Update '*var' as FOBOS algorithm with fixed learning rate.
tf.raw_ops.ApplyProximalGradientDescent(
var, alpha, l1, l2, delta, use_locking=False, name=None
)
prox_v = var - alpha * delta var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}
Args | |
|---|---|
var
|
A mutable Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, uint16, complex128, half, uint32, uint64.
Should be from a Variable().
|
alpha
|
A Tensor. Must have the same type as var.
Scaling factor. Must be a scalar.
|
l1
|
A Tensor. Must have the same type as var.
L1 regularization. Must be a scalar.
|
l2
|
A Tensor. Must have the same type as var.
L2 regularization. Must be a scalar.
|
delta
|
A Tensor. Must have the same type as var. The change.
|
use_locking
|
An optional bool. Defaults to False.
If True, the subtraction will be protected by a lock;
otherwise the behavior is undefined, but may exhibit less contention.
|
name
|
A name for the operation (optional). |
Returns | |
|---|---|
A mutable Tensor. Has the same type as var.
|