digits_recognition_neural_network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style=\"color:blue\" align=\"center\">Handwritten digits classification using neural network</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we will classify handwritten digits using a simple neural network which has only input and output layers. We will than add a hidden layer and see how the performance of the model improves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train) , (X_test, y_test) = keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c8a412f550>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(X_train[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train / 255\n",
    "X_test = X_test / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
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     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_flattened = X_train.reshape(len(X_train), 28*28)\n",
    "X_test_flattened = X_test.reshape(len(X_test), 28*28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_flattened.shape"
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  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "       0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.76470588,\n",
       "       0.31372549, 0.03529412, 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.21568627, 0.6745098 ,\n",
       "       0.88627451, 0.99215686, 0.99215686, 0.99215686, 0.99215686,\n",
       "       0.95686275, 0.52156863, 0.04313725, 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.53333333, 0.99215686, 0.99215686, 0.99215686,\n",
       "       0.83137255, 0.52941176, 0.51764706, 0.0627451 , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        ])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_flattened[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style='color:purple'>Very simple neural network with no hidden layers</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"digits_nn.jpg\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 5s 2ms/step - loss: 0.4691 - accuracy: 0.8762\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3038 - accuracy: 0.9153\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2836 - accuracy: 0.9211\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2731 - accuracy: 0.9235\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2666 - accuracy: 0.9260\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x1c89d217390>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(10, input_shape=(784,), activation='sigmoid')\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(X_train_flattened, y_train, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step - loss: 0.2687 - accuracy: 0.9262\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.26872265338897705, 0.9261999726295471]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_flattened, y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([3.2648511e-02, 1.6864298e-07, 4.2836424e-02, 9.2896128e-01,\n",
       "       1.9971631e-03, 6.6600017e-02, 1.0990414e-06, 9.9975038e-01,\n",
       "       5.0772585e-02, 5.6874293e-01], dtype=float32)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = model.predict(X_test_flattened)\n",
