tensorflow::ops::Cumsum

#include <math_ops.h>

Compute the cumulative sum of the tensor x along axis.

Summary

By default, this op performs an inclusive cumsum, which means that the first element of the input is identical to the first element of the output: ```prettyprint tf.cumsum([a, b, c]) ==> [a, a + b, a + b + c] ```

By setting the exclusive kwarg to True, an exclusive cumsum is performed instead: ```prettyprint tf.cumsum([a, b, c], exclusive=True) ==> [0, a, a + b] ```

By setting the reverse kwarg to True, the cumsum is performed in the opposite direction: ```prettyprint tf.cumsum([a, b, c], reverse=True) ==> [a + b + c, b + c, c] `` This is more efficient than using separatetf.reverse` ops.

The reverse and exclusive kwargs can also be combined: ```prettyprint tf.cumsum([a, b, c], exclusive=True, reverse=True) ==> [b + c, c, 0] ```

Arguments:

• scope: A Scope object

Returns:

• Output: The out tensor.

Public attributes

::tensorflow::Output out

Public functions

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

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