tf.contrib.bayesflow.entropy.entropy_shannon(p, z=None, n=None, seed=None, form=None, name='entropy_shannon') Monte Carlo or deterministic computation of Shannon's entropy. Depending on the kwarg form, this Op returns either the analytic entropy of the distribution p, or the sampled entropy: -n^{-1} sum_{i=1}^n p.log_prob(z_i), where z_i ~ p, \approx - E_p[ Log[p(Z)] ] = Entropy[p] User supplies either Tensor of samples z, or number of samples to draw n Args: p: tf.contrib.distribut

tf.contrib.graph_editor.ControlOutputs.get(op) return the control outputs of op.

tf.contrib.distributions.Mixture.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.losses.get_total_loss(add_regularization_losses=True, name='total_loss') Returns a tensor whose value represents the total loss. Notice that the function adds the given losses to the regularization losses. Args: add_regularization_losses: A boolean indicating whether or not to use the regularization losses in the sum. name: The name of the returned tensor. Returns: A Tensor whose value represents the total loss. Raises: ValueError: if losses is not iterable.

tf.matrix_set_diag(input, diagonal, name=None) Returns a batched matrix tensor with new batched diagonal values. Given input and diagonal, this operation returns a tensor with the same shape and values as input, except for the diagonals of the innermost matrices. These will be overwritten by the values in diagonal. The batched matrices must be square. The output is computed as follows: Assume input has k+1 dimensions [I, J, K, ..., N, N] and diagonal has k dimensions [I, J, K, ..., N]. Then th

int64 tensorflow::TensorShape::dim_size(int d) const Returns the number of elements in dimension d. REQUIRES: 0 <= d < dims()

class tf.QueueBase Base class for queue implementations. A queue is a TensorFlow data structure that stores tensors across multiple steps, and exposes operations that enqueue and dequeue tensors. Each queue element is a tuple of one or more tensors, where each tuple component has a static dtype, and may have a static shape. The queue implementations support versions of enqueue and dequeue that handle single elements, versions that support enqueuing and dequeuing a batch of elements at once. Se

tf.contrib.distributions.BetaWithSoftplusAB.a Shape parameter.

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

tf.contrib.distributions.BernoulliWithSigmoidP.entropy(name='entropy') Shanon entropy in nats.