tf.contrib.layers.summarize_tensors(tensors, summarizer=summarize_tensor) Summarize a set of tensors.

tf.contrib.distributions.Beta.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.distributions.MultivariateNormalDiag.is_continuous

tf.contrib.distributions.Categorical.std(name='std') Standard deviation.

tf.contrib.learn.monitors.StopAtStep.step_end(step, output)

tf.nn.rnn_cell.GRUCell.__init__(num_units, input_size=None, activation=tanh)

tf.contrib.graph_editor.detach_outputs(sgv, control_outputs=None) Detach the outputa of a subgraph view. Args: sgv: the subgraph view to be detached. This argument is converted to a subgraph using the same rules as the function subgraph.make_view. Note that sgv is modified in place. control_outputs: a util.ControlOutputs instance or None. If not None the control outputs are also detached. Returns: A tuple (sgv, output_placeholders) where sgv is a new subgraph view of the detached subgraph;

tf.contrib.distributions.Mixture.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.graph_editor.copy_with_input_replacements(sgv, replacement_ts, dst_graph=None, dst_scope='', src_scope='', reuse_dst_scope=False) Copy a subgraph, replacing some of its inputs. Note a replacement only happens if the tensor to be replaced is an input of the given subgraph. The inputs of a subgraph can be queried using sgv.inputs. Args: sgv: the source subgraph-view. This argument is converted to a subgraph using the same rules as the function subgraph.make_view. replacement_ts: dic

tf.self_adjoint_eig(tensor, name=None) Computes the eigen decomposition of a batch of self-adjoint matrices. Computes the eigenvalues and eigenvectors of the innermost N-by-N matrices in tensor such that tensor[...,:,:] * v[..., :,i] = e[..., i] * v[...,:,i], for i=0...N-1. Args: tensor: Tensor of shape [..., N, N]. Only the lower triangular part of each inner inner matrix is referenced. name: string, optional name of the operation. Returns: e: Eigenvalues. Shape is [..., N]. v: Eigenvec