inception_v3_1st_try.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BIH6uxkJCmcQ",
        "outputId": "cd858ef0-af7d-4a6e-a913-b6ce0cfd31ae"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "\u001b[31mERROR: Could not find a version that satisfies the requirement sickit-learn (from versions: none)\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[31mERROR: No matching distribution found for sickit-learn\u001b[0m\u001b[31m\n",
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          ]
        }
      ],
      "source": [
        "!pip install tensorflow\n",
        "!pip install matplotlib\n",
        "!pip install numpy\n",
        "!pip install sickit-learn\n",
        "!pip install seaborn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "gvW4HknOCdev",
        "outputId": "d0425c18-1210-4790-a2bf-a0a61c4bab79"
      },
      "outputs": [
        {
          "ename": "ImportError",
          "evalue": "cannot import name 'InceptionV4' from 'tensorflow.keras.applications.inception_v3' (/usr/local/lib/python3.10/dist-packages/keras/api/_v2/keras/applications/inception_v3/__init__.py)",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-bac93d665f89>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapplications\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minception_v3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mInceptionV4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreprocessing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mImageDataGenerator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mImportError\u001b[0m: cannot import name 'InceptionV4' from 'tensorflow.keras.applications.inception_v3' (/usr/local/lib/python3.10/dist-packages/keras/api/_v2/keras/applications/inception_v3/__init__.py)",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RvlrTpJZCpyg"
      },
      "outputs": [],
      "source": [
        "train_dir = 'path/to/train/directory'\n",
        "test_dir = 'path/to/test/directory'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Data augmentation\n",
        "train_datagen = ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        "    rotation_range=20,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    fill_mode='nearest')\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# GPU configuration\n",
        "physical_devices = tf.config.list_physical_devices('GPU')\n",
        "if physical_devices:\n",
        "    tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
        "\n",
        "base_model = InceptionV3(weights='imagenet', include_top=False, input_shape=(299, 299, 3))\n",
        "num_classes = 7\n",
        "x = base_model.output\n",
        "x = tf.keras.layers.GlobalAveragePooling2D()(x)\n",
        "x = tf.keras.layers.Dense(1024, activation='relu')(x)\n",
        "predictions = tf.keras.layers.Dense(num_classes, activation='softmax')(x)\n",
        "model = tf.keras.models.Model(inputs=base_model.input, outputs=predictions)\n",
        "\n",
        "for layer in base_model.layers:\n",
        "    layer.trainable = False\n",
        "\n",
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "train_generator = train_datagen.flow_from_directory(\n",
        "    train_dir,\n",
        "    target_size=(299, 299),\n",
        "    batch_size=32,\n",
        "    class_mode='categorical')\n",
        "\n",
        "test_generator = test_datagen.flow_from_directory(\n",
        "    test_dir,\n",
        "    target_size=(299, 299),\n",
        "    batch_size=32,\n",
        "    class_mode='categorical')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "history = model.fit(\n",
        "    train_generator,\n",
        "    steps_per_epoch=len(train_generator.filenames) // 32,\n",
        "    epochs=10,\n",
        "    validation_data=test_generator,\n",
        "    validation_steps=len(test_generator.filenames) // 32)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "test_loss, test_acc = model.evaluate(test_generator)\n",
        "print('Test accuracy:', test_acc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "y_pred = model.predict(test_generator)\n",
        "y_pred = np.argmax(y_pred, axis=1)\n",
        "y_true = test_generator.classes\n",
        "conf_matrix = confusion_matrix(y_true, y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(conf_matrix, annot=True, cmap='Blues')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "plt.subplot(2, 1, 2)\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "\n",
        "# Save the model\n",
        "model.save('inception_model.h5')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}