冒号后的代码是上面提到的报错的地方:for epoch in range(1): clone = maml_qiao.clone() # 进行预测 y_pred = clone(x1.float()) #y_pred = clone(x) #y_pred = rnn.forward(x.float()) error = loss(y_pred, y) print(epoch, error) clone.adapt(error) opt.zero_grad() error.backward() opt.step()
时间: 2024-04-23 14:24:24 浏览: 7
根据报错信息,可能是在 `clone.adapt(error)` 这一行代码中,`gradients` 变量未被正确赋值。你可以检查一下 `adapt()` 函数的实现,看看是否在函数中正确地定义了 `gradients` 变量,并且在函数调用时传入了正确的参数。此外,可能是在 `backward()` 函数中使用了不匹配的数据类型,因为该函数默认使用与输入 Tensor 相同的数据类型进行反向传播。你可以尝试将 `error` 的数据类型改为 `float`,例如:`clone.adapt(error.float())`。
相关问题
代码解释:for epoch in range(start_epoch, epochs): model.train()
这段代码是训练过程的核心部分,根据指定的 epoch 范围,循环遍历每个 epoch,并将模型设置为训练模式。在训练模式下,模型会根据输入的数据进行前向传播、计算损失、反向传播和参数更新等操作,从而不断优化模型的性能。在每个 epoch 结束后,会根据验证集的表现来判断是否需要保存模型,并记录一些指标,比如训练集和验证集的损失、精度等。
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:
```python
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。