gpu训练神经网络时报错
#plus 1 to avoid log(0)def MSLE_loss(pred, target):
log_pred = torch.log(pred + 1)
log_target = torch.log(target + 1)
loss = nn.MSELoss()(log_pred, log_target)
return loss
model = Net().cuda()
optimizer = optim.AdamW(model.parameters(),lr=1e-3,weight_decay=1e-3)
for epoch in range(3500):
pred = model(X_train)
pred = pred.squeeze()
loss = MSLE_loss(pred,y_train)
if epoch % 500 == 0 :
print(loss)
print(pred)
print(y_train)
loss.backward()
optimizer.step()
optimizer.zero_grad()
报错如下---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In, line 11
9 optimizer = optim.AdamW(model.parameters(),lr=1e-3,weight_decay=1e-3)
10 for epoch in range(3500):
---> 11 pred = model(X_train)
12 pred = pred.squeeze()
13 loss = MSLE_loss(pred,y_train)
File /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1553, in Module._wrapped_call_impl(self, *args, **kwargs)
1551 return self._compiled_call_impl(*args, **kwargs)# type: ignore
1552 else:
-> 1553 return self._call_impl(*args, **kwargs)
File /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1562, in Module._call_impl(self, *args, **kwargs)
1557 # If we don't have any hooks, we want to skip the rest of the logic in
1558 # this function, and just call forward.
1559 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1560 or _global_backward_pre_hooks or _global_backward_hooks
1561 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1562 return forward_call(*args, **kwargs)
1564 try:
1565 result = None
Cell In, line 23, in Net.forward(self, x)
21 def forward(self,x):
22 inputs = x
---> 23 layer1_out = torch.nn.functional.gelu(self.hidden_layer1(inputs))
24 layer1_out = self.dropout(layer1_out)
25 layer2_out = torch.nn.functional.gelu(self.hidden_layer2(layer1_out))
File /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1553, in Module._wrapped_call_impl(self, *args, **kwargs)
1551 return self._compiled_call_impl(*args, **kwargs)# type: ignore
1552 else:
-> 1553 return self._call_impl(*args, **kwargs)
File /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1562, in Module._call_impl(self, *args, **kwargs)
1557 # If we don't have any hooks, we want to skip the rest of the logic in
1558 # this function, and just call forward.
1559 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1560 or _global_backward_pre_hooks or _global_backward_hooks
1561 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1562 return forward_call(*args, **kwargs)
1564 try:
1565 result = None
File /opt/conda/lib/python3.10/site-packages/torch/nn/modules/linear.py:117, in Linear.forward(self, input)
116 def forward(self, input: Tensor) -> Tensor:
--> 117 return F.linear(input, self.weight, self.bias)
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument mat1 in method wrapper_CUDA_addmm) 报错显示您的张量在不同设备上：有的在GPU上（cuda:0），有的在CPU上。解决办法如下：
1. **将数据移动到GPU**：确保`X_train`和`y_train`在使用时都在GPU上。使用`.cuda()`方法将它们移到GPU上。例如：
X_train = X_train.cuda()
y_train = y_train.cuda()
2. **检查模型参数和其他张量**：确保模型的所有参数和输入张量都在同一设备上。可以在定义模型和数据加载时直接使用`.to(device)`，其中`device`是定义的计算设备，如：
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net().to(device)
X_train = X_train.to(device)
y_train = y_train.to(device)
确保所有的计算都在同一设备上，将解决这个错误。
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