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

Chapter 2
Rethinking the Training Loop
Spoilers
In this chapter, we will:
• build a function to perform training steps
• implement our own dataset class
• use data loaders to generate mini-batches
• build a function to perform mini-batch gradient descent
• evaluate our model
• integrate TensorBoard to monitor model training
• save / checkpoint our model to disk
• load our model from disk to resume training or to deploy
Jupyter Notebook
The Jupyter notebook corresponding to Chapter 2 [53] is part of the official Deep
Learning with PyTorch Step-by-Step repository on GitHub. You can also run it
directly in Google Colab [54] .
If you’re using a local installation, open your terminal or Anaconda prompt and
navigate to the PyTorchStepByStep folder you cloned from GitHub. Then, activate
the pytorchbook environment and run jupyter notebook:
$ conda activate pytorchbook
(pytorchbook)$ jupyter notebook
If you’re using Jupyter’s default settings, this link should open Chapter 2’s
notebook. If not, just click on Chapter02.ipynb on your Jupyter’s home page.
Imports
For the sake of organization, all libraries needed throughout the code used in any
Spoilers | 123