tf.real(input, name=None) Returns the real part of a complex number. Given a tensor input of complex numbers, this operation returns a tensor of type float32 or float64 that is the real part of each element in input. All elements in input must be complex numbers of the form (a + bj), where a is the real part returned by this operation and b is the imaginary part. For example: # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] tf.real(input) ==> [-2.25, 3.25] If input is already real, it is

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

size_t tensorflow::ThreadOptions::stack_size Thread stack size to use (in bytes).

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.value_type

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

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.__init__(mu, diag_large, v, diag_small=None, validate_args=False, allow_nan_stats=True, name='MultivariateNormalDiagPlusVDVT') Multivariate Normal distributions on R^k. For every batch member, this distribution represents k random variables (X_1,...,X_k), with mean E[X_i] = mu[i], and covariance matrix C_{ij} := E[(X_i - mu[i])(X_j - mu[j])] The user initializes this class by providing the mean mu, and a lightweight definition of C: C = S

tf.contrib.distributions.Mixture.cat

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.loss(final_loss, name='Loss')

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

tf.contrib.learn.monitors.SummarySaver.epoch_end(epoch) End epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've not begun an epoch, or epoch number does not match.