def forward(self, pred, target, trans_feat): total_loss = F.nll_loss(pred, target)

您想问这段代码的作用是什么吗？这段代码是一个 PyTorch 的神经网络的正向传播函数。其中，pred 是模型的预测输出，target 是真实的目标输出，trans_feat 是特征变换。这段代码的作用是计算模型的损失函数，使用的是负对数似然损失函数（nll_loss）。通过将预测输出与目标输出的差异计算损失，以便在反向传播时更新模型的参数。

相关问题

def predict(self, x_star, z_star): tf_dict = {self.x_tf: x_star, self.z_tf: z_star} u_real_star = self.sess.run(self.u_real_pred, tf_dict) u_imag_star = self.sess.run(self.u_imag_pred, tf_dict) return u_real_star, u_imag_star 解释一下代码

这是一个 Python 代码段，其中定义了一个名为 predict 的函数，该函数接受两个参数 x_star 和 z_star。这个函数使用 TensorFlow 库来计算 u_real_star 和 u_imag_star 的值，然后将它们作为返回值返回。具体来说，这个函数使用一个名为 self.x_tf 的 TensorFlow 张量来表示输入 x_star，使用一个名为 self.z_tf 的 TensorFlow 张量来表示输入 z_star。然后，它使用 TensorFlow 会话（self.sess）来运行两个名为 self.u_real_pred 和 self.u_imag_pred 的 TensorFlow 操作，这些操作分别计算 u_real_star 和 u_imag_star 的值。最后，这个函数将 u_real_star 和 u_imag_star 作为元组返回。

这段代码中加一个test loss功能 class LSTM(nn.Module): def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size, device): super().__init__() self.device = device self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.output_size = output_size self.num_directions = 1 # 单向LSTM self.batch_size = batch_size self.lstm = nn.LSTM(self.input_size, self.hidden_size, self.num_layers, batch_first=True) self.linear = nn.Linear(65536, self.output_size) def forward(self, input_seq): h_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device) c_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device) output, _ = self.lstm(input_seq, (h_0, c_0)) pred = self.linear(output.contiguous().view(self.batch_size, -1)) return pred if __name__ == '__main__': # 加载已保存的模型参数 saved_model_path = '/content/drive/MyDrive/危急值/model/dangerous.pth' device = 'cuda:0' lstm_model = LSTM(input_size=1, hidden_size=64, num_layers=1, output_size=3, batch_size=256, device='cuda:0').to(device) state_dict = torch.load(saved_model_path) lstm_model.load_state_dict(state_dict) dataset = ECGDataset(X_train_df.to_numpy()) dataloader = DataLoader(dataset, batch_size=256, shuffle=True, num_workers=0, drop_last=True) loss_fn = nn.CrossEntropyLoss() optimizer = optim.SGD(lstm_model.parameters(), lr=1e-4) for epoch in range(200000): print(f'epoch:{epoch}') lstm_model.train() epoch_bar = tqdm(dataloader) for x, y in epoch_bar: optimizer.zero_grad() x_out = lstm_model(x.to(device).type(torch.cuda.FloatTensor)) loss = loss_fn(x_out, y.long().to(device)) loss.backward() epoch_bar.set_description(f'loss:{loss.item():.4f}') optimizer.step() if epoch % 100 == 0 or epoch == epoch - 1: torch.save(lstm_model.state_dict(), "/content/drive/MyDrive/危急值/model/dangerous.pth") print("权重成功保存一次")

以下是加入 Test Loss 功能后的代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader

class LSTM(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size, batch_size, device):
        super().__init__()
        self.device = device
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.output_size = output_size
        self.num_directions = 1 # 单向LSTM
        self.batch_size = batch_size
        self.lstm = nn.LSTM(self.input_size, self.hidden_size, self.num_layers, batch_first=True)
        self.linear = nn.Linear(65536, self.output_size)

    def forward(self, input_seq):
        h_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device)
        c_0 = torch.randn(self.num_directions * self.num_layers, self.batch_size, self.hidden_size).to(self.device)
        output, _ = self.lstm(input_seq, (h_0, c_0))
        pred = self.linear(output.contiguous().view(self.batch_size, -1))
        return pred

if __name__ == '__main__':
    # 加载已保存的模型参数
    saved_model_path = '/content/drive/MyDrive/危急值/model/dangerous.pth'
    device = 'cuda:0'
    lstm_model = LSTM(input_size=1, hidden_size=64, num_layers=1, output_size=3, batch_size=256, device=device).to(device)
    state_dict = torch.load(saved_model_path)
    lstm_model.load_state_dict(state_dict)
    
    # 加载测试数据集
    test_dataset = ECGDataset(X_test_df.to_numpy())
    test_loader = DataLoader(test_dataset, batch_size=256, shuffle=False, num_workers=0, drop_last=False)
    
    # 定义损失函数和优化器
    loss_fn = nn.CrossEntropyLoss()
    optimizer = optim.SGD(lstm_model.parameters(), lr=1e-4)
    
    # 开始训练
    for epoch in range(200000):
        print(f'epoch:{epoch}')
        lstm_model.train()
        epoch_bar = tqdm(dataloader)
        for x, y in epoch_bar:
            optimizer.zero_grad()
            x_out = lstm_model(x.to(device).type(torch.cuda.FloatTensor))
            loss = loss_fn(x_out, y.long().to(device))
            loss.backward()
            epoch_bar.set_description(f'loss:{loss.item():.4f}')
            optimizer.step()
        
        # 计算测试集上的平均损失
        test_loss = 0.0
        lstm_model.eval()
        with torch.no_grad():
            for x, y in test_loader:
                x_out = lstm_model(x.to(device).type(torch.cuda.FloatTensor))
                loss = loss_fn(x_out, y.long().to(device))
                test_loss += loss.item() * x.size(0)
        test_loss /= len(test_dataset)
        print(f'Test Loss: {test_loss:.4f}')
        
        if epoch % 100 == 0 or epoch == epoch - 1:
            torch.save(lstm_model.state_dict(), "/content/drive/MyDrive/危急值/model/dangerous.pth")
            print("权重成功保存一次")

在这个代码中，我们首先加载了测试数据集 `test_dataset` 和测试数据加载器 `test_loader`。在每个 epoch 完成后，我们计算测试集上的平均损失值。我们使用 `lstm_model.eval()` 将模型设为评估模式，并利用 `torch.no_grad()` 避免计算梯度，以加速计算。最后，我们输出测试集上的平均损失值。