Static helper routines for PartialTensorShape. Includes a few common predicates on a partially known tensor shape. Member Details string tensorflow::PartialTensorShapeUtils::PartialShapeListString(const gtl::ArraySlice< PartialTensorShape > &shapes) bool tensorflow::PartialTensorShapeUtils::AreCompatible(const gtl::ArraySlice< PartialTensorShape > &shapes0, const gtl::ArraySlice< PartialTensorShape > &shapes1)

class tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor GammaWithSoftplusAlphaBetaTensor is a StochasticTensor backed by the distribution GammaWithSoftplusAlphaBeta.

tf.contrib.distributions.Gamma.entropy(name='entropy') Shanon entropy in nats. Additional documentation from Gamma: This is defined to be entropy = alpha - log(beta) + log(Gamma(alpha)) + (1-alpha)digamma(alpha) where digamma(alpha) is the digamma function.

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

uint64 tensorflow::EnvWrapper::NowMicros() override Returns the number of micro-seconds since some fixed point in time. Only useful for computing deltas of time.

void tensorflow::EnvWrapper::SleepForMicroseconds(int64 micros) override Sleeps/delays the thread for the prescribed number of micro-seconds.

tf.contrib.learn.run_n(output_dict, feed_dict=None, restore_checkpoint_path=None, n=1) Run output_dict tensors n times, with the same feed_dict each run. Args: output_dict: A dict mapping string names to tensors to run. Must all be from the same graph. feed_dict: dict of input values to feed each run. restore_checkpoint_path: A string containing the path to a checkpoint to restore. n: Number of times to repeat. Returns: A list of n dict objects, each containing values read from output_di

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.value_type

tf.contrib.learn.monitors.ExportMonitor.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.

tf.self_adjoint_eigvals(tensor, name=None) Computes the eigenvalues of one or more self-adjoint matrices. Args: tensor: Tensor of shape [..., N, N]. name: string, optional name of the operation. Returns: e: Eigenvalues. Shape is [..., N]. The vector e[..., :] contains the N eigenvalues of tensor[..., :, :].