Update relevant entries in '*var' according to the Ftrl-proximal scheme.
tf.raw_ops.ResourceSparseApplyFtrl(
var, accum, linear, grad, indices, lr, l1, l2, lr_power, use_locking=False,
multiply_linear_by_lr=False, name=None
)
That is for rows we have grad for, we update var, accum and linear as follows: accum_new = accum + grad * grad linear += grad - (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 accum = accum_new
Args | |
---|---|
var
|
A Tensor of type resource . Should be from a Variable().
|
accum
|
A Tensor of type resource . Should be from a Variable().
|
linear
|
A Tensor of type resource . Should be from a Variable().
|
grad
|
A 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 .
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.
|
lr
|
A Tensor . Must have the same type as grad .
Scaling factor. Must be a scalar.
|
l1
|
A Tensor . Must have the same type as grad .
L1 regularization. Must be a scalar.
|
l2
|
A Tensor . Must have the same type as grad .
L2 regularization. Must be a scalar.
|
lr_power
|
A Tensor . Must have the same type as grad .
Scaling factor. Must be a scalar.
|
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.
|
multiply_linear_by_lr
|
An optional bool . Defaults to False .
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
The created Operation. |