tf.contrib.rnn.GRUBlockCell.output_size

tf.FixedLenSequenceFeature.__getnewargs__() Return self as a plain tuple. Used by copy and pickle.

class tf.contrib.distributions.WishartCholesky The matrix Wishart distribution on positive definite matrices. This distribution is defined by a scalar degrees of freedom df and a lower, triangular Cholesky factor which characterizes the scale matrix. Using WishartCholesky is a constant-time improvement over WishartFull. It saves an O(nbk^3) operation, i.e., a matrix-product operation for sampling and a Cholesky factorization in log_prob. For most use-cases it often saves another O(nbk^3) opera

tf.SparseTensor.dtype The DType of elements in this tensor.

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.contrib.framework.add_arg_scope(func) Decorates a function with args so it can be used within an arg_scope. Args: func: function to decorate. Returns: A tuple with the decorated function func_with_args().

class tf.nn.rnn_cell.BasicRNNCell The most basic RNN cell.