optimizer_sgd  R Documentation 
Gradient descent (with momentum) optimizer
optimizer_sgd(
learning_rate = 0.01,
momentum = 0,
nesterov = FALSE,
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 = "SGD",
...
)
learning_rate 
A 
momentum 
float hyperparameter >= 0 that accelerates gradient descent in the relevant direction and dampens oscillations. Defaults to 0, i.e., vanilla gradient descent. 
nesterov 
boolean. Whether to apply Nesterov momentum.
Defaults to 
amsgrad 
ignored. 
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 
Update rule for parameter w
with gradient g
when momentum
is 0:
w = w  learning_rate * g
Update rule when momentum
is larger than 0:
velocity = momentum * velocity  learning_rate * g w = w + velocity
When nesterov=TRUE
, this rule becomes:
velocity = momentum * velocity  learning_rate * g w = w + momentum * velocity  learning_rate * g
Optimizer for use with compile.keras.engine.training.Model
.
Other optimizers:
optimizer_adadelta()
,
optimizer_adagrad()
,
optimizer_adam()
,
optimizer_adamax()
,
optimizer_ftrl()
,
optimizer_nadam()
,
optimizer_rmsprop()
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