Complete_Vit_SBERT_Bidirectional_LSTM_Model.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/09aryan/MEME_CLASSIFICATION_USING_SENTIMENT_ANALYSIS/blob/main/Complete_Vit_SBERT_Bidirectional_LSTM_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LDldwqfq4n33",
        "outputId": "4ca88aaf-1846-4ebb-999b-46504ef3fa5c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sentence-transformers\n",
            "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Collecting sentencepiece (from sentence-transformers)\n",
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            "Building wheels for collected packages: sentence-transformers\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125923 sha256=948b4c4b33bdf839a1aa2d76c6b627ed8b6714751170ba300cf01fbf5a6be160\n",
            "  Stored in directory: /root/.cache/pip/wheels/62/f2/10/1e606fd5f02395388f74e7462910fe851042f97238cbbd902f\n",
            "Successfully built sentence-transformers\n",
            "Installing collected packages: sentencepiece, sentence-transformers\n",
            "Successfully installed sentence-transformers-2.2.2 sentencepiece-0.1.99\n",
            "Collecting timm\n",
            "  Downloading timm-0.9.12-py3-none-any.whl (2.2 MB)\n",
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            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.7->timm) (1.3.0)\n",
            "Installing collected packages: timm\n",
            "Successfully installed timm-0.9.12\n",
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "!pip install sentence-transformers\n",
        "!pip install timm\n",
        "import tensorflow as tf\n",
        "from sentence_transformers import SentenceTransformer\n",
        "from tensorflow.keras.layers import Input, Dense, Reshape, Concatenate, Bidirectional, LSTM\n",
        "from tensorflow.keras.models import Model\n",
        "from sklearn.model_selection import train_test_split\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "import timm\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import cv2\n",
        "import os\n",
        "import re\n",
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')\n"
      ]
    },
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        {
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          "name": "stdout",
          "text": [
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      ],
      "source": [
        "def load_image_data_in_batches(folder_path, max_images=4501, target_size=(224, 224), batch_size=500):\n",
        "    images = []\n",
        "    file_names = sorted(os.listdir(folder_path), key=lambda x: int(re.findall(r'\\d+', x)[0]) if re.findall(r'\\d+', x) else 0)\n",
        "\n",
        "    for i in range(0, max_images, batch_size):\n",
        "        batch_file_names = file_names[i:i+batch_size]\n",
        "        batch_images = []\n",
        "\n",
        "        for file_name in batch_file_names:\n",
        "            file_path = os.path.join(folder_path, file_name)\n",
        "\n",
        "            try:\n",
        "                img = cv2.imread(file_path)\n",
        "\n",
        "                # Check if the image is loaded successfully\n",
        "                if img is not None and not img.size == 0:\n",
        "                    img = cv2.resize(img, target_size)\n",
        "                    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "                    batch_images.append(img)\n",
        "                else:\n",
        "                    print(f\"Error loading image {file_path}: Image not loaded successfully\")\n",
        "            except Exception as e:\n",
        "                print(f\"Error loading image {file_path}: {str(e)}\")\n",
        "\n",
        "        images.extend(batch_images)\n",
        "\n",
        "    return np.array(images)\n",
        "\n",
        "# Load labels.csv file\n",
        "labels_path = '/content/drive/MyDrive/labels.csv'\n",
        "labels_df = pd.read_csv(labels_path)\n",
        "\n",
        "# Select the first 3000 rows from labels dataframe\n",
        "labels_df_subset = labels_df.head(4500)\n",
        "\n",
        "# Extract text_corrected and overall_sentiment\n",
        "X_text = labels_df_subset['text_corrected'].tolist()\n",
        "y_sentiment = labels_df_subset['overall_sentiment'].values\n",
        "\n",
        "# Load only the first 3000 images\n",
        "images_folder_path = '/content/drive/MyDrive/images'\n",
        "X_images = load_image_data_in_batches(images_folder_path)\n",
        "\n",
        "# Assuming max_text_length is defined somewhere\n",
        "max_text_length = 300\n",
        "\n",
        "# Convert X_images to torch tensor\n",
        "batch_size = 500\n",
        "num_batches = len(X_images) // batch_size\n",
        "X_images_torch = []\n",
        "\n",
        "for i in range(num_batches):\n",
        "    start_idx = i * batch_size\n",
        "    end_idx = (i + 1) * batch_size\n",
