MNIST_Own_Attempt_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing Required Libraries\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "import numpy as np\n",
    "import os\n",
    "import random\n",
    "import cv2\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.config.experimental_run_functions_eagerly(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Downloading and storing the MNIST Dataset\n",
    "\n",
    "a,b = tf.keras.datasets.mnist.load_data(path='mnist.npz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Getting the train and test images and labels for each image\n",
    "train_X = a[0]\n",
    "train_y = a[1]\n",
    "test_X = b[0]\n",
    "test_y = b[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(60000,)\n",
      "(10000, 28, 28)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "#Printing shapes of the training and testing datasets\n",
    "\n",
    "print(train_X.shape)\n",
    "print(train_y.shape)\n",
    "print(test_X.shape)\n",
    "print(test_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index:  50693\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x16770191e88>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#Printing a random image from training dataset along with its label as the title of th image(plot)\n",
    "ran = random.randint(0,60000)\n",
    "print(\"Index: \", ran)\n",
    "plt.title(train_y[ran])\n",
    "plt.imshow(train_X[ran])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index:  54809\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x16773d87408>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#Printing another random image from training dataset along with its label as the title of th image(plot)\n",
    "ran = random.randint(0,60000)\n",
    "print(\"Index: \", ran)\n",
    "plt.title(train_y[ran])\n",
    "plt.imshow(train_X[ran])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index:  3388\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x16773dcd2c8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "#Printing a random image from testing dataset along with its label as the title of th image(plot)\n",
    "ran = random.randint(0,10000)\n",
    "print(\"Index: \", ran)\n",
    "plt.title(test_y[ran])\n",
    "plt.imshow(test_X[ran])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index:  2582\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x16773e5c708>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAEICAYAAACZA4KlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAO1ElEQVR4nO3dbYxc5XnG8euys7YbxyAbamODW2hwyotDCWxJImjj1g1vkWL4EAJVwC1QUwloIdAGQVP8JRV5A1ESoi5gYVJKggIIq0IU4qYiwYGyUAfbMQWXmGDseiEuYBCYtX33ww7RYmaeXebtDL7/P2k1s+eeZ8+tkS+fM/OcmccRIQB7vwlVNwCgOwg7kARhB5Ig7EAShB1IgrADSRB2IAnCjrpsH277322/YnuD7dOr7gmtIex4F9sfkHSvpH+VNEPSEkn/bPsjlTaGlpgr6LAn2/MlPSJpWtT+gdh+QNKjEfHlSptD0ziyox432Da/242gfQg76nlK0pCkv7HdZ/tESZ+S9