tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.mean(name='mean')

tf.contrib.distributions.LaplaceWithSoftplusScale.scale Distribution parameter for scale.

tf.contrib.bayesflow.entropy.elbo_ratio(log_p, q, z=None, n=None, seed=None, form=None, name='elbo_ratio') Estimate of the ratio appearing in the ELBO and KL divergence. With p(z) := exp{log_p(z)}, this Op returns an approximation of E_q[ Log[p(Z) / q(Z)] ] The term E_q[ Log[p(Z)] ] is always computed as a sample mean. The term E_q[ Log[q(z)] ] can be computed with samples, or an exact formula if q.entropy() is defined. This is controlled with the kwarg form. This log-ratio appears in differe

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.distributions.TransformedDistribution.is_continuous

tf.contrib.learn.LinearClassifier.get_estimator()

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

tf.contrib.distributions.Distribution.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.