Fully-connected RNN where the output is to be fed back to input.

layer_simple_rnn(object, units, activation = "tanh", use_bias = TRUE,
  return_sequences = FALSE, return_state = FALSE,
  go_backwards = FALSE, stateful = FALSE, unroll = FALSE,
  kernel_initializer = "glorot_uniform",
  recurrent_initializer = "orthogonal", bias_initializer = "zeros",
  kernel_regularizer = NULL, recurrent_regularizer = NULL,
  bias_regularizer = NULL, activity_regularizer = NULL,
  kernel_constraint = NULL, recurrent_constraint = NULL,
  bias_constraint = NULL, dropout = 0, recurrent_dropout = 0,
  input_shape = NULL, batch_input_shape = NULL, batch_size = NULL,
  dtype = NULL, name = NULL, trainable = NULL, weights = NULL)

Arguments

object	Model or layer object
units	Positive integer, dimensionality of the output space.
activation	Activation function to use. Default: hyperbolic tangent (`tanh`). If you pass `NULL`, no activation is applied (ie. "linear" activation: `a(x) = x`).
use_bias	Boolean, whether the layer uses a bias vector.
return_sequences	Boolean. Whether to return the last output in the output sequence, or the full sequence.
return_state	Boolean (default FALSE). Whether to return the last state in addition to the output.
go_backwards	Boolean (default FALSE). If TRUE, process the input sequence backwards and return the reversed sequence.
stateful	Boolean (default FALSE). If TRUE, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.
unroll	Boolean (default FALSE). If TRUE, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences.
kernel_initializer	Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs.
recurrent_initializer	Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state.
bias_initializer	Initializer for the bias vector.
kernel_regularizer	Regularizer function applied to the `kernel` weights matrix.
recurrent_regularizer	Regularizer function applied to the `recurrent_kernel` weights matrix.
bias_regularizer	Regularizer function applied to the bias vector.
activity_regularizer	Regularizer function applied to the output of the layer (its "activation")..
kernel_constraint	Constraint function applied to the `kernel` weights matrix.
recurrent_constraint	Constraint function applied to the `recurrent_kernel` weights matrix.
bias_constraint	Constraint function applied to the bias vector.
dropout	Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
recurrent_dropout	Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state.
input_shape	Dimensionality of the input (integer) not including the samples axis. This argument is required when using this layer as the first layer in a model.
batch_input_shape	Shapes, including the batch size. For instance, `batch_input_shape=c(10, 32)` indicates that the expected input will be batches of 10 32-dimensional vectors. `batch_input_shape=list(NULL, 32)` indicates batches of an arbitrary number of 32-dimensional vectors.
batch_size	Fixed batch size for layer
dtype	The data type expected by the input, as a string (`float32`, `float64`, `int32`...)
name	An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.
trainable	Whether the layer weights will be updated during training.
weights	Initial weights for layer.

Input shapes

3D tensor with shape (batch_size, timesteps, input_dim), (Optional) 2D tensors with shape (batch_size, output_dim).

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, units).
if return_sequences: 3D tensor with shape (batch_size, timesteps, units).
else, 2D tensor with shape (batch_size, units).

Masking

This layer supports masking for input data with a variable number of timesteps. To introduce masks to your data, use an embedding layer 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.

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 = c(...) to the first layer in your model. For functional models with 1 or more Input layers, pass batch_shape = c(...) to all the first layers in your model. This is the expected shape of your inputs including the batch size. It should be a vector of integers, e.g. c(32, 10, 100).
Specify shuffle = FALSE when calling fit().

To reset the states of your model, call 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 keyword argument states. The value of states should be a numpy array or list of numpy arrays representing the initial state of the RNN layer.

References

A Theoretically Grounded Application of Dropout in Recurrent Neural Networks