keras: layer_simple_rnn – R documentation

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keras

layer_simple_rnn

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

Description

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

Usage

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. 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 = 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). For dimensions which can vary (are not known ahead of time), use NULL in place of an integer, e.g. c(32, NULL, NULL).
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