layer_rnn: Base class for recurrent layers

Description

Base class for recurrent layers

Usage

layer_rnn(
  object,
  cell,
  return_sequences = FALSE,
  return_state = FALSE,
  go_backwards = FALSE,
  stateful = FALSE,
  unroll = FALSE,
  time_major = FALSE,
  ...,
  zero_output_for_mask = FALSE
)

Call arguments

inputs: Input tensor.
mask: Binary tensor of shape [batch_size, timesteps] indicating whether a given timestep should be masked. An individual TRUE entry indicates that the corresponding timestep should be utilized, while a FALSE entry indicates that the corresponding timestep should be ignored.
training: R or Python Boolean indicating whether the layer should behave in training mode or in inference mode. This argument is passed to the cell when calling it. This is for use with cells that use dropout.
initial_state: List of initial state tensors to be passed to the first call of the cell.
constants: List of constant tensors to be passed to the cell at each timestep.

Input shapes

N-D tensor with shape (batch_size, timesteps, ...), or (timesteps, batch_size, ...) when time_major = TRUE.

Output shape

if return_state: a list of tensors. The first tensor is the output. The remaining tensors are the last states, each with shape (batch_size, state_size), where state_size could be a high dimension tensor shape.
if return_sequences: N-D tensor with shape [batch_size, timesteps, output_size], where output_size could be a high dimension tensor shape, or [timesteps, batch_size, output_size] when time_major is TRUE
else, N-D tensor with shape [batch_size, output_size], where output_size could be a high dimension tensor shape.

Masking

This layer supports masking for input data with a variable number of timesteps. To introduce masks to your data, use layer_embedding() with the mask_zero parameter set to TRUE.

Statefulness in RNNs

You can set RNN layers to be 'stateful', which means that the states computed for the samples in one batch will be reused as initial states for the samples in the next batch. This assumes a one-to-one mapping between samples in different successive batches.

For intuition behind statefulness, there is a helpful blog post here: https://philipperemy.github.io/keras-stateful-lstm/

To enable statefulness:

Specify stateful = TRUE in the layer constructor.
Specify a fixed batch size for your model. For sequential models, pass batch_input_shape = list(...) to the first layer in your model. For functional models with 1 or more Input layers, pass batch_shape = list(...) to all the first layers in your model. This is the expected shape of your inputs including the batch size. It should be a list of integers, e.g. list(32, 10, 100). For dimensions which can vary (are not known ahead of time), use NULL in place of an integer, e.g. list(32, NULL, NULL).
Specify shuffle = FALSE when calling fit().

To reset the states of your model, call layer$reset_states() on either a specific layer, or on your entire model.

Initial State of RNNs

You can specify the initial state of RNN layers symbolically by calling them with the keyword argument initial_state. The value of initial_state should be a tensor or list of tensors representing the initial state of the RNN layer.

You can specify the initial state of RNN layers numerically by calling reset_states with the named argument states. The value of states should be an array or list of arrays representing the initial state of the RNN layer.

Passing external constants to RNNs

You can pass "external" constants to the cell using the constants named argument of RNN$__call__ (as well as RNN$call) method. This requires that the cell$call method accepts the same keyword argument constants. Such constants can be used to condition the cell transformation on additional static inputs (not changing over time), a.k.a. an attention mechanism.

Details

See the Keras RNN API guide for details about the usage of RNN API.