Yogi

Yogi

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.

Value

Optimizer for use with `keras::compile()`