CNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B6H27Ur6P-fY"
      },
      "outputs": [],
      "source": [
        "#import necessaries package, libray\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load MNIST dataset\n",
        "(x_train, y_train), (x_test, y_test) = datasets.mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Whh1IMNTQ5s4",
        "outputId": "73e24a5d-9bb3-40a7-ae14-8621089667fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot the digit with title\n",
        "plt.figure(figsize=(10,10))\n",
        "for i in range(9):\n",
        "  plt.subplot(3,3,i+1)\n",
        "  plt.imshow(x_train[i], cmap='gray')\n",
        "  plt.title(str(y_train[i]))\n",
        "  plt.axis('off')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 829
        },
        "id": "rJfclkDCDtN3",
        "outputId": "68ba1acc-8fc2-4144-a432-5797b9e6fabe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalize pixel values between 0 and 1\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0"
      ],
      "metadata": {
        "id": "mXTuosPaQ94w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert to float\n",
        "x_train = x_train[..., tf.newaxis].astype(\"float32\")\n",
        "x_test = x_test[..., tf.newaxis].astype(\"float32\")"
      ],
      "metadata": {
        "id": "QJuRFjZbRBJp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define CNN model\n",
        "model = models.Sequential()\n",
        "model.add(layers.Conv2D(32, (3, 3), activation='relu', padding ='same', input_shape=(28, 28, 1)))\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "model.add(layers.Conv2D(64, (3, 3), padding ='same', activation='relu'))\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "model.add(layers.Conv2D(128, (3, 3), padding ='same', activation='relu'))\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(64, activation='relu'))\n",
        "model.add(layers.Dense(10, activation='softmax'))"
      ],
      "metadata": {
        "id": "DgmsHR8yRESw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile the model\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "UNql1dzFRJNo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59aba08c-72f1-4a3a-d719-e4b29773e765"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 28, 28, 32)        320       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 14, 14, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 14, 14, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 7, 7, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 7, 7, 128)         73856     \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 6272)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 64)                401472    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                650       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 494,794\n",
            "Trainable params: 494,794\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "history = model.fit(x_train, y_train, epochs=5,\n",
        "                    validation_data=(x_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w3psW65NRMi3",
        "outputId": "3252cdfe-0433-47e6-8353-0a842a3daa9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 20s 5ms/step - loss: 0.1154 - accuracy: 0.9644 - val_loss: 0.0412 - val_accuracy: 0.9860\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0389 - accuracy: 0.9879 - val_loss: 0.0326 - val_accuracy: 0.9893\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.0274 - accuracy: 0.9916 - val_loss: 0.0209 - val_accuracy: 0.9927\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 9s 5ms/step - loss: 0.0195 - accuracy: 0.9938 - val_loss: 0.0273 - val_accuracy: 0.9917\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0163 - accuracy: 0.9947 - val_loss: 0.0363 - val_accuracy: 0.9903\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model\n",
        "test_loss, test_acc = model.evaluate(x_test, y_test, verbose=0)\n",
        "print(\"Test accuracy:\",100 - test_loss)"
      ],
      "metadata": {
        "id": "S1UspIEaTBCp",
        "outputId": "9b6ce607-b983-48fb-b54a-36de061ae368",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test accuracy: 99.9636972732842\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predicted the model\n",
        "test_predict = model.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QYRy3QqnF0Co",
        "outputId": "ad1abccc-17de-42a6-d0eb-e7b18d6bc51b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# show the predicated of tested data\n",
        "\n",
        "#set size of image\n",
        "plt.figure(figsize=(10,10))\n",
        "for i in range(9):\n",
        "\n",
        "  #subplot the testing data\n",
        "  plt.subplot(3,3,i+1)\n",
        "  plt.imshow(x_test[i], cmap='gray')\n",
        "\n",
        "  #set the predicated title\n",
        "  plt.title(np.argmax(test_predict[i]))\n",
        "  plt.axis('off')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 829
        },
        "id": "xMixnOqqHM7t",
        "outputId": "87438b87-3b2b-44f2-c857-e668f481dc05"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}