class tf.contrib.graph_editor.ControlOutputs The control outputs topology.

tf.contrib.distributions.MultivariateNormalDiag.mode(name='mode') Mode.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.__init__(alpha, beta, validate_args=False, allow_nan_stats=True, name='InverseGammaWithSoftplusAlphaBeta')

string tensorflow::Status::State::msg

tf.contrib.distributions.Normal.log_pdf(value, name='log_pdf') Log probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

tf.nn.rnn_cell.EmbeddingWrapper.__init__(cell, embedding_classes, embedding_size, initializer=None) Create a cell with an added input embedding. Args: cell: an RNNCell, an embedding will be put before its inputs. embedding_classes: integer, how many symbols will be embedded. embedding_size: integer, the size of the vectors we embed into. initializer: an initializer to use when creating the embedding; if None, the initializer from variable scope or a default one is used. Raises: TypeErro

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

tf.contrib.learn.monitors.BaseMonitor.end(session=None) Callback at the end of training/evaluation. Args: session: A tf.Session object that can be used to run ops. Raises: ValueError: if we've not begun a run.

tf.nn.rnn_cell.LSTMStateTuple.c Alias for field number 0

tf.contrib.learn.monitors.EveryN.__init__(every_n_steps=100, first_n_steps=1) Initializes an EveryN monitor. Args: every_n_steps: int, the number of steps to allow between callbacks. first_n_steps: int, specifying the number of initial steps during which the callbacks will always be executed, regardless of the value of every_n_steps. Note that this value is relative to the global step