Copy_of_Sentiment_Analysis_using_Computer_Vision.ipynb

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              "      <script>// Copyright 2017 Google LLC\n",
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              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
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              " * @fileoverview Helpers for google.colab Python module.\n",
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              "// This is roughly an async generator (not supported in the browser yet),\n",
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        {
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          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        }
      ],
      "source": [
        "!pip install -q kaggle\n",
        "from google.colab import files\n",
        "\n",
        "# Upload kaggle.json file\n",
        "files.upload()\n",
        "\n",
        "# Make a directory for Kaggle and move the kaggle.json file there\n",
        "!mkdir -p ~/.kaggle\n",
        "!mv kaggle.json ~/.kaggle/\n",
        "\n",
        "# Set the permissions for kaggle.json\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Download the dataset from Kaggle\n",
        "!kaggle datasets download -d nishantathawale/fer-dataset-for-3-class-sentiment-analysis\n",
        "# Unzip the downloaded dataset\n",
        "!unzip fer-dataset-for-3-class-sentiment-analysis.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7kDOhK9ojfVu",
        "outputId": "04368739-a660-4341-c5a1-3eac6752a9a4"
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      "outputs": [
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          "text": [
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow_addons\n",
        "import os\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, Dense, Dropout, LayerNormalization\n",
        "from keras.callbacks import ReduceLROnPlateau\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import classification_report, confusion_matrix"
      ],
      "metadata": {
        "id": "FEsGU0L6_5-K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the base directory where the dataset is located\n",
        "base_dir = '/content/FER_Custom_Dataset'\n",
        "\n",
        "# Define the class subfolders\n",
        "classes = ['Disappointed', 'interested', 'neutral']\n",
        "\n",
        "# Function to get file paths and labels\n",
        "def get_file_paths_and_labels(base_dir, classes):\n",
        "    file_paths = []\n",
        "    labels = []\n",
        "    for class_label, class_name in enumerate(classes):\n",
        "        class_dir = os.path.join(base_dir, class_name)\n",
        "        for root, _, files in os.walk(class_dir):\n",
        "            for file in files:\n",
        "                file_paths.append(os.path.join(root, file))\n",
        "                labels.append(class_label)\n",
        "    return np.array(file_paths), np.array(labels)\n",
        "\n",
        "# Get file paths and labels\n",
        "file_paths, labels = get_file_paths_and_labels(base_dir, classes)\n",
        "\n",
        "# Split the data into training, validation, and test sets (80%, 10%, 10%)\n",
        "train_paths, test_paths, train_labels, test_labels = train_test_split(\n",
        "    file_paths, labels, test_size=0.2, stratify=labels, random_state=42)\n",
        "\n",
        "val_paths, test_paths, val_labels, test_labels = train_test_split(\n",
        "    test_paths, test_labels, test_size=0.5, stratify=test_labels, random_state=42)\n",
        "\n",
        "# Check the number of samples in each set\n",
        "print(f'Train samples: {len(train_paths)}')\n",
        "print(f'Validation samples: {len(val_paths)}')\n",
        "print(f'Test samples: {len(test_paths)}')"
      ],
      "metadata": {
        "id": "VtJYYXmt_jrI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot data distribution\n",
        "import pandas as pd\n",
        "\n",
        "# Create a DataFrame for plotting\n",
        "df = pd.DataFrame({\n",
        "    'Set': ['Train', 'Validation', 'Test'],\n",
        "    'Count': [len(train_paths), len(val_paths), len(test_paths)]\n",
        "})\n",
        "\n",
        "# Plot pie chart\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.pie(df['Count'], labels=df['Set'], autopct='%1.1f%%', startangle=140)\n",
        "plt.title('Data Distribution')\n",
        "plt.show()\n",
        "\n",
        "# Function to convert image path to image array\n",
        "def path_to_image_array(image_path):\n",
        "    return img_to_array(load_img(image_path, target_size=(64, 64))) / 255.0\n",
        "\n",
        "# Function to generate batches of images and labels\n",
