tf.contrib.graph_editor.SubGraphView.output_index(t) Find the output index corresponding to given output tensor t. Args: t: the output tensor of this subgraph view. Returns: The index in the self.outputs list. Raises: Error: if t in not an output tensor.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.is_continuous

tf.ReaderBase.__init__(reader_ref, supports_serialize=False) Creates a new ReaderBase. Args: reader_ref: The operation that implements the reader. supports_serialize: True if the reader implementation can serialize its state.

tf.ReaderBase.reset(name=None) Restore a reader to its initial clean state. Args: name: A name for the operation (optional). Returns: The created Operation.

tf.contrib.learn.monitors.PrintTensor.begin(max_steps=None) Called at the beginning of training. When called, the default graph is the one we are executing. Args: max_steps: int, the maximum global step this training will run until. Raises: ValueError: if we've already begun a run.

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.value_type

tf.contrib.distributions.WishartCholesky.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.distributions.Chi2WithAbsDf.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.graph_editor.get_name_scope_ops(ops, scope) Get all the operations under the given scope path. Args: ops: an object convertible to a list of tf.Operation. scope: a scope path. Returns: A list of tf.Operation. Raises: TypeError: if ops cannot be converted to a list of tf.Operation.

tf.contrib.distributions.Bernoulli.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.