tensorflow::
ops::
ResourceApplyAdagrad
#include <training_ops.h>
Update '*var' according to the adagrad scheme.
Summary
accum += grad * grad var -= lr * grad * (1 / sqrt(accum))
Args:
- scope: A Scope object
- var: Should be from a Variable().
- accum: Should be from a Variable().
- lr: Scaling factor. Must be a scalar.
- grad: The gradient.
Optional attributes (see
Attrs
):
-
use_locking: 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.
Returns:
-
the created
Operation
Constructors and Destructors |
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ResourceApplyAdagrad
(const ::
tensorflow::Scope
& scope, ::
tensorflow::Input
var, ::
tensorflow::Input
accum, ::
tensorflow::Input
lr, ::
tensorflow::Input
grad)
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ResourceApplyAdagrad
(const ::
tensorflow::Scope
& scope, ::
tensorflow::Input
var, ::
tensorflow::Input
accum, ::
tensorflow::Input
lr, ::
tensorflow::Input
grad, const
ResourceApplyAdagrad::Attrs
& attrs)
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Public attributes |
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operation
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Public functions |
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operator::tensorflow::Operation
() const
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Public static functions |
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UpdateSlots
(bool x)
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UseLocking
(bool x)
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Structs |
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tensorflow::
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Optional attribute setters for ResourceApplyAdagrad . |
Public attributes
Public functions
ResourceApplyAdagrad
ResourceApplyAdagrad( const ::tensorflow::Scope & scope, ::tensorflow::Input var, ::tensorflow::Input accum, ::tensorflow::Input lr, ::tensorflow::Input grad )
ResourceApplyAdagrad
ResourceApplyAdagrad( const ::tensorflow::Scope & scope, ::tensorflow::Input var, ::tensorflow::Input accum, ::tensorflow::Input lr, ::tensorflow::Input grad, const ResourceApplyAdagrad::Attrs & attrs )
operator::tensorflow::Operation
operator::tensorflow::Operation() const