Pytorch: ValueError: Expected input batch_size (32) to match target batch_size (64) - python-3.x

Question

Tried to run the CNN examples on MNIST dataset, batch size=64, channel =1, n_h=28, n_w=28, n_iters = 1000. The program runs for first 500 interation and then gives the above mentioned error.
There are same topics already being discussed on the forum such as : topic 1
and topic 2, but none of them could help me identify the mistake in the following code:
class CNN_MNIST(nn.Module):
def __init__(self):
super(CNN_MNIST,self).__init__()
# convolution layer 1
self.cnn1 = nn.Conv2d(in_channels=1, out_channels= 32, kernel_size=5,
stride=1,padding=2)
# ReLU activation
self.relu1 = nn.ReLU()
# maxpool 1
self.maxpool1 = nn.MaxPool2d(kernel_size=2,stride=2)
# convolution 2
self.cnn2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5,
stride=1,padding=2)
# ReLU activation
self.relu2 = nn.ReLU()
# maxpool 2
self.maxpool2 = nn.MaxPool2d(kernel_size=2,stride=2)
# fully connected 1
self.fc1 = nn.Linear(7*7*64,1000)
# fully connected 2
self.fc2 = nn.Linear(1000,10)
def forward(self,x):
# convolution 1
out = self.cnn1(x)
# activation function
out = self.relu1(out)
# maxpool 1
out = self.maxpool1(out)
# convolution 2
out = self.cnn2(out)
# activation function
out = self.relu2(out)
# maxpool 2
out = self.maxpool2(out)
# flatten the output
out = out.view(out.size(0),-1)
# fully connected layers
out = self.fc1(out)
out = self.fc2(out)
return out
# model trainning
count = 0
loss_list = []
iteration_list = []
accuracy_list = []
for epoch in range(int(n_epochs)):
for i, (image,labels) in enumerate(train_loader):
train = Variable(image)
labels = Variable(labels)
# clear gradient
optimizer.zero_grad()
# forward propagation
output = cnn_model(train)
# calculate softmax and cross entropy loss
loss = error(output,label)
# calculate gradients
loss.backward()
# update the optimizer
optimizer.step()
count += 1
if count % 50 ==0:
# calculate the accuracy
correct = 0
total = 0
# iterate through the test data
for image, labels in test_loader:
test = Variable(image)
# forward propagation
output = cnn_model(test)
# get prediction
predict = torch.max(output.data,1)[1]
# total number of labels
total += len(labels)
# correct prediction
correct += (predict==labels).sum()
# accuracy
accuracy = 100*correct/float(total)
# store loss, number of iteration, and accuracy
loss_list.append(loss.data)
iteration_list.append(count)
accuracy_list.append(accuracy)
# print loss and accurcay as the algorithm progresses
if count % 500 ==0:
print('Iteration :{} Loss :{} Accuracy :
{}'.format(count,loss.item(),accuracy))
The error is as follows:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-19-9e93a242961b> in <module>
18
19 # calculate softmax and cross entropy loss
---> 20 loss = error(output,label)
21
22 # calculate gradients
~\Anaconda3\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
545 result = self._slow_forward(*input, **kwargs)
546 else:
--> 547 result = self.forward(*input, **kwargs)
548 for hook in self._forward_hooks.values():
549 hook_result = hook(self, input, result)
~\Anaconda3\lib\site-packages\torch\nn\modules\loss.py in forward(self, input, target)
914 def forward(self, input, target):
915 return F.cross_entropy(input, target, weight=self.weight,
--> 916 ignore_index=self.ignore_index, reduction=self.reduction)
917
918
~\Anaconda3\lib\site-packages\torch\nn\functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction)
1993 if size_average is not None or reduce is not None:
1994 reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 1995 return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
1996
1997
~\Anaconda3\lib\site-packages\torch\nn\functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
1820 if input.size(0) != target.size(0):
1821 raise ValueError('Expected input batch_size ({}) to match target batch_size ({}).'
-> 1822 .format(input.size(0), target.size(0)))
1823 if dim == 2:
1824 ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
ValueError: Expected input batch_size (32) to match target batch_size (64).

Answer 1

You are providing the wrong target to your loss:
loss = error(output, label)
While your loader gives you
for i, (image,labels) in enumerate(train_loader):
train = Variable(image)
labels = Variable(labels)
So you have a variable name labels (with s) from the loader, yet you feed label (no s) to your loss.
Batch size is the least of your worries.