iter = 0
for epoch in range(n_epoch):
for i, (images, labels) in enumerate(train_loader):####################### # USE GPU FOR MODEL # ####################### if torch.cuda.is_available(): images = Variable(images.view(-1, 28*28).cuda()) labels = Variable(labels.cuda()) else: images = Variable(images.view(-1, 28*28)) labels = Variable(labels) # Clear gradients w.r.t. parameters optimizer.zero_grad() print(images.shape) # Forward pass to get output/logits outputs = model(images) print(outputs.shape) # Calculate Loss: softmax --> cross entropy loss loss = criterion(outputs, labels) # Getting gradients w.r.t. parameters loss.backward() # Updating parameters optimizer.step() iter += 1 if iter % 500 == 0: # Calculate Accuracy correct = 0 total = 0 # Iterate through test dataset for images, labels in test_loader: ####################### # USE GPU FOR MODEL # ####################### images = Variable(images.view(-1, 12288).cuda()) # Forward pass only to get logits/output outputs = model(images) # Get predictions from the maximum value _, predicted = torch.max(outputs.data, 1) # Total number of labels total += labels.size(0) ####################### # USE GPU FOR MODEL # ####################### # Total correct predictions correct += (predicted.cpu() == labels.cpu()).sum() accuracy = 100 * correct / total # Print Loss print('Iteration: {}. Loss: {}. Accuracy: {}'.format(iter, loss.data, accuracy))
I’m new in pytorch. My model is initialized as such
input_dim = 28*28
output_dim = 2
model = FeedForward_NN(input_dim,output_dim)
I get a ValueError: Expected input batch_size (150) to match target batch_size (50)
whats the reason for this?
