tensorflow::ops::Cumprod

#include <math_ops.h>

Compute the cumulative product of the tensor x along axis.

Summary

By default, this op performs an inclusive cumprod, which means that the first element of the input is identical to the first element of the output:

```python tf.cumprod([a, b, c]) # => [a, a * b, a * b * c] ```

By setting the exclusive kwarg to True, an exclusive cumprod is performed instead:

```python tf.cumprod([a, b, c], exclusive=True) # => [1, a, a * b] ```

By setting the reverse kwarg to True, the cumprod is performed in the opposite direction:

```python tf.cumprod([a, b, c], reverse=True) # => [a * b * c, b * c, c] ```

This is more efficient than using separate tf.reverse ops.

The reverse and exclusive kwargs can also be combined:

```python tf.cumprod([a, b, c], exclusive=True, reverse=True) # => [b * c, c, 1] ```

Arguments:

• scope: A Scope object
• x: A Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half.
• axis: A Tensor of type int32 (default: 0). Must be in the range [-rank(x), rank(x)).

Optional attributes (see Attrs):

• exclusive: If True, perform exclusive cumprod.
• reverse: A bool (default: False).

Returns:

• Output: The out tensor.

Public attributes

::tensorflow::Output out

Public functions

 Cumprod(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input x,
  ::tensorflow::Input axis
)

 Cumprod(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input x,
  ::tensorflow::Input axis,
  const Cumprod::Attrs & attrs
)

::tensorflow::Node * node() const 

operator::tensorflow::Input() const 

operator::tensorflow::Output() const 

Public static functions

Attrs Exclusive(
  bool x
)

Attrs Reverse(
  bool x
)