digitRecog.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1e918ad12d0>"
      ]
     },
     "execution_count": 21,
     "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[9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 0, 4, 1, 9, 2, 1, 3, 1, 4, 3, 5, 3, 6, 1, 7, 2, 8, 6, 9],\n",
       "      dtype=uint8)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train/255\n",
    "X_test = X_test/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_flattened.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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.        , 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.        , 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.        , 0.01176471, 0.07058824, 0.07058824,\n",
       "       0.07058824, 0.49411765, 0.53333333, 0.68627451, 0.10196078,\n",
       "       0.65098039, 1.        , 0.96862745, 0.49803922, 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.11764706, 0.14117647, 0.36862745, 0.60392157,\n",
       "       0.66666667, 0.99215686, 0.99215686, 0.99215686, 0.99215686,\n",
       "       0.99215686, 0.88235294, 0.6745098 , 0.99215686, 0.94901961,\n",
       "       0.76470588, 0.25098039, 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.19215686, 0.93333333,\n",
       "       0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686,\n",
       "       0.99215686, 0.99215686, 0.99215686, 0.98431373, 0.36470588,\n",
       "       0.32156863, 0.32156863, 0.21960784, 0.15294118, 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.07058824, 0.85882353, 0.99215686, 0.99215686,\n",
       "       0.99215686, 0.99215686, 0.99215686, 0.77647059, 0.71372549,\n",
       "       0.96862745, 0.94509804, 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.31372549, 0.61176471, 0.41960784, 0.99215686, 0.99215686,\n",
       "       0.80392157, 0.04313725, 0.        , 0.16862745, 0.60392157,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.05490196,\n",
       "       0.00392157, 0.60392157, 0.99215686, 0.35294118, 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.        , 0.54509804,\n",
       "       0.99215686, 0.74509804, 0.00784314, 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.        , 0.04313725, 0.74509804, 0.99215686,\n",
       "       0.2745098 , 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.        , 0.1372549 , 0.94509804, 0.88235294, 0.62745098,\n",
       "       0.42352941, 0.00392157, 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",
       "       0.31764706, 0.94117647, 0.99215686, 0.99215686, 0.46666667,\n",
       "       0.09803922, 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.        , 0.17647059,\n",
       "       0.72941176, 0.99215686, 0.99215686, 0.58823529, 0.10588235,\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.        , 0.0627451 , 0.36470588,\n",
       "       0.98823529, 0.99215686, 0.73333333, 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.        , 0.97647059, 0.99215686,\n",
       "       0.97647059, 0.25098039, 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.        , 0.18039216, 0.50980392,\n",
       "       0.71764706, 0.99215686, 0.99215686, 0.81176471, 0.00784314,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.15294118,\n",
       "       0.58039216, 0.89803922, 0.99215686, 0.99215686, 0.99215686,\n",
       "       0.98039216, 0.71372549, 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.09411765, 0.44705882, 0.86666667, 0.99215686, 0.99215686,\n",
       "       0.99215686, 0.99215686, 0.78823529, 0.30588235, 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.09019608, 0.25882353, 0.83529412, 0.99215686,\n",
       "       0.99215686, 0.99215686, 0.99215686, 0.77647059, 0.31764706,\n",
       "       0.00784314, 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.07058824, 0.67058824, 0.85882353,\n",
       "       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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_flattened[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(10, activation='sigmoid', input_shape=[\n",
    "                       len(X_train_flattened[0])]),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    optimizer=\"adam\",\n",
    "    loss='sparse_categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 2s 863us/step - loss: 0.4656 - accuracy: 0.8777\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.3029 - accuracy: 0.9161\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 2s 843us/step - loss: 0.2832 - accuracy: 0.9201\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 1s 761us/step - loss: 0.2728 - accuracy: 0.9243\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 2s 888us/step - loss: 0.2667 - accuracy: 0.9255\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 2s 973us/step - loss: 0.2623 - accuracy: 0.9275\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 1s 790us/step - loss: 0.2582 - accuracy: 0.9286\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 1s 684us/step - loss: 0.2554 - accuracy: 0.9294\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x1e91bd387d0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_flattened, y_train, epochs=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 663us/step - loss: 0.2661 - accuracy: 0.9267\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.266145259141922, 0.9266999959945679]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_flattened, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1e91c31a8d0>"
