Neural Architecture Search (NAS) recurrent network cell.

Source: R/rnn.R

Neural Architecture Search (NAS) recurrent network cell.

layer_nas_cell(
  object,
  units,
  projection = NULL,
  use_bias = FALSE,
  kernel_initializer = "glorot_uniform",
  recurrent_initializer = "glorot_uniform",
  projection_initializer = "glorot_uniform",
  bias_initializer = "zeros",
  ...
)

Arguments

object

Model or layer object
units

int, The number of units in the NAS cell.
projection

(optional) int, The output dimensionality for the projection matrices. If None, no projection is performed.
use_bias

(optional) bool, If `TRUE` then use biases within the cell. This is `FALSE` by default.
kernel_initializer

Initializer for kernel weight.
recurrent_initializer

Initializer for recurrent kernel weight.
projection_initializer

Initializer for projection weight, used when projection is not `NULL`.
bias_initializer

Initializer for bias, used when `use_bias` is `TRUE`.
...

Additional keyword arguments.

Value

A tensor

Details

This implements the recurrent cell from the paper: https://arxiv.org/abs/1611.01578 Barret Zoph and Quoc V. Le. "Neural Architecture Search with Reinforcement Learning" Proc. ICLR 2017. The class uses an optional projection layer.