Knowledge Transfer

Shuffle the list before splitting else you won’t get all the classes in the three splits since these indices would be used by the Subset class to sample from the original dataset. Shuffling the elements of a tensor amounts to finding a permutation of its indices. The random_split function does exactly this:

Pinned memory is used as a staging area for transfers from the device to the host. We can avoid the cost of the transfer between pageable and pinned host arrays by directly allocating our host arrays in pinned memory.

It’s quite a bit faster due to the with torch.no_grad() context manager explicitly informing PyTorch that no gradients need to be computed. Context managers like with torch.no_grad(): can be used to control auto-grad’s behavior.

Using the related set_grad_enabled context, we can also condition the code to run with autograd enabled or disabled, according to a Boolean expression—typically indicating whether we are running in training or inference mode.

When the user specifies model.eval() and the model contains a batch normalization module, the running estimates are frozen and used for normalization. To unfreeze running estimates and return to using the minibatch statistics, we call model.train(), just as we did for dropout.

We can save tensors quickly this way but if we want to load them with the file format itself is not interoperable. We can’t read the tensor with software other than PyTorch. Depending on the use case, this may or may not be a limitation, but we should learn how to save tensors interoperably.

It is for the neural network to learn both deep patterns using the deep path and simple rules through the short path. In contrast, regular MLP forces all the data to flow through the entire stack of layers. These simple patterns in the data may end up being distorted by this sequence of transformations.

The stack function serves the same role as append in lists. It concatenates the sequence of tensors along a new dimension. It doesn’t change the original vector space but instead adds a new index to the new tensor, so you retain the ability to get the original tensor you added to the list by indexing in the new dimension.

A module can have one or more Parameters (its weights and bise) instances as attributes, which are tensors. A module can also have one or more submodules (subclasses of nn.Module) as attributes, and it will also be able to track their parameters.

Another popular learning rate schedule used with deep learning models is systematically dropping the learning rate at specific times during training.Decays the learning rate by gamma every step_size epochs.