public final class
ResourceSparseApplyAdagrad
Update relevant entries in '*var' and '*accum' according to the adagrad scheme.
That is for rows we have grad for, we update var and accum as follows: accum += grad * grad var -= lr * grad * (1 / sqrt(accum))
Nested Classes
class | ResourceSparseApplyAdagrad.Options | Optional attributes for ResourceSparseApplyAdagrad
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Constants
String | OP_NAME | The name of this op, as known by TensorFlow core engine |
Public Methods
static <T extends TType> ResourceSparseApplyAdagrad | |
static ResourceSparseApplyAdagrad.Options |
updateSlots(Boolean updateSlots)
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static ResourceSparseApplyAdagrad.Options |
useLocking(Boolean useLocking)
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Inherited Methods
Constants
public static final String OP_NAME
The name of this op, as known by TensorFlow core engine
Constant Value:
"ResourceSparseApplyAdagrad"
Public Methods
public static ResourceSparseApplyAdagrad create (Scope scope, Operand<?> var, Operand<?> accum, Operand<T> lr, Operand<T> grad, Operand<? extends TNumber> indices, Options... options)
Factory method to create a class wrapping a new ResourceSparseApplyAdagrad operation.
Parameters
scope | current scope |
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var | Should be from a Variable(). |
accum | Should be from a Variable(). |
lr | Learning rate. Must be a scalar. |
grad | The gradient. |
indices | A vector of indices into the first dimension of var and accum. |
options | carries optional attributes values |
Returns
- a new instance of ResourceSparseApplyAdagrad
public static ResourceSparseApplyAdagrad.Options useLocking (Boolean useLocking)
Parameters
useLocking | 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. |
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