src/MNIST_database_of_handwritten_digits_keras_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports\n",
    "\n",
    "`numpy`      import to manupulate arrays  \n",
    "`pandas`     import to create and modify dataframes  \n",
    "`matplotlib` to visulaize graphs  \n",
    "`seaborn`    build on matploblib, higher level graph functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the data\n",
    "Here we are taking the inbuilt function of keras to load the data from the server  \n",
    "The dataset file in present in the [Link to dataset in amazon server](https://s3.amazonaws.com/img-datasets/mnist.npz)  \n",
    "The inbuilt code \n",
    "```python\n",
    "def load_data(path='mnist.npz'):\n",
    "    \"\"\"Loads the MNIST dataset.\n",
    "    # Arguments\n",
    "        path: path where to cache the dataset locally\n",
    "            (relative to ~/.keras/datasets).\n",
    "    # Returns\n",
    "        Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.\n",
    "    \"\"\"\n",
    "    path = get_file(path,\n",
    "                    origin='https://s3.amazonaws.com/img-datasets/mnist.npz',\n",
    "                    file_hash='8a61469f7ea1b51cbae51d4f78837e45')\n",
    "    f = np.load(path)\n",
    "    x_train, y_train = f['x_train'], f['y_train']\n",
    "    x_test, y_test = f['x_test'], f['y_test']\n",
    "    f.close()\n",
    "    return (x_train, y_train), (x_test, y_test)\n",
    "```\n",
    "which downloads the data and unpacks and gives back as tuples of test, train splits   \n",
    "\n",
    "## Regarding Dataset\n",
    "Mnist is data set of handwritten digits of 0-9, [official Link](http://yann.lecun.com/exdb/mnist/)   \n",
    "60000 training samples  \n",
    "10000 testing samples   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Loading the data\n",
    "from keras.datasets import mnist\n",
    "(x_train,y_train),(x_test,y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understand the dataset\n",
    "Let see the size and shape of test and training tuples\n",
    "\n",
    "``On Execution``  \n",
    "There will be 60000 samples of 28*28 resolution of images in training set  \n",
    "There will be 10000 samples of 28*28 resolution of images in testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of training samples is  60000\n",
      "The size of testing samples is  10000\n",
      "The shape of training samples array is (60000, 28, 28)\n",
      "The shape of training labels is  (60000,)\n"
     ]
    }
   ],
   "source": [
    "print(\"The size of training samples is \",len(x_train))\n",
    "print(\"The size of testing samples is \",len(x_test))\n",
    "print(\"The shape of training samples array is\",np.shape(x_train))\n",
    "print(\"The shape of training labels is \",np.shape(y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing the data\n",
