MainVGG16Dataset3_0.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "mount_file_id": "1pPVheEFL4iFamX2JDMsBHw7AWV14nxbZ",
      "authorship_tag": "ABX9TyOK5xHQdWAHyY2LUCf5NmOF",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Joyfull-Joyal/DS-Lab/blob/main/MainVGG16Dataset3_0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "iuazMBxz-W1T",
        "outputId": "0f40978a-6894-4afd-e5f7-09468a47bc53"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 2320 images belonging to 29 classes.\n",
            "Found 290 images belonging to 29 classes.\n",
            "Found 290 images belonging to 29 classes.\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58889256/58889256 [==============================] - 1s 0us/step\n",
            "Epoch 1/10\n",
            "145/145 [==============================] - 943s 6s/step - loss: 3.4494 - accuracy: 0.0466 - val_loss: 3.2998 - val_accuracy: 0.1181\n",
            "Epoch 2/10\n",
            "145/145 [==============================] - 40s 274ms/step - loss: 3.3295 - accuracy: 0.0659 - val_loss: 3.2234 - val_accuracy: 0.2188\n",
            "Epoch 3/10\n",
            "145/145 [==============================] - 39s 271ms/step - loss: 3.1952 - accuracy: 0.1000 - val_loss: 3.0948 - val_accuracy: 0.2465\n",
            "Epoch 4/10\n",
            "145/145 [==============================] - 40s 275ms/step - loss: 3.0377 - accuracy: 0.1440 - val_loss: 2.9306 - val_accuracy: 0.3160\n",
            "Epoch 5/10\n",
            "145/145 [==============================] - 40s 275ms/step - loss: 2.8734 - accuracy: 0.1694 - val_loss: 2.7978 - val_accuracy: 0.3576\n",
            "Epoch 6/10\n",
            "145/145 [==============================] - 39s 272ms/step - loss: 2.7256 - accuracy: 0.2099 - val_loss: 2.6794 - val_accuracy: 0.3125\n",
            "Epoch 7/10\n",
            "145/145 [==============================] - 40s 274ms/step - loss: 2.5683 - accuracy: 0.2569 - val_loss: 2.5838 - val_accuracy: 0.3958\n",
            "Epoch 8/10\n",
            "145/145 [==============================] - 41s 284ms/step - loss: 2.4495 - accuracy: 0.2918 - val_loss: 2.5028 - val_accuracy: 0.4931\n",
            "Epoch 9/10\n",
            "145/145 [==============================] - 40s 278ms/step - loss: 2.3412 - accuracy: 0.3306 - val_loss: 2.4221 - val_accuracy: 0.4479\n",
            "Epoch 10/10\n",
            "145/145 [==============================] - 39s 272ms/step - loss: 2.2006 - accuracy: 0.3655 - val_loss: 2.3380 - val_accuracy: 0.4931\n",
            "18/18 [==============================] - 96s 6s/step - loss: 2.1847 - accuracy: 0.4792\n",
            "145/145 [==============================] - 38s 265ms/step - loss: 1.9475 - accuracy: 0.6151\n",
            "1/1 [==============================] - 0s 182ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 2.1847\n",
            "Test accuracy: 47.92\n",
            "Train loss: 1.9475\n",
            "Train accuracy: 61.51\n",
            "Recall: 0.48\n",
            "Precision: 0.42\n",
            "F1-score: 0.40\n",
            "RMSE: 5.99\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.applications import VGG16\n",
        "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import recall_score, precision_score, f1_score, mean_squared_error, confusion_matrix\n",
        "import seaborn as sns  # Added seaborn for visualization\n",
        "\n",
        "# Define your data directories\n",
        "train_dir = '/content/drive/MyDrive/Dataset/Dataset3.0/train'\n",
        "val_dir = '/content/drive/MyDrive/Dataset/Dataset3.0/validation'\n",
        "test_dir = '/content/drive/MyDrive/Dataset/Dataset3.0/test'\n",
        "\n",
        "# Define image size and batch size\n",
        "img_size = (224, 224)\n",
        "batch_size = 16\n",
        "\n",
        "# Create data generators\n",
        "train_datagen = ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        "    rotation_range=30,\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",
        "\n",
        "val_datagen = ImageDataGenerator(rescale=1./255)\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    train_dir,\n",
        "    target_size=img_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical'\n",
        ")\n",
        "\n",
        "val_generator = val_datagen.flow_from_directory(\n",
        "    val_dir,\n",
        "    target_size=img_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical'\n",
        ")\n",
        "\n",
        "test_generator = test_datagen.flow_from_directory(\n",
        "    test_dir,\n",
        "    target_size=img_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical'\n",
        ")\n",
        "\n",
        "# Load the pre-trained VGG16 model\n",
        "base_model = VGG16(weights='imagenet', include_top=False)\n",
        "\n",
        "# Add custom classification layers\n",
        "x = base_model.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "x = Dense(1024, activation='relu')(x)\n",
        "x = Dropout(0.5)(x)  # Add dropout for regularization\n",
        "x = Dense(512, activation='relu')(x)\n",
        "x = Dropout(0.5)(x)  # Add dropout for regularization\n",
        "predictions = Dense(29, activation='softmax')(x)\n",
        "\n",
        "model = Model(inputs=base_model.input, outputs=predictions)\n",
        "\n",
        "# Freeze the layers in the base model\n",
        "for layer in base_model.layers:\n",
        "    layer.trainable = False\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "history = model.fit(\n",
        "    train_generator,\n",
        "    steps_per_epoch=train_generator.n // batch_size,\n",
        "    epochs=10,\n",
        "    validation_data=val_generator,\n",
        "    validation_steps=val_generator.n // batch_size\n",
        ")\n",
        "\n",
        "# Evaluate the model on the test data\n",
        "test_loss, test_accuracy = model.evaluate(test_generator, steps=test_generator.n // batch_size)\n",
        "train_loss, train_accuracy = model.evaluate(train_generator, steps=train_generator.n // batch_size)\n",
        "\n",
        "# Calculate recall\n",
        "y_true = []\n",
        "y_pred = []\n",
        "for i in range(test_generator.n // batch_size):\n",
        "    batch_x, batch_y = next(test_generator)\n",
        "    y_true.extend(np.argmax(batch_y, axis=1))\n",
        "    y_pred.extend(np.argmax(model.predict(batch_x), axis=1))\n",
        "\n",
        "# Calculate confusion matrix\n",
        "cm = confusion_matrix(y_true, y_pred)\n",
        "\n",
        "# Visualize the confusion matrix as a heatmap\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['A', 'B', 'C', 'D', 'del', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'nothing', 'O', 'P', 'Q', 'R', 'S', 'space', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'], yticklabels=['A', 'B', 'C', 'D', 'del', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'nothing', 'O', 'P', 'Q', 'R', 'S', 'space', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'])\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()\n",
        "\n",
        "# Calculate other metrics\n",
        "recall = recall_score(y_true, y_pred, average='macro')\n",
        "precision = precision_score(y_true, y_pred, average='macro')\n",
        "f1 = f1_score(y_true, y_pred, average='macro')\n",
        "rmse = np.sqrt(mean_squared_error(y_true, y_pred))\n",
        "\n",
        "# Display metrics\n",
        "print(f'Test loss: {test_loss:.4f}')\n",
        "print(f'Test accuracy: {test_accuracy * 100:.2f}')\n",
        "print(f'Train loss: {train_loss:.4f}')\n",
        "print(f'Train accuracy: {train_accuracy * 100:.2f}')\n",
        "print(f'Recall: {recall:.2f}')\n",
        "print(f'Precision: {precision:.2f}')\n",
        "print(f'F1-score: {f1:.2f}')\n",
        "print(f'RMSE: {rmse:.2f}')\n",
        "\n",
        "# Save the trained model\n",
        "model.save('Modeltest/sign_language_interprter_vgg16_with_recall 10-16 more layer.h5')"
      ]
    }
  ]
}