labels with Predicted Values for disply\n",
    "sample_fp_fn['label'] = sample_fp_fn.apply(\n",
    "    lambda row: row['prediction'], axis=1)\n",
    "\n",
    "# Dropping the 'prediction', 'FP_FN' Columns\n",
    "sample_fp_fn.drop(columns=['prediction', 'FP_FN'], inplace=True)\n",
    "\n",
    "print(sample_fp_fn.shape)\n",
    "\n",
    "# Finally Displaying only Flase Predictions\n",
    "dispaly_images(sample_fp_fn.sample(36).values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b31d698",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  },
  "toc": {
   "base_numbering": 1,
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   "number_sections": true,
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
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}