pytorch training loop ends with ''int' object has no attribute 'size' exception

Question

The code I am posting below is just a small part of the application:

def train(self, training_reviews, training_labels):
        
        # make sure out we have a matching number of reviews and labels
        assert(len(training_reviews) == len(training_labels))
        
        # Keep track of correct predictions to display accuracy during training 
        correct_so_far = 0
        
        # Remember when we started for printing time statistics
        start = time.time()
        
        
        criterion = nn.CrossEntropyLoss()
        optimizer =  torch.optim.SGD(self.parameters(), lr=self.learning_rate)

        # loop through all the given reviews and run a forward and backward pass,
        # updating weights for every item
        for i in range(len(training_reviews)):
            
            # TODO: Get the next review and its correct label
            review = training_reviews[i]
            label = training_labels[i]
            print('processing item ',i)
            self.update_input_layer(review)
            output = self.forward(torch.from_numpy(self.layer_0).float()) 
            target = self.get_target_for_label(label)
            print('output ',output)
            print('target ',target)
            loss = criterion(output, target)

...
mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)
mlp.train(reviews[:-1000],labels[:-1000])

and it ends with the exception in the title line when evaluating:

loss = criterion(output, target)

prior to that, the variables are as follows:

output  tensor([[0.5803]], grad_fn=<SigmoidBackward>)
target  1

Answer 1

Target should be a torch.Tensor variable. Use torch.tensor([target]) .

Additionally, you may want to use batches (so there are N samples and shape of torch.tensor is (N,) , same for target ).

Also see introductory tutorial about PyTorch, as you're not using batches, not running optimizer or not using torch.utils.data.Dataset and torch.utils.data.DataLoader as you probably should.