    "Let us try to visualise the few samples of data so, we could get an idea of how the data looks like  \n",
    "`On Execution`  \n",
    "We can see ten images of "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xs7FmtsDMppf4paqmTwDM7FQzm25mM8zstBK9TEHvpexz6OViZnOAHiGEWBZLCmJmbYBZQH9gLjAVGBpCSK+SUAUxs/8BegCrhRD2Sbs9aTKzPsDXwB0hhK3Sbk8lMLOtgHFAL2Ax8ChwQgjh7VQblkFTLq1HL2B2COHdEMJiaj6cA1NuU0Uws/WBvYFb0m5LJQghTAE+S7sdFaYb8FII4ZsQwhLgGaDxsrApiXlAD8BjZvZq7SnhrV0n4IN6P8+tvU3gWuAsoPLOFJNKMR3oY2btzWwlYC9gg5TblKWi6qEX2c4hhHlmtjbwuJm9WRt5tFa5dt/FOd/WAma2D7AghPCqmfVNuz1SmUIIM83sCuBxaqajXgeWpNuqbNFG6CGEebVfFwD3UzPl0JrNpWFEsT4wL6W2VJKdgf1q11zGAf3M7K50mySVKIRwawhh+xBCH2qmpCpq/hwiHdDNbGUzW9W/B/ag5pKpNZsKdDWzLmbWFhgCTEq5TakLIYwMIawfQuhMTZ88GUI4NOVmSQWqvdrHzDYEDgD+mm6LssU65bIOcH/twb7LAn8JITyabpPSFUJYYmbDgclAG2BsCGFGys2SCmRmfwX6AmuZ2VzgtyGEW9NtVUWYYGbtgf8CJ4UQPk+7QZmiTVsUEWltopxyERFpjTSgi4hEQgO6iEgkNKCLiERCA7qISCQ0oIuIREIDuohIJDSgi4hEQgO6iEgkNKCLiERCA7qISCQ0oIuIREIDuohIJDSgi4hEQgO6iEgkNKCLiERCA7qISCQ0oIuIREIDuohIJDSgi4hEQgO6iEgkNKCLiERCA7qISCQ0oIuIREIDuohIJDSgi4hEQgO6iEgkNKCLiERCA7qISCQ0oIuIREIDuohIJDSgi4hEQgO6iEgkNKCLiERCA7qISCQ0oIuIREIDuohIJDSgi4hEQgO6iEgkNKCLiERCA7qISCQ0oIuIREIDuohIJDSgi4hEQgO6iEgkChrQzWyAmb1lZrPN7JxiNUpERFrOQghL90CzNsAsoD8wF5gKDA0hvFG85omISL4KidB7AbNDCO+GEBYD44CBxWmWiIi01LIFPLYT8EG9n+cCOzT1ADNbusuB6vNJCKFDPndUn2RTn+TWWvolhGD53re19Al5flYKGdBzdXpW55rZMGBYAa9Tjd5r6pfqk2zqk9xaab80qZX2SbOfFShsDn1H4IIQwp61P48ECCFc1sRjWstf01dDCD3yuaP6JJv6JLfW0i+K0HPK67NSyBz6VKCrmXUxs7bAEGBSAc8nIiIFWOoplxDCEjMbDkwG2gBjQwgzitYyERFpkULm0AkhPAw8XKS2iIhIAbRTVEQkEhrQRUQiUdCUi1SHUaNGAXDKKacAMH36dAD22WcfAN57L6+MKBEpkieeeAIAs5qEnn79+hXleRWhi4hEotVE6KuuuioAq6yyCgB77703AB061Gy+uuaaawD4/vvvU2hdaXTu3BmAQw89FIAff/wRgG7dugGwxRZbAK0rQt9ss80AWG655QDo06cPADfeeCOQ9FFzJk6cCMCQIUPqblu8eHHR2pkW75eddtoJgEsvvRSAnXfeObU2xeIPf/hD3ffev3fccUdRX0MRuohIJKKN0D06PfvsswHYcccdAdhqq61y3r9jx45AMs8cg48//hiAKVOmALDffvul2ZxUbLnllgAceeSRABx00EEALLNMTSyz3nrrAUlknu/Oae/Lm266qe620047DYCvvvqqwFanZ/XVVwfgqaeeAuDDDz8EYN11123ws+Tv8ssvB+BXv/pV3W3//e9/gWQuvVgUoYuIRCKaCN3ngz1KOuSQQwBYccUVgWQ1+YMPagpELly4EEjmkwcPHgwkc6lvvvlmOZpdUosWLQJa1xx5pssuqykttNdee5Xk+Q8//PC672+99VYAnn/++ZK8Vho8MleEvvR69+4NJOsTAM899xwA48ePL+prKUIXEYmEBnQRkUhU7ZSLL95cccUVABx88MFAkp6Y6e233wZgzz33BJLLH59aWWuttRp8jcEaa6wBwDbbbJNyS9Lz+OOPA9lTLgsWLACSaRJfJM1MW/T0sl133bWk7axUPlXZmnlq63nnnQfA0KFDAfjss8+afJzfzxMx3nnnnbrfjRgxoujtBEXoIiLRqNoIfdCgQQAce+yxTd7P/yr2798fSBZFN9100xK2rjKstNJKAGy44YY5f9+zZ08guUqJcfF09OjRADzwwAMNbve0seYW+VZbbTUgKZfgaY6u/vO+8sorhTW2Anka5worrJByS9IzZswYALp27QpA9+7dgWRhszHnnnsuAO3btwfguOOOq/vd66+/XvR2giJ0EZFoVG2E7htEMs2ZMweAqVOnAsnGIo/MnacrxmzevHkA3H777QBccMEFDX7vP3/xxRcA3HDDDeVqWtksWbIEyP7/ny9fc2nXrl3O38+dO7fu+5jKRmTq0aPm9LOXXnop5ZaU3zfffAPkf7Wy7bbbArDRRhsBybpMOa5yFKGLiESiaiN0n48aNqzm8O/HHnsMgNmzZwNJFkNj1llnnRK2rrJcdNFFQHaELo3zolv+OfMNapnOP//8srWpHPyK5ssvvwSSbLJNNtkktTalxf/d/