Gluon Recurrent Neural Network API

Overview

This document lists the recurrent neural network API in Gluon:

.. currentmodule:: mxnet.gluon.rnn

Recurrent Layers

Recurrent layers can be used in Sequential with other regular neural network layers. For example, to construct a sequence labeling model where a prediction is made for each time-step:

model = mx.gluon.nn.Sequential()
with model.name_scope():
    model.add(mx.gluon.nn.Embedding(30, 10))
    model.add(mx.gluon.rnn.LSTM(20))
    model.add(mx.gluon.nn.Dense(5, flatten=False))
model.initialize()
model(mx.nd.ones((2,3)))

.. autosummary::
    :nosignatures:

    RNN
    LSTM
    GRU

Recurrent Cells

Recurrent cells allows fine-grained control when defining recurrent models. User can explicit step and unroll to construct complex networks. It provides more flexibility but is slower than recurrent layers. Recurrent cells can be stacked with SequentialRNNCell:

model = mx.gluon.rnn.SequentialRNNCell()
with model.name_scope():
    model.add(mx.gluon.rnn.LSTMCell(20))
    model.add(mx.gluon.rnn.LSTMCell(20))
states = model.begin_state(batch_size=32)
inputs = mx.nd.random.uniform(shape=(5, 32, 10))
outputs = []
for i in range(5):
    output, states = model(inputs[i], states)
    outputs.append(output)

.. autosummary::
    :nosignatures:

    RNNCell
    LSTMCell
    GRUCell
    RecurrentCell
    SequentialRNNCell
    BidirectionalCell
    DropoutCell
    ZoneoutCell
    ResidualCell

API Reference

.. automodule:: mxnet.gluon.rnn
    :members:
    :imported-members: