# tf.raw_ops.CumulativeLogsumexp

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Compute the cumulative product of the tensor `x` along `axis`.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.raw_ops.CumulativeLogsumexp`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/CumulativeLogsumexp)

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    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.

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| ## Args ||
|---|---|
| `x` | A `Tensor`. Must be one of the following types: `bfloat16`, `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). |

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| ## Returns ||
|---|---|
| A `Tensor`. Has the same type as `x`. ||

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