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

import numpy as np
import datetime
import torch
import torch.optim as optim
import torch.nn as nn
import torch.functional as F
from torch.utils.data import DataLoader, TensorDataset, random_split
from torch.utils.tensorboard import SummaryWriter
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('fivethirtyeight')
Going Classy
So far, the %%writefile magic has helped us to organize the code into three distinct
parts: data preparation, model configuration, and model training. At the end of
Chapter 2, though, we bumped into some of its limitations, like being unable to
choose a different number of epochs without having to edit the model training
code.
Clearly, this situation is not ideal. We need to do better. We need to go classy; that
is, we need to build a class to handle the model training part.
I am assuming you have a working knowledge of object-oriented
programming (OOP) in order to benefit the most from this
chapter. If that’s not the case, and if you didn’t do it in Chapter 1,
now is the time to follow tutorials like Real Python’s "Object-
Oriented Programming (OOP) in Python 3" [63] and "Supercharge
Your Classes With Python super()." [64]
The Class
Let’s start by defining our class with a rather unoriginal name: StepByStep. We’re
starting it from scratch: Either we don’t specify a parent class, or we inherit it from
the fundamental object class. I personally prefer the latter, so our class definition
looks like this:
176 | Chapter 2.1: Going Classy