我收到错误，
输入类型 (torch.cuda.ByteTensor(和权重类型 (torch.cuda.FloatTensor( 应相同
以下是我的代码，
device = torch.device('cuda:0')
trainData = torchvision.datasets.FashionMNIST('/content/', train=True, transform=None, target_transform=None, download=True)
testData = torchvision.datasets.FashionMNIST('/content/', train=False, transform=None, target_transform=None, download=True)
class Net(nn.Module):
def __init__(self):
super().__init__()
'''
Network Structure:
input >
(1)Conv2D > (2)MaxPool2D >
(3)Conv2D > (4)MaxPool2D >
(5)Conv2D > (6)MaxPool2D >
(7)Linear > (8)LinearOut
'''
# Creating the convulutional Layers
self.conv1 = nn.Conv2d(in_channels=CHANNELS, out_channels=32, kernel_size=3)
self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3)
self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3)
self.flatten = None
# Creating a Random dummy sample to get the Flattened Dimensions
x = torch.randn(CHANNELS, DIM, DIM).view(-1, CHANNELS, DIM, DIM)
x = self.convs(x)
# Creating the Linear Layers
self.fc1 = nn.Linear(self.flatten, 512)
self.fc2 = nn.Linear(512, CLASSES)
def convs(self, x):
# Creating the MaxPooling Layers
x = F.max_pool2d(F.relu(self.conv1(x)), kernel_size=(2, 2))
x = F.max_pool2d(F.relu(self.conv2(x)), kernel_size=(2, 2))
x = F.max_pool2d(F.relu(self.conv3(x)), kernel_size=(2, 2))
if not self.flatten:
self.flatten = x[0].shape[0] * x[0].shape[1] * x[0].shape[2]
return x
# FORWARD PASS
def forward(self, x):
x = self.convs(x)
x = x.view(-1, self.flatten)
sm = F.relu(self.fc1(x))
x = F.softmax(self.fc2(sm), dim=1)
return x, sm
x_train, y_train = training_set
x_train, y_train = x_train.to(device), y_train.to(device)
optimizer = optim.Adam(net.parameters(), lr=LEARNING_RATE)
loss_func = nn.MSELoss()
loss_log = []
for epoch in range(EPOCHS):
for i in tqdm(range(0, len(x_train), BATCH_SIZE)):
x_batch = x_train[i:i+BATCH_SIZE].view(-1, CHANNELS, DIM, DIM).to(device)
y_batch = y_train[i:i+BATCH_SIZE].to(device)
net.zero_grad()
output, sm = net(x_batch)
loss = loss_func(output, y_batch.float())
loss.backward()
optimizer.step()
loss_log.append(loss)
# print(f"Epoch : {epoch} || Loss : {loss}")
return loss_log
train_set = (trainData.train_data, trainData.train_labels)
test_set = (testData.test_data, testData.test_labels)
EPOCHS = 5
LEARNING_RATE = 0.001
BATCH_SIZE = 32
net = Net().to(device)
loss_log = train(net, train_set, EPOCHS, LEARNING_RATE, BATCH_SIZE)
这是我得到的错误，
RuntimeError Traceback (most recent call last)
<ipython-input-8-0db1a1b4e37d> in <module>()
5 net = Net().to(device)
6
----> 7 loss_log = train(net, train_set, EPOCHS, LEARNING_RATE, BATCH_SIZE)
6 frames
<ipython-input-6-7de4a78e3736> in train(net, training_set, EPOCHS, LEARNING_RATE, BATCH_SIZE)
13
14 net.zero_grad()
---> 15 output, sm = net(x_batch)
16 loss = loss_func(output, y_batch.float())
17 loss.backward()
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
539 result = self._slow_forward(*input, **kwargs)
540 else:
--> 541 result = self.forward(*input, **kwargs)
542 for hook in self._forward_hooks.values():
543 hook_result = hook(self, input, result)
<ipython-input-5-4fddc427892a> in forward(self, x)
41 # FORWARD PASS
42 def forward(self, x):
---> 43 x = self.convs(x)
44 x = x.view(-1, self.flatten)
45 sm = F.relu(self.fc1(x))
<ipython-input-5-4fddc427892a> in convs(self, x)
31
32 # Creating the MaxPooling Layers
---> 33 x = F.max_pool2d(F.relu(self.conv1(x)), kernel_size=(2, 2))
34 x = F.max_pool2d(F.relu(self.conv2(x)), kernel_size=(2, 2))
35 x = F.max_pool2d(F.relu(self.conv3(x)), kernel_size=(2, 2))
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
539 result = self._slow_forward(*input, **kwargs)
540 else:
--> 541 result = self.forward(*input, **kwargs)
542 for hook in self._forward_hooks.values():
543 hook_result = hook(self, input, result)
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py in forward(self, input)
343
344 def forward(self, input):
--> 345 return self.conv2d_forward(input, self.weight)
346
347 class Conv3d(_ConvNd):
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/conv.py in conv2d_forward(self, input, weight)
340 _pair(0), self.dilation, self.groups)
341 return F.conv2d(input, weight, self.bias, self.stride,
--> 342 self.padding, self.dilation, self.groups)
343
344 def forward(self, input):
RuntimeError: Input type (torch.cuda.ByteTensor) and weight type (torch.cuda.FloatTensor) should be the same
我仔细检查了我的神经网络和输入是否都在 GPU 中。我仍然收到此错误，我不明白为什么！
有人，请帮助我摆脱这个错误。
将输入x_batch转换为浮动。在通过模型之前使用x_batch = x_batch.float()。