        "    X_batch = torch.tensor(X_images[start_idx:end_idx].transpose(0, 3, 1, 2), dtype=torch.float32)\n",
        "    X_images_torch.append(X_batch)\n",
        "\n",
        "X_images_torch = torch.cat(X_images_torch, dim=0)\n",
        "\n",
        "\n",
        "# Convert text data to a list of strings\n",
        "X_text = [str(text) for text in X_text]\n",
        "\n",
        "# Load SBERT model\n",
        "sbert_model = SentenceTransformer('paraphrase-MiniLM-L6-v2')\n",
        "\n",
        "# Tokenize text using the SentenceTransformer tokenizer\n",
        "batch_size = 500\n",
        "num_batches = len(X_text) // batch_size\n",
        "X_text_embeddings = []\n",
        "\n",
        "for i in range(num_batches):\n",
        "    start_idx = i * batch_size\n",
        "    end_idx = (i + 1) * batch_size\n",
        "    X_text_batch = sbert_model.encode(X_text[start_idx:end_idx], convert_to_tensor=True)\n",
        "    X_text_embeddings.append(X_text_batch)\n",
        "\n",
        "X_text_embeddings = torch.cat(X_text_embeddings, dim=0)\n",
        "# Convert torch tensor to numpy array\n",
        "# Convert torch tensor to numpy array\n",
        "X_text_embeddings_np = X_text_embeddings.cpu().numpy()\n",
        "\n",
        "# Convert numpy array to TensorFlow tensor\n",
        "X_text_embeddings_tf = tf.convert_to_tensor(X_text_embeddings_np, dtype=tf.float32)\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "itKPOXARD9By"
      },
      "outputs": [],
      "source": [
        "# Load labels.csv file\n",
        "labels_path = '/content/drive/MyDrive/labels.csv'\n",
        "labels_df = pd.read_csv(labels_path)\n",
        "\n",
        "# Select the first 3000 rows from labels dataframe\n",
        "labels_df_subset = labels_df.head(4500)\n",
        "\n",
        "# Extract text_corrected and overall_sentiment\n",
        "X_text = labels_df_subset['text_corrected'].tolist()\n",
        "y_sentiment = labels_df_subset['overall_sentiment'].values\n",
        "# Assuming max_text_length is defined somewhere\n",
        "max_text_length = 300"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jYL_PMvhwUZU",
        "outputId": "d183be2a-efbe-4557-901f-12614ec45d79"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3000"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "len(X_images_torch)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3rcpSRwbDqMs",
        "outputId": "f44a6aba-7ee5-46df-8983-b6d083ebdb75"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "TensorShape([3000, 384])"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_text_embeddings_tf.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TERUnxpOETh9",
        "outputId": "4554cd04-8bd3-40b7-c23e-2450e23d39c9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(3000, 384)"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_text_embeddings_np.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "c6cc445cb0064d7c8c1e955c225af441",
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            "a11d13105ed343ab9baecb6448859f11",
            "9337b1eaf125428992d6e05baa6e42b9",
            "0bf7257e4f9546379bd9a7e486fcc25c",
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            "0d548883b6ed416f90dcb4973cf86651",
            "c5ac60bb81744d7fb8818dab1d11805f"
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        },
        "id": "k8kXer4zKb9W",
        "outputId": "afb12897-c3e4-4e30-a5d3-d8f0aedb74da"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/346M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c6cc445cb0064d7c8c1e955c225af441"
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          "metadata": {}
        }
      ],
      "source": [
        "# Function to process images in batches\n",
        "vit_model = timm.create_model('vit_base_patch16_224', pretrained=True)\n",
        "def process_images_in_batches(images_torch, model, batch_size=500):\n",
        "    num_batches = len(images_torch) // batch_size\n",
        "    processed_images = []\n",
        "\n",
        "    for i in range(num_batches):\n",
        "        start_idx = i * batch_size\n",
        "        end_idx = (i + 1) * batch_size\n",
        "        X_images_batch = images_torch[start_idx:end_idx]\n",
        "\n",
        "        with torch.no_grad():\n",
        "            image_embedding = model(X_images_batch)\n",
        "\n",
        "        # Flatten the image embedding\n",
        "        image_embedding = torch.flatten(image_embedding, start_dim=1)\n",
        "\n",
        "        # Convert to NumPy array\n",
        "        image_embedding = image_embedding.numpy()\n",
        "        processed_images.append(image_embedding)\n",
        "\n",
        "    processed_images = np.concatenate(processed_images, axis=0)\n",
        "    return processed_images\n",
        "\n",
        "\n",
        "image_embedding = process_images_in_batches(X_images_torch, vit_model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wcT_BQBWRD0K",
