tf.nn.rnn_cell.MultiRNNCell.__call__(inputs, state, scope=None) Run this multi-layer cell on inputs, starting from state.

tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.loss(final_loss, name='Loss')

tf.parse_single_example(serialized, features, name=None, example_names=None) Parses a single Example proto. Similar to parse_example, except: For dense tensors, the returned Tensor is identical to the output of parse_example, except there is no batch dimension, the output shape is the same as the shape given in dense_shape. For SparseTensors, the first (batch) column of the indices matrix is removed (the indices matrix is a column vector), the values vector is unchanged, and the first (batch_s

tf.train.string_input_producer(string_tensor, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, name=None) Output strings (e.g. filenames) to a queue for an input pipeline. Args: string_tensor: A 1-D string tensor with the strings to produce. num_epochs: An integer (optional). If specified, string_input_producer produces each string from string_tensor num_epochs times before generating an OutOfRange error. If not specified, string_input_producer can cycle through the s

tf.contrib.distributions.Chi2WithAbsDf.mode(name='mode') Mode. Additional documentation from Gamma: The mode of a gamma distribution is (alpha - 1) / beta when alpha > 1, and NaN otherwise. If self.allow_nan_stats is False, an exception will be raised rather than returning NaN.

tf.TFRecordReader.num_records_produced(name=None) Returns the number of records this reader has produced. This is the same as the number of Read executions that have succeeded. Args: name: A name for the operation (optional). Returns: An int64 Tensor.

tf.train.limit_epochs(tensor, num_epochs=None, name=None) Returns tensor num_epochs times and then raises an OutOfRange error. Args: tensor: Any Tensor. num_epochs: A positive integer (optional). If specified, limits the number of steps the output tensor may be evaluated. name: A name for the operations (optional). Returns: tensor or OutOfRange. Raises: ValueError: if num_epochs is invalid.

tf.train.input_producer(input_tensor, element_shape=None, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, summary_name=None, name=None) Output the rows of input_tensor to a queue for an input pipeline. Args: input_tensor: A tensor with the rows to produce. Must be at least one-dimensional. Must either have a fully-defined shape, or element_shape must be defined. element_shape: (Optional.) A TensorShape representing the shape of a row of input_tensor, if it cannot be

tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.entropy(name='entropy')

tf.contrib.distributions.MultivariateNormalCholesky.log_sigma_det(name='log_sigma_det') Log of determinant of covariance matrix.