handwrittendigits.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bed3eda7-2552-4381-904a-6870ef0fb26a",
   "metadata": {},
   "source": [
    "# IMPORT LIBRARIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "045df607-c65a-471a-b3bf-5c0da10c1e73",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "766213db-08eb-4991-94a7-297952c51999",
   "metadata": {},
   "source": [
    " ## Loading dataset from keras library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dd3ccc95-836f-4286-8bb2-ff5fca3591bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
      "\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 0us/step\n"
     ]
    }
   ],
   "source": [
    "(X_train, y_train) , (X_test, y_test) = keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "47d951ef-1636-461e-8779-5a060cb7529c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000 10000\n"
     ]
    }
   ],
   "source": [
    "## number of training and testing samples\n",
    "\n",
    "print(len(X_train),len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "43b71ff3-b381-4546-b9f3-79a3491a3501",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## sample size in pixle: 28 x 28 pixels\n",
    "X_train[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "68671a7b-9f2c-435e-b0d4-2f85ade788bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "          0,   0]], dtype=uint8)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "becc7824-16ef-43b2-a38e-0fd65958689a",
   "metadata": {},
   "source": [
    "> the 0 indicates black where 255 indicates white(hex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8c71ce87-c5c2-498c-928e-d421d869a792",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1694c3150>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mm5o1a1b8dXV1tSRp4cKFevnll/Xcc8/pypUrWrJkiT766CNNmTJFr732mgKBQOq6BgBkFZ9zzlk38VnRaFTBYFBlekRDfcOs20EG++1fT/Ze8/BPk7rWd3/3bzzX/N/pSXwQ/HqP9xrASLe7pgbtUSQSUU5Ozm3PZW06AIA5wggAYI4wAgCYI4wAAOYIIwCAOcIIAGCOMAIAmCOMAADmCCMAgDnCCABgjjACAJgjjAAA5lL65XrAQPLAn/3Wc813x3tf8FSSNhf9T881M79d5bkmsPO45xogEzAzAgCYI4wAAOYIIwCAOcIIAGCOMAIAmCOMAADmCCMAgDnCCABgjjACAJgjjAAA5ggjAIA5wggAYI4wAgCYY9VuZK2eSxHPNR3PPJDUtS7sveK55gc/2uq5ZuW/e9RzjSS5U0HPNYV/cSyJCznvNYCYGQEABgDCCABgjjACAJgjjAAA5ggjAIA5wggAYI4wAgCYI4wAAOYIIwCAOcIIAGCOMAIAmCOMAADmWCgV+Izrv34nqbrH//w/ea7Ztvq/eq55a6r3xVUlSVO9lzw4aqnnmrGbWj3XdJ8777kG2YeZEQDAHGEEADBHGAEAzBFGAABzhBEAwBxhBAAwRxgBAMwRRgAAc4QRAMAcYQQAMEcYAQDMEUYAAHM+55yzbuKzotGogsGgyvSIhvqGWbcDpI374695rsl54b2krvV3f3AgqTqv7j/0fc81f/TnkaSu1dN0Lqk69J9ud00N2qNIJKKcnJzbnsvMCABgjjACAJjzHEaHDx/WvHnzVFBQIJ/Pp927dyccX7RokXw+X8I2dWoSX6YCABg0PIdRV1eXJkyYoLq6us89Z+7cuWptbY1v+/fv/1JNAgCym+dveq2oqFBFRcVtz/H7/QqFQkk3BQAYXNLynlFDQ4Py8vI0btw4LV68WO3t7Z97biwWUzQaTdgAAINLysOooqJC27Zt08GDB7Vu3TqdOHFCs2fPViwWu+X5tbW1CgaD8a2wsDDVLQEABjjPv6a7kwULFsT/XFJSokmTJqmoqEj79u1TZWVln/NXrlyp6urq+OtoNEogAcAgk/Iwulk4HFZRUZGamppuedzv98vv96e7DQDAAJb2zxl1dHSopaVF4XA43ZcCAGQozzOjy5cv6913342/bm5u1ltvvaXc3Fzl5uaqpqZGjz32mMLhsM6fP6/nn39eo0eP1qOPPprSxgEA2cNzGL355puaNWtW/PWN93sWLlyojRs36vTp09q6dasuXbqkcDisWbNmaefOnQoEAqnrGgCQVTyHUVlZmW63tuqBA/2zICMAIHuk/QEGALfm+19vea75+Ft5SV1r8oJlnmve+LMfe675x1l/47nm33+13HONJEWmJ1WGAYqFUgEA5ggjAIA5wggAYI4wAgCYI4wAAOYIIwCAOcIIAGCOMAIAmCOMAADmCCMAgDnCCABgjjACAJhjoVQgg/R80J5UXf5fea/75LluzzUjfcM912z66n/3XCNJDz+63HPNyFfeSOpaSD9mRgAAc4QRAMAcYQQAMEcYAQDMEUYAAHOEEQDAHGEEADBHGAEAzBFGAABzhBEAwBxhBAAwRxgBAMyxUCpg5Pr0r3mu+adv353UtUq+dt5zTTKLnibjJx/+66TqRu55M8WdwBIzIwCAOcIIAGCOMAIAmCOMAADmCCMAgDnCCABgjjACAJgjjAAA5ggjAIA5wggAYI4wAgCYI4wAAOZYKBX4DN+kkqTqfvun3hcV3fTHWzzXzLj7quea/