OQnPwFg5syZQOPz3yuvvDKQzAr4GpZf1dx7772la2wtRegiIpGo2gjd54eXNur0Yl2tSWO51pKUijjnnHOAJAuq/nbt+qZNmwYk2TKx8PWUZ599FkgOQWlNNthgAyC5OvOrluHDhwNJ0btMXoLb1/d8jCpn6WFF6CIikajaCL05XgbX57Uy+byYe+GFFwB48cUXS9uwFLW0RGwMvIzyYYcdBsDuu++e83677LIL0HjfeElcj+AffvhhAL799tuitVXS5Ts677//fiDZNX799dcD8Mwzz+R8nO/69BLN7pJLLilFM5ukCF1EJBJVH6H7SrLv3vrtb38LZNfuaGz+2Oe5jjrqKAB++OGH0jVWysajrUmTJgGN75bNl88p+67B1sZ3O8Zi2WWToc+PaGysro+vt40cORJI5srXXHNNIJkz97o3fqzczTffXLo30AhF6CIikai6CN2zDrbbbjsAJkyYACRHyPmcpkfePic+YMAAIInonf+lPuCAAwAYNWoUEMeBv5JETc1VDWwuA8izPX7+858D8MgjjxSriVUhtuML6x/ufcsttwDJ+ol/BnxPi++S9a8DBw4EoFOnTkAy9nj2y9FHH13StjdFEbqISCSqIkJv27Zt3fcead93330N7nPhhRcC8OSTTwLJMWA+z+W3Zx4S3aFDByA5quz9998HGlbRi6VGR2NRqNd7jqmWi1dH7Nu3L5DMk06ePBmA7777rsnHH3PMMQCcfPLJJWphZfNDomPLQ/dzE2677ba623wvgefg//KXvwTg888/B+Dqq68Gkpr4Hqn7VZ9H9p4V43WD/LNXvw56qSlCFxGJhJUzJ9nMWvRiPl/+u9/9ru62M888s8F9fC7T84z9r6xH3p4vvP322wPJ3PiVV14JJBG7z4u5f/zjH3Xf+6lI/hfb+W7BHF4NIfRo6r25lvZJITyDp7H/51tvvTUAb7zxRileviL7pDFew+TTTz9tcPu+++4LFG0OPe8+gfL2y4EHHgjA3/72NyBZm/JsslLWzg8h5H1MUkv7xK/UvRIiwMUXXww0jNrr8/fsWSue9ZIZobu//OUvQMMDxIsgr8+KInQRkUhU5Bx6mzZtgKTaWf3z9xYtWgQkO/bGjRsHJJG5z2/5fLBnw/iZoieccAKQzBH6iTR+dqTX9Ki/qu/nUjqfI+vSpctSv8c03HTTTQAcf/zxOX/vlStPO+20srWpUnkd9NbK65c4j0aXX375NJpTNBMnTgQarsE1Vyvf58Yz19/8zFBfr3H1a+SXmyJ0EZFINBuhm9kGwB3AusCPwJgQwigzWxO4B+gMzAEGhxA+b+x5WsIjRY/M/cQQSKJLr3/eu3dvINnp6XnCXr/a5999fizzr7HX6Hj00UcbfPW/vpCservTTz99Kd9Zuvzs0Nj4Wssee+xRd5vPlba01op/jnw/Qmvlkax/ZrbYYgsguXo78cQT02lYgVry/9XXUXwnqF/Ne9bK+PHji9y6wuUToS8BzgghdAN6AyeZWXfgHOCJEEJX4Inan0VEJCUtznIxs4nADbX/9Q0hzDezjsDTIYTNm3lsXi82f/58IMlUqZ8H7hGDV1H0utWZvE6655eXuUZLRWd0zJo1C8g+hcbz1L1Pi5w/W/Q+8QqJ5513HgD9+/ev+52vbzQ3P+r7FLz2j1fWW3XVVRvczyN9X1vxNZgCVWyWi7v22muB5MrFT/pqLo+/EKXMcmkJr93ia3m+E7Rnz55A2efK8/qstGhR1Mw6A9sBLwPrhBDmA9QO6ms38phhwLCWvE7s1CfZ1Ce5qV+yqU8al/eAbmarABOA00IIXzVXG8OFEMYAY2qfI6+/ph9++CGQROj1V9a32WabBvf1PPMpU6YAyQ7POXPmAJVZPXFp+qSYZsyYAcDGG2/c4PY0TzJamj7xTKbM7AOAs846C4CFCxc2+Rwe1fs+hcwr1qeffhqA0aNHA0WLzPOW9melXjuAyqhxVOo+8Rz1Y4891l8PSCptppnF0py8slzMbDlqBvO7Qwie7/NR7VQLtV+bPpVZRERKqtkB3WpC8VuBmSGEa+r9ahJwRO33RwATi988ERHJVz5TLjsDhwH/MjPf634ucDkw3syOAd4HDipWo7xY1P777w8kl8MACxbUXAiMHTsWSLbjV8KlYLXwS0ffxh4j30DWUv75evDBBwE49dRTgdIuAlYDT9nzEhl+TFuMfCOhT73cddddQHJ4TiVrdkAPITwHNDZhvltxmyMiIkurIrf++0LWnXfe2eCrFIcX35o5cyYA3bp1S7M5S80P5fUSt0cccUQT927IUzJ901rmEXOZ27lbq8GDBwNJ6rB/ZmLmmxA9XdE3WVUDbf0XEYlERZfPrWIVvbEoJSXrE09r9YgdkpKo7dq1A5J0Vp8f9ajLU2RTUvEbi7z4nV/F+caqai2fW8VUPldEpDVRhF4aitCzqU+yVXyEngZF6DkpQhcRaU00oIuIREIDuohIJDSgi4hEQgO6iEgkyr1T9BNgUe3XGKxF7veyUQueI7Y+gdz9oj4prE8gvn5Rn2QraEwpa9oigJm90pJUrUpWrPcSU59Acd6P+qS0z1MJ1CfZCn0vmnIREYmEBnQRkUikMaCPSeE1S6VY7yWmPoHivB/1SWmfpxKoT7IV9F7KPocuIiKloSkXEZFIlG1AN7MBZvaWmc02s3PK9brFYmYbmNlTZjbTzGaY2am1t19gZv8xs2m1/+3Vwuet2n5Rn2RTn+RWin5Rn+QQQij5f0Ab4B1gY6At8DrQvRyvXcT30BHYvvb7VYFZQHfgAmBEa+wX9Yn6JK1+UZ/k/q9cEXovYHYI4d0QwmJgHDCwTK9dFCGE+SGEf9Z+vxCYCXQq8Gmrul/UJ9nUJ7mVoF/UJzmUa0DvBHxQ7+e5FP4hT42ZdQa2A16uvWm4mf2fmY01s3YteKpo+kV9kk19kluR+kV9kkO5BvRcBeurMr3GzFYBJgCnhRC+AkYDmwDbAvOBq1vydDluq7p+UZ9kU5/kVsR+UZ/kUK4BfS6wQb2f1wfmlem1i8bMlqOm4+8OIdwHEEL4KITwQwjhR+BP1FwK5qvq+0V9kk19kluR+0V9kkO5BvSpQFcz62JmbYEhwKQyvXZRmJkBtwIzQwjX1Lu9Y727DQKmt+Bpq7pf1CfZ1Ce5laBf1Cc5lKXaYghhiZkNByZTszo9NoQwoxyvXUQ7A4cB/zKzabW3nQsMNbNtqbncmwMcn+8TRtAv6pNs6pPcitov6pPctFNURCQS2ikqIhIJDegiIpHQgC4iEgkN6CIikdCALiISiSgH9MaqmLV2tduIF5hZS3Kgo2ZmK5jZ/5rZ67WflQvTblPa9DlpnJm1MbPXzOyhtNuSS5QDOrAEOCOE0A3oDZxkZt1TblMluB0YkHYjKsz3QL8QwjbUbLUeYGa9U25T2m5Hn5PGnEpNEa2KFOWAXqKKd1UvhDAF+CztdlSSUOPr2h+Xq/2vVW/O0OckNzNbH9iKQNK4AAABCUlEQVQbuCXttjQmygG9vhxVzEQaqL2MngYsAB4PIeizIrlcC5wF/Jh2QxoT9YCeo4qZSJbaQkjbUlPgqZeZbZV2m6SymNk+wIIQwqtpt6Up0Q7ouaqYiTQlhPAF8DSaP5ZsOwP7mdkcag7T6Gdmd6XbpGxRDuiNVTETyWRmHcxsjdrvVwR2B95Mt1VSaUIII0MI64cQOlNT2fHJEMKhKTcrS5QDOkkVs35LddBqpMzsr8CLwOZmNtfMjkm7TRWgI/CUmf0fNSVZHw8hVGRKWrnoc1K9VG1RRCQSsUboIiKtjgZ0EZFIaEAXEYmEBnQRkUhoQBcRiYQGdBGRSGhAFxGJhAZ0EZFI/D8LWQ5kbccbtwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the data\n",
    "fig, ax = plt.subplots(2, 5, sharex=True, sharey=True)\n",
    "index = 0\n",
    "for row in ax:\n",
    "    for col in row:\n",
    "        col.set_xlabel(str(y_train[index]))\n",
    "        col.imshow(x_train[index],cmap='gray')\n",
    "        index+=1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing the data\n",
    "\n",
    "## Vairables\n",
    "`image_width`,`image_height`,`image_channels` would describe the dimentions of the image  \n",
    "`classes` to detemine how many catogories of samples present in out dataset. By nature mnist have 0-9 images to ten classes  \n",
    "\n",
    "## Creating sparse vector representation\n",
    "`to_categorical` is converting into one hot encoding. Means each vector is represented by one hot encoding.\n",
    "0 --> [1,0,0,0,0,0,0,0,0,0]   \n",
    "1 --> [0,1,0,0,0,0,0,0,0,0]  \n",
    "2 --> [0,0,1,0,0,0,0,0,0,0]   \n",
    "and similarly goes on \n",
    "\n",
    "`np.expand_dims` would just increst one dimentions in the end. Like .... [1,2,3] to [[1],[2],[3]]  \n",
    "\n",
    "\n",
    "`Line16`,`Line17`is normalization we are divinding all the pixal values by 255. so all the numerical values are converted between 0 and 1\n",
    ">Note: since its a simple dataset there is not much of processing required to attain good accuracies. For all real time datasest preprocessing like normalizing , standadising , on hot encoding, filling the missing values, transforming features, feeding the data in batches and all other type of preprocessing is required\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vairables\n",
    "image_width = 28\n",
    "image_height = 28\n",
    "image_channels = 1\n",
    "input_shape = (image_width,image_height,image_channels)\n",
    "classes =  10\n",
    "\n",
