tf.contrib.distributions.MultivariateNormalCholesky.dtype The DType of Tensors handled by this Distribution.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.is_reparameterized

tf.contrib.distributions.InverseGamma.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.bayesflow.stochastic_tensor.StudentTTensor.value(name='value')

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.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.

tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.errors.UnimplementedError.__init__(node_def, op, message) Creates an UnimplementedError.

tf.contrib.graph_editor.SubGraphView.copy() Return a copy of itself. Note that this class is a "view", copying it only create another view and does not copy the underlying part of the tf.Graph. Returns: A new instance identical to the original one.

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

tf.contrib.learn.monitors.GraphDump.epoch_begin(epoch) Begin epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've already begun an epoch, or epoch < 0.