RuntimeError: expected scalar type Double but found Float
RuntimeError: expected scalar type Double but found Float
我是 PyTorch 的新手,我从我的 cnn 层收到以下错误:“RuntimeError:预期标量类型为 Double,但发现为 Float”。我将每个元素转换为 .astype(np.double)
但错误消息仍然存在。然后在转换 Tensor
后尝试使用 .double()
并再次出现错误消息。
这是我的代码以便更好地理解:
import torch.nn as nn
class CNN(nn.Module):
# Contructor
def __init__(self, shape):
super(CNN, self).__init__()
self.cnn1 = nn.Conv1d(in_channels=shape, out_channels=32, kernel_size=3)
self.act1 = torch.nn.ReLU()
# Prediction
def forward(self, x):
x = self.cnn1(x)
x = self.act1(x)
return x
X_train_reshaped = np.zeros([X_train.shape[0],int(X_train.shape[1]/depth),depth])
for i in range(X_train.shape[0]):
for j in range(X_train.shape[1]):
X_train_reshaped[i][int(j/3)][j%3] = X_train[i][j].astype(np.double)
X_train = torch.tensor(X_train_reshaped)
y_train = torch.tensor(y_train)
# Dataset w/o any tranformations
train_dataset_normal = CustomTensorDataset(tensors=(X_train, y_train), transform=None)
train_loader = torch.utils.data.DataLoader(train_dataset_normal, shuffle=True, batch_size=16)
model = CNN(X_train.shape[1]).to(device)
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())
# Train the model
#how to implement batch_size??
for epoch in range(epochno):
#for i, (dataX, labels) in enumerate(X_train_reshaped,y_train):
for i, (dataX, labels) in enumerate(train_loader):
dataX = dataX.to(device)
labels = labels.to(device)
# Forward pass
outputs = model(dataX)
loss = criterion(outputs, labels)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
下面是我收到的错误:
RuntimeError Traceback (most recent call last)
<ipython-input-39-d99b62b3a231> in <module>
14
15 # Forward pass
---> 16 outputs = model(dataX.double())
17 loss = criterion(outputs, labels)
18
~\torch\nn\modules\module.py in _call_impl(self, *input, **kwargs)
887 result = self._slow_forward(*input, **kwargs)
888 else:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),
<ipython-input-27-7510ac2f1f42> in forward(self, x)
22 # Prediction
23 def forward(self, x):
---> 24 x = self.cnn1(x)
25 x = self.act1(x)
~\torch\nn\modules\module.py in _call_impl(self, *input, **kwargs)
887 result = self._slow_forward(*input, **kwargs)
888 else:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),
~\torch\nn\modules\conv.py in forward(self, input)
261
262 def forward(self, input: Tensor) -> Tensor:
--> 263 return self._conv_forward(input, self.weight, self.bias)
264
265
~\torch\nn\modules\conv.py in _conv_forward(self, input, weight, bias)
257 weight, bias, self.stride,
258 _single(0), self.dilation, self.groups)
--> 259 return F.conv1d(input, weight, bias, self.stride,
260 self.padding, self.dilation, self.groups)
261
RuntimeError: expected scalar type Double but found Float
我不知道是我还是 Pytorch,但错误消息试图说以某种方式转换为浮点数。因此在 forward pass
内,我通过将 dataX
转换为 float
解决了问题,如下所示: outputs = model(dataX.float())
同意 aysebilgegunduz 的观点。应该是Pytorch的问题,我也遇到同样的错误信息。
只需将类型更改为其他类型即可解决问题。
您可以通过以下方式检查输入张量的类型:
data.type()
更改类型的一些有用函数:
data.float()
data.double()
data.long()
我是 PyTorch 的新手,我从我的 cnn 层收到以下错误:“RuntimeError:预期标量类型为 Double,但发现为 Float”。我将每个元素转换为 .astype(np.double)
但错误消息仍然存在。然后在转换 Tensor
后尝试使用 .double()
并再次出现错误消息。
这是我的代码以便更好地理解:
import torch.nn as nn
class CNN(nn.Module):
# Contructor
def __init__(self, shape):
super(CNN, self).__init__()
self.cnn1 = nn.Conv1d(in_channels=shape, out_channels=32, kernel_size=3)
self.act1 = torch.nn.ReLU()
# Prediction
def forward(self, x):
x = self.cnn1(x)
x = self.act1(x)
return x
X_train_reshaped = np.zeros([X_train.shape[0],int(X_train.shape[1]/depth),depth])
for i in range(X_train.shape[0]):
for j in range(X_train.shape[1]):
X_train_reshaped[i][int(j/3)][j%3] = X_train[i][j].astype(np.double)
X_train = torch.tensor(X_train_reshaped)
y_train = torch.tensor(y_train)
# Dataset w/o any tranformations
train_dataset_normal = CustomTensorDataset(tensors=(X_train, y_train), transform=None)
train_loader = torch.utils.data.DataLoader(train_dataset_normal, shuffle=True, batch_size=16)
model = CNN(X_train.shape[1]).to(device)
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters())
# Train the model
#how to implement batch_size??
for epoch in range(epochno):
#for i, (dataX, labels) in enumerate(X_train_reshaped,y_train):
for i, (dataX, labels) in enumerate(train_loader):
dataX = dataX.to(device)
labels = labels.to(device)
# Forward pass
outputs = model(dataX)
loss = criterion(outputs, labels)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
下面是我收到的错误:
RuntimeError Traceback (most recent call last)
<ipython-input-39-d99b62b3a231> in <module>
14
15 # Forward pass
---> 16 outputs = model(dataX.double())
17 loss = criterion(outputs, labels)
18
~\torch\nn\modules\module.py in _call_impl(self, *input, **kwargs)
887 result = self._slow_forward(*input, **kwargs)
888 else:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),
<ipython-input-27-7510ac2f1f42> in forward(self, x)
22 # Prediction
23 def forward(self, x):
---> 24 x = self.cnn1(x)
25 x = self.act1(x)
~\torch\nn\modules\module.py in _call_impl(self, *input, **kwargs)
887 result = self._slow_forward(*input, **kwargs)
888 else:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),
~\torch\nn\modules\conv.py in forward(self, input)
261
262 def forward(self, input: Tensor) -> Tensor:
--> 263 return self._conv_forward(input, self.weight, self.bias)
264
265
~\torch\nn\modules\conv.py in _conv_forward(self, input, weight, bias)
257 weight, bias, self.stride,
258 _single(0), self.dilation, self.groups)
--> 259 return F.conv1d(input, weight, bias, self.stride,
260 self.padding, self.dilation, self.groups)
261
RuntimeError: expected scalar type Double but found Float
我不知道是我还是 Pytorch,但错误消息试图说以某种方式转换为浮点数。因此在 forward pass
内,我通过将 dataX
转换为 float
解决了问题,如下所示: outputs = model(dataX.float())
同意 aysebilgegunduz 的观点。应该是Pytorch的问题,我也遇到同样的错误信息。
只需将类型更改为其他类型即可解决问题。
您可以通过以下方式检查输入张量的类型:
data.type()
更改类型的一些有用函数:
data.float()
data.double()
data.long()