model/BiLSTM_model.py

import torch
import torch.nn as nn

class BiLSTM(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super(BiLSTM, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, bidirectional=True)
        self.fc = nn.Linear(hidden_size*2, output_size)  # Умножаем на 2 из-за двунаправленности

    def forward(self, x):
        h0 = torch.zeros(self.num_layers*2, x.size(0), self.hidden_size).to(x.device)  # 2 для bidirectional
        c0 = torch.zeros(self.num_layers*2, x.size(0), self.hidden_size).to(x.device)

        out, _ = self.lstm(x, (h0, c0))
        out = self.fc(out[:, -1, :])
        return out
    
# device = 'cpu'    
# model = BiLSTM().to(device)