tf.contrib.distributions.ExponentialWithSoftplusLam.is_continuous

tf.contrib.graph_editor.SubGraphView.inputs The input tensors of this subgraph view.

tf.contrib.learn.monitors.CaptureVariable.__init__(var_name, every_n=100, first_n=1) Initializes a CaptureVariable monitor.

tf.contrib.distributions.Exponential.get_batch_shape() Shape of a single sample from a single event index as a TensorShape

tf.contrib.distributions.BetaWithSoftplusAB.log_prob(value, name='log_prob') Log probability density/mass function (depending

tf.contrib.distributions.Categorical.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given

tf.contrib.learn.TensorFlowRNNRegressor.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None)

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.value(name='value')

virtual Status tensorflow::Session::PRunSetup(const std::vector< string > &input_names, const std::vector< string > &output_names, const std::vector<

tf.contrib.distributions.MultivariateNormalCholesky.log_survival_function(value, name='log_survival_function') Log survival function