models.py

import torch
import torch.nn as nn

class LSTM_Net(nn.Module):
    def __init__(self, embedding, embedding_dim, hidden_dim, output_dim, num_layers, dropout=0.2, fix_embedding=True):
        super(LSTM_Net, self).__init__()
        # 製作 embedding layer
        self.embedding = torch.nn.Embedding(embedding.size(0),embedding.size(1))
        self.embedding.weight = torch.nn.Parameter(embedding)
        # 是否將 embedding fix 住，如果 fix_embedding 為 False，在訓練過程中，embedding 也會跟著被訓練
        self.embedding.weight.requires_grad = False if fix_embedding else True
        self.embedding_dim = embedding.size(1)
        self.hidden_dim = hidden_dim
        self.num_layers = num_layers
        self.dropout = dropout
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers=num_layers, batch_first=True)
        self.classifier = nn.Sequential(
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, output_dim),
            nn.GELU()
        )
    def forward(self, inputs):
        inputs = self.embedding(inputs)
        x, _ = self.lstm(inputs, None)
        # x 的 dimension (batch, seq_len, hidden_size)
        # 取用 LSTM 最後一層的 hidden state
        x = x[:, -1, :] 
        x = self.classifier(x)
        return x