hRz1zzXHP+wOLmLXW9Nrg4DEjMjAIA5wggAYI4wAgCYI4wAAOYIIwCAOcIIAGCOMAIAmCOMAADmCCMAgDnCCABgjjACAJgjjAAA5ggjAIA5Vu1GRhhaXOS55p++W+C5pmbBDs81kvTY711Mqm4ge/6DSZ5rGn881XPNv9hyzHMNsg8zIwCAOcIIAGDOUxjV1tZq8uTJCgQCysvL0/z583X27NmEc5xzqqmpUUFBgUaMGKGysjKdOXMmpU0DALKLpzBqbGxUVVWVjh8/rvr6enV3d6u8vFxdXV3xc9auXav169errq5OJ06cUCgU0pw5c9TZ2Zny5gEA2cHTAwyvvvpqwuvNmzcrLy9PJ0+e1IwZM+Sc04YNG7Rq1SpVVlZKkrZs2aL8/Hxt375dTz31VJ+fGYvFFIvF4q+j0Wgyfw8AQAb7Uu8ZRSIRSVJubq4kqbm5WW1tbSovL4+f4/f7NXPmTB09evSWP6O2tlbBYDC+FRYWfpmWAAAZKOkwcs6purpa06dPV0lJiSSpra1NkpSfn59wbn5+fvzYzVauXKlIJBLfWlpakm0JAJChkv6c0dKlS/X222/ryJEjfY75fL6E1865Pvtu8Pv98vv9ybYBAMgCSc2Mli1bpr179+rQoUMaM2ZMfH8oFJKkPrOg9vb2PrMlAABu8BRGzjktXbpUu3bt0sGDB1VcXJxwvLi4WKFQSPX19fF9V69eVWNjo0pLS1PTMQAg63j6NV1VVZW2b9+uPXv2KBAIxGdAwWBQI0aMkM/n0/Lly7VmzRqNHTtWY8eO1Zo1azRy5Eg9+eSTafkLAAAyn6cw2rhxoySprKwsYf/mzZu1aNEiSdJzzz2nK1euaMmSJfroo480ZcoUvfbaawoEAilpGACQfXzOOWfdxGdFo1EFg0GV6REN9Q2zbge3MfSr9yZVF5kY9lyz4Iev3vmkmzz9++c81wx0K1q9L0QqScde8r7oae7L/9v7ha73eK9B1up219SgPYpEIsrJybntuaxNBwAwRxgBAMwRRgAAc4QRAMAcYQQAMEcYAQDMEUYAAHOEEQDAHGEEADBHGAEAzBFGAABzhBEAwFzS3/SKgWtoOOS55sO/HeW55pniRs81kvRE4IOk6gaypf883XPNP2z8muea0X//fzzXSFJu57Gk6oD+wswIAGCOMAIAmCOMAADmCCMAgDnCCABgjjACAJgjjAAA5ggjAIA5wggAYI4wAgCYI4wAAOYIIwCAOcIIAGCOVbv7ydU/meS95j9+mNS1nv/D/Z5rykd0JXWtgeyDniuea2bsXZHUte7/z//ouSb3kveVtK97rgAyAzMjAIA5wggAYI4wAgCYI4wAAOYIIwCAOcIIAGCOMAIAmCOMAADmCCMAgDnCCABgjjACAJgjjAAA5lgotZ+cn+899387/pdp6CR1Xrx0X1J1P24s91zj6/F5rrn/R82ea8Z+8IbnGknqSaoKwA3MjAAA5ggjAIA5wggAYI4wAgCYI4wAAOYIIwCAOcIIAGCOMAIAmCOMAADmCCMAgDnCCABgjjACAJjzOeecdROfFY1GFQwGVaZHNNQ3zLodAECSut01NWiPIpGIcnJybnsuMyMAgDnCCABgzlMY1dbWavLkyQoEAsrLy9P8+fN19uzZhHMWLVokn8+XsE2dOjWlTQMAsounMGpsbFRVVZWOHz+u+vp6dXd3q7y8XF1dXQnnzZ07V62trfFt//79KW0aAJBdPH3T66uvvprwevPmzcrLy9PJkyc1Y8aM+H6/369QKJSaDgEAWe9LvWcUiUQkSbm5uQn7GxoalJeXp3Hjxmnx4sVqb2//3J8Ri8UUjUYTNgDA4JJ0GDnnVF1drenTp6ukpCS+v6KiQtu2bdPBgwe1bt06nThxQrNnz1YsFrvlz6mtrVUwGIxvhYWFybYEAMhQSX/OqKqqSvv27dORI0c0ZsyYzz2vtbVVRUVF2rFjhyorK/scj8ViCUEVjUZVWFjI54wAIMN5+ZyRp/eMbli2bJn27t2rw4cP3zaIJCkcDquoqEhNTU23PO73++X3+5NpAwCQJTyFkXNOy5Yt0yuvvKKGhgYVFxffsaajo0MtLS0Kh8NJNwkAyG6e3jOqqqrSL37xC23fvl2BQEBtbW1qa2vTlStXJEmXL1/Ws88+q2PHjun8+fNqaGjQvHnzNHr0aD366KNp+QsAADKfp5nRxo0bJUllZWUJ+zdv3qxFixZpyJAhOn36tLZu3apLly4pHA5r1qxZ2rlzpwKBQMqaBgBkF8+/prudESNG6MCBA1+qIQDA4MPadAAAc4QRAMAcYQQAMEcYAQDMEUYAAHOEEQDAHGEEADBHGAEAzBFGAABzhBEAwBxhBAAwRxgBAMwRRgAAc4QRAMAcYQQAMEcYAQDMEUYAAHOEEQDAHGEEADBHGAEAzBFGAABzhBEAwBxhBAAwRxgBAMwRRgAAc0OtG7iZc06S1K1rkjNuBgCQtG5dk/Tpv9dvZ8CFUWdnpyTpiPYbdwIASIXOzk4Fg8HbnuNzXySy+tH169f1/vvvKxAIyOfzJRyLRqMqLCxUS0uLcnJyjDocGBiLXoxDL8bhU4xFr4EwDs45dXZ2qqCgQHfddft3hQbczOiuu+7SmDFjbntOTk7OoL7JPoux6MU49GIcPsVY9LIehzvNiG7gAQYAgDnCCABgLqPCyO/3a/Xq1fL7/datmGMsejEOvRiHTzEWvTJtHAbcAwwAgMEno2ZGAIDsRBgBAMwRRgAAc4QRAMAcYQQAMEcYAQDMEUYAAHOEEQDA3P8DZ6yam7DUFooAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(X_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f89491bc-0a5e-4e2d-a50e-163c6bc1da33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the final number\n",
    "y_train[0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ea21996a-2f31-4d49-b8be-04db81b9add5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "923edc5a-34e1-4b4f-af42-a9d8b1377598",
   "metadata": {},
   "source": [
    "> flatten our dataset\n",
    "> > convert our 28x28 dataset into a 1D array\n",
    "> > use pandas function reshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b27e73e5-60ae-452b-99f7-18f15fe74356",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ada6284-f34b-4037-a29d-768b729a1aa4",