        "outputId": "3a4f851b-2136-438a-f0f1-0226d700138d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(3000, 1000)"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "image_embedding.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zldDBV4tRLt7",
        "outputId": "beeaad9a-7e77-447d-ae44-0278d59c40d1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(3000, 384)"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_text_embeddings_np.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wYIZvnM36ORg"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.layers import Dropout\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "from tensorflow.keras.layers import SpatialDropout1D\n",
        "\n",
        "# Assuming create_model function is already defined\n",
        "def create_model(input_dim_text, input_dim_image, num_classes):\n",
        "    text_input = Input(shape=(input_dim_text,), dtype=tf.float32, name='text_input')\n",
        "    image_input = Input(shape=(input_dim_image,), name='image_input')\n",
        "\n",
        "    # Embedding layer for text data\n",
        "    embedded_text = Dense(128, activation='relu')(text_input)\n",
        "\n",
        "    # Reshape the embedded_text to a 3D tensor for Bidirectional LSTM\n",
        "    reshaped_text = Reshape((-1, 128))(embedded_text)\n",
        "\n",
        "    # Bidirectional LSTM layers for text data\n",
        "    lstm_text = Bidirectional(LSTM(128, return_sequences=True))(reshaped_text)\n",
        "    lstm_text = SpatialDropout1D(0.2)(lstm_text)\n",
        "    lstm_text = Bidirectional(LSTM(64))(lstm_text)\n",
        "    lstm_text = Dropout(0.5)(lstm_text)\n",
        "\n",
        "    # Concatenate text and image embeddings\n",
        "    concatenated_features = Concatenate()([lstm_text, image_input])\n",
        "\n",
        "    # Dense layers\n",
        "    dense_output = Dense(128, activation='relu')(concatenated_features)\n",
        "    dense_output = BatchNormalization()(dense_output)\n",
        "    dense_output = Dropout(0.5)(dense_output)\n",
        "\n",
        "    # Output layer with softmax for multi-class classification\n",
        "    sentiment_output = Dense(num_classes, activation='softmax', name='sentiment')(dense_output)\n",
        "\n",
        "    # Define the model\n",
        "    model = tf.keras.models.Model(inputs=[text_input, image_input], outputs=[sentiment_output])\n",
        "\n",
        "\n",
        "\n",
        "    optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)\n",
        "    model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sHG7UybKXXLi",
        "outputId": "80916664-ebe1-41b6-a684-65ff2975be11"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " text_input (InputLayer)     [(None, 384)]                0         []                            \n",
            "                                                                                                  \n",
            " image_input (InputLayer)    [(None, 1000)]               0         []                            \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)   (None, 1384)                 0         ['text_input[0][0]',          \n",
            "                                                                     'image_input[0][0]']         \n",
            "                                                                                                  \n",
            " tf.expand_dims (TFOpLambda  (None, 1, 1384)              0         ['concatenate[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " bidirectional (Bidirection  (None, 256)                  1549312   ['tf.expand_dims[0][0]']      \n",
            " al)                                                                                              \n",
            "                                                                                                  \n",
            " dense (Dense)               (None, 64)                   16448     ['bidirectional[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization (Batch  (None, 64)                   256       ['dense[0][0]']               \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " dropout (Dropout)           (None, 64)                   0         ['batch_normalization[0][0]'] \n",
            "                                                                                                  \n",
            " sentiment (Dense)           (None, 5)                    325       ['dropout[0][0]']             \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 1566341 (5.98 MB)\n",
            "Trainable params: 1566213 (5.97 MB)\n",
            "Non-trainable params: 128 (512.00 Byte)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QRM65k3RguTV",
        "outputId": "c79276bc-89cd-4122-c2f1-d86f64f577e5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3600 450 450\n",
            "Epoch 1/7\n",
            "58/58 [==============================] - 9s 35ms/step - loss: 1.9544 - accuracy: 0.2378 - val_loss: 2.7135 - val_accuracy: 0.0764\n",
            "Epoch 2/7\n",
