T - data type for output() outputpublic final class FractionalAvgPoolGrad<T extends java.lang.Number> extends PrimitiveOp implements Operand<T>
Unlike FractionalMaxPoolGrad, we don't need to find arg_max for FractionalAvgPoolGrad, we just need to evenly back-propagate each element of out_backprop to those indices that form the same pooling cell. Therefore, we just need to know the shape of original input tensor, instead of the whole tensor.
| Modifier and Type | Class | Description |
|---|---|---|
static class |
FractionalAvgPoolGrad.Options |
Optional attributes for
FractionalAvgPoolGrad |
operation| Modifier and Type | Method | Description |
|---|---|---|
Output<T> |
asOutput() |
Returns the symbolic handle of a tensor.
|
static <T extends java.lang.Number> |
create(Scope scope,
Operand<java.lang.Long> origInputTensorShape,
Operand<T> outBackprop,
Operand<java.lang.Long> rowPoolingSequence,
Operand<java.lang.Long> colPoolingSequence,
FractionalAvgPoolGrad.Options... options) |
Factory method to create a class to wrap a new FractionalAvgPoolGrad operation to the graph.
|
Output<T> |
output() |
4-D.
|
static FractionalAvgPoolGrad.Options |
overlapping(java.lang.Boolean overlapping) |
clone, finalize, getClass, notify, notifyAll, wait, wait, waitequals, hashCode, toStringpublic static <T extends java.lang.Number> FractionalAvgPoolGrad<T> create(Scope scope, Operand<java.lang.Long> origInputTensorShape, Operand<T> outBackprop, Operand<java.lang.Long> rowPoolingSequence, Operand<java.lang.Long> colPoolingSequence, FractionalAvgPoolGrad.Options... options)
scope - current graph scopeorigInputTensorShape - Original input tensor shape for `fractional_avg_pool`outBackprop - 4-D with shape `[batch, height, width, channels]`. Gradients
w.r.t. the output of `fractional_avg_pool`.rowPoolingSequence - row pooling sequence, form pooling region with
col_pooling_sequence.colPoolingSequence - column pooling sequence, form pooling region with
row_pooling sequence.options - carries optional attributes valuespublic static FractionalAvgPoolGrad.Options overlapping(java.lang.Boolean overlapping)
overlapping - When set to True, it means when pooling, the values at the boundary
of adjacent pooling cells are used by both cells. For example:
`index 0 1 2 3 4`
`value 20 5 16 3 7`
If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [41/3, 26/3] for fractional avg pooling.
public Output<T> 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<T extends java.lang.Number>OperationBuilder.addInput(Output)