tf.raw_ops.ApplyAdagrad
Update '*var' according to the adagrad scheme.
tf.raw_ops.ApplyAdagrad(
var, accum, lr, grad, use_locking=False, update_slots=True, name=None
)
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. Scaling factor. Must be a scalar. |
grad |
A Tensor. Must have the same type as var. The gradient. |
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. |
© 2022 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/raw_ops/ApplyAdagrad