Lazy Adam — optimizer_lazy

Lazy Adam

optimizer_lazy_adam(
  learning_rate = 0.001,
  beta_1 = 0.9,
  beta_2 = 0.999,
  epsilon = 1e-07,
  amsgrad = FALSE,
  name = "LazyAdam",
  clipnorm = NULL,
  clipvalue = NULL,
  decay = NULL,
  lr = NULL
)

Arguments

learning_rate	A Tensor or a floating point value. or a schedule that is a tf.keras.optimizers.schedules.LearningRateSchedule The learning rate.
beta_1	A float value or a constant float tensor. The exponential decay rate for the 1st moment estimates.
beta_2	A float value or a constant float tensor. The exponential decay rate for the 2nd moment estimates.
epsilon	A small constant for numerical stability. This epsilon is "epsilon hat" in Adam: A Method for Stochastic Optimization. Kingma et al., 2014 (in the formula just before Section 2.1), not the epsilon in Algorithm 1 of the paper.
amsgrad	boolean. Whether to apply AMSGrad variant of this algorithm from the paper "On the Convergence of Adam and beyond". Note that this argument is currently not supported and the argument can only be False.
name	Optional name for the operations created when applying gradients. Defaults to "LazyAdam".
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()`

Examples


if (FALSE) {
keras_model_sequential() %>%
  layer_dense(32, input_shape = c(784)) %>%
  compile(
    optimizer = optimizer_lazy_adam(),
    loss='binary_crossentropy',
    metrics='accuracy'
  )
}