MFq20IrOI1HXbaPknSDRo7mg5JelLQjIs6rtDE0jbBjXGyvkrQ8Iv6p6l7QHE7jUZfto2xPsf1B25dLmi3p1orbQgsIOxo5W9IWjbx2Xyjp0xGxo9qW0ApO44EkOLIDSRB2IAnCDiRB2IEkPtDNnU3y5Jiiqd3cJZDKm3pdb8WOepc7txZ22ydLul7SREk3R8Q1pcdP0VR93Atb2SWAgkdjZcNa06fxtidK+rakUyQdIeks20c0+/cAdFYrr9mPk7QhIp6NiLckfU/Sova0BaDdWgn7gZKeH/X7ptq2d7C9xPag7cFhcQEWUJVWwl7vTYB3XY4XEQMR0R8R/X2a3MLuALSilbBvkjR31O8HSdrcWjsAOqWVsD8maZ7tQ2xPknSmpBXtaQtAuzU99RYRO21fJOnfNDL1tiwi1rWtMwBt1dI8e0TcJ+m+NvUCoIO4XBZIgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQREtLNtveKGm7pF2SdkZEfzuaAtB+LYW95o8i4qU2/B0AHcRpPJBEq2EPSQ/Yftz2knoPsL3E9qDtwWHtaHF3AJrV6mn88RGx2fZMSQ/afioiHhr9gIgYkDQgSft4RrS4PwBNaunIHhGba7dDku6RdFw7mgLQfk2H3fZU29Pevi/pRElr29UYgPZq5TR+lqR7bL/9d/4lIu5vS1d435gwbVqxvu30+Q1rCy79aXHsV2etLtYX/vyzxXrfyZsb1mLnzuLYvVHTYY+IZyX9Xht7AdBBTL0BSRB2IAnCDiRB2IEkCDuQRDs+CIO9mI89sljf/OXdxfrg73+r6X0Pj3G95f2H31OsH3PJxQ1rc76xqpmW3tc4sgNJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEsyzJ/f0jeXvG7nnlBuK9SMn9e4/odd/a1fVLfQUjuxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kETvTpJi3CbMP6xhbdGdPy6OPWnqtcX6F9afU6xvXTOrWF//p40/z/6fO1wc+6PXjijWv7TfumL9sH8caljLOAPPkR1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkmCe/X1g4pG/W6yf+YMfNqwdNfmF4thzLrmsWH/58InF+voLy593L7n63POL9W1ffL1Yf/7N6cV6bGk8z57RmEd228tsD9leO2rbDNsP2n6mdlt+1gFUbjyn8bdKOnmPbVdIWhkR8yStrP0OoIeNGfaIeEjStj02L5K0vHZ/uaTT2twXgDZr9g26WRGxRZJqtzMbPdD2EtuDtgeHtaPJ3QFoVcffjY+IgYjoj4j+Pk3u9O4ANNBs2Lfani1JtVve9gR6XLNhXyFpce3+Ykn3tqcdAJ0y5jy77TskLZC0v+1Nkq6WdI2kO22fJ+mXkj7XySb3dhOP+EixftoPyp9JP3bK8w1rF1x+SXHs/55Q/kz5stNvLNYffrOvWL/i75c0rO23oXHfknTTR8vrr5//ZPmz9jNff6pYz2bMsEfEWQ1KC9vcC4AO4nJZIAnCDiRB2IEkCDuQBGEHkuAjrj1g/aX7Fuv37lOeonr4zakNawv+blVx7JX7P16srx0uT81dee4Fxfq+//FIw9rQuZ8sjj1qUvnjtW/tLNfxThzZgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJ5tl7wKJj/qul8cdPGS7UVhfH3vLKIcX63ef/SbE+cdUTxXrJXUu/XqxvHWNd5QOvLX+8Fu/EkR1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkmCefS/wDy99tGHt7mULimPnLFtTrHv7z5pp6ddKX5M9xQ8Xx/7BbZcX6wc//NOmesqKIzuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJME8ew94+vNzi/XPTjm0WPeWoYa1A35V/t743cVq657/zP4Na9MnTCmOnbqp3d3kNuaR3fYy20O2147attT2C7ZX135O7WybAFo1ntP4WyWdXGf7dRFxdO3nvva2BaDdxgx7RDwkaVsXegHQQa28QXeR7Sdrp/nTGz3I9hLbg7YHh7Wjhd0BaEWzYf+OpA9LOlrSFknfbPTAiBiIiP6I6O/T5CZ3B6BVTYU9IrZGxK6I2C3pJknHtbctAO3WVNhtzx716+mS1jZ6LIDeMOY8u+07JC2QtL/tTZKulrTA9tGSQtJGSeVFulG0a8Mvqm6hab86r7zG+ppLb2xY+8XwG8WxMx/bXqxHsYo9jRn2iDirzuZbOtALgA7iclkgCcIOJEHYgSQIO5AEYQeS4COuKJowbVqx/om/LC/ZPByN110+adWFxbGHDLb2NdZ4J47sQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AE8+woembpkcX6ijnfLtYHXjm4YW3eVa8Ux+4sVvFecWQHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSSYZ09u41fKXwW9+vPXFet3bD+oWP/uVz7TsLbvs48Ux6K9OLIDSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBLjWbJ5rqTbJB0gabekgYi43vYMSd+XdLBGlm0+IyL+r3OtopGJ++zTsLbl7PnFsfd/4WvF+pvhYv3rN59RrM+5fVWxju4Zz5F9p6TLIuJwSZ+QdKHtIyRdIWllRMyTtLL2O4AeNWbYI2JLRDxRu79d0npJB0