如何在nn.LSTM pytorch中进行R2评分

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 = 1
hidden_size = 32
num_layers = 2
output_size = 1

model = LSTMModel(input_size, hidden_size, num_layers, output_size)
model.to(device)

criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

num_epochs = 100
for epoch in range(num_epochs):
    model.train()
    outputs = model(inputs)
    loss = criterion(outputs, labels)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

model.eval()
with torch.no_grad():
    predicted = model(test_inputs)
    r2_score = 1 - (torch.sum((predicted - test_labels) ** 2) / torch.sum((test_labels - torch.mean(test_labels)) ** 2))
    print("R2 Score: {:.2f}".format(r2_score.item()))