optimizer_yogi: Yogi
In henry090/tfaddons: Interface to 'TensorFlow SIG Addons'

optimizer_yogi

R Documentation

Yogi

Description

Yogi

Usage

optimizer_yogi(
  learning_rate = 0.01,
  beta1 = 0.9,
  beta2 = 0.999,
  epsilon = 0.001,
  l1_regularization_strength = 0,
  l2_regularization_strength = 0,
  initial_accumulator_value = 1e-06,
  activation = "sign",
  name = "Yogi",
  clipnorm = NULL,
  clipvalue = NULL,
  decay = NULL,
  lr = NULL
)

Arguments

`learning_rate`	A Tensor or a floating point value. The learning rate.
`beta1`	A float value or a constant float tensor. The exponential decay rate for the 1st moment estimates.
`beta2`	A float value or a constant float tensor. The exponential decay rate for the 2nd moment estimates.
`epsilon`	A constant trading off adaptivity and noise.
`l1_regularization_strength`	A float value, must be greater than or equal to zero.
`l2_regularization_strength`	A float value, must be greater than or equal to zero.
`initial_accumulator_value`	The starting value for accumulators. Only positive values are allowed.
`activation`	Use hard sign or soft tanh to determin sign.
`name`	Optional name for the operations created when applying gradients. Defaults to "Yogi".
`clipnorm`	is clip gradients by norm.
`clipvalue`	is clip gradients by value.
`decay`	is included for backward compatibility to allow time inverse decay of learning rate.
`lr`	is included for backward compatibility, recommended to use learning_rate instead.