Building a CNN for classifying real-world images

So far, we have learned how to perform image classification on the Fashion-MNIST dataset. In this section, we'll do the same for a more real-world scenario, where the task is to classify images containing cats or dogs. We will also learn about how the accuracy of the dataset varies when we change the number of images available for training.

We will be working on a dataset available in Kaggle: https://www.kaggle.com/tongpython/cat-and-dog.

The code for this section is available as Cats_Vs_Dogs.ipynb in the Chapter04 folder of this book's GitHub repository - https://tinyurl.com/mcvp-packt Be sure to copy the URL from the notebook in GitHub to avoid any issue while reproducing the results

1. Import the necessary packages:

import torchvision
import torch.nn as nn
import torch
import torch.nn.functional as F
from torchvision import transforms,models,datasets
from PIL import Image
from torch import optim
device = 'cuda' if torch.cuda...