   "metadata": {},
   "source": [
    "## Scaling the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "977342c7-a006-4238-b5de-51730fc689cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train / 255\n",
    "X_test = X_test / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "87e54a62-a675-45ca-b812-c61644c1469c",
   "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": 26,
   "id": "56fdd259-4f28-4cce-a873-9b3263a16911",
   "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",
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      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_flattened [0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c7e9ba7-a67a-42de-8cb1-ebd89c4aa6d0",
   "metadata": {},
   "source": [
    "# Creating a Simple Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6b22b72d-8dd7-498f-98d8-ca0a6a0bfcf9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.11/site-packages/keras/src/layers/core/dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 381us/step - accuracy: 0.8132 - loss: 0.7255\n",
      "Epoch 2/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 386us/step - accuracy: 0.9149 - loss: 0.3092\n",
      "Epoch 3/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 383us/step - accuracy: 0.9212 - loss: 0.2861\n",
      "Epoch 4/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 382us/step - accuracy: 0.9225 - loss: 0.2747\n",
      "Epoch 5/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 371us/step - accuracy: 0.9248 - loss: 0.2682\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x17379c1d0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " ##sequential: stack of layer in the NN\n",
    "\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(10,input_shape=(784,),activation='sigmoid')\n",
    "    ## Dense: All neuron in one layer are connected with every other neuron in the the other layer\n",
    "    ])\n",
    "\n",
    "##optimizer: train effeciently\n",
    "model.compile(\n",
    "    optimizer='adam',\n",
    "    loss='sparse_categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")\n",
    "\n",
    "##here we train are model\n",
    "model.fit(X_train_flattened,y_train, epochs=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2d74a9bd-a756-473f-a272-a99bba613f74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 330us/step - accuracy: 0.9130 - loss: 0.3047\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.2669297754764557, 0.9236000180244446]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_flattened,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "886211f9-f123-4f1a-865f-c565ca9c162f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 316us/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test_flattened)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "203914c2-341e-4f97-b222-9175895ae078",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x316a76150>"
      ]
     },
     "execution_count": 30,
     "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[18])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7a90e693-bf1d-4157-a2ee-ca3a84f09fe2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8.1169172e-05, 3.8749949e-06, 9.3897946e-02, 4.4520387e-01,\n",
       "       1.1992645e-04, 5.5846315e-02, 2.5315311e-02, 1.6618045e-05,\n",