    "y_predicted[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1c8a0db0ad0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wgubPny9Jeu2115Sdna1du3Zp8WLvn0AKAEh+cX3NqLGxUc3NzSouLo7cFwgEVFBQoNra2h7XhMNhhUKhqBsAYGCJa4yam5slSdnZ2VH3Z2dnRx77ssrKSgUCgcgtLy8vniMBAPoB83fTVVRUqL29PXI7efKk9UgAgF4W1xgFg0FJUktLS9T9LS0tkce+zO/3Ky0tLeoGABhY4hqj/Px8BYNBVVVVRe4LhUI6ePCgCgsL47krAEAS8fxuurNnz6q+/sqlTxobG3XkyBFlZGRo7NixWrVqlX784x/rzjvvVH5+vl588UXl5uZqwYIF8ZwbAJBEPMfo0KFDeuCBByJfl5eXS5KWLFmirVu36tlnn1VnZ6eWL1+utrY23Xfffdq7d6+GDRsWv6kBAEnF55xz1kP8pVAopEAgoCLN1xAfFzUE+pPW/+H9x/G16/8xpn1t/H8TPa/ZN2eC5zUXm3p+JzBu7KLrUrV2q729/YbvBzB/Nx0AAMQIAGCOGAEAzBEjAIA5YgQAMEeMAADmiBEAwBwxAgCYI0YAAHPECABgjhgBAMwRIwCAOc9X7QYwMAwZl+d5zT8+7/2ip0N9gz2vkaQd/1Dsec2optqY9oXE48wIAGCOGAEAzBEjAIA5YgQAMEeMAADmiBEAwBwxAgCYI0YAAHPECABgjhgBAMwRIwCAOWIEADBHjAAA5rhqN4AefbL6Ns9rpvt9ntf83wufe14jSRkfn4tpHfomzowAAOaIEQDAHDECAJgjRgAAc8QIAGCOGAEAzBEjAIA5YgQAMEeMAADmiBEAwBwxAgCYI0YAAHNcKBUYAMJ/Pd3zmg//5qUY9uT3vGLF00/HsB9p+B8+iGkd+ibOjAAA5ogRAMAcMQIAmCNGAABzxAgAYI4YAQDMESMAgDliBAAwR4wAAOaIEQDAHDECAJgjRgAAc1woFRgATpR6/3vnLT7vFz19pPG/eV4zYu9/eF4jSS6mVeirODMCAJgjRgAAc55jtG/fPs2bN0+5ubny+XzatWtX1ONLly6Vz+eLus2dOzde8wIAkpDnGHV2dmrq1KnatGnTNbeZO3eumpqaIrft27ff1JAAgOTm+Q0MpaWlKi0tve42fr9fwWAw5qEAAANLQl4zqq6uVlZWlu6++26tWLFCra2t19w2HA4rFApF3QAAA0vcYzR37ly99tprqqqq0k9/+lPV1NSotLRUly5d6nH7yspKBQKByC0vLy/eIwEA+ri4/57R4sWLI3+ePHmypkyZogkTJqi6ulqzZ8++avuKigqVl5dHvg6FQgQJAAaYhL+1e/z48crMzFR9fX2Pj/v9fqWlpUXdAAADS8JjdOrUKbW2tionJyfRuwIA9FOef0x39uzZqLOcxsZGHTlyRBkZGcrIyND69eu1cOFCBYNBNTQ06Nlnn9Udd9yhkpKSuA4OAEgenmN06NAhPfDAA5Gvv3i9Z8mSJdq8ebOOHj2qX//612pra1Nubq7mzJmjH/3oR/L7vV/nCgAwMHiOUVFRkZy79iUKf/e7393UQACAgYerdgP9yKDU1JjWfff+/Z7XhLrPe15z5n+N97zGH/53z2uQfLhQKgDAHDECAJgjRgAAc8QIAGCOGAEAzBEjAIA5YgQAMEeMAADmiBEAwBwxAgCYI0YAAHPECABgjgulAv3I8XX3xrRuT+YvPK+Zf3yh5zX+33LRU8SGMyMAgDliBAAwR4wAAOaIEQDAHDECAJgjRgAAc8QIAGCOGAEAzBEjAIA5YgQAMEeMAADmiBEAwBwXSgWMtP/3b3lec3TRKzHtq+Fil+c1Z386xvMav5o8rwEkzowAAH0AMQIAmCNGAABzxAgAYI4YAQDMESMAgDliBAAwR4wAAOaIEQDAHDECAJgjRgAAc8QIAGCOC6UCcTDktlzPa1a9+KbnNX5fbP/KLv6P73peM/rf/j2mfQGx4MwIAGCOGAEAzBEjAIA5YgQAMEeMAADmiBEAwBwxAgCYI0YAAHPECABgjhgBAMwRIwCAOWIEADBHjAAA5rhqN/AXfENi+1di6p5Tntf87S2tnte83pHleY0kZb/o/e+d3THtCYgNZ0YAAHPECABgzlOMKisrNX36dKWmpiorK0sLFixQXV1d1Dbnz59XWVmZRo0apVtuuUULFy5US0tLXIcGACQXTzGqqalRWVmZDhw4oHfeeUddXV2aM2eOOjs7I9usXr1ab7/9tnbs2KGamhqdPn1aDz/8cNwHBwAkD0+v1u7duzfq661btyorK0uHDx/WrFmz1N7erl/+8pfatm2bvvOd70iStmzZoq997Ws6cOCAvvWtb131PcPhsMLhcOTrUCgUyz8HAKAfu6nXjNrb2yVJGRkZkqTDhw+rq6tLxcXFkW0mTpyosWPHqra2tsfvUVlZqUAgELnl5eXdzEgAgH4o5hh1d3dr1apVmjlzpiZNmiRJam5uVkpKitLT06O2zc7OVnNzc4/fp6KiQu3t7ZHbyZMnYx0JANBPxfx7RmVlZTp27Jj2799/UwP4/X75/f6b+h4AgP4tpjOjlStXas+ePXr//fc1ZsyYyP3BYFAXLlxQW1tb1PYtLS0KBoM3NSgAIHl5ipFzTitXrtTOnTv13nvvKT8/P+rxadOmaejQoaqqqorcV1dXpxMnTqiwsDA+EwMAko6nH9OVlZVp27Zt2r17t1JTUyOvAwUCAQ0fPlyBQEBPPPGEysvLlZGRobS0ND311FMqLCzs8Z10AABIHmO0efNmSVJRUVHU/Vu2bNHSpUslSS+99JIGDRqkhQsXKhwOq6SkRL/4xS/iMiwAIDn5nHPOeoi/FAqFFAgEVKT5GuIbaj0OBhjftHtjWvev//JPcZ6kZ39VURbTuvTXev7VCiCRLrouVWu32tvblZaWdt1tuTYdAMAcMQIAmCNGAABzxAgAYI4YAQDMESMAgDliBAAwR4wAAOaIEQDAHDECAJgjRgAAc8QIAGAu5k96Bfq6wffc5XnN8jd2J2CSnt3zK+8XPb39nw4kYBLAHmdGAABzxAgAYI4YAQDMESMAgDliBAAwR4wAAOaIEQDAHDECAJgjRgAAc8QIAGCOGAEAzBEjAIA5YgQAMMdVu5G0PvnerZ7XzBsRSsAkPRtTfcH7IufiPwjQB3BmBAAwR4wAAOaIEQDAHDECAJgjRgAAc8QIAGCOGAEAzBEjAIA5YgQAMEeMAADmiBEAwBwxAgCY40Kp6BfOz5vheU3VvL+PYU8jYlgD4GZxZgQAMEeMAADmiBEAwBwxAgCYI0YAAHPECABgjhgBAMwRIwCAOWIEADBHjAAA5ogRAMAcMQIAmONCqegXTs8c7HnN2CG9d9HT1zuyPK8ZGrrgeY3zvALoHzgzAgCYI0YAAHOeYlRZWanp06crNTVVWVlZWrBggerq6qK2KSoqks/ni7o9+eSTcR0aAJBcPMWopqZGZWVlOnDggN555x11dXVpzpw56uzsjNpu2bJlampqitw2bNgQ16EBAMnF0xsY9u7dG/X11q1blZWVpcOHD2vWrFmR+0eMGKFgMBifCQEASe+mXjNqb2+XJGVkZETd//rrryszM1OTJk1SRUWFzp07d83vEQ6HFQqFom4AgIEl5rd2d3d3a9WqVZo5c6YmTZoUuf/RRx/VuHHjlJubq6NHj+q5555TXV2d3nrrrR6/T2VlpdavXx/rGACAJBBzjMrKynTs2DHt378/6v7ly5dH/jx58mTl5ORo9uzZamho0IQJE676PhUVFSovL498HQqFlJeXF+tYAIB+KKYYrVy5Unv27NG+ffs0ZsyY625bUFAgSaqvr+8xRn6/X36/P5YxAABJwlOMnHN66qmntHPnTlVXVys/P/+Ga44cOSJJysnJiWlAAEDy8xSjsrIybdu2Tbt371Zqaqqam5slSYFAQMOHD1dDQ4O2bdumBx98UKNGjdLRo0e1evVqzZo1S1OmTEnIPwAAoP/zFKPNmzdLuvyLrX9py5YtWrp0qVJSUvTuu+/q5ZdfVmdnp/Ly8rRw4UK98MILcRsYAJB8PP+Y7nry8vJUU1NzUwMBlipb74lpXW3J7Z7XuKb/E9O+gGTEtekAAOaIEQDAHDECAJgjRgAAc8QIAGCOGAEAzBEjAIA5YgQAMEeMAADmiBEAwBwxAgCYI0YAAHPECABgLuaPHQd60/i/q/W85sG/+0YCJrmW5l7cF5B8ODMCAJgjRgAAc8QIAGCOGAEAzBEjAIA5YgQAMEeMAADmiBEAwBwxAgCYI0YAAHPECABgrs9dm845J0m6qC7JGQ8DAIjZRXVJuvLf9evpczHq6OiQJO3Xb40nAQDEQ0dHhwKBwHW38bmvkqxe1N3drdOnTys1NVU+ny/qsVAopLy8PJ08eVJpaWlGE9rjOFzBsbiM43AFx+KyvnAcnHPq6OhQbm6uBg26/qtCfe7MaNCgQRozZsx1t0lLSxvQT7IvcByu4FhcxnG4gmNxmfVxuNEZ0Rd4AwMAwBwxAgCY61cx8vv9Wrt2rfx+v/UopjgOV3AsLuM4XMGxuKy/HYc+9wYGAMDA06/OjAAAyYkYAQDMESMAgDliBAAwR4wAAOaIEQDAHDECAJgjRgAAc/8FLLL7yQs9WeQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(X_test[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**np.argmax finds a maximum element  from an array and returns the index of it**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(y_predicted[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted_labels = [np.argmax(i) for i in y_predicted]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7, 2, 1, 0, 4]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted_labels[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
       "array([[ 962,    0,    1,    1,    0,    6,    6,    3,    1,    0],\n",
       "       [   0, 1115,    3,    2,    0,    1,    4,    2,    8,    0],\n",
       "       [   9,    8,  928,   12,   10,    3,   13,   12,   34,    3],\n",
       "       [   4,    0,   19,  911,    1,   31,    4,   13,   20,    7],\n",
       "       [   1,    1,    2,    1,  930,    0,   14,    4,    8,   21],\n",
       "       [  10,    3,    3,   21,   11,  784,   22,    7,   24,    7],\n",
       "       [  12,    3,    6,    1,    8,   10,  915,    2,    1,    0],\n",
       "       [   1,    5,   23,    3,    7,    0,    0,  959,    2,   28],\n",
       "       [  10,    8,    6,   19,   10,   28,   10,   13,  861,    9],\n",
       "       [  11,    7,    1,    7,   44,    7,    0,   30,    5,  897]])>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = tf.math.confusion_matrix(labels=y_test,predictions=y_predicted_labels)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(95.72222222222221, 0.5, 'Truth')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "plt.figure(figsize = (10,7))\n",
