|---| | [ View source on GitHub](https://github.com/tensorflow/probability/blob/v0.25.0/tensorflow_probability/python/stats/sample_stats.py#L935-L984) | Computes `log(average(to_probs(logits)))` in a numerically stable manner. tfp.stats.log_average_probs( logits, sample_axis=0, event_axis=None, keepdims=False, validate_args=False, name=None ) The meaning of `to_probs` is controlled by the `event_axis` argument. When `event_axis` is `None`, `to_probs = tf.math.sigmoid` and otherwise `to_probs = lambda x: tf.math.log_softmax(x, axis=event_axis)`. `sample_axis` and `event_axis` should have a null intersection. This requirement is always verified when `validate_args` is `True`.


| ## Args || |---|---| | `logits` | A `float` `Tensor` representing logits. | | `sample_axis` | Scalar or vector `Tensor` designating axis holding samples, or `None` (meaning all axis hold samples). Default value: `0` (leftmost dimension). | | `event_axis` | Scalar or vector `Tensor` designating the axis representing categorical logits. Default value: `None` (i.e., Bernoulli logits). | | `keepdims` | Boolean. Whether to keep the sample axis as singletons. Default value: `False` (i.e., squeeze the reduced dimensions). | | `validate_args` | Python `bool`, default `False`. When `True` distribution parameters are checked for validity despite possibly degrading runtime performance. When `False` invalid inputs may silently render incorrect outputs. Default value: `False` (i.e., do not validate args). | | `name` | Python `str` name prefixed to Ops created by this function. Default value: `None` (i.e., `'log_average_probs'`). |



| ## Returns || |---|---| | `log_avg_probs` | The natural log of the average of probs computed from logits. |