Update relevant entries in 'var' and 'accum' according to the adagrad scheme.
tf.raw_ops.ResourceSparseApplyAdagrad(
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 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 , qint16 , quint16 , uint16 , complex128 , half , uint32 , uint64 .
Learning rate. 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.
|