    "# Creating sparse vector representation\n",
    "from keras.utils import to_categorical\n",
    "y_train_sparse = to_categorical(y_train,num_classes=classes)\n",
    "y_test_sparse = to_categorical(y_test,num_classes=classes)\n",
    "\n",
    "x_train = np.expand_dims(x_train, axis=3)\n",
    "x_test = np.expand_dims(x_test, axis=3)\n",
    "\n",
    "x_train = x_train/255 # Normalizing the x_trian\n",
    "x_test = x_test/255 # Normalizing the X_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training varibles\n",
    "These Training varbles are hyper parameters for neural network training.   \n",
    "`epochs` : each epoch is forward propagation + backward propagation over the whole dataset once is called one epoch.  \n",
    "`learning_rate` : the magnitude in which the weights are modified one the acquired loss.   \n",
    "`learning_rate_decay` : there can be high leanring rate at the beining of the training when the loss is high. Over a period of time the learning rate can reduce for fine training of network.  \n",
    "`batch_size` : the data is fed to the network in batches of 32 samples at each time. This batch feeding is done all over the whole dataset.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training varibles\n",
    "epochs = 10\n",
    "learning_rate = 0.001\n",
    "learning_rate_decay = 0.000001\n",
    "batch_size = 32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Netowork Model\n",
    "`Line 6` : we are building a keras sequential model  \n",
    "`Line 32` : we are using stochastic gradient decent optimizer  \n",
    "`Line 36` : compiling the model to check if the model is build properly.  \n",
    "\n",
    "The loss function being used is `categorical_crossentropy` since its a multi class classification\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building the model\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D,MaxPooling2D,Dropout,Flatten,Activation,Dense\n",
    "from keras.optimizers import SGD\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "# Layer 1\n",
    "model.add(Conv2D(64,kernel_size=(3,3), strides=(1, 1), padding='valid', data_format=\"channels_last\", input_shape = input_shape))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(rate=0.25))\n",
    "# Layer 2\n",
    "model.add(Conv2D(32,kernel_size =(3,3), strides=(1, 1), padding='valid'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(rate=0.25))\n",
    "\n",
    "# Layer 3\n",
    "model.add(Flatten())\n",
    "model.add(Dense(512))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(rate=0.25))\n",
    "\n",
    "# Layer 4\n",
    "model.add(Dense(256))\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "# Layer 5\n",
    "model.add(Dense(10,activation='softmax'))\n",
    "\n",
    "# optimizer\n",
    "optimizer = SGD(lr = learning_rate, momentum=0.0, decay=learning_rate_decay, nesterov=True)\n",
