Sparse update entries in 'var' and 'accum' according to FOBOS algorithm.
tf.raw_ops.ResourceSparseApplyProximalAdagrad(
var, accum, lr, l1, l2, grad, indices, use_locking=False, name=None
)
That is for rows we have grad for, we update var and accum as follows:
accum += grad * grad
prox_v = var
prox_v -= lr * grad * (1 / sqrt(accum))
var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0}
Args |
|---|
var
|
A Tensor of type resource. Should be from a Variable().
|
accum
|
A Tensor of type resource. Should be from a Variable().
|
lr
|
A Tensor. Must be one of the following types: float32, float64, int32, uint8, int16, int8, complex64, int64, qint8, quint8, qint32, bfloat16, qint16, quint16, uint16, complex128, half, uint32, uint64.
Learning rate. Must be a scalar.
|
l1
|
A Tensor. Must have the same type as lr.
L1 regularization. Must be a scalar.
|
l2
|
A Tensor. Must have the same type as lr.
L2 regularization. Must be a scalar.
|
grad
|
A Tensor. Must have the same type as lr. The gradient.
|
indices
|
A Tensor. Must be one of the following types: int32, int64.
A vector of indices into the first dimension of var and accum.
|
use_locking
|
An optional bool. Defaults to False.
If True, updating of the var and accum tensors will be protected by
a lock; otherwise the behavior is undefined, but may exhibit less contention.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
|
The created Operation.
|