        "def data_generator(file_paths, labels, batch_size):\n",
        "    while True:\n",
        "        indices = np.arange(len(file_paths))\n",
        "        np.random.shuffle(indices)\n",
        "        for start in range(0, len(file_paths), batch_size):\n",
        "            end = min(start + batch_size, len(file_paths))\n",
        "            batch_indices = indices[start:end]\n",
        "            batch_images = [path_to_image_array(file_paths[i]) for i in batch_indices]\n",
        "            batch_labels = [labels[i] for i in batch_indices]\n",
        "            yield np.array(batch_images), np.array(batch_labels)\n",
        "\n",
        "# Parameters\n",
        "batch_size = 32\n",
        "\n",
        "# Create generators\n",
        "train_generator = data_generator(train_paths, train_labels, batch_size)\n",
        "val_generator = data_generator(val_paths, val_labels, batch_size)\n",
        "test_generator = data_generator(test_paths, test_labels, batch_size)\n",
        "\n",
        "# Calculate the number of steps per epoch for training, validation, and testing\n",
        "train_steps = len(train_paths) // batch_size\n",
        "val_steps = len(val_paths) // batch_size\n",
        "test_steps = len(test_paths) // batch_size"
      ],
      "metadata": {
        "id": "NVW_x2Bg_oLx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, Dense, Dropout, BatchNormalization\n",
        "\n",
        "# Build the CNN model with Batch Normalization\n",
        "model = Sequential([\n",
        "    Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)),\n",
        "    BatchNormalization(),\n",
        "    MaxPooling2D(2, 2),\n",
        "\n",
        "    Conv2D(64, (3, 3), activation='relu'),\n",
        "    BatchNormalization(),\n",
        "    MaxPooling2D(2, 2),\n",
        "\n",
        "    Conv2D(128, (3, 3), activation='relu'),\n",
        "    BatchNormalization(),\n",
        "    MaxPooling2D(2, 2),\n",
        "\n",
        "    Conv2D(256, (3, 3), activation='relu'),\n",
        "    BatchNormalization(),\n",
        "    MaxPooling2D(2, 2),\n",
        "\n",
        "    GlobalAveragePooling2D(),\n",
        "    Dense(128, activation='relu'),\n",
        "    Dropout(0.5),\n",
        "    Dense(3, activation='softmax')\n",
        "])\n",
        "\n",
        "# Compile the model with AdamW optimizer\n",
        "model.compile(loss='sparse_categorical_crossentropy',\n",
        "              optimizer=tf.keras.optimizers.AdamW(learning_rate=1e-3),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# Callbacks\n",
        "callbacks = [ReduceLROnPlateau(monitor='val_loss', factor=0.4, patience=3)]\n",
        "\n",
        "# Train the CNN model\n",
        "history = model.fit(\n",
        "    train_generator,\n",
        "    steps_per_epoch=train_steps,\n",
        "    epochs=25,\n",
        "    validation_data=val_generator,\n",
        "    validation_steps=val_steps,\n",
        "    callbacks=callbacks\n",
        ")\n",
        "\n",
        "# Evaluate the model\n",
        "test_loss, test_acc = model.evaluate(test_generator, steps=test_steps)\n",
        "print('Test accuracy:', test_acc)"
      ],
      "metadata": {
        "id": "L_PMnUPc_rwW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot training & validation accuracy/loss values\n",
        "def plot_training_history(history):\n",
        "    acc = history.history['accuracy']\n",
        "    val_acc = history.history['val_accuracy']\n",
        "    loss = history.history['loss']\n",
        "    val_loss = history.history['val_loss']\n",
        "\n",
        "    epochs = range(len(acc))\n",
        "\n",
        "    plt.figure(figsize=(12, 5))\n",
        "\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.plot(epochs, acc, 'r', label='Training accuracy')\n",
        "    plt.plot(epochs, val_acc, 'b', label='Validation accuracy')\n",
        "    plt.title('Training and validation accuracy')\n",
        "    plt.legend()\n",
        "\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.plot(epochs, loss, 'r', label='Training loss')\n",
        "    plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "    plt.title('Training and validation loss')\n",
        "    plt.legend()\n",
        "\n",
        "    plt.show()\n",
        "\n",
        "plot_training_history(history)"
      ],
      "metadata": {
        "id": "qWfPuHw-_wne"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the model\n",
        "model.save('cnn_model.h5')"
      ],
      "metadata": {
        "id": "FUVuvpkgsPW9",
        "outputId": "10df63bb-b1c2-4148-e02e-41f6aa9cd238",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/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"
          ]
        }
      ]
    }
  ]
}