tf.raw_ops.SparseApplyAdagrad
Update relevant entries in 'var' and 'accum' according to the adagrad scheme.
tf.raw_ops.SparseApplyAdagrad(
var, accum, lr, 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 mutable 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. 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. |
update_slots | An optional bool. Defaults to True. |
name | A name for the operation (optional). |
| Returns | |
|---|---|
A mutable Tensor. Has the same type as var. |
© 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/SparseApplyAdagrad