我无法让数据类型匹配，如果我将张量更改为长整型，损失函数要求长整型而模型要求浮点数。 张量的形状为42000，1，28，28和42000。 我不确定在哪里可以更改所需的数据类型以适应模型或损失函数。
我不确定是否需要使用数据加载器（dataloader），使用变量（variable）也无效。
dataloaders_train = torch.utils.data.DataLoader(Xt_train, batch_size=64)
dataloaders_test = torch.utils.data.DataLoader(Yt_train, batch_size=64)
class Network(nn.Module):
def __init__(self):
super().__init__()
self.hidden = nn.Linear(42000, 256)
self.output = nn.Linear(256, 10)
self.sigmoid = nn.Sigmoid()
self.softmax = nn.Softmax(dim=1)
def forward(self, x):
x = self.hidden(x)
x = self.sigmoid(x)
x = self.output(x)
x = self.softmax(x)
return x
model = Network()
input_size = 784
hidden_sizes = [28, 64]
output_size = 10
model = nn.Sequential(nn.Linear(input_size, hidden_sizes[0]),
nn.ReLU(),
nn.Linear(hidden_sizes[0], hidden_sizes[1]),
nn.ReLU(),
nn.Linear(hidden_sizes[1], output_size),
nn.Softmax(dim=1))
print(model)
criterion = nn.NLLLoss()
optimizer = optim.SGD(model.parameters(), lr=0.003)
epochs = 5
for e in range(epochs):
running_loss = 0
for images, labels in zip(dataloaders_train, dataloaders_test):
images = images.view(images.shape[0], -1)
#images, labels = Variable(images), Variable(labels)
print(images.dtype)
print(labels.dtype)
optimizer.zero_grad()
output = model(images)
loss = criterion(output, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
else:
print(f"Training loss: {running_loss}")
其中提供
RuntimeError Traceback (most recent call last)
<ipython-input-128-68109c274f8f> in <module>
11
12 output = model(images)
---> 13 loss = criterion(output, labels)
14 loss.backward()
15 optimizer.step()
/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
530 result = self._slow_forward(*input, **kwargs)
531 else:
--> 532 result = self.forward(*input, **kwargs)
533 for hook in self._forward_hooks.values():
534 hook_result = hook(self, input, result)
/opt/conda/lib/python3.6/site-packages/torch/nn/modules/loss.py in forward(self, input, target)
202
203 def forward(self, input, target):
--> 204 return F.nll_loss(input, target, weight=self.weight, ignore_index=self.ignore_index, reduction=self.reduction)
205
206
/opt/conda/lib/python3.6/site-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
1836 .format(input.size(0), target.size(0)))
1837 if dim == 2:
-> 1838 ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
1839 elif dim == 4:
1840 ret = torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
RuntimeError: expected scalar type Long but found Float