      ]
     },
     "execution_count": 34,
     "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_test[9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 602us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([5.4053548e-05, 5.9959571e-09, 2.6140242e-06, 2.8377565e-04,\n",
       "       8.0115396e-01, 1.3838185e-03, 2.0863120e-06, 9.2713726e-01,\n",
       "       2.8767785e-01, 9.9270660e-01], dtype=float32)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(X_test_flattened)\n",
    "y_pred[9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(y_pred[9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predicted_labels = [np.argmax(i) for i in y_pred]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7, 2, 1, 0, 4, 1, 4, 9, 6, 9]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted_labels[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 2, 1, 0, 4, 1, 4, 9, 5, 9], dtype=uint8)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
       "array([[ 964,    0,    1,    2,    0,    4,    6,    2,    1,    0],\n",
       "       [   0, 1118,    3,    2,    0,    1,    4,    2,    5,    0],\n",
       "       [   5,    9,  932,   15,    7,    3,   13,    9,   36,    3],\n",
       "       [   3,    0,   19,  930,    0,   20,    2,   12,   19,    5],\n",
       "       [   1,    1,    6,    2,  904,    0,   16,    9,   11,   32],\n",
       "       [   9,    3,    2,   40,    7,  770,   18,    8,   30,    5],\n",
       "       [  11,    3,    7,    1,    7,   10,  916,    2,    1,    0],\n",
       "       [   1,    6,   24,    5,    4,    0,    0,  964,    3,   21],\n",
       "       [   8,   16,    6,   20,    8,   25,   12,   13,  861,    5],\n",
       "       [  11,    8,    1,   10,   24,    6,    0,   34,    7,  908]])>"
      ]
     },
     "execution_count": 43,
     "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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(95.72222222222221, 0.5, 'Truth')"
      ]
     },
     "execution_count": 44,
     "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": [
    "Using Hidden Layer\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2760 - accuracy: 0.9219\n",
      "Epoch 2/8\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.1259 - accuracy: 0.9628\n",
      "Epoch 3/8\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.0862 - accuracy: 0.9743\n",
      "Epoch 4/8\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0652 - accuracy: 0.9803\n",
      "Epoch 5/8\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0513 - accuracy: 0.9844\n",
      "Epoch 6/8\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0409 - accuracy: 0.9877\n",
      "Epoch 7/8\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0325 - accuracy: 0.9903\n",
      "Epoch 8/8\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0277 - accuracy: 0.9918\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x1e943e09710>"
      ]
     },
     "execution_count": 45,
     "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=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 860us/step - loss: 0.0891 - accuracy: 0.9740\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.08914750069379807, 0.9739999771118164]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_flattened, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 666us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(95.72222222222221, 0.5, 'Truth')"
      ]
     },
     "execution_count": 47,
     "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": [
    "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": [
    "Using Flatten layer so that we don't have to call .reshape on input dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 3s 1ms/step - loss: 0.2780 - accuracy: 0.9213\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.1255 - accuracy: 0.9621\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0862 - accuracy: 0.9739\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0659 - accuracy: 0.9797\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0520 - accuracy: 0.9836\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0416 - accuracy: 0.9872\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0345 - accuracy: 0.9896\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0278 - accuracy: 0.9912\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0229 - accuracy: 0.9932\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0186 - accuracy: 0.9946\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x1e952596fd0>"
      ]
     },
     "execution_count": 48,
     "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": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 752us/step - loss: 0.0847 - accuracy: 0.9769\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.08473709970712662, 0.9768999814987183]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, y_test)"
   ]
  }
 ],
 "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}