Modifier and Type | Class and Description |
---|---|
static class |
ApplyAdagradDa.Options
Optional attributes for
ApplyAdagradDa |
operation
Modifier and Type | Method and Description |
---|---|
Output<T> |
asOutput()
Returns the symbolic handle of a tensor.
|
static <T> ApplyAdagradDa<T> |
create(Scope scope,
Operand<T> var,
Operand<T> gradientAccumulator,
Operand<T> gradientSquaredAccumulator,
Operand<T> grad,
Operand<T> lr,
Operand<T> l1,
Operand<T> l2,
Operand<Long> globalStep,
ApplyAdagradDa.Options... options)
Factory method to create a class wrapping a new ApplyAdagradDa operation.
|
Output<T> |
out()
Same as "var".
|
static ApplyAdagradDa.Options |
useLocking(Boolean useLocking) |
equals, hashCode, op, toString
public static <T> ApplyAdagradDa<T> create(Scope scope, Operand<T> var, Operand<T> gradientAccumulator, Operand<T> gradientSquaredAccumulator, Operand<T> grad, Operand<T> lr, Operand<T> l1, Operand<T> l2, Operand<Long> globalStep, ApplyAdagradDa.Options... options)
scope
- current scopevar
- Should be from a Variable().gradientAccumulator
- Should be from a Variable().gradientSquaredAccumulator
- Should be from a Variable().grad
- The gradient.lr
- Scaling factor. Must be a scalar.l1
- L1 regularization. Must be a scalar.l2
- L2 regularization. Must be a scalar.globalStep
- Training step number. Must be a scalar.options
- carries optional attributes valuespublic static ApplyAdagradDa.Options useLocking(Boolean useLocking)
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.public Output<T> asOutput()
Operand
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.
asOutput
in interface Operand<T>
OperationBuilder.addInput(Output)
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