optimizer_adam  R Documentation 
Optimizer that implements the Adam algorithm
optimizer_adam(
learning_rate = 0.001,
beta_1 = 0.9,
beta_2 = 0.999,
epsilon = 1e07,
amsgrad = FALSE,
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 = "Adam",
...
)
learning_rate 
A 
beta_1 
A float value or a constant float tensor, or a callable that takes no arguments and returns the actual value to use. The exponential decay rate for the 1st moment estimates. Defaults to 0.9. 
beta_2 
A float value or a constant float tensor, or a callable that takes no arguments and returns the actual value to use. The exponential decay rate for the 2nd moment estimates. Defaults to 0.999. 
epsilon 
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 1e7. 
amsgrad 
Boolean. Whether to apply AMSGrad variant of this algorithm from
the paper "On the Convergence of Adam and beyond". Defaults to 
weight_decay 
Float, defaults to NULL. If set, weight decay is applied. 
clipnorm 
Float. If set, the gradient of each weight is individually clipped so that its norm is no higher than this value. 
clipvalue 
Float. If set, the gradient of each weight is clipped to be no higher than this value. 
global_clipnorm 
Float. If set, the gradient of all weights is clipped so that their global norm is no higher than this value. 
use_ema 
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. 
ema_momentum 
Float, defaults to 0.99. Only used if 
ema_overwrite_frequency 
Int or NULL, defaults to NULL. Only used if

jit_compile 
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. 
name 
String. The name to use for momentum accumulator weights created by the optimizer. 
... 
Used for backward and forward compatibility 
Adam optimization is a stochastic gradient descent method that is based on adaptive estimation of firstorder and secondorder moments.
According to Kingma et al., 2014, the method is "computationally efficient, has little memory requirement, invariant to diagonal rescaling of gradients, and is well suited for problems that are large in terms of data/parameters".
Optimizer for use with compile.keras.engine.training.Model
.
Other optimizers:
optimizer_adadelta()
,
optimizer_adagrad()
,
optimizer_adamax()
,
optimizer_ftrl()
,
optimizer_nadam()
,
optimizer_rmsprop()
,
optimizer_sgd()
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