Error while executing CrossEntropyLoss() in PyTorch - neural-network

Question

My dataset contains images of shape [3,28,28]. I have written the following code:
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.layer1 = nn.Sequential(nn.Conv2d(3, 28, kernel_size=5, stride=1, padding=2),nn.ReLU(),nn.MaxPool2d(kernel_size=2, stride=2))
self.layer2 = nn.Sequential(nn.Conv2d(28, 56, kernel_size=5, stride=1, padding=2),nn.ReLU(),nn.MaxPool2d(kernel_size=2, stride=2))
self.drop_out = nn.Dropout()
self.fc1 = nn.Linear(7 * 7 * 56, 1000)
self.fc2 = nn.Linear(1000, 10)
def forward(self, x):
out = self.layer1(x)
out = self.layer2(out)
out = out.reshape(out.size(0), -1)
out = self.drop_out(out)
out = self.fc1(out)
out = self.fc2(out)
return out
model = ConvNet()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
total_step = len(loader_train)
for e in range(num_epochs):
print("Epoch ", e+1,": ")
for i, (images, labels) in enumerate(loader_train):
optimizer.zero_grad()
actual_out = model(images)
loss = criterion(actual_out, labels)
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.3f}' .format(e+1, num_epochs, i+1, total_step, loss.item()))
However, I'm getting the following error:
AttributeError Traceback (most recent call last)
in
8 actual_out = model(images)
9
---> 10 loss = criterion(actual_out, labels)
11 loss.backward()
AttributeError: 'tuple' object has no attribute 'size'
I converted labels into a tensor by the following method:
target_out = torch.empty(batch_size,dtype=torch.long).random_(labels)
loss = criterion(actual_out, target_out)
But that generates:
TypeError Traceback (most recent call last)
in
---> 11 target_out = torch.empty(batch_size,dtype=torch.long).random_(labels)
12 loss = criterion(actual_out, target_out)
TypeError: random_() received an invalid combination of arguments - got (tuple), but expected one of:
(*, torch.Generator generator)
(int from, int to, *, torch.Generator generator)
(int to, *, torch.Generator generator)

Answer 1

Your labels object is a tuple and you want to convert it to a tensor of dtype long.
You can do this via:
torch.tensor(labels, dtype=torch.long)

Answer 2

Assuming this is how your train_loader is structured, you can reshape in the training loop rather than in the forward() function. I've given an example change it as your need.
Also during the backward, clear the grad, the .to(device) is optional, if you have a GPU
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
# origin shape: [100, 1, 28, 28]
# resized: [100, 784]
images = images.reshape(-1, 28*28).to(device)
labels = labels.to(device)
# Forward pass
outputs = model(images)
loss = criterion(outputs, labels)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()