W3cubDocs

/TensorFlow 2.4

tf.raw_ops.ResourceSparseApplyAdagradV2

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

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/r2.4/api_docs/python/tf/raw_ops/ResourceSparseApplyAdagradV2