In our introduction to linear regression, we walked through various components including the data, the model, the loss function, and the optimization algorithm. Indeed, linear regression is one of the simplest machine learning models. Training it, however, uses many of the same components that other models in this book require. Therefore, before diving into the implementation details it is worth designing some of the APIs that we use throughout. Treating components in deep learning as objects, we can start by defining classes for these objects and their interactions. This object-oriented design for implementation will greatly streamline the presentation and you might even want to use it in your projects.
Inspired by open-source libraries such as PyTorch Lightning, at a high level we wish to have three classes: (i) Module contains models, losses, and optimization methods; (ii) DataModule provides data loaders for training and validation; (iii) both classes are combined using the Trainer class, which allows us to train models on a variety of hardware platforms. Most code in this book adapts Module and DataModule. We will touch upon the Trainer class only when we discuss GPUs, CPUs, parallel training, and optimization algorithms.
import time import numpy as np from d2l import torch as d2l import torch from torch import nn
We need a few utilities to simplify object-oriented programming in Jupyter notebooks. One of the challenges is that class definitions tend to be fairly long blocks of code. Notebook readability demands short code fragments, interspersed with explanations, a requirement incompatible with the style of programming common for Python libraries. The first utility function allows us to register functions as methods in a class after the class has been created. In fact, we can do so even after we have created instances of the class! It allows us to split the implementation of a class into multiple code blocks.
def add_to_class(Class): #@save """Register functions as methods in created class.""" def wrapper(obj): setattr(Class, obj.__name__, obj) return wrapper
Let’s have a quick look at how to use it. We plan to implement a class A with a method do. Instead of having code for both A and do in the same code block, we can first declare the class A and create an instance a.
class A: def __init__(self): self.b = 1 a = A()
Next we define the method do as we normally would, but not in class A’s scope. Instead, we decorate this method by add_to_class with class A as its argument. In doing so, the method is able to access the member variables of A just as we would expect had it been included as part of A’s definition. Let’s see what happens when we invoke it for the instance a.
@add_to_class(A) def do(self): print('Class attribute "b" is', self.b) a.do()
The second one is a utility class that saves all arguments in a class’s __init__ method as class attributes. This allows us to extend constructor call signatures implicitly without additional code.
class HyperParameters: #@save """The base class of hyperparameters.""" def save_hyperparameters(self, ignore=[]): raise NotImplemented
We defer its implementation into . To use it, we define our class that inherits from HyperParameters and calls save_hyperparameters in the __init__ method.
# Call the fully implemented HyperParameters class saved in d2l class B(d2l.HyperParameters): def __init__(self, a, b, c): self.save_hyperparameters(ignore=['c']) print('self.a =', self.a, 'self.b =', self.b) print('There is no self.c =', not hasattr(self, 'c')) b = B(a=1, b=2, c=3)
The final utility allows us to plot experiment progress interactively while it is going on. In deference to the much more powerful (and complex) TensorBoard we name it ProgressBoard. The implementation is deferred to . For now, let’s simply see it in action.
The draw method plots a point (x, y) in the figure, with label specified in the legend. The optional every_n smooths the line by only showing $1/n$ points in the figure. Their values are averaged from the $n$ neighbor points in the original figure.
class ProgressBoard(d2l.HyperParameters): #@save """The board that plots data points in animation.""" def __init__(self, xlabel=None, ylabel=None, xlim=None, ylim=None, xscale='linear', yscale='linear', ls=['-', '--', '-.', ':'], colors=['C0', 'C1', 'C2', 'C3'], fig=None, axes=None, figsize=(3.5, 2.5), display=True): self.save_hyperparameters() def draw(self, x, y, label, every_n=1): raise NotImplemented
In the following example, we draw sin and cos with a different smoothness. If you run this code block, you will see the lines grow in animation.
board = d2l.ProgressBoard('x') for x in np.arange(0, 10, 0.1): board.draw(x, np.sin(x), 'sin', every_n=2) board.draw(x, np.cos(x), 'cos', every_n=10)
The Module class is the base class of all models we will implement. At the very least we need three methods. The first, __init__, stores the learnable parameters, the training_step method accepts a data batch to return the loss value, and finally, configure_optimizers returns the optimization method, or a list of them, that is used to update the learnable parameters. Optionally we can define validation_step to report the evaluation measures. Sometimes we put the code for computing the output into a separate forward method to make it more reusable.
