Digitrecognition.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wi4Xl6gYIyIh"
      },
      "outputs": [],
      "source": [
        "import tensorflow\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense,Flatten"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train,y_train),(X_test,y_test)=keras.datasets.mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sTTWXuZZdeVJ",
        "outputId": "f5ded38a-e069-4b31-b4fd-7b809e08ffc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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[1m0s\u001b[0m 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2TdDEGfMdtB1",
        "outputId": "efd35fa1-46d4-4cee-8b8d-a9fd4dfc43b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0PPDqwvWdxBH",
        "outputId": "c8f76e34-b85a-4c97-9199-d88bd7e55a3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sHMu2Yv2g7PT",
        "outputId": "91c4c3b3-239e-4d92-8603-9ae43841b909"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(X_train[2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "zmAggbCyhOgR",
        "outputId": "4e9a5105-7932-4252-f0a3-c003d74e35e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fbf3870a5c0>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train=X_train/255\n",
        "X_test=X_test/255"
      ],
      "metadata": {
        "id": "EbeQRZk7i6wI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dMkOSRYTkstE",
        "outputId": "b26659da-9338-4e26-ae48-70d81a0af693"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "        0.07058824, 0.49411765, 0.53333333, 0.68627451, 0.10196078,\n",
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              "        0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686,\n",
              "        0.99215686, 0.99215686, 0.98431373, 0.36470588, 0.32156863,\n",
              "        0.32156863, 0.21960784, 0.15294118, 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.07058824, 0.85882353, 0.99215686,\n",
              "        0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.77647059,\n",
              "        0.71372549, 0.96862745, 0.94509804, 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.31372549, 0.61176471,\n",
              "        0.41960784, 0.99215686, 0.99215686, 0.80392157, 0.04313725,\n",
              "        0.        , 0.16862745, 0.60392157, 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        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.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.54509804, 0.99215686, 0.74509804, 0.00784314,\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.        , 0.04313725, 0.74509804, 0.99215686, 0.2745098 ,\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.        , 0.1372549 , 0.94509804, 0.88235294,\n",
              "        0.62745098, 0.42352941, 0.00392157, 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.        , 0.31764706, 0.94117647,\n",
              "        0.99215686, 0.99215686, 0.46666667, 0.09803922, 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.        , 0.17647059,\n",
              "        0.72941176, 0.99215686, 0.99215686, 0.58823529, 0.10588235,\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",
              "        0.0627451 , 0.36470588, 0.98823529, 0.99215686, 0.73333333,\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.        , 0.97647059, 0.99215686, 0.97647059,\n",
              "        0.25098039, 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.        , 0.18039216,\n",
              "        0.50980392, 0.71764706, 0.99215686, 0.99215686, 0.81176471,\n",
              "        0.00784314, 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.15294118, 0.58039216, 0.89803922,\n",
              "        0.99215686, 0.99215686, 0.99215686, 0.98039216, 0.71372549,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        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.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.09019608, 0.25882353,\n",
              "        0.83529412, 0.99215686, 0.99215686, 0.99215686, 0.99215686,\n",
              "        0.77647059, 0.31764706, 0.00784314, 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.07058824, 0.67058824, 0.85882353, 0.99215686,\n",
              "        0.99215686, 0.99215686, 0.99215686, 0.76470588, 0.31372549,\n",
              "        0.03529412, 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.21568627,\n",
              "        0.6745098 , 0.88627451, 0.99215686, 0.99215686, 0.99215686,\n",
              "        0.99215686, 0.95686275, 0.52156863, 0.04313725, 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "        0.        , 0.        , 0.        ],\n",
              "       [0.        , 0.        , 0.        , 0.        , 0.53333333,\n",
              "        0.99215686, 0.99215686, 0.99215686, 0.83137255, 0.52941176,\n",
              "        0.51764706, 0.0627451 , 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.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Artificial Neural Network"
      ],
      "metadata": {
        "id": "cMu3-rwDk7uc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=Sequential()\n",
        "model.add(Flatten(input_shape=(28,28)))\n",
        "model.add(Dense(128,activation='relu'))\n",
        "model.add(Dense(10,activation='softmax'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sdUOw3Jfk5gV",
