Hand_written_digit_classification_using_Neural_Network.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "86e70504",
      "metadata": {
        "id": "86e70504"
      },
      "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": null,
      "id": "b07db98f",
      "metadata": {
        "id": "b07db98f"
      },
      "outputs": [],
      "source": [
        "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8ba83a3a",
      "metadata": {
        "id": "8ba83a3a",
        "outputId": "c64365d6-4b71-481e-90b2-e18f84595b50"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(x_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0ef70c0d",
      "metadata": {
        "id": "0ef70c0d",
        "outputId": "93c88bd9-62e1-4713-cc83-a9cc276d005a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4b1949b4",
      "metadata": {
        "id": "4b1949b4",
        "outputId": "c93b5861-091e-4b59-f838-442f9f6043d7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(28, 28)"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train[0].shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f1e5fb71",
      "metadata": {
        "id": "f1e5fb71",
        "outputId": "3ce3f456-b664-4400-a59f-9c1a602a9f70"
      },
      "outputs": [
        {
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          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3597b87f",
      "metadata": {
        "id": "3597b87f"
      },
      "source": [
        "# Just be used to with how it works"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dd92d4b0",
      "metadata": {
        "id": "dd92d4b0",
        "outputId": "a236d0e7-b06a-4f29-b474-8afc41e10bf7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x195714de350>"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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0lO0natvWSVppe5lGLhvukHRRC+YD0GITuTrwY0ljXV8svwk/gCmBFYNAcpQAkBwlACRHCQDJUQJAcpQAkBwlACRHCQDJUQJAcpQAkBwlACRHCQDJUQJAcpQAkBwlACRX93MHmroz+zVJL43aNF/S7rYNcPCYrzGdPF8nzyY1f76jIuLDYwVtLYFf2bndFxE9lQ1QB/M1ppPn6+TZpPbOx9MBIDlKAEiu6hLYUPH+62G+xnTyfJ08m9TG+Sp9TQBA9ao+EwBQMUoASI4SAJKjBIDkKAEguf8BsRZSmAIzL0AAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.matshow(x_train[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6544a112",
      "metadata": {
        "id": "6544a112",
        "outputId": "af33db7c-20c8-42ef-e99c-c1630c6ebca1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "033930c0",
      "metadata": {
        "id": "033930c0",
        "outputId": "cc129983-fb2b-4b5b-eaaf-7fefb3e6bd18"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x195735c6e60>"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.matshow(x_train[2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e8b14725",
      "metadata": {
        "id": "e8b14725",
        "outputId": "95115e8b-59a4-467e-c723-059b8d4a9320"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train[2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f5e0c543",
      "metadata": {
        "id": "f5e0c543",
        "outputId": "f062f1bb-1bbd-4c5c-b880-10e8b26f4660"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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0lO0natvWSVppe5lGLhvukHRRC+YD0GITuTrwY0ljXV8svwk/gCmBFYNAcpQAkBwlACRHCQDJUQJAcpQAkBwlACRHCQDJUQJAcpQAkBwlACRHCQDJUQJAcpQAkBwlACRX93MHmroz+zVJL43aNF/S7rYNcPCYrzGdPF8nzyY1f76jIuLDYwVtLYFf2bndFxE9lQ1QB/M1ppPn6+TZpPbOx9MBIDlKAEiu6hLYUPH+62G+xnTyfJ08m9TG+Sp9TQBA9ao+EwBQMUoASI4SAJKjBIDkKAEguf8BsRZSmAIzL0AAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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M4YZBft9C2z22e/Zpb+MTA2iqIZWA7S71FcBdEXGfJEXEjog4EBG9km6TdMpAvzciFkdEd0R0d2lcs+YG0CRDeXfAkm6XtDEibuy3fXq/p50vaX3zxwPQakN5d+CTki6UtM722tq2qyXNtz1bUkjaLOmSFswHoMWG8u7A45IG+jnkh5s/DoB2Y8UgkBwlACRHCQDJUQJAcpQAkBwlACRHCQDJUQJAcpQAkBwlACRHCQDJUQJAcpQAkBwlACRHCQDJOSLatzP7PyVt6bfpCEmvtm2A4WO+xnTyfJ08m9T8+X4jIj44UNDWEviVnds9EdFd2QB1MF9jOnm+Tp5Nau98XA4AyVECQHJVl8DiivdfD/M1ppPn6+TZpDbOV+lrAgCqV/WZAICKUQJAcpQAkBwlACRHCQDJ/Q/RizKgx+JmHQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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hACRHCADJEQJAcoQAkBwhACRHCADJ1TpPwPbrkjaPWDRb0o7aGjhw9NeeXu6vl3uTOt/f0RHxK6MVag2BX1i5PRAR/Y010AL9taeX++vl3qR6+2N3AEiOEACSazoEljW8/lborz293F8v9ybV2F+jxwQANK/pkQCAhhECQHKEAJAcIQAkRwgAyf0vts8M0PJ3db0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "for i in range(0 , 5):\n",
        "    plt.matshow(x_train[i])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eca12a2b",
      "metadata": {
        "id": "eca12a2b",
        "outputId": "d28b301d-57c3-4b64-af7a-e334d2545eaf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "5\n",
            "0\n",
            "4\n",
            "1\n",
            "9\n"
          ]
        }
      ],
      "source": [
        "for i in range(0, 5):\n",
        "    print(y_train[i])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "599e3b18",
      "metadata": {
        "id": "599e3b18"
      },
      "source": [