class Module(d2l.nn_Module, d2l.HyperParameters): #@save """The base class of models.""" def __init__(self, plot_train_per_epoch=2, plot_valid_per_epoch=1): super().__init__() self.save_hyperparameters() self.board = ProgressBoard() def loss(self, y_hat, y): raise NotImplementedError def forward(self, X): assert hasattr(self, 'net'), 'Neural network is defined' return self.net(X) def plot(self, key, value, train): """Plot a point in animation.""" assert hasattr(self, 'trainer'), 'Trainer is not inited' self.board.xlabel = 'epoch' if train: x = self.trainer.train_batch_idx / \ self.trainer.num_train_batches n = self.trainer.num_train_batches / \ self.plot_train_per_epoch else: x = self.trainer.epoch + 1 n = self.trainer.num_val_batches / \ self.plot_valid_per_epoch self.board.draw(x, d2l.numpy(d2l.to(value, d2l.cpu())), ('train_' if train else 'val_') + key, every_n=int(n)) def training_step(self, batch): l = self.loss(self(*batch[:-1]), batch[-1]) self.plot('loss', l, train=True) return l def validation_step(self, batch): l = self.loss(self(*batch[:-1]), batch[-1]) self.plot('loss', l, train=False) def configure_optimizers(self): raise NotImplementedError
You may notice that Module is a subclass of nn.Module, the base class of neural networks in PyTorch. It provides convenient features for handling neural networks. For example, if we define a forward method, such as forward(self, X), then for an instance a we can invoke this method by a(X). This works since it calls the forward method in the built-in __call__ method. You can find more details and examples about nn.Module in .
The DataModule class is the base class for data. Quite frequently the __init__ method is used to prepare the data. This includes downloading and preprocessing if needed. The train_dataloader returns the data loader for the training dataset. A data loader is a (Python) generator that yields a data batch each time it is used. This batch is then fed into the training_step method of Module to compute the loss. There is an optional val_dataloader to return the validation dataset loader. It behaves in the same manner, except that it yields data batches for the validation_step method in Module.
class DataModule(d2l.HyperParameters): #@save """The base class of data.""" def get_dataloader(self, train): raise NotImplementedError def train_dataloader(self): return self.get_dataloader(train=True) def val_dataloader(self): return self.get_dataloader(train=False)
The Trainer class trains the learnable parameters in the Module class with data specified in DataModule. The key method is fit, which accepts two arguments: model, an instance of Module, and data, an instance of DataModule. It then iterates over the entire dataset max_epochs times to train the model. As before, we will defer the implementation of this method to later chapters.
class Trainer(d2l.HyperParameters): #@save """The base class for training models with data.""" def __init__(self, max_epochs, num_gpus=0, gradient_clip_val=0): self.save_hyperparameters() assert num_gpus == 0, 'No GPU support yet' def prepare_data(self, data): self.train_dataloader = data.train_dataloader() self.val_dataloader = data.val_dataloader() self.num_train_batches = len(self.train_dataloader) self.num_val_batches = (len(self.val_dataloader) if self.val_dataloader is not None else 0) def prepare_model(self, model): model.trainer = self model.board.xlim = [0, self.max_epochs] self.model = model def fit_epoch(self): raise NotImplementedError
To highlight the object-oriented design for our future deep learning implementation, the above classes simply show how their objects store data and interact with each other. We will keep enriching implementations of these classes, such as via @add_to_class, in the rest of the book. Moreover, these fully implemented classes are saved in the D2L library, a lightweight toolkit that makes structured modeling for deep learning easy. In particular, it facilitates reusing many components between projects without changing much at all. For instance, we can replace just the optimizer, just the model, just the dataset, etc.; this degree of modularity pays dividends throughout the book in terms of conciseness and simplicity (this is why we added it) and it can do the same for your own projects.
save_hyperparameters statement in the B class. Can you still print self.a and self.b? Optional: if you have dived into the full implementation of the HyperParameters class, can you explain why?