.. _sec_rnn_gluon:
Concise Implementation of Recurrent Neural Networks
===================================================
While :numref:`sec_rnn_scratch` was instructive to see how recurrent
neural networks (RNNs) are implemented, this is not convenient or fast.
This section will show how to implement the same language model more
efficiently using functions provided by Gluon. We begin as before by
reading the “Time Machine” corpus.
.. code:: python
import d2l
from mxnet import np, npx
from mxnet.gluon import nn, rnn
npx.set_np()
batch_size, num_steps = 32, 35
train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)
Defining the Model
------------------
Gluon’s ``rnn`` module provides a recurrent neural network
implementation (beyond many other sequence models). We construct the
recurrent neural network layer ``rnn_layer`` with a single hidden layer
and 256 hidden units, and initialize the weights.
.. code:: python
num_hiddens = 256
rnn_layer = rnn.RNN(num_hiddens)
rnn_layer.initialize()
Initializing the state is straightforward. We invoke the member function
``rnn_layer.begin_state(batch_size)``. This returns an initial state for
each element in the minibatch. That is, it returns an object of size
(hidden layers, batch size, number of hidden units). The number of
hidden layers defaults to be 1. In fact, we have not even discussed yet
what it means to have multiple layers—this will happen in
:numref:`sec_deep_rnn`. For now, suffice it to say that multiple
layers simply amount to the output of one RNN being used as the input
for the next RNN.
.. code:: python
batch_size = 1
state = rnn_layer.begin_state(batch_size=batch_size)
len(state), state[0].shape
.. parsed-literal::
:class: output
(1, (1, 1, 256))
With a state variable and an input, we can compute the output with the
updated state.
.. code:: python
num_steps = 1
X = np.random.uniform(size=(num_steps, batch_size, len(vocab)))
Y, state_new = rnn_layer(X, state)
Y.shape, len(state_new), state_new[0].shape
.. parsed-literal::
:class: output
((1, 1, 256), 1, (1, 1, 256))
Similar to :numref:`sec_rnn_scratch`, we define an ``RNNModel`` block
by subclassing the ``Block`` class for a complete recurrent neural
network. Note that ``rnn_layer`` only contains the hidden recurrent
layers, we need to create a separate output layer. While in the previous
section, we have the output layer within the ``rnn`` block.
.. code:: python
# Saved in the d2l package for later use
class RNNModel(nn.Block):
def __init__(self, rnn_layer, vocab_size, **kwargs):
super(RNNModel, self).__init__(**kwargs)
self.rnn = rnn_layer
self.vocab_size = vocab_size
self.dense = nn.Dense(vocab_size)
def forward(self, inputs, state):
X = npx.one_hot(inputs.T, self.vocab_size)
Y, state = self.rnn(X, state)
# The fully connected layer will first change the shape of Y to
# (num_steps * batch_size, num_hiddens). Its output shape is
# (num_steps * batch_size, vocab_size).
output = self.dense(Y.reshape(-1, Y.shape[-1]))
return output, state
def begin_state(self, *args, **kwargs):
return self.rnn.begin_state(*args, **kwargs)
Training and Predicting
-----------------------
Before training the model, let’s make a prediction with the a model that
has random weights.
.. code:: python
ctx = d2l.try_gpu()
model = RNNModel(rnn_layer, len(vocab))
model.initialize(force_reinit=True, ctx=ctx)
d2l.predict_ch8('time traveller', 10, model, vocab, ctx)
.. parsed-literal::
:class: output
'time travellervmjznnngii'
As is quite obvious, this model does not work at all. Next, we call
``train_ch8`` with the same hyper-parameters defined in
:numref:`sec_rnn_scratch` and train our model with Gluon.
.. code:: python
num_epochs, lr = 500, 1
d2l.train_ch8(model, train_iter, vocab, lr, num_epochs, ctx)
.. parsed-literal::
:class: output
Perplexity 1.2, 190212 tokens/sec on gpu(0)
time traveller you can show black is white by argument said fil
traveller you can show black is white by argument said fil
.. figure:: output_rnn-gluon_d31a58_13_1.svg
Compared with the last section, this model achieves comparable
perplexity, albeit within a shorter period of time, due to the code
being more optimized.
Summary
-------
- Gluon’s ``rnn`` module provides an implementation at the recurrent
neural network layer.
- Gluon’s ``nn.RNN`` instance returns the output and hidden state after
forward computation. This forward computation does not involve output
layer computation.
- As before, the computational graph needs to be detached from previous
steps for reasons of efficiency.
Exercises
---------
1. Compare the implementation with the previous section.
- Why does Gluon’s implementation run faster?
- If you observe a significant difference beyond speed, try to find
the reason.
2. Can you make the model overfit?
- Increase the number of hidden units.
- Increase the number of iterations.
- What happens if you adjust the clipping parameter?
3. Implement the autoregressive model of the introduction to the current
chapter using an RNN.
4. What happens if you increase the number of hidden layers in the RNN
model? Can you make the model work?
5. How well can you compress the text using this model?
- How many bits do you need?
- Why does not everyone use this model for text compression? Hint:
what about the compressor itself?
`Discussions `__
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.. |image0| image:: ../img/qr_rnn-gluon.svg