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

Imports
For the sake of organization, all libraries needed throughout the code used in any
given chapter are imported at its very beginning. For this chapter, we’ll need the
following imports:
import numpy as np
import torch
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset, random_split, \
TensorDataset
from torch.nn.utils import rnn as rnn_utils
from data_generation.square_sequences import generate_sequences
from stepbystep.v4 import StepByStep
Sequences
In this third part of the book, we’ll dive into a new kind of input: sequences! So far,
each data point has been considered in and of itself; that is, each data point has had
a label to call its own. An image of a hand was classified as "rock," "paper," or
"scissors" based on its pixel values alone, without paying any attention to other
images' pixel values. This won’t be the case anymore.
In sequence problems, an ordered sequence of data points shares a single
label—emphasis on being ordered.
"Why is ordered so important?"
If the data points aren’t ordered, even if they share a single label, they are not a
sequence, but rather a collection of data points.
Let’s think of a slightly contrived example: greyscale images with shuffled pixels.
Each pixel has a single value, but a pixel alone doesn’t have a label. It is the
collection of shuffled pixels, the shuffled image, that has a label: a duck, a dog, or a
cat (labeled before shuffling the pixels, of course).
588 | Chapter 8: Sequences