tf.contrib.distributions.Distribution.log_pmf(value, name='log_pmf') Log probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.distributions.TransformedDistribution.is_continuous

tf.contrib.distributions.Poisson.log_pdf(value, name='log_pdf') Log probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

tf.contrib.distributions.Laplace.name Name prepended to all ops created by this Distribution.

tf.contrib.training.NextQueuedSequenceBatch.sequence An int32 vector, length batch_size: the sequence index of each entry. When an input is split up, the sequence values 0, 1, ..., sequence_count - 1 are assigned to each split. Returns: An int32 vector Tensor.

tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.entropy(name='entropy')

tf.contrib.distributions.Distribution.get_batch_shape() Shape of a single sample from a single event index as a TensorShape. Same meaning as batch_shape. May be only partially defined. Returns: batch_shape: TensorShape, possibly unknown.

class tf.contrib.distributions.Categorical Categorical distribution. The categorical distribution is parameterized by the log-probabilities of a set of classes.

tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.entropy(name='entropy')

class tf.errors.UnimplementedError Raised when an operation has not been implemented. Some operations may raise this error when passed otherwise-valid arguments that it does not currently support. For example, running the tf.nn.max_pool() operation would raise this error if pooling was requested on the batch dimension, because this is not yet supported.