       "       1.7583324e-02, 5.2248124e-06], dtype=float32)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[18]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "21e54f25-5d86-46fc-beef-8a1c298be766",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(y_pred[18])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1f47d981-ff6e-4dcf-ad32-ebfe3ab85493",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_label=[np.argmax(i) for i in y_pred]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "736d65d2-4304-439e-af7b-2993c42f1580",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
       "array([[ 959,    0,    2,    2,    0,    7,    6,    3,    1,    0],\n",
       "       [   0, 1113,    3,    2,    0,    1,    4,    2,   10,    0],\n",
       "       [   4,    7,  929,   18,    7,    4,   13,   10,   35,    5],\n",
       "       [   2,    0,   19,  936,    0,   19,    2,   10,   14,    8],\n",
       "       [   1,    1,    3,    2,  908,    0,   12,    4,    7,   44],\n",
       "       [   7,    1,    4,   48,   10,  766,   17,    6,   25,    8],\n",
       "       [  11,    3,    6,    1,    7,   13,  914,    2,    1,    0],\n",
       "       [   1,    5,   23,   10,    9,    0,    0,  944,    2,   34],\n",
       "       [   8,    7,    8,   34,   10,   33,    9,   12,  838,   15],\n",
       "       [  10,    7,    2,   14,   23,    5,    0,   18,    1,  929]],\n",
       "      dtype=int32)>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = tf.math.confusion_matrix(labels=y_test,predictions=y_pred_label)\n",
    "## we do not want to use the array rather the max predicted values\n",
    "\n",
    "cm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11daef65-e407-4c8a-83ff-e005d241d949",
   "metadata": {},
   "source": [
    "## visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "00d346a2-a067-4044-8b94-0ea32f3df3ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d0890dd1-bfe0-42e4-a9f9-f50ff4fa90a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(95.72222222222221, 0.5, 'Actual')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(cm, annot=True,fmt='d')\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86854b26-0eaa-48db-ab17-326c512b3c50",
   "metadata": {},
   "source": [
    "# Adding a Hidden Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2fbc45d0-ac8d-4f1c-911d-a2973c20f82e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.11/site-packages/keras/src/layers/core/dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 910us/step - accuracy: 0.8716 - loss: 0.4475\n",
      "Epoch 2/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 818us/step - accuracy: 0.9613 - loss: 0.1330\n",
      "Epoch 3/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 751us/step - accuracy: 0.9745 - loss: 0.0867\n",
      "Epoch 4/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 744us/step - accuracy: 0.9809 - loss: 0.0640\n",
      "Epoch 5/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 784us/step - accuracy: 0.9844 - loss: 0.0525\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x149dc3c10>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(100,input_shape=(784,),activation='relu'),\n",
    "    ## Dense: All neuron in one layer are connected with every other neuron in the the other layer\n",
    "    keras.layers.Dense(10,activation='sigmoid')\n",
    "])\n",
    "\n",
    "##optimizer: train effeciently\n",
    "model.compile(\n",
    "    optimizer='adam',\n",
    "    loss='sparse_categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")\n",
    "\n",
    "##here we train are model\n",
