Class ApplyGradientDescent<T extends TType>
java.lang.Object
org.tensorflow.op.RawOp
org.tensorflow.op.train.ApplyGradientDescent<T>
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Nested Class Summary
Nested ClassesModifier and TypeClassDescriptionstatic classApplyGradientDescent.Inputs<T extends TType>static classOptional attributes forApplyGradientDescent -
Field Summary
FieldsModifier and TypeFieldDescriptionstatic final StringThe name of this op, as known by TensorFlow core engine -
Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionasOutput()Returns the symbolic handle of the tensor.static <T extends TType>
ApplyGradientDescent<T> create(Scope scope, Operand<T> var, Operand<T> alpha, Operand<T> delta, ApplyGradientDescent.Options... options) Factory method to create a class wrapping a new ApplyGradientDescent operation.out()Gets out.static ApplyGradientDescent.OptionsuseLocking(Boolean useLocking) Sets the useLocking option.
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Field Details
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OP_NAME
The name of this op, as known by TensorFlow core engine- See Also:
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Constructor Details
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ApplyGradientDescent
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Method Details
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create
@Endpoint(describeByClass=true) public static <T extends TType> ApplyGradientDescent<T> create(Scope scope, Operand<T> var, Operand<T> alpha, Operand<T> delta, ApplyGradientDescent.Options... options) Factory method to create a class wrapping a new ApplyGradientDescent operation.- Type Parameters:
T- data type forApplyGradientDescentoutput and operands- Parameters:
scope- current scopevar- Should be from a Variable().alpha- Scaling factor. Must be a scalar.delta- The change.options- carries optional attribute values- Returns:
- a new instance of ApplyGradientDescent
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useLocking
Sets the useLocking option.- Parameters:
useLocking- IfTrue, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention.- Returns:
- this Options instance.
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out
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asOutput
Description copied from interface:OperandReturns the symbolic handle of the 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.
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