layer_simple_rnn_cell: Cell class for SimpleRNN
In keras: R Interface to 'Keras'

layer_simple_rnn_cell

R Documentation

Cell class for SimpleRNN

Description

Cell class for SimpleRNN

Usage

layer_simple_rnn_cell(
  units,
  activation = "tanh",
  use_bias = TRUE,
  kernel_initializer = "glorot_uniform",
  recurrent_initializer = "orthogonal",
  bias_initializer = "zeros",
  kernel_regularizer = NULL,
  recurrent_regularizer = NULL,
  bias_regularizer = NULL,
  kernel_constraint = NULL,
  recurrent_constraint = NULL,
  bias_constraint = NULL,
  dropout = 0,
  recurrent_dropout = 0,
  ...
)

Arguments

`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, (default `TRUE`), whether the layer uses a bias vector.
`kernel_initializer`	Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. Default: `glorot_uniform`.
`recurrent_initializer`	Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state. Default: `orthogonal`.
`bias_initializer`	Initializer for the bias vector. Default: `zeros`.
`kernel_regularizer`	Regularizer function applied to the `kernel` weights matrix. Default: `NULL`.
`recurrent_regularizer`	Regularizer function applied to the `recurrent_kernel` weights matrix. Default: `NULL`.
`bias_regularizer`	Regularizer function applied to the bias vector. Default: `NULL`.
`kernel_constraint`	Constraint function applied to the `kernel` weights matrix. Default: `NULL`.
`recurrent_constraint`	Constraint function applied to the `recurrent_kernel` weights matrix. Default: `NULL`.
`bias_constraint`	Constraint function applied to the bias vector. Default: `NULL`.
`dropout`	Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. Default: 0.
`recurrent_dropout`	Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. Default: 0.
`...`	standard layer arguments.