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Diffstat (limited to 'tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.BernoulliWithSigmoidProbs.md')
| -rw-r--r-- | tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.BernoulliWithSigmoidProbs.md | 29 |
1 files changed, 14 insertions, 15 deletions
diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.BernoulliWithSigmoidProbs.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.BernoulliWithSigmoidProbs.md index 7c51c70b9b..b0a926f8ed 100644 --- a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.BernoulliWithSigmoidProbs.md +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard6/tf.contrib.distributions.BernoulliWithSigmoidProbs.md @@ -10,21 +10,20 @@ Bernoulli with `probs = nn.sigmoid(logits)`. #### `tf.contrib.distributions.BernoulliWithSigmoidProbs.allow_nan_stats` {#BernoulliWithSigmoidProbs.allow_nan_stats} -Python boolean describing behavior when a stat is undefined. +Python `bool` describing behavior when a stat is undefined. -Stats return +/- infinity when it makes sense. E.g., the variance -of a Cauchy distribution is infinity. However, sometimes the -statistic is undefined, e.g., if a distribution's pdf does not achieve a -maximum within the support of the distribution, the mode is undefined. -If the mean is undefined, then by definition the variance is undefined. -E.g. the mean for Student's T for df = 1 is undefined (no clear way to say -it is either + or - infinity), so the variance = E[(X - mean)^2] is also -undefined. +Stats return +/- infinity when it makes sense. E.g., the variance of a +Cauchy distribution is infinity. However, sometimes the statistic is +undefined, e.g., if a distribution's pdf does not achieve a maximum within +the support of the distribution, the mode is undefined. If the mean is +undefined, then by definition the variance is undefined. E.g. the mean for +Student's T for df = 1 is undefined (no clear way to say it is either + or - +infinity), so the variance = E[(X - mean)**2] is also undefined. ##### Returns: -* <b>`allow_nan_stats`</b>: Python boolean. +* <b>`allow_nan_stats`</b>: Python `bool`. - - - @@ -222,7 +221,7 @@ Indicates that `batch_shape == []`. ##### Returns: -* <b>`is_scalar_batch`</b>: `Boolean` `scalar` `Tensor`. +* <b>`is_scalar_batch`</b>: `bool` scalar `Tensor`. - - - @@ -239,7 +238,7 @@ Indicates that `event_shape == []`. ##### Returns: -* <b>`is_scalar_event`</b>: `Boolean` `scalar` `Tensor`. +* <b>`is_scalar_event`</b>: `bool` scalar `Tensor`. - - - @@ -383,8 +382,8 @@ param_shapes with static (i.e. `TensorShape`) shapes. This is a class method that describes what key/value arguments are required to instantiate the given `Distribution` so that a particular shape is -returned for that instance's call to `sample()`. Assumes that -the sample's shape is known statically. +returned for that instance's call to `sample()`. Assumes that the sample's +shape is known statically. Subclasses should override class method `_param_shapes` to return constant-valued tensors when constant values are fed. @@ -532,7 +531,7 @@ survival_function(x) = P[X > x] #### `tf.contrib.distributions.BernoulliWithSigmoidProbs.validate_args` {#BernoulliWithSigmoidProbs.validate_args} -Python boolean indicated possibly expensive checks are enabled. +Python `bool` indicating possibly expensive checks are enabled. - - - |