    "model.fit(X_train_flattened,y_train, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8bf89111-6960-49fc-ab70-2b923f842a2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 435us/step - accuracy: 0.9703 - loss: 0.0912\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.07846023887395859, 0.9742000102996826]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test_flattened,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "49edbe82-78fb-4a16-a7b7-01323a3fb9e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 387us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(95.72222222222221, 0.5, 'Actual')"
      ]
     },
     "execution_count": 49,
     "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_pred = model.predict(X_test_flattened)\n",
    "\n",
    "y_pred_label=[np.argmax(i) for i in y_pred]\n",
    "cm = tf.math.confusion_matrix(labels=y_test,predictions=y_pred_label)\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.heatmap(cm, annot=True,fmt='d')\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4519561-fe42-4a40-8e72-23532ff02a81",
   "metadata": {},
   "source": [
    "*Flattening Array using Keras*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5ac6f369-7b6e-4aa5-9798-7cb0a1d5d897",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.11/site-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n",
      "/opt/anaconda3/lib/python3.11/site-packages/keras/src/layers/core/dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 860us/step - accuracy: 0.8767 - loss: 0.4527\n",
      "Epoch 2/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 922us/step - accuracy: 0.9615 - loss: 0.1319\n",
      "Epoch 3/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 834us/step - accuracy: 0.9742 - loss: 0.0862\n",
      "Epoch 4/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 799us/step - accuracy: 0.9806 - loss: 0.0649\n",
      "Epoch 5/5\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 923us/step - accuracy: 0.9854 - loss: 0.0489\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x14a3527d0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28,28)),\n",
    "    keras.layers.Dense(100,input_shape=(784,),activation='relu'),\n",
    "    ## Dense: All neuron in one layer are connected with every other neuron in the the other layer\n",
    "    keras.layers.Dense(10,activation='sigmoid')\n",
    "])\n",
    "\n",
    "##optimizer: train effeciently\n",
    "model.compile(\n",
    "    optimizer='adam',\n",
    "    loss='sparse_categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")\n",
    "\n",
    "##here we train are model\n",
    "model.fit(X_train,y_train, epochs=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7b117cb8-5e84-44ea-8895-b0a65337b65e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 424us/step - accuracy: 0.9719 - loss: 0.0891\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.07650121301412582, 0.9754999876022339]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "eacda6a4-9acf-4cbf-9871-b0d19e846c41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 429us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(95.72222222222221, 0.5, 'Actual')"
      ]
     },
     "execution_count": 53,
     "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_pred = model.predict(X_test)\n",
    "\n",
    "y_pred_label=[np.argmax(i) for i in y_pred]\n",
    "cm = tf.math.confusion_matrix(labels=y_test,predictions=y_pred_label)\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.heatmap(cm, annot=True,fmt='d')\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85812603-a19a-4b24-b882-5aed09202b78",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}