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

We can convert the former into the latter using a one-liner:
example_tensor = torch.as_tensor(example / 255).float()
Moreover, we can use this line of code to convert our whole Numpy dataset into
tensors so they become an appropriate input to our composed transformation.
Data Preparation
The first step of data preparation, as in previous chapters, is to convert our
features and labels from Numpy arrays to PyTorch tensors:
# Builds tensors from numpy arrays BEFORE split
x_tensor = torch.as_tensor(images / 255).float()
y_tensor = torch.as_tensor(labels.reshape(-1, 1)).float()
The only difference is that we scaled the images to get them into the expected [0.0,
1.0] range.
Dataset Transforms
Next, we use both tensors to build a Dataset, but not a simple TensorDataset like
before. Once again, we’ll build our own custom dataset that is capable of handling
transformations. Its code is actually quite simple:
Data Preparation | 283