tf.contrib.graph_editor.matcher.control_input_ops(*args) Add input matches.

tf.contrib.distributions.Categorical.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

bool tensorflow::PartialTensorShape::IsCompatibleWith(const PartialTensorShape &shape) const Return true iff the ranks match, and if the dimensions all either match or one is unknown.

tf.contrib.distributions.Categorical.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

class tf.errors.UnknownError Unknown error. An example of where this error may be returned is if a Status value received from another address space belongs to an error-space that is not known to this address space. Also errors raised by APIs that do not return enough error information may be converted to this error.

tf.nn.rnn_cell.GRUCell.output_size

class tf.contrib.bayesflow.stochastic_tensor.StochasticTensor StochasticTensor is a BaseStochasticTensor backed by a distribution.

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

tf.contrib.distributions.Dirichlet.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Additional documentation from Dirichlet: Note that the input must be a non-negative tensor with dtype dtype and whose shape can be broadcast with self.alpha. For fixed leading dimensions, the last dimension represents counts for the corresponding Dirichlet distribution in self.alpha. x is only legal if it sums up to one. Args: value: float or double Tensor.

tf.contrib.distributions.MultivariateNormalCholesky.mu