在Pytorch中测试LSTM的实现

import torch
import torch.nn as nn

class LSTMModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super(LSTMModel, 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)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        out, _ = self.lstm(x, (h0, c0))
        out = self.fc(out[:, -1, :])
        return out

input_size = 10
hidden_size = 20
num_layers = 2
output_size = 1

model = LSTMModel(input_size, hidden_size, num_layers, output_size)
model.load_state_dict(torch.load('lstm_model.pth'))

test_data = torch.randn(1, 5, input_size)  # 输入数据形状为(batch_size, sequence_length, input_size)

model.eval()
with torch.no_grad():
    output = model(test_data)

print(output)