Training a neural network

To train a neural network, we must perform the following steps:

1. Import the relevant packages.
2. Build a dataset that can fetch data one data point at a time.
3. Wrap the DataLoader from the dataset.
4. Build a model and then define the loss function and the optimizer.
5. Define two functions to train and validate a batch of data, respectively.
6. Define a function that will calculate the accuracy of the data.
7. Perform weight updates based on each batch of data over increasing epochs.

In the following lines of code, we'll perform each of the following steps:

The following code is available as Steps_to_build_a_neural_network_on_FashionMNIST.ipynb in the Chapter03 folder of this book's GitHub repository - https://tinyurl.com/mcvp-packt

1. Import the relevant packages and the FMNIST dataset:

from torch.utils.data import Dataset, DataLoader
import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
device = "cuda" if...