tf.contrib.bayesflow.stochastic_tensor.ExponentialTensor.distribution

tf.contrib.distributions.StudentT.__init__(df, mu, sigma, validate_args=False, allow_nan_stats=True, name='StudentT') Construct Student's t distributions. The distributions have degree of freedom df, mean mu, and scale sigma. The parameters df, mu, and sigma must be shaped in a way that supports broadcasting (e.g. df + mu + sigma is a valid operation). Args: df: Floating point tensor, the degrees of freedom of the distribution(s). df must contain only positive values. mu: Floating point tens

tf.contrib.distributions.Mixture.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.contrib.distributions.Normal.allow_nan_stats Python boolean 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 un

tf.contrib.learn.monitors.LoggingTrainable.end(session=None)

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.input_dict

tf.contrib.learn.monitors.GraphDump.set_estimator(estimator) A setter called automatically by the target estimator. If the estimator is locked, this method does nothing. Args: estimator: the estimator that this monitor monitors. Raises: ValueError: if the estimator is None.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.is_reparameterized

tf.contrib.distributions.WishartFull.allow_nan_stats Python boolean 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

tf.contrib.distributions.Bernoulli.__init__(logits=None, p=None, dtype=tf.int32, validate_args=False, allow_nan_stats=True, name='Bernoulli') Construct Bernoulli distributions. Args: logits: An N-D Tensor representing the log-odds of a positive event. Each entry in the Tensor parametrizes an independent Bernoulli distribution where the probability of an event is sigmoid(logits). p: An N-D Tensor representing the probability of a positive event. Each entry in the Tensor parameterizes an indep