d2l-pytorch-notebooks/chapter_recurrent-neural-networks/rnn-concise.ipynb

Concise Implementation of Recurrent Neural Networks

🏷️sec_rnn-concise

Like most of our from-scratch implementations, :numref:sec_rnn-scratch was designed to provide insight into how each component works. But when you are using RNNs every day or writing production code, you will want to rely more on libraries that cut down on both implementation time (by supplying library code for common models and functions) and computation time (by optimizing the heck out of these library implementations). This section will show you how to implement the same language model more efficiently using the high-level API provided by your deep learning framework. We begin, as before, by loading The Time Machine dataset.

In [1]:

import torch
from torch import nn
from torch.nn import functional as F
from d2l import torch as d2l

[Defining the Model]

We define the following class using the RNN implemented by high-level APIs.

In [2]:

class RNN(d2l.Module):  #@save
    """The RNN model implemented with high-level APIs."""
    def __init__(self, num_inputs, num_hiddens):
        super().__init__()
        self.save_hyperparameters()
        self.rnn = nn.RNN(num_inputs, num_hiddens)

    def forward(self, inputs, H=None):
        return self.rnn(inputs, H)

Inheriting from the RNNLMScratch class in :numref:sec_rnn-scratch, the following RNNLM class defines a complete RNN-based language model. Note that we need to create a separate fully connected output layer.

In [3]:

class RNNLM(d2l.RNNLMScratch):  #@save
    """The RNN-based language model implemented with high-level APIs."""
    def init_params(self):
        self.linear = nn.LazyLinear(self.vocab_size)

    def output_layer(self, hiddens):
        return self.linear(hiddens).swapaxes(0, 1)

Training and Predicting

Before training the model, let's [make a prediction with a model initialized with random weights.] Given that we have not trained the network, it will generate nonsensical predictions.

In [4]:

data = d2l.TimeMachine(batch_size=1024, num_steps=32)
rnn = RNN(num_inputs=len(data.vocab), num_hiddens=32)
model = RNNLM(rnn, vocab_size=len(data.vocab), lr=1)
model.predict('it has', 20, data.vocab)

Out [4]:

'it hasoadd dd dd dd dd dd '

Next, we [train our model, leveraging the high-level API].

In [5]:

trainer = d2l.Trainer(max_epochs=100, gradient_clip_val=1, num_gpus=1)
trainer.fit(model, data)

Compared with :numref:sec_rnn-scratch, this model achieves comparable perplexity, but runs faster due to the optimized implementations. As before, we can generate predicted tokens following the specified prefix string.

In [6]:

model.predict('it has', 20, data.vocab, d2l.try_gpu())

Out [6]:

'it has and the trave the t'

Summary

High-level APIs in deep learning frameworks provide implementations of standard RNNs. These libraries help you to avoid wasting time reimplementing standard models. Moreover, framework implementations are often highly optimized, leading to significant (computational) performance gains when compared with implementations from scratch.

Exercises

1. Can you make the RNN model overfit using the high-level APIs?
2. Implement the autoregressive model of :numref:sec_sequence using an RNN.

Discussions