@Operator public final class FakeQuantWithMinMaxVars extends PrimitiveOp implements Operand<Float>
and `max` to 'outputs' tensor of same shape as `inputs`.
`[min; max]` define the clamping range for the `inputs` data. `inputs` values are quantized into the quantization range (`[0; 2^num_bits - 1]` when `narrow_range` is false and `[1; 2^num_bits - 1]` when it is true) and then de-quantized and output as floats in `[min; max]` interval. `num_bits` is the bitwidth of the quantization; between 2 and 16, inclusive.
This operation has a gradient and thus allows for training `min` and `max` values.
| Modifier and Type | Class and Description |
|---|---|
static class |
FakeQuantWithMinMaxVars.Options
Optional attributes for
FakeQuantWithMinMaxVars |
operation| Modifier and Type | Method and Description |
|---|---|
Output<Float> |
asOutput()
Returns the symbolic handle of a tensor.
|
static FakeQuantWithMinMaxVars |
create(Scope scope,
Operand<Float> inputs,
Operand<Float> min,
Operand<Float> max,
FakeQuantWithMinMaxVars.Options... options)
Factory method to create a class to wrap a new FakeQuantWithMinMaxVars operation to the graph.
|
static FakeQuantWithMinMaxVars.Options |
narrowRange(Boolean narrowRange) |
static FakeQuantWithMinMaxVars.Options |
numBits(Long numBits) |
Output<Float> |
outputs() |
equals, hashCode, toStringpublic static FakeQuantWithMinMaxVars create(Scope scope, Operand<Float> inputs, Operand<Float> min, Operand<Float> max, FakeQuantWithMinMaxVars.Options... options)
scope - current graph scopeinputs - min - max - options - carries optional attributes valuespublic static FakeQuantWithMinMaxVars.Options numBits(Long numBits)
numBits - public static FakeQuantWithMinMaxVars.Options narrowRange(Boolean narrowRange)
narrowRange - public Output<Float> asOutput()
OperandInputs 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<Float>OperationBuilder.addInput(Output)Copyright © 2015–2019. All rights reserved.