            "58/58 [==============================] - 1s 13ms/step - loss: 1.5697 - accuracy: 0.3465 - val_loss: 2.3250 - val_accuracy: 0.0472\n",
            "Epoch 3/7\n",
            "58/58 [==============================] - 1s 19ms/step - loss: 1.4567 - accuracy: 0.3889 - val_loss: 1.7466 - val_accuracy: 0.2444\n",
            "Epoch 4/7\n",
            "58/58 [==============================] - 1s 18ms/step - loss: 1.3667 - accuracy: 0.4247 - val_loss: 1.6086 - val_accuracy: 0.2708\n",
            "Epoch 5/7\n",
            "58/58 [==============================] - 1s 18ms/step - loss: 1.2893 - accuracy: 0.4736 - val_loss: 1.4930 - val_accuracy: 0.3583\n",
            "Epoch 6/7\n",
            "58/58 [==============================] - 1s 18ms/step - loss: 1.1556 - accuracy: 0.5451 - val_loss: 1.4046 - val_accuracy: 0.3917\n",
            "Epoch 7/7\n",
            "58/58 [==============================] - 1s 19ms/step - loss: 1.0011 - accuracy: 0.6431 - val_loss: 1.5998 - val_accuracy: 0.4319\n",
            "9/9 [==============================] - 0s 5ms/step - loss: 1.5706 - accuracy: 0.4356\n",
            "Test Loss: 1.5706454515457153\n",
            "Test Accuracy: 0.4355555474758148\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from tensorflow.keras.layers import Input, Bidirectional, LSTM, Dense, Concatenate\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "label_encoder = LabelEncoder()\n",
        "y_sentiment_numerical = label_encoder.fit_transform(y_sentiment)\n",
        "\n",
        "# Convert numerical labels to one-hot encoding\n",
        "y_sentiment_onehot = to_categorical(y_sentiment_numerical)\n",
        "# Convert NumPy array to TensorFlow tensor\n",
        "numpy_array = X_text_embeddings.cpu().numpy()\n",
        "\n",
        "text_tensor = tf.convert_to_tensor(numpy_array, dtype=tf.float32)\n",
        "image_tensor = tf.convert_to_tensor(image_embedding, dtype=tf.float32)\n",
        "\n",
        "# Split the data into train, validation, and test sets using NumPy arrays\n",
        "X_text_train_np, X_text_temp_np, X_images_train_np, X_images_temp_np, y_sentiment_train_np, y_sentiment_temp_np = train_test_split(\n",
        "    text_tensor.numpy(), image_tensor.numpy(), y_sentiment_onehot, test_size=0.2, random_state=42, stratify=y_sentiment_numerical)\n",
        "\n",
        "X_text_val_np, X_text_test_np, X_images_val_np, X_images_test_np, y_sentiment_val_np, y_sentiment_test_np = train_test_split(\n",
        "    X_text_temp_np, X_images_temp_np, y_sentiment_temp_np, test_size=0.5, random_state=42, stratify=y_sentiment_temp_np)\n",
        "\n",
        "# Convert NumPy arrays back to TensorFlow tensors\n",
        "X_text_train = tf.convert_to_tensor(X_text_train_np, dtype=tf.float32)\n",
        "X_images_train = tf.convert_to_tensor(X_images_train_np, dtype=tf.float32)\n",
        "y_sentiment_train = tf.convert_to_tensor(y_sentiment_train_np, dtype=tf.float32)\n",
        "\n",
        "X_text_val = tf.convert_to_tensor(X_text_val_np, dtype=tf.float32)\n",
        "X_images_val = tf.convert_to_tensor(X_images_val_np, dtype=tf.float32)\n",
        "y_sentiment_val = tf.convert_to_tensor(y_sentiment_val_np, dtype=tf.float32)\n",
        "\n",
        "X_text_test = tf.convert_to_tensor(X_text_test_np, dtype=tf.float32)\n",
        "X_images_test = tf.convert_to_tensor(X_images_test_np, dtype=tf.float32)\n",
        "y_sentiment_test = tf.convert_to_tensor(y_sentiment_test_np, dtype=tf.float32)\n",
        "\n",
        "# Check the number of samples after the split\n",
        "print(len(X_text_train), len(X_text_val), len(X_text_test))\n",
        "\n",
        "# Create the model\n",
        "num_classes = 5  # Replace with the actual number of classes\n",
        "model = create_model(input_dim_text=len(X_text_train[0]), input_dim_image=len(X_images_train[0]), num_classes=num_classes)\n",
        "\n",
        "# Reduce the learning rate\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)\n",
        "\n",
        "# Compile the model with the new optimizer\n",
        "model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# ... (unchanged)\n",
        "\n",
        "# Add early stopping\n",
        "early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
        "\n",
        "model.fit([X_text_train.numpy(), X_images_train], y_sentiment_train,\n",
        "          validation_split=0.2,  # 20% of the training data will be used for validation\n",
        "          epochs=7, batch_size=50, callbacks=[early_stopping])\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "loss, accuracy = model.evaluate([X_text_test, X_images_test], y_sentiment_test, batch_size=50)\n",
        "\n",
        "print(f\"Test Loss: {loss}\")\n",
        "print(f\"Test Accuracy: {accuracy}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gq5F7aXlImJ_",
        "outputId": "cee4440d-391b-4d42-f39b-bfd48ce396c9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10/10 [==============================] - 1s 5ms/step\n"
          ]
        }
      ],
      "source": [
