tff.learning.optimizers.build_adam

Returns a tff.learning.optimizers.Optimizer for Adam.

tff.learning.optimizers.build_adam(
    learning_rate: float,
    beta_1: float = 0.9,
    beta_2: float = 0.999,
    epsilon: float = 1e-07
) -> tff.learning.optimizers.Optimizer

The Adam optimizer is based on Adam: A Method for Stochastic Optimization

The update rule given learning rate lr, epsilon eps, accumulator acc, preconditioner s, iteration t, weights w and gradients g is:

acc = beta_1 * acc + (1 - beta_1) * g
s = beta_2 * s + (1 - beta_2) * g**2
normalized_lr = lr * sqrt(1 - beta_2**t) / (1 - beta_1**t)
w = w - normalized_lr * acc / (sqrt(s) + eps)

Args
`learning_rate`	A positive `float` for learning rate.
`beta_1`	A `float` between `0.0` and `1.0` for the decay used to track the previous gradients.
`beta_2`	A `float` between `0.0` and `1.0` for the decay used to track the magnitude (second moment) of previous gradients.
`epsilon`	A small non-negative `float`, used to maintain numerical stability.

tff.learning.optimizers.build_adam

Args