DikshaRai_Handwritten_Digit_Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DikshaRai_Handwritten-Digit-Classification.ipynb",
      "provenance": [],
      "mount_file_id": "1z4cGVASpqtdBAqVJ3r1zcyETZIdwyjGf",
      "authorship_tag": "ABX9TyMENrUuP4Xnwge9iq3drp63",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/diksharai9/Handwritten-Digit-Classification/blob/main/DikshaRai_Handwritten_Digit_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kT3OfgRkJy46"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow\n",
        "import keras\n",
        "\n",
        "\n",
        "\n",
        "from keras.datasets import mnist"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2cw1PWUjJ-S9",
        "outputId": "86f12572-c1ea-4610-e660-59bd46c7bdf0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "11501568/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape , X_test.shape , y_train.shape , y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pztOQDVTKGH4",
        "outputId": "a0850bba-3fdd-45c3-c1c1-a6c543ca6eff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 28, 28), (10000, 28, 28), (60000,), (10000,))"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(X_train[5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "P8N8PruAKLwO",
        "outputId": "39a6ac7a-c9da-4564-e889-15ad7682dc29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f732479e290>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zrgZ47+o5/Xk9wr5G0hQze5+ZXSDpU5JW1KGPtzGz4dmBE5nZcEkfUeNNRb1C0oLs/gJJT9Wxl9/TKNN4500zrjq/d3Wf/tzda/4n6Tr1HZH/jaS76tFDTl+TJD2f/W2ud2+SlqvvY90p9R3buFXSaEldkrZL+h9Joxqot0ckbZS0QX3BaqtTb3PU9xF9g6T12d919X7vEn3V5H3j57JAEBygA4Ig7EAQhB0IgrADQRB2IAjCDgRB2IEg/h8CIWRCsmbzCQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_input_img(i):\n",
        "  plt.imshow(X_train[i],cmap='binary')\n",
        "  plt.title(y_train[i])\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "JwKPWJ4_KTiw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(10):\n",
        "  plot_input_img(i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "zH5VxqHNKYIP",
        "outputId": "e6bb5e7e-7fb3-47b1-de6d-dcfc6ecf72e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Thllm/O/KvO+aXf68VWWUfZekSUN+/lntuq4QEbtq3/dJWqPuW4p67+EVdGvf95U8z9910zLewy0zri6478pc/ryMsm+QdI7tn9s+TtLNkl4qYY4fsT2m9sKJbI+RdJW6bynqlyTNqV2eI+nFEmf5nm5ZxrveMuMq+b4rffnziOj4l6TrNPiK/CeSlpQxQ525fiHpf2pfW8ueTdJKDT6s+1aDr23cKekfJK2XtF3Sa5LGddFs/yHpfUmbNVis3pJmu0yDD9E3S9pU+7qu7PsuMVdH7jfeLgtkghfogExQdiATlB3IBGUHMkHZgUxQdiATlB3IxP8DQpFwmDBahugAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VdFf2drUree0zWDeNnY5qGu9OqTPN+F+Vue+anf68VWWEfZ+kaSOeT82WdQV335f9PiTpJXXfVNQHT82gm/0+VHI/f9VN03jXm2ZcXbDvypz+vIywvyepz8y+a2bjJN0s6ZUS+vgGMzs3O3EiMztX0o/UfVNRvyKpP3vcL2lTib18TbdM4503zbhK3nelT3/u7h3/kbRQtTPy/yPp38roIaevv5O0Pfv5oOzeJG1Q7W3d/6l2buM2SRdIekPSR5L+S9KkLuptvaQdkt5XLVi9JfV2lWpv0d+XNJj9LCx73yX66sh+4+uyQBCcoAOCIOxAEIQdCIKwA0EQdiAIwg4EQdiBIP4fZlqSXevLgeMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pIo19I/+6pFuq6KFJX6dL+q/G386qe5P0mMYO6/5PY99tLJf0x5Kek/SapH+VNG2AentE0iuSXtZYsGZW1Nt8jR2ivyxpe+Pvoqrfu0JffXnfOF0WSIIv6IAkCDuQBGEHkiDsQBKEHUiCsANJEHYgif8Hxu5duG7SzdAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qr/neXdLvzOw9M5tTdDP9GOnuB7L7ByWNLLKZflSdxruVzplmvG3eu3qmP8+LA3Tnm+TuP5I0VdKD2cfVtuS938Haaey0pmm8W6Wfacb/qsj3rt7pz/MqIuz7JY3u8/i72bK24O77s7/dklar/aaiPnR2Bt3sb3fB/fxVO03j3d8042qD967I6c+LCPtmSdea2ffNbKCkn0haW0Af5zGzwdmBE5nZYEmT1X5TUa+VNDu7P1vSmgJ7+Zp2mca70jTjKvi9K3z6c3dv+U3SNPUekf+TpH8poocKff1A0gfZbWfRvUlaod6PdafVe2zjPknfkfS2pN2S/kvSFW3U239I2i5pm3qDNaqg3iap9yP6Nklbs9u0ot+7RF8ted/4uSwQBAfogCAIOxAEYQeCIOxAEIQdCIKwA0EQdiCI/wPg9KSgiM6sVgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2T5Z0pfpvKurNklYUt1dI2lRhL1/TL9N4N5pmXBUfu8qnP4+Inl8kLdb4O/JvSfqXKnpo0NdfSvqf4vJq1b1JekzjT+s+0/h7Gysl/YWk7ZLekPSMpNP7qLdHJO2W9IrGgzW7ot4WaPwp+iuSdhWXxVUfu5K+enLc+LgskARv0AFJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEv8P2f5dd44LkV8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X_train.astype(np.float32)/255\n",
        "X_test = X_test.astype(np.float32)/255\n",
        "\n",
        "X_train= np.expand_dims(X_train, -1)\n",
        "X_test= np.expand_dims(X_test, -1)"
      ],
      "metadata": {
        "id": "T_22svDEKf5p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iNIrLZK6KkOQ",
        "outputId": "dfcfafc6-d4ec-4506-8e1d-64e571f8f68c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense \n",
        "from keras.layers import Dropout\n",
        "from keras.layers import Flatten\n",
        "from keras.layers import Conv2D\n",
        "from keras.layers import MaxPooling2D"
      ],
      "metadata": {
        "id": "eaMXhBrSLg2v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import to_categorical\n",
        "y_train = to_categorical(y_train, 10)\n",
        "y_test = to_categorical(y_test, 10)\n",
        "y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kEu0sV_vLlDX",
        "outputId": "42526cdd-9aec-44ac-d8ad-4f410966d6dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HqTlWe-ULxmA",
        "outputId": "8693c72b-92d0-4c61-c1a9-aed8a0c74a6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., ..., 0., 0., 0.],\n",
              "       [1., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 1., 0.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model=Sequential()"
      ],
      "metadata": {
        "id": "Mzh0Tna5L3dF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(Conv2D(32,(3,3),input_shape=(28,28,1),activation='relu'))\n",
        "model.add(MaxPooling2D((2,2)))\n",
        "model.add(Conv2D(64,(3,3),activation='relu'))\n",
        "model.add(MaxPooling2D((2,2)))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Dense(10, activation='softmax'))"
      ],
      "metadata": {
        "id": "vyAIkyy4L9Q9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TMjkllBDMB3H",
        "outputId": "a2a126c1-56ef-493e-8962-8f538dc64407"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 13, 13, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 11, 11, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 5, 5, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 1600)              0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 1600)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 10)                16010     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 34,826\n",
            "Trainable params: 34,826\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss = keras.losses.categorical_crossentropy,metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "_N6wT04VMK31"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.callbacks import EarlyStopping,ModelCheckpoint\n",
        "\n",
        "es=EarlyStopping(monitor='val_acc',min_delta=0.01,patience=4,verbose=1)\n",
        "\n",
        "mc=ModelCheckpoint(\"./bestmodel.h5\",monitor='val_acc',verbose=1,save_best_only=True)"
      ],
      "metadata": {