        "# Flatten 2 dimension array into 1 Dimension so that we can pass it to input layer of neural network"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a18abf37",
      "metadata": {
        "id": "a18abf37",
        "outputId": "1c430cf5-c020-492b-818a-dcd044e41ed5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train.shape\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "de0dd212",
      "metadata": {
        "scrolled": true,
        "id": "de0dd212",
        "outputId": "011adeda-c51d-40d9-b7c8-1a9d3db7e76c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(x_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "01655f00",
      "metadata": {
        "id": "01655f00"
      },
      "outputs": [],
      "source": [
        "# Scaled dataset to convert each val btw 0 and 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "73dbc6b3",
      "metadata": {
        "id": "73dbc6b3"
      },
      "outputs": [],
      "source": [
        "x_train = x_train/255\n",
        "x_test = x_test/255"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e6f9519b",
      "metadata": {
        "id": "e6f9519b"
      },
      "outputs": [],
      "source": [
        "x_train_flatten  = x_train.reshape(len(x_train),28*28)  # we need (60000, 28,28) in the form of (60000,784)\n",
        "x_test_flatten = x_test.reshape(len(x_test), 28*28)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "34e790bf",
      "metadata": {
        "id": "34e790bf",
        "outputId": "37883d63-f747-471d-8387-aaf791989327"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(60000, 784)"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train_flatten.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ed705492",
      "metadata": {
        "id": "ed705492",
        "outputId": "8c21b073-b4a5-4fe8-af5e-2551d9709e42"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(10000, 784)"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_test_flatten.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f717e5b3",
      "metadata": {
        "id": "f717e5b3",
        "outputId": "88d88978-eba9-4dcf-f465-b7cabb2b4358"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
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              "       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": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_train_flatten[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d513ccae",
      "metadata": {
        "id": "d513ccae"
      },
      "source": [
        "# Create simple neural network"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d1ec4729",
      "metadata": {
        "id": "d1ec4729",
        "outputId": "2d6155a4-137b-4429-c0ef-b7d07479fad7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 10s 3ms/step - loss: 0.4656 - accuracy: 0.8794\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3038 - accuracy: 0.9143\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2832 - accuracy: 0.9202\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2733 - accuracy: 0.9229\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2668 - accuracy: 0.9255\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x19570e11810>"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Dense(10, input_shape= ( 784,), activation = 'sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(\n",
        "    optimizer = 'adam',\n",
        "    loss ='sparse_categorical_crossentropy',\n",
        "    metrics = ['accuracy']\n",
        ")\n",
        "\n",
        "model.fit(x_train_flatten, y_train, epochs = 5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c31e47eb",
      "metadata": {
        "id": "c31e47eb",
        "outputId": "4eb5c43d-137c-4d0e-a8f0-d386c321d616"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.2695 - accuracy: 0.9245\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[0.26945385336875916, 0.9244999885559082]"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.evaluate(x_test_flatten,y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7bfd272b",
      "metadata": {
        "id": "7bfd272b"
      },
      "source": [
        "# Check"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "21ede40f",
      "metadata": {
        "id": "21ede40f",
        "outputId": "610eea2e-e9b0-47ad-abd3-ca7d4b426d1f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x19571057700>"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.matshow(x_test[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "214cf9e5",
      "metadata": {
        "id": "214cf9e5",
        "outputId": "93fb82e1-1dd8-419b-b07c-a760f0f2b94b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([5.0382674e-01, 4.4019222e-03, 9.9955082e-01, 3.9453551e-01,\n",
              "       9.5582353e-10, 8.5756469e-01, 9.2686188e-01, 1.2890480e-12,\n",
              "       1.1907300e-01, 3.7349306e-09], dtype=float32)"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_predicted = model.predict(x_test_flatten)\n",