    "sn.heatmap(cm, annot=True, fmt='d')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Truth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style='color:purple'>Using hidden layer</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2801 - accuracy: 0.9202\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1281 - accuracy: 0.9619\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0897 - accuracy: 0.9732\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0687 - accuracy: 0.9791\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0540 - accuracy: 0.9833\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x1c8c0ec5fd0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(100, input_shape=(784,), activation='relu'),\n",
    "    keras.layers.Dense(10, activation='sigmoid')\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(X_train_flattened, y_train, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step - loss: 0.0747 - accuracy: 0.9764\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.07473749667406082, 0.9764000177383423]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_flattened,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(69.0, 0.5, 'Truth')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_predicted = model.predict(X_test_flattened)\n",
    "y_predicted_labels = [np.argmax(i) for i in y_predicted]\n",
    "cm = tf.math.confusion_matrix(labels=y_test,predictions=y_predicted_labels)\n",
    "\n",
    "plt.figure(figsize = (10,7))\n",
    "sn.heatmap(cm, annot=True, fmt='d')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Truth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style='color:purple'>Using Flatten layer so that we don't have to call .reshape on input dataset</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 7s 3ms/step - loss: 0.2748 - accuracy: 0.9227\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1224 - accuracy: 0.9642\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0855 - accuracy: 0.9745\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0653 - accuracy: 0.9801\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0509 - accuracy: 0.9842\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0423 - accuracy: 0.9870\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0337 - accuracy: 0.9898\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0284 - accuracy: 0.9909\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0236 - accuracy: 0.9928\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0190 - accuracy: 0.9945\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x1c8c25520d0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    keras.layers.Dense(100, activation='relu'),\n",
    "    keras.layers.Dense(10, activation='sigmoid')\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(X_train, y_train, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 1ms/step - loss: 0.0813 - accuracy: 0.9779\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.08133944123983383, 0.9779000282287598]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test,y_test)"
   ]
  }
 ],
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