That is for rows we have grad for, we update var as follows:
prox_v = var - alpha * grad
var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0}
Args
var
A Tensor of type resource. Should be from a Variable().
alpha
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.
Scaling factor. Must be a scalar.
l1
A Tensor. Must have the same type as alpha.
L1 regularization. Must be a scalar.
l2
A Tensor. Must have the same type as alpha.
L2 regularization. Must be a scalar.
grad
A Tensor. Must have the same type as alpha. 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, the subtraction will be protected by a lock;
otherwise the behavior is undefined, but may exhibit less contention.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2022-10-27 UTC."],[],[]]