tf.raw_ops.CumulativeLogsumexp
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/r2.3/api_docs/python/tf/raw_ops/CumulativeLogsumexp