Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Let’s take one mini-batch of images from our training set to illustrate how it works:
dummy_xs, dummy_ys = next(iter(train_loader))
dummy_xs.shape
Output
torch.Size([16, 1, 5, 5])
Our dummy mini-batch has 16 images, one channel each, dimensions five-by-five
pixels. What if we flatten this mini-batch?
flattener = nn.Flatten()
dummy_xs_flat = flattener(dummy_xs)
print(dummy_xs_flat.shape)
print(dummy_xs_flat[0])
Output
torch.Size([16, 25])
tensor([-1., -1., -1., -1., -1., -1., -1., -1., -1., -1., 1., -1.,
-1., -1., -1., -1., 1., -1., -1., -1., -1., -1., 1., -1., -1.])
By default, it preserves the first dimension such that we keep the number of data
points in the mini-batch, but it collapses the remaining dimensions. If we look at the
first element of the flattened mini-batch, we find a long tensor with 25 (1 x 5 x 5)
elements in it. If our images had three channels, the tensor would be 75 (3 x 5 x 5)
elements long.
"Don’t we lose information when we flatten pixels?"
Sure we do! And that’s why convolutional neural networks (CNNs), which we’ll
cover in the next chapter, are so successful: They do not lose this information. But,
for now, let’s do it really old style and flatten the pixels. It will make it much simpler
to illustrate a couple of other concepts before we get to the fancier CNNs.
Now, assuming the flattened version of our dataset, I ask you:
Data Preparation | 297