Using target size that is different to the input size

1

Afternoon,

I have a Autoencoder working now, but at the start i get the following warning:

UserWarning: Using a target size (torch.Size([16, 2048])) that is different to the input size (torch.Size([16, 1, 2048])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.

16 is my batch size and the vector length is 2048, so i would expect to see 16,1,2048. I cannot understand where 16,2048 comes from…

how do I reshape the target size to 16,1,2048 to solve the probelm so that the warning goes away?

It looks like the input vector size is 16,2048, and using data=data.unsqueeze(1) changes this to 16,1,2048 but i still have the warning…

thanks,

Chaslie

2

If you are already changing the tensor shape in target then you shouldnt be getting the warning. Can you you post your code?

3

hi Tahir,

I get the warning after the loss function below:

for epoch in range(num_epochs):
    for data, target in train_loader:
        print("data=",data.shape)# this gives the size as [16,2048]
        data = data.cuda()
        z_loc, z_scale = model.Encoder(data)
        z = model.reparam(z_loc, z_scale)
        out = model.Decoder(z)
        loss = loss_fn(out, data, z_loc, z_scale)
        optimizer.zero_grad()

Which is after I have used the unsqueeze here:

    def forward(self, data):
        data=data.unsqueeze(1)
       print("data_mod=",data.shape) # this reshapes to [16,1,2048]