paJGl57WHLJZ3WqSYBtO49vWa3fbCkj0l6VNKsiNgijfyHIGlmu5sD0D7jDrvtD0m6S9IlEfHqexi3xPag7cFh7WimRwBtMK6w2+7TSNBvj4i7a5u32p5dq8+WNFRvbEQMRER/RPT3aXI7egbQhDHDbtuSbpG0PiKuHVVaIWlx7f5iSfe2vz0A7TKej7geL+lsSWtsr65tu1LSNZLutH2epF9K+lxnWoT7JhXrG65o/HXP6xbfMMZf/41itf8bFxfrc65jau39YsywR8RPJDWabF3Y3nYAdApX0AFJEHYgCcIOJEHYgSQIO5AEYQeS4Kuke8CEadOK9a23zynW1x37rYa1l3a9URx70vV/W6zPZh59r8GRHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSYJ69CyYePq9Y/9Sdq4v1i6evLNb//LkTG9a2nTO9OHb2BubRs+DIDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJMM/eBjv/+Nhi/aqbbynWPzl5V7F+2Pf/qlg/9IuPFKovF8ciD47sQBKEHUiCsANJEHYgCcIOJEHYgSQIO5DEmPPstudKuk3SAZJ2SxqIiOttL5X0F5JerD30yoi4r1ON9rKXDy2vn37Nc6cW62989cBifd4PB4v1KFaBEeO5qGanpMsi4gnb0yQ9bvvBWu26iPhG59oD0C5jhj0itkjaUru/3fZ6SeVDEYCe855es9s+WNLHJD1a23SR7SdtL7Nd9/uPbC+xPWh7cFg7WmoWQPPGHXbbH5J0l6RLIuJVSd+R9GFJR2vkyP/NeuMiYiAi+iOiv0+T29AygGaMK+y2+zQS9Nsj4m5JioitEbErInZLuknScZ1rE0Crxgy7bUu6RdL6iLh21PbZox52uqS17W8PQLs4ojxxY/sEST+WtEYjU2+SdKWkszRyCh+SNkq6oPZmXkP7eEZ83AtbbBlAI4/GSr0a21yvNp53438iqd7glHPqwPsVV9ABSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSGPPz7G3dmf2ipOdGbdpf0ktda+C96dXeerUvid6a1c7efjsifrNeoathf9fO7cGI6K+sgYJe7a1X+5LorVnd6o3TeCAJwg4kUXXYByref0mv9tarfUn01qyu9Fbpa3YA3VP1kR1AlxB2IIlKwm77ZNv/bXuD7Suq6KER2xttr7G92nZ5reTO97LM9pDttaO2zbD9oO1nard119irqLeltl+oPXerbZfXqu5cb3Nt/8j2etvrbP91bXulz12hr648b11/zW57oqSnJX1a0iZJj0k6KyJ+3tVGGrC9UVJ/RFR+AYbtP5T0mqTbImJ+bdvXJG2LiGtq/1FOj4gv9UhvSyW9VvUy3rXVimaPXmZc0mmS/kwVPneFvs5QF563Ko7sx0naEBHPRsRbkr4naVEFffS8iHhI0rY9Ni+StLx2f7lG/rF0XYPeekJEbImIJ2r3t0t6e5nxSp+7Ql9dUUXYD5T0/KjfN6m31nsPSQ/Yftz2kqqbqWPW28ts1W5nVtzPnsZcxrub9lhmvGeeu2aWP29VFWGvt5RUL83/HR8Rx0g6RdKFtdNVjM+4lvHuljrLjPeEZpc/b1UVYd8kae6o3w+StLmCPuqKiM212yFJ96j3lqLe+vYKurXboYr7+bVeWsa73jLj6oHnrsrlz6sI+2OS5tk+xPYkSWdKWlFBH+9ie2rtjRPZnirpRPXeUtQrJC2u3V8s6d4Ke3mHXlnGu9Ey46r4uat8+fOI6PqPpFM18o78/0i6qooeGvT1O5J+VvtZV3Vvku7QyGndsEbOiM6TtJ+klZKeqd3O6KHevquRpb2f1EiwZlfU2wkaeWn4pKTVtZ9Tq37uCn115XnjclkgCa6gA5Ig7EAShB1IgrADSRB2IAnCDiRB2IEk/h+8IVTGGakiDgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Printing another random image from testing dataset along with its label as the title of th image(plot)\n",
    "ran = random.randint(0,10000)\n",
    "print(\"Index: \", ran)\n",
    "plt.title(test_y[ran])\n",
    "plt.imshow(test_X[ran])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Initializing some model parameters\n",
    "num_of_pixels = 784\n",
    "num_of_classes = 10\n",
    "batch_size = 200\n",
    "epochs = 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784)\n",
      "(10000, 784)\n"
     ]
    }
   ],
   "source": [
    "#Reshaping the images from 28*28 arrays to a 784 vector for training and testing\n",
    "train_X_act = train_X.reshape(-1, 784)\n",
    "test_X_act = test_X.reshape(-1, 784)\n",
    "print(train_X_act.shape)\n",
    "print(test_X_act.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#One-Hot Encoding the target labels for training\n",
