# PyTorch

Published:

This lesson covers PyTorch Tutorial, https://pytorch.org/tutorials/beginner/basics/intro.html

# 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
• target_transform
• to modify the labels
• both accept callables containing the transformation logic.
• torchvision.transforms
• offers several commonly-used transforms out of the box
• FashionMNIST
• features are in PIL Image format
• labels are integers
• For training, we need the
• features as normalized tensors
• labels as one-hot encoded tensors
• To make these transformations, we use ToTensor and Lambda.
topic = "pytorch"
lesson = 4

from n import *
home, models_path = get_project_dir("FashionMNIST")
print(home)

/home/naneja/datasets/n/FashionMNIST

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

# zero tensor of size 10
tt = torch.zeros(10, dtype=torch.float)

# assigns value 1 on the index given by input y
tt = lambda y: tt.scatter_(dim=0,
index=torch.tensor(y),
value=1)

# Lambda transforms user defined lambda function
tt = Lambda(tt)

ds = datasets.FashionMNIST(
root=home,
train=True,
transform=ToTensor(),
target_transform=tt
)


## ToTensor()

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

## Lambda Transforms

• 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))




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