class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1=nn.Sequential(
nn.Conv2d(
in_channels=1,
out_channels=16,
kernel_size=3,
stride=1,
padding=1,
),
nn.ReLU(),
)
self.conv2 = nn.Sequential(
nn.Conv2d(16, 32, 3, 1, 1),
nn.ReLU(), # activation
)
self.out = nn.Linear(32 * 7 * 7, 5)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = x.view(x.size(0), -1)
output = self.out(x)
return output, x
cnn = CNN().cuda()
print(cnn)
EPOCH=10
optimizer = optim.SGD(cnn.parameters(), lr=0.1, momentum=0.9,weight_decay=1e-4,nesterov=False)
criterion = torch.nn.CrossEntropyLoss()
def train(cnn,train_loader,EPOCH):
cnn.train()
for epoch in range(EPOCH):
for step,(data,target) in enumerate(train_loader):
data,target=data.cuda(),target.cuda()
data,target=Variable(data),Variable(target)
optimizer.zero_grad()
output=cnn(data)
loss=criterion(output,target)
loss.backward()
optimizer.step()
if step % 10 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, step * len(data), len(train_loader.dataset),
100. * step / len(train_loader), loss.data[0]))
def test(cnn, test_loader):
cnn.eval()
test_loss = 0
correct = 0
for data, target in test_loader:
data, target = data.cuda(), target.cuda()
data, target = Variable(data, volatile=True), Variable(target)
output = cnn(data)
test_loss += criterion(output, target).data[0] # Variable.data
pred = output.data.max(1)[1] # get the index of the max log-probability
correct += pred.eq(target.data).cpu().sum()
test_loss = test_loss
test_loss /= len(test_loader) # loss function already averages over batch size
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
if __name__ == "__main__":
train(cnn, train_loader, EPOCH=10)
test(cnn, test_loader)
this is a tuple…
1 Like
Then,I don’t know how to solve it,that’s …?Could you help me?Thanks!!!
In the forward method just return output instead of (output, x) or use loss = criterion(output[0], target) in the training loop.
1 Like
@ztttkx in the future, please dont write: “please help me”, it’s redundant, all posts are looking for help here.
1 Like
Good!!! Thank you! I’ve solved it!
you are right!