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

• defining our StepByStep class
• understanding the purpose of the constructor (__init__()) method
• defining the arguments of the constructor method
• defining class' attributes to store arguments, placeholders, and variables we need
to keep track of
• defining functions as attributes, using higher-order functions and the class'
attributes to build functions that perform training and validation steps
• understanding the difference between public, protected, and private methods,
and Python’s "relaxed" approach to it
• creating methods to set data loaders and TensorBoard integration
• (re)implementing training methods: _mini_batch() and train()
• implementing saving and loading methods: save_checkpoint() and
load_checkpoint()
• implementing a method for making predictions that takes care of all
boilerplate code regarding Numpy-to-PyTorch conversion and back
• implementing methods to plot losses and add the model’s graph to
TensorBoard
• instantiating our StepByStep class and running a classy pipeline: configuring
the model, loading the data, training the model, making predictions,
checkpointing, and resuming training. The whole nine yards!
Congratulations! You have developed a fully functioning class that implements all
methods relevant to model training and evaluation. From now on, we’ll use it over
and over again to tackle different tasks and models. Next stop: classification!
[61] https://github.com/dvgodoy/PyTorchStepByStep/blob/master/Chapter02.1.ipynb
[62] https://colab.research.google.com/github/dvgodoy/PyTorchStepByStep/blob/master/Chapter02.1.ipynb
[63] https://realpython.com/python3-object-oriented-programming/
[64] https://realpython.com/python-super/
[65] https://www.w3schools.com/python/ref_func_setattr.asp
[66] https://pytorch.org/docs/stable/notes/randomness.html
[67] https://github.com/dvgodoy/PyTorchStepByStep/blob/master/stepbystep/v0.py
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