DenseNet121_Dataset3_0_epoch_10_8.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "mount_file_id": "13ZKliajF0ujgVdcv-O6wNPuoKSeWKmPO",
      "authorship_tag": "ABX9TyMCVQotpPfsyJrUrCPd5Hte",
      "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/sign_language_interprter_Main-Project/blob/DenseNet121/DenseNet121_Dataset3_0_epoch_10_8.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": "h8N5dMEXGZvK",
        "outputId": "1695e3ad-ff48-40c7-c45f-f806b4cf70f9"
      },
      "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/densenet/densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "29084464/29084464 [==============================] - 0s 0us/step\n",
            "Epoch 1/10\n",
            "290/290 [==============================] - 935s 3s/step - loss: 3.0457 - accuracy: 0.1668 - val_loss: 2.7171 - val_accuracy: 0.2569\n",
            "Epoch 2/10\n",
            "290/290 [==============================] - 45s 154ms/step - loss: 2.1983 - accuracy: 0.3397 - val_loss: 2.2698 - val_accuracy: 0.4201\n",
            "Epoch 3/10\n",
            "290/290 [==============================] - 43s 147ms/step - loss: 1.7997 - accuracy: 0.4491 - val_loss: 2.0194 - val_accuracy: 0.4688\n",
            "Epoch 4/10\n",
            "290/290 [==============================] - 43s 150ms/step - loss: 1.5393 - accuracy: 0.5177 - val_loss: 1.9082 - val_accuracy: 0.4826\n",
            "Epoch 5/10\n",
            "290/290 [==============================] - 42s 146ms/step - loss: 1.4160 - accuracy: 0.5642 - val_loss: 1.6921 - val_accuracy: 0.5035\n",
            "Epoch 6/10\n",
            "290/290 [==============================] - 43s 148ms/step - loss: 1.3456 - accuracy: 0.5720 - val_loss: 1.6199 - val_accuracy: 0.5417\n",
            "Epoch 7/10\n",
            "290/290 [==============================] - 44s 151ms/step - loss: 1.2495 - accuracy: 0.5983 - val_loss: 1.6826 - val_accuracy: 0.4861\n",
            "Epoch 8/10\n",
            "290/290 [==============================] - 42s 145ms/step - loss: 1.2476 - accuracy: 0.6065 - val_loss: 1.5775 - val_accuracy: 0.6146\n",
            "Epoch 9/10\n",
            "290/290 [==============================] - 45s 153ms/step - loss: 1.1709 - accuracy: 0.6379 - val_loss: 1.6046 - val_accuracy: 0.4896\n",
            "Epoch 10/10\n",
            "290/290 [==============================] - 42s 146ms/step - loss: 1.1215 - accuracy: 0.6491 - val_loss: 1.5446 - val_accuracy: 0.5972\n",
            "36/36 [==============================] - 97s 3s/step - loss: 1.2890 - accuracy: 0.6319\n",
            "290/290 [==============================] - 41s 140ms/step - loss: 0.6315 - accuracy: 0.8358\n",
            "1/1 [==============================] - 3s 3s/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 44ms/step\n",
            "1/1 [==============================] - 0s 50ms/step\n",
            "1/1 [==============================] - 0s 43ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 42ms/step\n",
            "1/1 [==============================] - 0s 44ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 44ms/step\n",
            "1/1 [==============================] - 0s 47ms/step\n",
            "1/1 [==============================] - 0s 48ms/step\n",
            "1/1 [==============================] - 0s 44ms/step\n",
            "1/1 [==============================] - 0s 46ms/step\n",
            "1/1 [==============================] - 0s 46ms/step\n",
            "1/1 [==============================] - 0s 49ms/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": "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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 1.2890\n",
            "Test accuracy: 63.19\n",
            "Train loss: 0.6315\n",
            "Train accuracy: 83.58\n",
            "Recall: 0.63\n",
            "Precision: 0.59\n",
            "F1-score: 0.57\n",
            "RMSE: 5.93\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.applications.densenet import DenseNet121, preprocess_input\n",
        "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout\n",
        "from tensorflow.keras.models import Model\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 = 8\n",
        "\n",
        "# Create data generators\n",
        "train_datagen = ImageDataGenerator(\n",
        "    preprocessing_function=preprocess_input,\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 DenseNet121 model with reduced complexity\n",
        "base_model = DenseNet121(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
        "\n",
        "# Add a global average pooling layer and a dense layer for classification\n",
        "x = GlobalAveragePooling2D()(base_model.output)\n",
        "x = Dropout(0.5)(x)  # Add a dropout layer with a dropout rate of 0.5\n",
        "x = Dense(128, activation='relu')(x)  # Add a dense layer with fewer units\n",
        "x = Dropout(0.3)(x)  # Add another dropout layer\n",
        "predictions = Dense(29, activation='softmax')(x)  # Assuming 29 classes\n",
        "\n",
        "# Create the model\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=0.001), 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('/content/drive/MyDrive/Project/Modeltest/sign_language_interprter_DenseNet121_Dataset3.0_epoch_10_8.h5')"
      ]
    }
  ]
}