Optimizer that implements the RMSprop algorithm
optimizer_rmsprop(
learning_rate = 0.001,
rho = 0.9,
momentum = 0,
epsilon = 1e-07,
centered = FALSE,
weight_decay = NULL,
clipnorm = NULL,
clipvalue = NULL,
global_clipnorm = NULL,
use_ema = FALSE,
ema_momentum = 0.99,
ema_overwrite_frequency = 100L,
jit_compile = TRUE,
name = "RMSprop",
...
)Optimizer for use with compile.keras.engine.training.Model.
Initial value for the learning rate:
either a floating point value,
or a tf.keras.optimizers.schedules.LearningRateSchedule instance.
Defaults to 0.001.
float, defaults to 0.9. Discounting factor for the old gradients.
float, defaults to 0.0. If not 0.0., the optimizer tracks the
momentum value, with a decay rate equals to 1 - momentum.
A small constant for numerical stability. This epsilon is "epsilon hat" in the Kingma and Ba paper (in the formula just before Section 2.1), not the epsilon in Algorithm 1 of the paper. Defaults to 1e-7.
Boolean. If TRUE, gradients are normalized by the estimated
variance of the gradient; if FALSE, by the uncentered second moment.
Setting this to TRUE may help with training, but is slightly more
expensive in terms of computation and memory. Defaults to FALSE.
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
The gist of RMSprop is to:
Maintain a moving (discounted) average of the square of gradients
Divide the gradient by the root of this average
This implementation of RMSprop uses plain momentum, not Nesterov momentum.
The centered version additionally maintains a moving average of the gradients, and uses that average to estimate the variance.
Other optimizers:
optimizer_adadelta(),
optimizer_adagrad(),
optimizer_adamax(),
optimizer_adam(),
optimizer_ftrl(),
optimizer_nadam(),
optimizer_sgd()