        "id": "iQcCRU-uMPn9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cb=[es,mc]"
      ],
      "metadata": {
        "id": "Fy2GHb4TMUMt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "his=model.fit(X_train , y_train , epochs=50, validation_split=0.3  )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yK1emV-pMrHV",
        "outputId": "333e5f9c-83be-43c5-f883-ffb386d143f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "1313/1313 [==============================] - 43s 32ms/step - loss: 0.2434 - accuracy: 0.9262 - val_loss: 0.0774 - val_accuracy: 0.9777\n",
            "Epoch 2/50\n",
            "1313/1313 [==============================] - 43s 33ms/step - loss: 0.0919 - accuracy: 0.9719 - val_loss: 0.0672 - val_accuracy: 0.9799\n",
            "Epoch 3/50\n",
            "1313/1313 [==============================] - 41s 31ms/step - loss: 0.0707 - accuracy: 0.9775 - val_loss: 0.0560 - val_accuracy: 0.9831\n",
            "Epoch 4/50\n",
            "1313/1313 [==============================] - 41s 31ms/step - loss: 0.0591 - accuracy: 0.9820 - val_loss: 0.0448 - val_accuracy: 0.9862\n",
            "Epoch 5/50\n",
            "1313/1313 [==============================] - 41s 31ms/step - loss: 0.0545 - accuracy: 0.9831 - val_loss: 0.0406 - val_accuracy: 0.9881\n",
            "Epoch 6/50\n",
            "1313/1313 [==============================] - 40s 31ms/step - loss: 0.0469 - accuracy: 0.9851 - val_loss: 0.0399 - val_accuracy: 0.9878\n",
            "Epoch 7/50\n",
            "1313/1313 [==============================] - 40s 31ms/step - loss: 0.0427 - accuracy: 0.9863 - val_loss: 0.0410 - val_accuracy: 0.9879\n",
            "Epoch 8/50\n",
            "1313/1313 [==============================] - 41s 31ms/step - loss: 0.0407 - accuracy: 0.9870 - val_loss: 0.0386 - val_accuracy: 0.9891\n",
            "Epoch 9/50\n",
            "1313/1313 [==============================] - 40s 31ms/step - loss: 0.0371 - accuracy: 0.9882 - val_loss: 0.0362 - val_accuracy: 0.9887\n",
            "Epoch 10/50\n",
            "1313/1313 [==============================] - 41s 31ms/step - loss: 0.0349 - accuracy: 0.9887 - val_loss: 0.0352 - val_accuracy: 0.9892\n",
            "Epoch 11/50\n",
            "1313/1313 [==============================] - 40s 31ms/step - loss: 0.0329 - accuracy: 0.9891 - val_loss: 0.0423 - val_accuracy: 0.9882\n",
            "Epoch 12/50\n",
            "1313/1313 [==============================] - 41s 31ms/step - loss: 0.0328 - accuracy: 0.9896 - val_loss: 0.0373 - val_accuracy: 0.9897\n",
            "Epoch 13/50\n",
            "1313/1313 [==============================] - 41s 31ms/step - loss: 0.0294 - accuracy: 0.9907 - val_loss: 0.0397 - val_accuracy: 0.9887\n",
            "Epoch 14/50\n",
            "1313/1313 [==============================] - 41s 31ms/step - loss: 0.0286 - accuracy: 0.9905 - val_loss: 0.0344 - val_accuracy: 0.9902\n",
            "Epoch 15/50\n",
            "1313/1313 [==============================] - 40s 31ms/step - loss: 0.0258 - accuracy: 0.9912 - val_loss: 0.0366 - val_accuracy: 0.9900\n",
            "Epoch 16/50\n",
            "1313/1313 [==============================] - 40s 31ms/step - loss: 0.0256 - accuracy: 0.9915 - val_loss: 0.0354 - val_accuracy: 0.9904\n",
            "Epoch 17/50\n",
            "1313/1313 [==============================] - 40s 31ms/step - loss: 0.0263 - accuracy: 0.9910 - val_loss: 0.0340 - val_accuracy: 0.9899\n",
            "Epoch 18/50\n",