        "outputId": "5de68a91-6051-45d7-d34b-2118c0663df0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "63Rp1IT1mdCF",
        "outputId": "2c595c21-ec75-4cb4-887a-c90e348310a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_4\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_4\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ flatten_4 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m784\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_10 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │         \u001b[38;5;34m100,480\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_11 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │           \u001b[38;5;34m1,290\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ flatten_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">784</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │         <span style=\"color: #00af00; text-decoration-color: #00af00\">100,480</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m101,770\u001b[0m (397.54 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">101,770</span> (397.54 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m101,770\u001b[0m (397.54 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">101,770</span> (397.54 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"Adam\", metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "bQWWNHHwmvDa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history=model.fit(X_train,y_train,epochs=10,validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_R2uOidHnCux",
        "outputId": "9f148145-de9e-4659-958d-e488f17439a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 5ms/step - accuracy: 0.8664 - loss: 0.4748 - val_accuracy: 0.9546 - val_loss: 0.1531\n",
            "Epoch 2/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9594 - loss: 0.1362 - val_accuracy: 0.9661 - val_loss: 0.1134\n",
            "Epoch 3/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.9753 - loss: 0.0862 - val_accuracy: 0.9707 - val_loss: 0.0993\n",
            "Epoch 4/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 3ms/step - accuracy: 0.9803 - loss: 0.0633 - val_accuracy: 0.9732 - val_loss: 0.0889\n",
            "Epoch 5/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 5ms/step - accuracy: 0.9857 - loss: 0.0473 - val_accuracy: 0.9736 - val_loss: 0.0825\n",
            "Epoch 6/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9892 - loss: 0.0355 - val_accuracy: 0.9707 - val_loss: 0.0963\n",
            "Epoch 7/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.9910 - loss: 0.0302 - val_accuracy: 0.9762 - val_loss: 0.0838\n",
            "Epoch 8/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.9936 - loss: 0.0224 - val_accuracy: 0.9778 - val_loss: 0.0784\n",
            "Epoch 9/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - accuracy: 0.9947 - loss: 0.0184 - val_accuracy: 0.9766 - val_loss: 0.0862\n",
            "Epoch 10/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9962 - loss: 0.0134 - val_accuracy: 0.9762 - val_loss: 0.0902\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Prediction**"
      ],
      "metadata": {
        "id": "pYzLGr6rnjsT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_prob=model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IxM-1KaDnd86",
        "outputId": "72a92a0a-3333-481c-d63e-1b978a572989"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred=y_prob.argmax(axis=-1)"
      ],
      "metadata": {
        "id": "EgrCNp9pnxHn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6y7R0Pa4n1cm",
        "outputId": "d7d67ea1-6535-4503-9208-c17600232bcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9789"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss']) # Use the 'history' variable directly\n",
        "plt.plot(history.history['val_loss'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "O796_e66sJTF",
        "outputId": "ebc65238-b483-444c-a18f-632646406ef9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7da0e74f4d00>]"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "3H9i43y_oq-a",
        "outputId": "4142e724-5ec2-480c-e5d1-22812da86dd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7da0e7360700>]"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(X_test[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "2_qwaEpMs7V-",
        "outputId": "b1c4397e-4d94-4248-ef24-da1e46e1c1c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7da0e71da260>"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(X_test[0].reshape(1,28,28))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0KtmjuKptI4s",
        "outputId": "512f5260-58a1-4e34-8e3c-e1a6c8a9e401"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5.3319127e-09, 8.0924921e-13, 1.8850498e-07, 8.1607377e-06,\n",
              "        1.8653217e-14, 1.7773221e-09, 3.0023998e-15, 9.9998951e-01,\n",
              "        6.1414438e-09, 2.1619142e-06]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Output of the prediction of test 1**"
      ],
      "metadata": {
        "id": "ZS0O7VjduRxc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(X_test[0].reshape(1,28,28)).argmax(axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "olHrNh4MtNtE",
        "outputId": "efa638ed-e4f9-410b-e006-de0b6bc71118"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([7])"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    }
  ]
}