    "train_y_act = to_categorical(train_y, 10)\n",
    "test_y_act = to_categorical(test_y, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 10)                7850      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 20)                220       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                210       \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                110       \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 10)                110       \n",
      "=================================================================\n",
      "Total params: 8,500\n",
      "Trainable params: 8,500\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Creating a Deep Learning model with 3 Dense Layers\n",
    "model = keras.Sequential(\n",
    "[\n",
    "    layers.Dense(10, input_shape = (784,), activation = 'relu'),\n",
    "    layers.Dense(20, activation = 'relu'),\n",
    "    layers.Dense(10, activation = 'relu'),\n",
    "    layers.Dense(10, activation = 'relu'),\n",
    "    layers.Dense(num_of_classes, activation = 'softmax'),\n",
    "]\n",
    ")\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#If model exists, uncomment this and execute\n",
    "model = tf.keras.models.load_model('MNIST_Model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Else, uncomment these lines and run\n",
    "#model.compile(Adam(lr = 0.01), loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "#history = model.fit(train_X_act, train_y_act, validation_split=0.1, epochs = epochs, batch_size = batch_size, verbose=1, shuffle=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50/50 [==============================] - 1s 13ms/step - loss: 0.4664 - accuracy: 0.8719\n",
      "Test Loss:  0.4664106070995331\n",
      "Test Accuracy 0.8719000220298767\n"
     ]
    }
   ],
   "source": [
    "#Testing the model using the test data\n",
    "result = model.evaluate(test_X_act, test_y_act, batch_size = batch_size)\n",
    "print(\"Test Loss: \", result[0])\n",
    "print(\"Test Accuracy\", result[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Saving the model\n",
    "model.save(\"MNIST_Model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:  5\n"
     ]
    },
    {
     "data": {
      "image/png": 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tP2J7Sd3NjOPAiBiSRv/xSJpRcz/v1XQa7256zzTjPfPatTP9eVl1hH28qaR6afxvQUQcI+lUSV9pvF1Fa1qaxrtbxplmvCe0O/15WXWEfZ2k2WPuHyxpfQ19jCsi1jeuN0q6Q703FfWGHTPoNq431tzPb/XSNN7jTTOuHnjt6pz+vI6wPyxpru1DbE+W9DlJy2vo431sT2t8cSLb0yR9Qr03FfVySYsbtxdLurPGXt6lV6bxLppmXDW/drVPfx4RXb9IOk2j38g/J+mSOnoo6OtQSb9qXJ6ouzdJt2j0bd2wRt8RnSdpf0krJD3buJ7eQ719T9Ljkh7TaLBm1dTbSRr9aPiYpJWNy2l1v3aJvrryunG4LJAJjqADMkHYgUwQdiAThB3IBGEHMkHYgUwQdiAT/w+LOXBpgC4REAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking the prediction of the model on a random picture from the test data\n",
