# Recurrent Nets and their Computational Graph¶

In this section, you will learn how to write

• recurrent nets with full backprop,
• recurrent nets with truncated backprop,
• evaluation of networks with few memory.

After reading this section, you will be able to:

• Handle input sequences of variable length
• Truncate upper stream of the network during forward computation
• Use volatile variables to prevent network construction

## Recurrent Nets¶

Recurrent nets are neural networks with loops. They are often used to learn from sequential input/output. Given an input stream $$x_1, x_2, \dots, x_t, \dots$$ and the initial state $$h_0$$, a recurrent net iteratively updates its state by $$h_t = f(x_t, h_{t-1})$$, and at some or every point in time $$t$$, it outputs $$y_t = g(h_t)$$. If we expand the procedure along the time axis, it looks like a regular feed-forward network except that same parameters are periodically used within the network.

Here we learn how to write a simple one-layer recurrent net. The task is language modeling: given a finite sequence of words, we want to predict the next word at each position without peeking the successive words. Suppose that there are 1,000 different word types, and that we use 100 dimensional real vectors to represent each word (a.k.a. word embedding).

Before writing the forward computation, we have to define parameterized functions:

model = FunctionSet(
embed  = F.EmbedID(1000, 100),
x_to_h = F.Linear(100,   50),
h_to_h = F.Linear( 50,   50),
h_to_y = F.Linear( 50, 1000),
)
optimizer = optimizers.SGD()
optimizer.setup(model.collect_parameters())


Here EmbedID is a parameterized function class for word embedding. It converts input integers into corresponding fixed-dimensional embedding vectors. Other Linear layers represent the transformation as their names indicate. Here we use 50 hidden units.

Then, we can write down the forward computation. Suppose that the input word sequence is given as a list of integer arrays. The forward computation is simply written with a for loop:

def forward_one_step(h, cur_word, next_word, volatile=False):
word = Variable(cur_word, volatile=volatile)
t    = Variable(next_word, volatile=volatile)
x    = F.tanh(model.embed(word))
h    = F.tanh(model.x_to_h(x) + model.h_to_h(h))
y    = model.h_to_y(h)
loss = F.softmax_cross_entropy(y, t)
return h, loss

def forward(x_list, volatile=False):
h = Variable(np.zeros((50,), dtype=np.float32), volatile=volatile)
loss = 0
for cur_word, next_word in zip(x_list, x_list[1:]):
h, new_loss = forward_one_step(h, cur_word, next_word, volatile=volatile)
loss += new_loss
return loss


We implemented the one-step-forward computation as a separate function, which is a best practice of writing recurrent nets for higher extensibility. Ignore the argument volatile for now, we will review it in the next subsection. The forward function is very simple and no special care needs to be taken with respect to the length of the input sequence. This code actually handles variable length input sequences without any tricks.

Of course, the accumulated loss is a Variable object with the full history of computation. So we can just call its backward() method to compute gradients of the total loss according to the model parameters:

optimizer.zero_grads()
loss = forward(x_list)
loss.backward()
optimizer.update()


Do not forget to call Optimizer.zero_grads() before the backward computation!

## Truncate the Graph by Unchaining¶

Learning from very long sequences is also a typical use case of recurrent nets. Suppose that the input and state sequence is too long to fit into memory. In such cases, we often truncate the backpropagation into a short time range. This technique is called truncated backprop. It is heuristic, and it makes the gradients biased. However, this technique works well in practice if the time range is long enough.

How to implement truncated backprop in Chainer? Chainer has a smart mechanism to achieve truncation, called backward unchaining. It is implemented in the Variable.unchain_backward() method. Backward unchaining starts from the Variable object, and it chops the computation history backwards from the variable. The chopped variables are disposed automatically (if they are not referenced explicitly from any other user object). As a result, they are no longer a part of computation history, and are not involved in backprop anymore.

Let’s write an example of truncated backprop. Here we use the same network as the one used in the previous subsection. Suppose that we are given a very long sequence, and we want to run backprop truncated at every 30 time steps. We can write truncated backprop using the forward_one_step function that we wrote above.

h = Variable(np.zeros((50,), dtype=np.float32))
loss   = 0
count  = 0
seqlen = len(x_list[1:])

for cur_word, next_word in zip(x_list, x_list[1:]):
h, new_loss = forward_one_step(h, cur_word, next_word)
loss  += new_loss
count += 1
if count % 30 == 0 or count == seqlen:
loss.backward()
loss.unchain_backward()
optimizer.update()


State is updated at foward_one_step, and the losses are accumulated to loss variable. At each 30 steps, backprop takes place at the accumulated loss. Then, the unchain_backward() method is called, which deletes the computation history backward from the accumulated loss. Note that the latest state h itself is not lost, since above code holds a reference to it.

The implementation of truncated backprop is simple, and since there is no complicated trick on it, we can generalize this method to different situations. For example, we can easily extend the above code to use different schedules between backprop timing and truncation length.

## Network Evaluation without Storing the Computation History¶

On evaluation of recurrent nets, there is typically no need to store the computation history. While unchaining enables us to walk through unlimited length of sequences with limited memory, it is a bit of a work-around.

As an alternative, Chainer provides an evaluation mode of forward computation which does not store the computation history. This is enabled by just passing volatile flag to all input variables. Such variables are called volatile variables.

Warning

It is not allowed to mix volatile and non-volatile variables as arguments to same function.

Remember that our forward function accepts volatile argument. So we can enable volatile forward computation by just passing volatile=True to this function:

loss = forward(x_list, volatile=True)


Volatile variables are also useful to evaluate feed-forward networks.

Variable’s volatility can be changed directly by setting the Variable.volatile attribute. This enables us to combine a fixed feature extractor network and a trainable predictor network. For example, suppose that we want to train a feed-forward network predictor_func, which is located on top of another fixed pretrained network fixed_func. We want to train predictor_func without storing the computation history for fixed_func. This is simply done by following code snippets (suppose x_data and y_data indicate input data and label, respectively):

x = Variable(x_data, volatile=True)
feat = fixed_func(x)
feat.volatile = False
y = predictor_func(feat)
y.backward()


At first, the input variable x is volatile, so fixed_func is executed in volatile mode, i.e. without memorizing the computation history. Then the intermediate variable feat is manually set to non-volatile, so predictor_func is executed in non-volatile mode, i.e., with memorizing the history of computation. Since the history of computation is only memorized between variables feat and y, the backward computation stops at the feat variable.

In this section we have demonstrated how to write recurrent nets in Chainer and some fundamental techniques to manage the history of computation (a.k.a. computational graph). The example in the examples/ptb directory implements truncated backprop learning of a LSTM language model from the Penn Treebank corpus. In the next section, we will review how to use GPU(s) in Chainer.