            "1313/1313 [==============================] - 40s 31ms/step - loss: 0.0239 - accuracy: 0.9917 - val_loss: 0.0387 - val_accuracy: 0.9895\n",
            "Epoch 19/50\n",
            "1313/1313 [==============================] - 40s 31ms/step - loss: 0.0232 - accuracy: 0.9921 - val_loss: 0.0319 - val_accuracy: 0.9916\n",
            "Epoch 20/50\n",
            "1313/1313 [==============================] - 41s 31ms/step - loss: 0.0217 - accuracy: 0.9927 - val_loss: 0.0338 - val_accuracy: 0.9915\n",
            "Epoch 21/50\n",
            "1313/1313 [==============================] - 40s 31ms/step - loss: 0.0221 - accuracy: 0.9925 - val_loss: 0.0349 - val_accuracy: 0.9908\n",
            "Epoch 22/50\n",
            "1313/1313 [==============================] - 40s 30ms/step - loss: 0.0217 - accuracy: 0.9932 - val_loss: 0.0348 - val_accuracy: 0.9908\n",
            "Epoch 23/50\n",
            "1313/1313 [==============================] - 40s 31ms/step - loss: 0.0216 - accuracy: 0.9924 - val_loss: 0.0335 - val_accuracy: 0.9911\n",
            "Epoch 24/50\n",
            "1313/1313 [==============================] - 40s 30ms/step - loss: 0.0198 - accuracy: 0.9934 - val_loss: 0.0329 - val_accuracy: 0.9921\n",
            "Epoch 25/50\n",
            "1313/1313 [==============================] - 40s 31ms/step - loss: 0.0214 - accuracy: 0.9928 - val_loss: 0.0359 - val_accuracy: 0.9910\n",
            "Epoch 26/50\n",
            "1313/1313 [==============================] - 40s 31ms/step - loss: 0.0180 - accuracy: 0.9937 - val_loss: 0.0372 - val_accuracy: 0.9902\n",
            "Epoch 27/50\n",
            "1313/1313 [==============================] - 40s 31ms/step - loss: 0.0200 - accuracy: 0.9933 - val_loss: 0.0365 - val_accuracy: 0.9908\n",
            "Epoch 28/50\n",
            "1313/1313 [==============================] - 41s 31ms/step - loss: 0.0175 - accuracy: 0.9942 - val_loss: 0.0337 - val_accuracy: 0.9908\n",
            "Epoch 29/50\n",
            "1313/1313 [==============================] - 41s 31ms/step - loss: 0.0150 - accuracy: 0.9947 - val_loss: 0.0366 - val_accuracy: 0.9904\n",
            "Epoch 30/50\n",
            "1313/1313 [==============================] - 41s 31ms/step - loss: 0.0167 - accuracy: 0.9941 - val_loss: 0.0349 - val_accuracy: 0.9911\n",
            "Epoch 31/50\n",
            "1313/1313 [==============================] - 41s 31ms/step - loss: 0.0174 - accuracy: 0.9939 - val_loss: 0.0371 - val_accuracy: 0.9908\n",
            "Epoch 32/50\n",
            "1313/1313 [==============================] - 41s 31ms/step - loss: 0.0164 - accuracy: 0.9942 - val_loss: 0.0383 - val_accuracy: 0.9905\n",
            "Epoch 33/50\n",
            "1313/1313 [==============================] - 41s 31ms/step - loss: 0.0155 - accuracy: 0.9947 - val_loss: 0.0406 - val_accuracy: 0.9908\n",
            "Epoch 34/50\n",
            "1313/1313 [==============================] - 41s 31ms/step - loss: 0.0166 - accuracy: 0.9941 - val_loss: 0.0362 - val_accuracy: 0.9913\n",
            "Epoch 35/50\n",
            "1313/1313 [==============================] - 41s 31ms/step - loss: 0.0160 - accuracy: 0.9945 - val_loss: 0.0348 - val_accuracy: 0.9915\n",
            "Epoch 36/50\n",
            "1313/1313 [==============================] - 43s 32ms/step - loss: 0.0152 - accuracy: 0.9952 - val_loss: 0.0348 - val_accuracy: 0.9921\n",
            "Epoch 37/50\n",
