tensorflow:: ops:: ApplyAdagrad
#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:
Output
: Same as "var".
Constructors and Destructors |
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ApplyAdagrad(const ::tensorflow::Scope & scope, ::tensorflow::Input var, ::tensorflow::Input accum, ::tensorflow::Input lr, ::tensorflow::Input grad)
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ApplyAdagrad(const ::tensorflow::Scope & scope, ::tensorflow::Input var, ::tensorflow::Input accum, ::tensorflow::Input lr, ::tensorflow::Input grad, const ApplyAdagrad::Attrs & attrs)
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Public attributes |
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operation
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out
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Public functions |
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node() const
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::tensorflow::Node *
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operator::tensorflow::Input() const
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operator::tensorflow::Output() 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:: |
Optional attribute setters for ApplyAdagrad. |
Public attributes
operation
Operation operation
out
::tensorflow::Output out
Public functions
ApplyAdagrad
ApplyAdagrad( const ::tensorflow::Scope & scope, ::tensorflow::Input var, ::tensorflow::Input accum, ::tensorflow::Input lr, ::tensorflow::Input grad )
ApplyAdagrad
ApplyAdagrad( const ::tensorflow::Scope & scope, ::tensorflow::Input var, ::tensorflow::Input accum, ::tensorflow::Input lr, ::tensorflow::Input grad, const ApplyAdagrad::Attrs & attrs )
node
::tensorflow::Node * node() const
operator::tensorflow::Input
operator::tensorflow::Input() const
operator::tensorflow::Output
operator::tensorflow::Output() const
Public static functions
UpdateSlots
Attrs UpdateSlots( bool x )
UseLocking
Attrs UseLocking( bool x )