Optimizer that implements the Adamax algorithm
optimizer_adamax(
learning_rate = 0.001,
beta_1 = 0.9,
beta_2 = 0.999,
epsilon = 1e-07,
weight_decay = NULL,
clipnorm = NULL,
clipvalue = NULL,
global_clipnorm = NULL,
use_ema = FALSE,
ema_momentum = 0.99,
ema_overwrite_frequency = NULL,
jit_compile = TRUE,
name = "Adamax",
...
)Optimizer for use with compile.keras.engine.training.Model.
A tf.Tensor, floating point value, a schedule that is a
tf.keras.optimizers.schedules.LearningRateSchedule, or a callable
that takes no arguments and returns the actual value to use. The
learning rate. Defaults to 0.001.
A float value or a constant float tensor. The exponential decay rate for the 1st moment estimates.
A float value or a constant float tensor. The exponential decay rate for the exponentially weighted infinity norm.
A small constant for numerical stability.
Float, defaults to NULL. If set, weight decay is applied.
Float. If set, the gradient of each weight is individually clipped so that its norm is no higher than this value.
Float. If set, the gradient of each weight is clipped to be no higher than this value.
Float. If set, the gradient of all weights is clipped so that their global norm is no higher than this value.
Boolean, defaults to FALSE. If TRUE, exponential moving average (EMA) is applied. EMA consists of computing an exponential moving average of the weights of the model (as the weight values change after each training batch), and periodically overwriting the weights with their moving average.
Float, defaults to 0.99. Only used if use_ema=TRUE. This is # noqa: E501
the momentum to use when computing the EMA of the model's weights:
new_average = ema_momentum * old_average + (1 - ema_momentum) * current_variable_value.
Int or NULL, defaults to NULL. Only used if
use_ema=TRUE. Every ema_overwrite_frequency steps of iterations, we
overwrite the model variable by its moving average. If NULL, the optimizer # noqa: E501
does not overwrite model variables in the middle of training, and you
need to explicitly overwrite the variables at the end of training
by calling optimizer.finalize_variable_values() (which updates the model # noqa: E501
variables in-place). When using the built-in fit() training loop, this
happens automatically after the last epoch, and you don't need to do
anything.
Boolean, defaults to TRUE. If TRUE, the optimizer will use XLA # noqa: E501 compilation. If no GPU device is found, this flag will be ignored.
String. The name to use for momentum accumulator weights created by the optimizer.
Used for backward and forward compatibility
Adamax, a variant of Adam based on the infinity norm, is a first-order gradient-based optimization method. Due to its capability of adjusting the learning rate based on data characteristics, it is suited to learn time-variant process, e.g., speech data with dynamically changed noise conditions. Default parameters follow those provided in the paper (see references below).
Initialization:
m = 0 # Initialize initial 1st moment vector
u = 0 # Initialize the exponentially weighted infinity norm
t = 0 # Initialize timestep
The update rule for parameter w with gradient g is described at the end
of section 7.1 of the paper (see the referenece section):
t += 1
m = beta1 * m + (1 - beta) * g
u = max(beta2 * u, abs(g))
current_lr = learning_rate / (1 - beta1 ** t)
w = w - current_lr * m / (u + epsilon)
Other optimizers:
optimizer_adadelta(),
optimizer_adagrad(),
optimizer_adam(),
optimizer_ftrl(),
optimizer_nadam(),
optimizer_rmsprop(),
optimizer_sgd()