[Python] Pytorch model accuracy test

I'm using Pytorch to classify a series of images. The NN is defined as follows:

model = models.vgg16(pretrained=True)
model.cuda()
for param in model.parameters(): param.requires_grad = False

classifier = nn.Sequential(OrderedDict([
('fc1', nn.Linear(25088, 4096)),
('relu', nn.ReLU()),
('fc2', nn.Linear(4096, 102)),
('output', nn.LogSoftmax(dim=1))
]))

model.classifier = classifier


The criterions and optimizers are as follows:

criterion = nn.NLLLoss()
optimizer = optim.Adam(model.classifier.parameters(), lr=0.001)


My validation function is as follows:

def validation(model, testloader, criterion):
test_loss = 0
accuracy = 0
for images, labels in testloader:

images.resize_(images.shape[0], 784)

output = model.forward(images)
test_loss += criterion(output, labels).item()

ps = torch.exp(output)
equality = (labels.data == ps.max(dim=1)[1])
accuracy += equality.type(torch.FloatTensor).mean()

return test_loss, accuracy


This is the piece of code that is throwing the following error:

RuntimeError: input has less dimensions than expected
​

epochs = 3
print_every = 40
steps = 0
running_loss = 0
testloader = dataloaders['test']

# change to cuda
model.to('cuda')

for e in range(epochs):
running_loss = 0
for ii, (inputs, labels) in enumerate(dataloaders['train']):
steps += 1

inputs, labels = inputs.to('cuda'), labels.to('cuda')

optimizer.zero_grad()

# Forward and backward passes
outputs = model.forward(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()

running_loss += loss.item()

if steps % print_every == 0:
model.eval()
with torch.no_grad():
test_loss, accuracy = validation(model, testloader, criterion)

print("Epoch: {}/{}.. ".format(e+1, epochs),
"Training Loss: {:.3f}.. ".format(running_loss/print_every),
"Test Loss: {:.3f}.. ".format(test_loss/len(testloader)),
"Test Accuracy: {:.3f}".format(accuracy/len(testloader)))

running_loss = 0


Any help?

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