tf.raw_ops.SparseApplyAdagradDA
Update entries in 'var' and 'accum' according to the proximal adagrad scheme.
tf.raw_ops.SparseApplyAdagradDA(
var,
gradient_accumulator,
gradient_squared_accumulator,
grad,
indices,
lr,
l1,
l2,
global_step,
use_locking=False,
name=None
)
Args |
var
|
A mutable Tensor . Must be one of the following types: float32 , float64 , int32 , uint8 , int16 , int8 , complex64 , int64 , qint8 , quint8 , qint32 , bfloat16 , qint16 , quint16 , 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.
|
indices
|
A Tensor . Must be one of the following types: int32 , int64 .
A vector of indices into the first dimension of var and accum.
|
lr
|
A Tensor . Must have the same type as var .
Learning rate. 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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Last updated 2024-01-23 UTC.
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