tf.contrib.crf.CrfForwardRnnCell.__init__()

tf.contrib.crf.CrfForwardRnnCell.__init__(transition_params) Initialize the CrfForwardRnnCell. Args: transition_params: A [num_tags, num_tags] matrix of binary potentials. This matrix is expanded into a [1, num_tags, num_tags] in preparation for the broadcast summation occurring within the cell.

tf.WholeFileReader.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.bayesflow.stochastic_tensor.CategoricalTensor.dtype

tf.WholeFileReader.supports_serialize

tf.WholeFileReader.supports_serialize Whether the Reader implementation can serialize its state.

tf.contrib.distributions.Bernoulli.prob()

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.

tf.contrib.graph_editor.get_name_scope_ops()

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.Chi2WithAbsDf.log_pmf()

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.distributions.WishartCholesky.sample()

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.bayesflow.stochastic_tensor.WishartCholeskyTensor.value_type

tf.ReaderBase.__init__()

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.