# Recurrent neural networks¶

Warning

This section is very much work in progress!

This tutorial explains recurrent bricks in Blocks. Readers unfamiliar with
bricks should start with the *bricks overview* first
and continue with this tutorial afterwards.

## Quickstart example¶

As a starting example, we’ll be building an RNN which accumulates the input it receives (figure above). The equation describing that RNN is

```
>>> import numpy
>>> import theano
>>> from theano import tensor
>>> from blocks import initialization
>>> from blocks.bricks import Identity
>>> from blocks.bricks.recurrent import SimpleRecurrent
>>> x = tensor.tensor3('x')
>>> rnn = SimpleRecurrent(
... dim=3, activation=Identity(), weights_init=initialization.Identity())
>>> rnn.initialize()
>>> h = rnn.apply(x)
>>> f = theano.function([x], h)
>>> print(f(numpy.ones((3, 1, 3), dtype=theano.config.floatX)))
[[[ 1. 1. 1.]]
[[ 2. 2. 2.]]
[[ 3. 3. 3.]]]...
```

Let’s modify that example so that the RNN accumulates two times the input it receives (figure below).

The equation for the RNN is

```
>>> from blocks.bricks import Linear
>>> doubler = Linear(
... input_dim=3, output_dim=3, weights_init=initialization.Identity(2),
... biases_init=initialization.Constant(0))
>>> doubler.initialize()
>>> h_doubler = rnn.apply(doubler.apply(x))
>>> f = theano.function([x], h_doubler)
>>> print(f(numpy.ones((3, 1, 3), dtype=theano.config.floatX)))
[[[ 2. 2. 2.]]
[[ 4. 4. 4.]]
[[ 6. 6. 6.]]]...
```

Note that in order to double the input we had to apply a `bricks.Linear`

brick to `x`

, even though

is what is usually thought of as the RNN equation. The reason why recurrent bricks work that way is it allows greater flexibility and modularity: \(\mathbf{W}\mathbf{x}_t\) can be replaced by a whole neural network if we want.

## Initial states¶

Recurrent models all have in common that their initial state has to be specified. However, in constructing our toy examples, we omitted to pass \(\mathbf{h}_0\) when applying the recurrent brick. What happened?

It turns out that recurrent bricks set that initial state to zero if it’s not passed as argument, which is a good sane default in most cases, but we can just as well set it explicitly.

We will modify the starting example so that it accumulates the input it receives, but starting from one instead of zero (figure above):

```
>>> h0 = tensor.matrix('h0')
>>> h = rnn.apply(inputs=x, states=h0)
>>> f = theano.function([x, h0], h)
>>> print(f(numpy.ones((3, 1, 3), dtype=theano.config.floatX),
... numpy.ones((1, 3), dtype=theano.config.floatX)))
[[[ 2. 2. 2.]]
[[ 3. 3. 3.]]
[[ 4. 4. 4.]]]...
```

## Reverse¶

Todo

Say something about the `reverse`

argument

## Getting initial states back¶

Todo

Say something about the `return_initial_states`

argument

## Iterate (or not)¶

The `apply`

method of a recurrent brick accepts an `iterate`

argument,
which defaults to `True`

. It is the reason for passing above a tensor of one
more dimension than described in `recurrent.SimpleRecurrent.apply()`

- the
extra first dimension corresponds to the length of the sequence we are iterating
over.

Setting `iterate`

to `False`

causes the `apply`

method to compute only
one step in the sequence.

This is very useful when you’re trying to combine multiple recurrent layers in a network.

Imagine you’d like to build a network with two recurrent layers. The second layer accumulates the output of the first layer, while the first layer accumulates the input of the network and the output of the second layer (see figure below).

Here’s how you can create a recurrent brick that encapsulate the two layers:

```
>>> from blocks.bricks.recurrent import BaseRecurrent, recurrent
>>> class FeedbackRNN(BaseRecurrent):
... def __init__(self, dim, **kwargs):
... super(FeedbackRNN, self).__init__(**kwargs)
... self.dim = dim
... self.first_recurrent_layer = SimpleRecurrent(
... dim=self.dim, activation=Identity(), name='first_recurrent_layer',
... weights_init=initialization.Identity())
... self.second_recurrent_layer = SimpleRecurrent(
... dim=self.dim, activation=Identity(), name='second_recurrent_layer',
... weights_init=initialization.Identity())
... self.children = [self.first_recurrent_layer,
... self.second_recurrent_layer]
...
... @recurrent(sequences=['inputs'], contexts=[],
... states=['first_states', 'second_states'],
... outputs=['first_states', 'second_states'])
... def apply(self, inputs, first_states=None, second_states=None):
... first_h = self.first_recurrent_layer.apply(
... inputs=inputs, states=first_states + second_states, iterate=False)
... second_h = self.second_recurrent_layer.apply(
... inputs=first_h, states=second_states, iterate=False)
... return first_h, second_h
...
... def get_dim(self, name):
... return (self.dim if name in ('inputs', 'first_states', 'second_states')
... else super(FeedbackRNN, self).get_dim(name))
...
>>> x = tensor.tensor3('x')
>>> feedback = FeedbackRNN(dim=3)
>>> feedback.initialize()
>>> first_h, second_h = feedback.apply(inputs=x)
>>> f = theano.function([x], [first_h, second_h])
>>> for states in f(numpy.ones((3, 1, 3), dtype=theano.config.floatX)):
... print(states)
[[[ 1. 1. 1.]]
[[ 3. 3. 3.]]
[[ 8. 8. 8.]]]
[[[ 1. 1. 1.]]
[[ 4. 4. 4.]]
[[ 12. 12. 12.]]]...
```

There’s a lot of things going on here!

We defined a recurrent brick class called `FeedbackRNN`

whose constructor
initializes two `bricks.recurrent.SimpleRecurrent`

bricks as its
children.

The class has a `get_dim`

method whose purpose is to tell the dimensionality
of each input to the brick’s `apply`

method.

The core of the class resides in its `apply`

method. The `@recurrent`

decorator is used to specify which of the arguments to the method are sequences
to iterate over, what is returned when the method is called and which of those
returned values correspond to recurrent states. Its
relationship with the `inputs`

and `outputs`

arguments to the
`@application`

decorator is as follows:

`outputs`

, like in`@application`

, defines everything that’s returned by`apply`

, including recurrent outputs`states`

is a subset of`outputs`

that corresponds to recurrent outputs, which means that the union of`sequences`

and`states`

forms what would be`inputs`

in`@application`

Notice how no call to `theano.scan()`

is being made. This is because the
implementation of `apply`

is responsible for computing one time step of the
recurrent application of the brick. It takes states at time \(t - 1\) and
inputs at time \(t\) and produces the output for time \(t\). The rest is
all handled by the `@recurrent`

decorator behind the scenes.

This is why the `iterate`

argument of the `apply`

method is so useful: it
allows to combine multiple recurrent brick applications within another `apply`

implementation.

Tip

When looking at a recurrent brick’s documentation, keep in mind that the
parameters to its `apply`

method are explained in terms of a single
iteration, *i.e.* with the assumption that `iterate = False`

.

## See Also¶

- LSTM implementation:
`bricks.recurrent.LSTM`

- GRU implementation:
`bricks.recurrent.GatedRecurrent`

- Bidirectional RNNs:
`bricks.recurrent.Bidirectional`

- Deep recurrent networks (stacked RNNs):
`bricks.recurrent.RecurrentStack`