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

import random
import numpy as np
from PIL import Image
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, \
WeightedRandomSampler, SubsetRandomSampler
from torchvision.transforms import Compose, ToTensor, Normalize,\
ToPILImage, RandomHorizontalFlip, Resize
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
%matplotlib inline
from data_generation.image_classification import generate_dataset
from stepbystep.v0 import StepByStep
Classifying Images
Enough already with simple data points: Let’s classify images! Although the data is
different, it is still a classification problem, so we will try to predict which class an
image belongs to.
First, let’s generate some images to work with (so we don’t have to use MNIST! [78] ).
266 | Chapter 4: Classifying Images