tf.raw_ops.ResourceSparseApplyAdagradV2

Update relevant entries in 'var' and 'accum' according to the adagrad scheme.

Compat aliases for migration

tf.compat.v1.raw_ops.ResourceSparseApplyAdagradV2

tf.raw_ops.ResourceSparseApplyAdagradV2(
    var, accum, lr, epsilon, grad, indices, use_locking=False, update_slots=True,
    name=None
)

That is for rows we have grad for, we update var and accum as follows: accum += grad * grad var -= lr * grad * (1 / sqrt(accum))

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`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Learning rate. Must be a scalar.
`epsilon`	A `Tensor`. Must have the same type as `lr`. Constant factor. 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.
`update_slots`	An optional `bool`. Defaults to `True`.
`name`	A name for the operation (optional).

Returns
The created Operation.

© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/raw_ops/ResourceSparseApplyAdagradV2

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