layer_nas_cell: Neural Architecture Search (NAS) recurrent network cell.

In tfaddons: Interface to 'TensorFlow SIG Addons'

Description

Neural Architecture Search (NAS) recurrent network cell.

Usage

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.

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.

Value

A tensor

tfaddons documentation built on July 2, 2020, 2:12 a.m.