            "1313/1313 [==============================] - 42s 32ms/step - loss: 0.0159 - accuracy: 0.9943 - val_loss: 0.0362 - val_accuracy: 0.9914\n",
            "Epoch 38/50\n",
            "1313/1313 [==============================] - 42s 32ms/step - loss: 0.0156 - accuracy: 0.9947 - val_loss: 0.0348 - val_accuracy: 0.9918\n",
            "Epoch 39/50\n",
            "1313/1313 [==============================] - 42s 32ms/step - loss: 0.0150 - accuracy: 0.9951 - val_loss: 0.0386 - val_accuracy: 0.9915\n",
            "Epoch 40/50\n",
            "1313/1313 [==============================] - 42s 32ms/step - loss: 0.0147 - accuracy: 0.9947 - val_loss: 0.0369 - val_accuracy: 0.9918\n",
            "Epoch 41/50\n",
            "1313/1313 [==============================] - 42s 32ms/step - loss: 0.0150 - accuracy: 0.9951 - val_loss: 0.0379 - val_accuracy: 0.9918\n",
            "Epoch 42/50\n",
            "1313/1313 [==============================] - 42s 32ms/step - loss: 0.0137 - accuracy: 0.9954 - val_loss: 0.0403 - val_accuracy: 0.9913\n",
            "Epoch 43/50\n",
            "1313/1313 [==============================] - 42s 32ms/step - loss: 0.0136 - accuracy: 0.9955 - val_loss: 0.0402 - val_accuracy: 0.9911\n",
            "Epoch 44/50\n",
            "1313/1313 [==============================] - 42s 32ms/step - loss: 0.0137 - accuracy: 0.9952 - val_loss: 0.0435 - val_accuracy: 0.9904\n",
            "Epoch 45/50\n",
            "1313/1313 [==============================] - 42s 32ms/step - loss: 0.0142 - accuracy: 0.9949 - val_loss: 0.0400 - val_accuracy: 0.9911\n",
            "Epoch 46/50\n",
            "1313/1313 [==============================] - 42s 32ms/step - loss: 0.0124 - accuracy: 0.9957 - val_loss: 0.0384 - val_accuracy: 0.9908\n",
            "Epoch 47/50\n",
            "1313/1313 [==============================] - 41s 31ms/step - loss: 0.0141 - accuracy: 0.9953 - val_loss: 0.0368 - val_accuracy: 0.9917\n",
            "Epoch 48/50\n",
            "1313/1313 [==============================] - 41s 31ms/step - loss: 0.0119 - accuracy: 0.9956 - val_loss: 0.0414 - val_accuracy: 0.9916\n",
            "Epoch 49/50\n",
            "1313/1313 [==============================] - 41s 31ms/step - loss: 0.0119 - accuracy: 0.9960 - val_loss: 0.0398 - val_accuracy: 0.9908\n",
            "Epoch 50/50\n",
            "1313/1313 [==============================] - 41s 31ms/step - loss: 0.0121 - accuracy: 0.9956 - val_loss: 0.0388 - val_accuracy: 0.9914\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import h5py"
      ],
      "metadata": {
        "id": "VBv2pFhmX5xe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import load_model"
      ],
      "metadata": {
        "id": "2GiZ4mMrXmyw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('bestmodel.h5')"
      ],
      "metadata": {
        "id": "Ie0BjFXVYGGt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_S = keras.models.load_model('bestmodel.h5')"
      ],
      "metadata": {
        "id": "O_wBkpuUU5Vd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score=model_S.evaluate(X_test , y_test)\n",
        "print(f\"the accuracy of the model is{score[1]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WX3RrjdGYS7Q",
        "outputId": "9d863dcd-2954-419e-d93a-163ea53b213a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 3s 9ms/step - loss: 0.0301 - accuracy: 0.9921\n",
            "the accuracy of the model is0.9921000003814697\n"
          ]
        }
      ]
    }
  ]
}