tf.raw_ops.ApplyAdagradDA

Update '*var' according to the proximal adagrad scheme.

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().
gradient_accumulator A mutable Tensor. Must have the same type as var. Should be from a Variable().
gradient_squared_accumulator A mutable Tensor. Must have the same type as var. Should be from a Variable().
grad A Tensor. Must have the same type as var. The gradient.
lr A Tensor. Must have the same type as var. Scaling factor. Must be a scalar.
l1 A Tensor. Must have the same type as var. L1 regularization. Must be a scalar.
l2 A Tensor. Must have the same type as var. L2 regularization. Must be a scalar.
global_step A Tensor of type int64. Training step number. Must be a scalar.
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.
name A name for the operation (optional).
Returns
A mutable Tensor. Has the same type as var.

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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/ApplyAdagradDA