        "predictions = model.predict([X_text_test, X_images_test])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NGGX_R6aJCLO",
        "outputId": "5b680b17-f4ef-4d5d-b2c1-973cdc71527e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15/15 [==============================] - 0s 4ms/step\n",
            "Distribution of Predicted Labels:\n",
            "Label 4: 19 samples\n",
            "Label 2: 355 samples\n",
            "Label 1: 76 samples\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "from collections import Counter\n",
        "\n",
        "# Assuming you have a trained model named 'model'\n",
        "# Make predictions on the test set\n",
        "y_pred_probs = model.predict([X_text_test, X_images_test])\n",
        "\n",
        "# Convert predicted probabilities to class labels\n",
        "y_pred_labels = np.argmax(y_pred_probs, axis=1)\n",
        "\n",
        "# Count the occurrences of each predicted label\n",
        "predicted_label_distribution = Counter(y_pred_labels)\n",
        "\n",
        "# Print the distribution\n",
        "print(\"Distribution of Predicted Labels:\")\n",
        "for label, count in predicted_label_distribution.items():\n",
        "    print(f\"Label {label}: {count} samples\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, f1_score, precision_score, recall_score\n",
        "\n",
        "# Assuming model_v3 is the latest model you trained\n",
        "y_pred = model.predict([X_text_test, X_images_test])\n",
        "y_pred_classes = np.argmax(y_pred, axis=1)\n",
        "y_true_classes = np.argmax(y_sentiment_test, axis=1)\n",
        "\n",
        "# Confusion Matrix\n",
        "conf_mat = confusion_matrix(y_true_classes, y_pred_classes)\n",
        "print(\"Confusion Matrix:\")\n",
        "print(conf_mat)\n",
        "\n",
        "# Classification Report\n",
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(y_true_classes, y_pred_classes, target_names=label_encoder.classes_))\n",
        "\n",
        "# Accuracy\n",
        "accuracy = accuracy_score(y_true_classes, y_pred_classes)\n",
        "print(f\"\\nAccuracy: {accuracy}\")\n",
        "\n",
        "# F1 Score\n",
        "f1 = f1_score(y_true_classes, y_pred_classes, average='weighted')\n",
        "print(f\"F1 Score: {f1}\")\n",
        "\n",
        "# Precision\n",
        "precision = precision_score(y_true_classes, y_pred_classes, average='weighted')\n",
        "print(f\"Precision: {precision}\")\n",
        "\n",
        "# Recall\n",
        "recall = recall_score(y_true_classes, y_pred_classes, average='weighted')\n",
        "print(f\"Recall: {recall}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gyHpDLAHKGPq",
        "outputId": "305c6085-bd01-4ef1-9a8d-8679014e2d12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15/15 [==============================] - 0s 6ms/step\n",
            "Confusion Matrix:\n",
            "[[  0   2  24   0   4]\n",
            " [  0  28 111   0   4]\n",
            " [  0  32 163   0   6]\n",
            " [  0   1   9   0   0]\n",
            " [  0  13  48   0   5]]\n",
            "\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "     negative       0.00      0.00      0.00        30\n",
            "      neutral       0.37      0.20      0.26       143\n",
            "     positive       0.46      0.81      0.59       201\n",
            "very_negative       0.00      0.00      0.00        10\n",
            "very_positive       0.26      0.08      0.12        66\n",
            "\n",
            "     accuracy                           0.44       450\n",
            "    macro avg       0.22      0.22      0.19       450\n",
            " weighted avg       0.36      0.44      0.36       450\n",
            "\n",
            "\n",
            "Accuracy: 0.43555555555555553\n",
            "F1 Score: 0.36040763091917016\n",
            "Precision: 0.3607617164978173\n",
            "Recall: 0.43555555555555553\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are 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/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are 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/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are 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/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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Plot Confusion Matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(conf_mat, annot=True, fmt='d', cmap='Blues', xticklabels=label_encoder.classes_, yticklabels=label_encoder.classes_)\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "MxCJWOXAKy-x",
        "outputId": "33b5f97c-7f94-41ff-e317-1bad76834e1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
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      ]
    }
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    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyMgVpKNkGEqhxMHwPOF98gd",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
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