    "\n",
    "# Model Compilation\n",
    "model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training \n",
    "Training is the process of feeding the data to neural network and modifiying the weights of the model using the the backpropagation algorithm. The backpropagation using loss the function acquires the loss over batch size of data and does a backpropagation to modify the weights in such a way the in the next epoch the loss would be less when compared to the current epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 15s 246us/step - loss: 2.2471 - acc: 0.2229 - val_loss: 2.0851 - val_acc: 0.5802\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 10s 158us/step - loss: 1.5214 - acc: 0.5330 - val_loss: 0.7091 - val_acc: 0.7990\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 9s 153us/step - loss: 0.8373 - acc: 0.7201 - val_loss: 0.4701 - val_acc: 0.8677\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 9s 154us/step - loss: 0.6544 - acc: 0.7852 - val_loss: 0.3801 - val_acc: 0.8904\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 9s 151us/step - loss: 0.5557 - acc: 0.8212 - val_loss: 0.3248 - val_acc: 0.9078\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 9s 148us/step - loss: 0.4845 - acc: 0.8467 - val_loss: 0.2823 - val_acc: 0.9185\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 9s 148us/step - loss: 0.4320 - acc: 0.8651 - val_loss: 0.2511 - val_acc: 0.9288\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 9s 147us/step - loss: 0.3865 - acc: 0.8777 - val_loss: 0.2240 - val_acc: 0.9350\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 9s 152us/step - loss: 0.3529 - acc: 0.8903 - val_loss: 0.2036 - val_acc: 0.9416\n"
     ]
    }
   ],
   "source": [
    "# Training the model\n",
    "model_history = model.fit(x=x_train, y=y_train_sparse, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test,y_test_sparse), shuffle=True, initial_epoch=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results\n",
    "using the trained model we try to predict what are the values of images in the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 53us/step\n"
     ]
    }
   ],
   "source": [
    "# Results\n",
    "y_pred = model.predict(x_test,  verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Verifying the results\n",
    "cheking the results how good they are with the first 10 samples.   \n",
    "Plotting the graphs of test and train set accuracies and loss values. \n",
    "> NOTE: This plot is a very curicial step. These plots would tell us how good the model converges and if there is any overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ground truths of first 10 images in test set [7 2 1 0 4 1 4 9 5 9]\n",
      "Predicted values of first 10 image in test set [7 2 1 0 4 1 4 9 5 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"
    },
    {
     "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": [
    "# Verifying the results\n",
    "print(\"Ground truths of first 10 images in test set\",np.array(y_test[0:10]))\n",
    "print(\"Predicted values of first 10 image in test set\",np.argmax(y_pred[0:10],axis=1))\n",
    "\n",
    "\n",
    "loss = model_history.history['loss']\n",
    "val_loss = model_history.history['val_loss']\n",
    "plt.plot(loss,label='train')\n",
    "plt.plot(val_loss,label='test')\n",
    "plt.title('loss Graph')\n",
    "plt.ylabel('precentage')\n",
    "plt.xlabel('epochs')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "acc = model_history.history['acc']\n",
    "val_acc = model_history.history['val_acc']\n",
    "plt.plot(acc,label='train')\n",
    "plt.plot(val_acc,label='test')\n",
    "plt.title('Accuracy Graph')\n",
    "plt.ylabel('precentage')\n",
    "plt.xlabel('epochs')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visulizing the results\n",
    "checking the results by visulizing them and creating a confusion matrix. The values of precession and accuracy can be obtained by the help of confusion matrix and f1 scores to compare this architecure with other architectures of neural networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted    0     1    2    3    4    5    6    7    8    9\n",
      "Actual                                                      \n",
      "0          968     0    1    0    0    1    6    1    3    0\n",
      "1            0  1113    3    2    0    0    4    0   13    0\n",
      "2           13     0  941   12   15    0   10   16   24    1\n",
      "3            1     1   13  951    1   10    1    5   21    6\n",
      "4            1     2    3    0  930    0   13    1    4   28\n",
      "5           10     1    1   24    3  813   18    1   15    6\n",
      "6            7     3    2    1    5   10  928    0    2    0\n",
      "7            3     8   30    3    4    1    0  938    5   36\n",
      "8            6     2    2   15    6    7    6    9  902   19\n",
      "9           12     7    5   10   14    3    0   12   14  932\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25b031c5be0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visulizing the results\n",
    "y_pred = np.argmax(y_pred,axis=1)\n",
    "y_pred = pd.Series(y_pred, name='Predicted')\n",
    "y_test = pd.Series(y_test, name='Actual')\n",
    "df_confusion  = pd.crosstab(y_test,y_pred, rownames=['Actual'], colnames=['Predicted'])\n",
    "print(df_confusion)\n",
    "plt.figure(figsize = (20,20))\n",
    "plt.xlabel('xlabel', fontsize=18)\n",
    "plt.ylabel('ylabel', fontsize=18)\n",
    "plt.title('Confusion Matrix',fontsize=20)\n",
    "sns.heatmap(df_confusion, annot=True,fmt=\"d\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}