model/bilstm_model.py

import torch
import torch.nn as nn

class BiLSTMSentiment(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes, dropout=0.2):
        super(BiLSTMSentiment, self).__init__()
        
        # ✅ BiLSTM Layer
        self.bilstm = nn.LSTM(
            input_size=input_size,
            hidden_size=hidden_size,
            batch_first=True,
            bidirectional=True
        )

        # ✅ Batch Normalization
        self.batch_norm = nn.BatchNorm1d(hidden_size * 2)

        # ✅ Dropout
        self.dropout = nn.Dropout(dropout)

        # ✅ Fully Connected Layer
        self.fc = nn.Linear(hidden_size * 2, num_classes)

    def forward(self, x):
        batch_size = x.size(0)

        # สร้าง initial hidden/cell states สำหรับ 2 directions
        h_0 = torch.zeros(2, batch_size, self.bilstm.hidden_size).to(x.device)
        c_0 = torch.zeros(2, batch_size, self.bilstm.hidden_size).to(x.device)

        # เพิ่ม dim ให้สอดคล้องกับ LSTM: [batch, seq_len=1, input_size]
        #x = x.unsqueeze(1)  # [batch_size, 1, input_size]
        #หลังลบ x = x.unsqueeze(1) แล้ว ตัวแปร x จะมี shape [batch, seq_len=1, input_size=769]

        out, _ = self.bilstm(x, (h_0, c_0))  # out: [batch, seq_len, hidden*2]
        out = out[:, -1, :]  # เอา timestep สุดท้าย: [batch, hidden*2]

        # ✅ BatchNorm + Dropout + Linear
        out = self.batch_norm(out)
        out = self.dropout(out)
        out = self.fc(out)
        return out