That is for rows we have grad for, we update var as follows:
Args
var
A mutable 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.
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.
grad
A Tensor. Must have the same type as var. 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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[],null,["# tf.raw_ops.SparseApplyProximalGradientDescent\n\nSparse update '\\*var' as FOBOS algorithm with fixed learning rate.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.raw_ops.SparseApplyProximalGradientDescent`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/SparseApplyProximalGradientDescent)\n\n\u003cbr /\u003e\n\n tf.raw_ops.SparseApplyProximalGradientDescent(\n var, alpha, l1, l2, grad, indices, use_locking=False, name=None\n )\n\nThat is for rows we have grad for, we update var as follows:\n\n\\\\\\[prox_v = var - alpha \\* grad\\\\\\]\n\n\\\\\\[var = sign(prox_v)/(1+alpha\\*l2) \\* max{\\|prox_v\\|-alpha\\*l1,0}\\\\\\]\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|---------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `var` | A mutable `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`. Should be from a Variable(). |\n| `alpha` | A `Tensor`. Must have the same type as `var`. Scaling factor. Must be a scalar. |\n| `l1` | A `Tensor`. Must have the same type as `var`. L1 regularization. Must be a scalar. |\n| `l2` | A `Tensor`. Must have the same type as `var`. L2 regularization. Must be a scalar. |\n| `grad` | A `Tensor`. Must have the same type as `var`. The gradient. |\n| `indices` | A `Tensor`. Must be one of the following types: `int32`, `int64`. A vector of indices into the first dimension of var and accum. |\n| `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. |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A mutable `Tensor`. Has the same type as `var`. ||\n\n\u003cbr /\u003e"]]
