Compute the cumulative product of the tensor `x`

along `axis`

.

tf.raw_ops.CumulativeLogsumexp( x, axis, exclusive=False, reverse=False, name=None )

By default, this op performs an inclusive cumulative log-sum-exp, which means that the first element of the input is identical to the first element of the output:

tf.math.cumulative_logsumexp([a, b, c]) # => [a, log(exp(a) + exp(b)), log(exp(a) + exp(b) + exp(c))]

By setting the `exclusive`

kwarg to `True`

, an exclusive cumulative log-sum-exp is performed instead:

tf.cumulative_logsumexp([a, b, c], exclusive=True) # => [-inf, a, log(exp(a) * exp(b))]

Note that the neutral element of the log-sum-exp operation is `-inf`

, however, for performance reasons, the minimal value representable by the floating point type is used instead.

By setting the `reverse`

kwarg to `True`

, the cumulative log-sum-exp is performed in the opposite direction.

Args | |
---|---|

`x` | A `Tensor` . Must be one of the following types: `half` , `float32` , `float64` . A `Tensor` . Must be one of the following types: `float16` , `float32` , `float64` . |

`axis` | A `Tensor` . Must be one of the following types: `int32` , `int64` . A `Tensor` of type `int32` (default: 0). Must be in the range `[-rank(x), rank(x))` . |

`exclusive` | An optional `bool` . Defaults to `False` . If `True` , perform exclusive cumulative log-sum-exp. |

`reverse` | An optional `bool` . Defaults to `False` . A `bool` (default: False). |

`name` | A name for the operation (optional). |

Returns | |
---|---|

A `Tensor` . Has the same type as `x` . |

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/raw_ops/CumulativeLogsumexp