        "y_predicted[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "678fac6b",
      "metadata": {
        "id": "678fac6b",
        "outputId": "91aa93f2-e812-4f65-99b2-bbcbd8722b32"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.argmax(y_predicted[1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5d2ce56a",
      "metadata": {
        "id": "5d2ce56a"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f747f605",
      "metadata": {
        "id": "f747f605",
        "outputId": "7d01b8e1-5c3c-4bb1-bc67-10831c34eec9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([7, 2, 1, 0, 4], dtype=uint8)"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "40954061",
      "metadata": {
        "id": "40954061",
        "outputId": "f5350df6-ce24-40d9-a811-7f4be6d4d78c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[7, 2, 1, 0, 4]"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_predicted_label = [np.argmax(i) for i in y_predicted]\n",
        "y_predicted_label[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fe9220dc",
      "metadata": {
        "id": "fe9220dc"
      },
      "outputs": [],
      "source": [
        "cm = tf.math.confusion_matrix (labels= y_test, predictions = y_predicted_label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2ab59936",
      "metadata": {
        "id": "2ab59936",
        "outputId": "141ad6bc-28fa-427a-d79b-cb37ba267e44"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'truth')"
            ]
          },
          "execution_count": 29,
          "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": [
        "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",
      "id": "ce7c0ef8",
      "metadata": {
        "id": "ce7c0ef8"
      },
      "source": [
        "# Adding hidden layer to our model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7ddd62db",
      "metadata": {
        "id": "7ddd62db",
        "outputId": "2c20ca76-3f7d-4024-b7e1-39c2127a7e82"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2687 - accuracy: 0.9247\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.1201 - accuracy: 0.9643\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0849 - accuracy: 0.9746\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0642 - accuracy: 0.9804\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0507 - accuracy: 0.9844\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x195737aa4d0>"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model = keras. Sequential([\n",
        "    keras.layers.Dense(100,input_shape= ( 784,), activation = 'relu'),#hidden layer with 100 neurons\n",
        "    keras.layers.Dense(10,activation = 'sigmoid')#no need to tell input size to each layer\n",
        "])\n",
        "\n",
        "model. compile(\n",
        "    optimizer = 'adam',\n",
        "    loss ='sparse_categorical_crossentropy',\n",
        "    metrics =['accuracy']\n",
        ")\n",
        "\n",
        "model.fit(x_train_flatten, y_train, epochs= 5)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ad2bd978",
      "metadata": {
        "id": "ad2bd978"
      },
      "source": [
        "# if you dont want to flatten the input array then use this"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ea98c981",
      "metadata": {
        "id": "ea98c981",
        "outputId": "a874646c-90da-407f-c782-7fb36aed2be5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.2405 - accuracy: 0.9304\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.1020 - accuracy: 0.9700\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.0687 - accuracy: 0.9793\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.0495 - accuracy: 0.9847\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.0377 - accuracy: 0.9883\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x19579d85ed0>"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model = keras. Sequential([\n",
        "    keras.layers.Flatten(input_shape=(28,28)),\n",
        "    keras.layers.Dense(200, activation = 'relu'),\n",
        "    keras.layers.Dense(10,activation = 'sigmoid')\n",
        "])\n",
        "\n",
        "model. compile(\n",
        "    optimizer = 'adam',\n",
        "    loss ='sparse_categorical_crossentropy',\n",
        "    metrics =['accuracy']\n",
        ")\n",
        "\n",
        "model.fit(x_train, y_train, epochs= 5)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fde53841",
      "metadata": {
        "id": "fde53841",
        "outputId": "1d846856-1368-443d-948b-c4a45093b493"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 1s 4ms/step - loss: 0.0764 - accuracy: 0.9768\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[0.07643250375986099, 0.9768000245094299]"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.evaluate(x_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "51416814",
      "metadata": {
        "id": "51416814"
      },
      "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": "3.10.2"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}