main.py

# ================================================================ #
#                            Transforms                            #
# ================================================================ #

""" Data does not always come in its final processed form that is required for training machine learning algorithms.
We use transforms to perform some manipulation of the data and make it suitable for training. All TorchVision
datasets have two parameters -transform to modify the features and target_transform to modify the labels - that
accept callables containing the transformation logic. The torchvision.transforms module offers several commonly-used
transforms out of the box. The FashionMNIST features are in PIL Image format, and the labels are integers. For
training, we need the features as normalized tensors, and the labels as one-hot encoded tensors. To make these
transformations, we use ToTensor and Lambda."""

from torchvision import datasets
from torchvision.transforms import ToTensor, Lambda

dataset = datasets.FashionMNIST(
    root="data",
    train=True,
    download=True,
    transform=ToTensor(),
    target_transform=Lambda(lambda y: torch.zeros(10, dtype=torch.float).scatter_(0, torch.tensor(y), value=1))
)

""" ToTensor() ToTensor converts a PIL image or NumPy ndarray into a FloatTensor. and scales the image’s pixel 
intensity values in the range [0., 1.] """

""" Lambda transforms apply any user-defined lambda function. Here, we define a function to turn the integer into a 
one-hot encoded tensor. It first creates a zero tensor of size 10 (the number of labels in our dataset) and calls 
scatter_ which assigns a value=1 on the index as given by the label y. """

target_transform = Lambda(lambda y: torch.zeros(
    10, dtype=torch.float).scatter_(dim=0, index=torch.tensor(y), value=1))