tfp.math.reduce_logmeanexp

Computes log(mean(exp(input_tensor))).

tfp.math.reduce_logmeanexp(
    input_tensor,
    axis=None,
    keepdims=False,
    experimental_named_axis=None,
    experimental_allow_all_gather=False,
    name=None
)

Reduces input_tensor along the dimensions given in axis. Unless keepdims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keepdims is true, the reduced dimensions are retained with length 1.

If axis has no entries, all dimensions are reduced, and a tensor with a single element is returned.

This function is more numerically stable than log(reduce_mean(exp(input))). It avoids overflows caused by taking the exp of large inputs and underflows caused by taking the log of small inputs.

Args
`input_tensor`	The tensor to reduce. Should have numeric type.
`axis`	The dimensions to reduce. If `None` (the default), reduces all dimensions. Must be in the range `[-rank(input_tensor), rank(input_tensor))`.
`keepdims`	Boolean. Whether to keep the axis as singleton dimensions. Default value: `False` (i.e., squeeze the reduced dimensions).
`experimental_named_axis`	A `str or list of`str`axis names to additionally reduce over. Providing`None`will not reduce over any axes. </td> </tr><tr> <td>`experimental_allow_all_gather`<a id="experimental_allow_all_gather"></a> </td> <td> Allow using an`all_gather`-based fallback under TensorFlow when computing the distributed maximum. This fallback is only efficient when`axis`reduces away most of the dimensions of`input_tensor`. </td> </tr><tr> <td>`name`<a id="name"></a> </td> <td> Python`str`name prefixed to Ops created by this function. Default value:`None`(i.e.,`'reduce_logmeanexp'`).

Returns
`log_mean_exp`	The reduced tensor.

tfp.math.reduce_logmeanexp

Args

Returns