    "ran = random.randint(0,10000)\n",
    "result = model.predict(test_X_act[ran].reshape(-1,784))\n",
    "plt.title(test_y[ran])\n",
    "plt.imshow(test_X[ran])\n",
    "result_temp = np.where(result == np.amax(result))\n",
    "print(\"Prediction: \", result_temp[1][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:  9\n"
     ]
    },
    {
     "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": [
    "#Checking the prediction of the model on a random picture from the test data\n",
    "ran = random.randint(0,10000)\n",
    "result = model.predict(test_X_act[ran].reshape(-1,784))\n",
    "plt.title(test_y[ran])\n",
    "plt.imshow(test_X[ran])\n",
    "result_temp = np.where(result == np.amax(result))\n",
    "print(\"Prediction: \", result_temp[1][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:  1\n"
     ]
    },
    {
     "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": [
    "#Checking the prediction of the model on a random picture from the test data\n",
    "ran = random.randint(0,10000)\n",
    "result = model.predict(test_X_act[ran].reshape(-1,784))\n",
    "plt.title(test_y[ran])\n",
    "plt.imshow(test_X[ran])\n",
    "result_temp = np.where(result == np.amax(result))\n",
    "print(\"Prediction: \", result_temp[1][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:  4\n"
     ]
    },
    {
     "data": {
      "image/png": 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qCPtMSa+MuL1F3bXee0i63/bjtpfU3cwopkXEVmn4H4+kqTX3s6+my3h30j7LjHfNa9fK8udl1RH20ZaS6qb5vwURcaKk0yVd3Hi7irEZ0zLenTLKMuNdodXlz8uqI+xbJPWOuD1L0ms19DGqiHitcblN0t3qvqWoX393Bd3G5baa+/l/3bSM92jLjKsLXrs6lz+vI+xrJc2xfYTtSZLOk7Syhj7ex/aBjS9OZPtASZ9V9y1FvVLS4sb1xZLuqbGX9+iWZbyLlhlXza9d7cufR0TH/ySdoeFv5J+X9Gd19FDQ15GSnmz8bai7N0krNPy2blDD74gulHSopFWSnm1cTumi3r4r6WlJT2k4WDNq6u3XNfzR8ClJ6xp/Z9T92iX66sjrxuGyQCY4gg7IBGEHMkHYgUwQdiAThB3IBGEHMkHYgUz8H+RrZoKI34h/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking the prediction of the model on a random picture from the test data\n",
    "ran = random.randint(0,10000)\n",
    "result = model.predict(test_X_act[ran].reshape(-1,784))\n",
    "plt.title(test_y[ran])\n",
    "plt.imshow(test_X[ran])\n",
    "result_temp = np.where(result == np.amax(result))\n",
    "print(\"Prediction: \", result_temp[1][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:  9\n"
     ]
    },
    {
     "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": [
    "#Checking the prediction of the model on a random picture from the test data\n",
    "ran = random.randint(0,10000)\n",
    "result = model.predict(test_X_act[ran].reshape(-1,784))\n",
    "plt.title(test_y[ran])\n",
    "plt.imshow(test_X[ran])\n",
    "result_temp = np.where(result == np.amax(result))\n",
    "print(\"Prediction: \", result_temp[1][0])"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}