Base class for recurrent layers
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 )
What to call the new
A RNN cell instance or a list of RNN cell instances. A RNN cell is a class that has:
The shape format of the
standard layer arguments.
See the Keras RNN API guide for details about the usage of RNN API.
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
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
N-D tensor with shape
(batch_size, timesteps, ...),
(timesteps, batch_size, ...) when
time_major = TRUE.
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
could be a high dimension tensor shape.
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
else, N-D tensor with shape
[batch_size, output_size], where
output_size could be a high dimension tensor shape.
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
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:
stateful = TRUE in the layer constructor.
Specify a fixed batch size for your model. For sequential models,
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),
NULL in place of an integer, e.g.
list(32, NULL, NULL).
shuffle = FALSE when calling
To reset the states of your model, call
layer$reset_states() on either
a specific layer, or on your entire model.
You can specify the initial state of RNN layers symbolically by calling them
with the keyword argument
initial_state. The value of initial_state should
be a tensor or list of tensors representing the initial state of the RNN
You can specify the initial state of RNN layers numerically by calling
reset_states with the named argument
states. The value of
be an array or list of arrays representing the initial state of the RNN
You can pass "external" constants to the cell using the
RNN$__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.