DenseNet121_Dataset2_0_epoch_10_8.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "mount_file_id": "1BE6V3t_xLu5tvAyoD0xrxUCzAVISNL_X",
      "authorship_tag": "ABX9TyOh7MR0fjPuxdUAcDHxzB+M",
      "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_Dataset2_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": "gZ3i-8ZsNcM9",
        "outputId": "823e20e8-44e0-4b6a-85bd-66b4bbdfc47c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 7888 images belonging to 29 classes.\n",
            "Found 986 images belonging to 29 classes.\n",
            "Found 986 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",
            "986/986 [==============================] - 3722s 4s/step - loss: 2.8335 - accuracy: 0.1928 - val_loss: 2.8836 - val_accuracy: 0.2500\n",
            "Epoch 2/10\n",
            "986/986 [==============================] - 153s 155ms/step - loss: 2.2181 - accuracy: 0.3332 - val_loss: 2.6952 - val_accuracy: 0.2652\n",
            "Epoch 3/10\n",
            "986/986 [==============================] - 151s 153ms/step - loss: 2.0069 - accuracy: 0.3812 - val_loss: 2.6360 - val_accuracy: 0.2835\n",
            "Epoch 4/10\n",
            "986/986 [==============================] - 152s 154ms/step - loss: 1.8805 - accuracy: 0.4156 - val_loss: 2.5233 - val_accuracy: 0.2764\n",
            "Epoch 5/10\n",
            "986/986 [==============================] - 156s 158ms/step - loss: 1.8105 - accuracy: 0.4251 - val_loss: 2.4855 - val_accuracy: 0.3018\n",
            "Epoch 6/10\n",
            "986/986 [==============================] - 150s 152ms/step - loss: 1.7729 - accuracy: 0.4409 - val_loss: 2.2970 - val_accuracy: 0.3150\n",
            "Epoch 7/10\n",
            "986/986 [==============================] - 152s 154ms/step - loss: 1.7461 - accuracy: 0.4478 - val_loss: 2.5014 - val_accuracy: 0.2764\n",
            "Epoch 8/10\n",
            "986/986 [==============================] - 155s 158ms/step - loss: 1.7047 - accuracy: 0.4604 - val_loss: 2.3363 - val_accuracy: 0.3791\n",
            "Epoch 9/10\n",
            "986/986 [==============================] - 152s 154ms/step - loss: 1.6934 - accuracy: 0.4635 - val_loss: 2.3052 - val_accuracy: 0.3049\n",
            "Epoch 10/10\n",
            "986/986 [==============================] - 150s 152ms/step - loss: 1.6743 - accuracy: 0.4650 - val_loss: 2.3514 - val_accuracy: 0.3140\n",
            "123/123 [==============================] - 413s 3s/step - loss: 2.5032 - accuracy: 0.3161\n",
            "986/986 [==============================] - 144s 146ms/step - loss: 1.2112 - accuracy: 0.6604\n",
            "1/1 [==============================] - 2s 2s/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
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          ]
        },
        {
          "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: 2.5032\n",
            "Test accuracy: 31.61\n",
            "Train loss: 1.2112\n",
            "Train accuracy: 66.04\n",
            "Recall: 0.32\n",
            "Precision: 0.24\n",
            "F1-score: 0.24\n",
            "RMSE: 9.57\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/Dataset2.0/train'\n",
        "val_dir = '/content/drive/MyDrive/Dataset/Dataset2.0/validation'\n",
        "test_dir = '/content/drive/MyDrive/Dataset/Dataset2.0/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/Dataset/Test/sign_language_interprter_DenseNet121_Dataset2.0_epoch_10_8.h5')"
      ]
    }
  ]
}