Split Custom PyTorch DataSet into Training, Testing and Validation set using random_split
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:
PyTorch:Difference between “tensor.detach()” vs “with torch.nograd()”
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.
PyTorch: What does model.train()?
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.
How to save and load PyTorch Tensor to file?
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.
Concatenate two layers using keras.layers.concatenate() example
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.
Concatenates PyTorch tensors using Stack and Cat with Dimension
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.