That is for rows we have grad for, we update var and accum 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().
accum
A mutable Tensor. Must have the same type as var.
Should be from a Variable().
lr
A Tensor. Must have the same type as var.
Learning rate. 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, updating of the var and accum tensors 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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26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.SparseApplyAdagrad\n\nUpdate relevant entries in '*var' and '*accum' according to the adagrad scheme.\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.SparseApplyAdagrad`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/SparseApplyAdagrad)\n\n\u003cbr /\u003e\n\n tf.raw_ops.SparseApplyAdagrad(\n var,\n accum,\n lr,\n grad,\n indices,\n use_locking=False,\n update_slots=True,\n name=None\n )\n\nThat is for rows we have grad for, we update var and accum as follows:\n\n\\\\\\[accum += grad \\* grad\\\\\\]\n\n\\\\\\[var -= lr \\* grad \\* (1 / sqrt(accum))\\\\\\]\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| `accum` | A mutable `Tensor`. Must have the same type as `var`. Should be from a Variable(). |\n| `lr` | A `Tensor`. Must have the same type as `var`. Learning rate. 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`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. |\n| `update_slots` | An optional `bool`. Defaults to `True`. |\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"]]
