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:
Nested Classes
class | SparseApplyAdagradV2.Options | Optional attributes for SparseApplyAdagradV2
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Public Methods
Output<T> |
asOutput()
Returns the symbolic handle of a tensor.
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static <T, U extends Number> SparseApplyAdagradV2<T> | |
Output<T> |
out()
Same as "var".
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static SparseApplyAdagradV2.Options |
updateSlots(Boolean updateSlots)
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static SparseApplyAdagradV2.Options |
useLocking(Boolean useLocking)
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Inherited Methods
Public Methods
public Output<T> asOutput ()
Returns the symbolic handle of a tensor.
Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.
public static SparseApplyAdagradV2<T> create (Scope scope, Operand<T> var, Operand<T> accum, Operand<T> lr, Operand<T> epsilon, Operand<T> grad, Operand<U> indices, Options... options)
Factory method to create a class wrapping a new SparseApplyAdagradV2 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. |
epsilon | Constant factor. 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 SparseApplyAdagradV2
public static SparseApplyAdagradV2.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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