tf.contrib.distributions.Beta.b

tf.contrib.distributions.Beta.b Shape parameter.

tf.contrib.graph_editor.SubGraphView.input_index()

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

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.clone()

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.clone(name=None, **dist_args)

tf.contrib.learn.BaseEstimator.model_dir

tf.contrib.learn.BaseEstimator.model_dir

tf.contrib.distributions.MultivariateNormalFull.is_reparameterized

tf.contrib.distributions.MultivariateNormalFull.is_reparameterized

tf.contrib.bayesflow.stochastic_tensor.BetaTensor.value()

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

tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.graph

tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.graph

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.is_continuous

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.is_continuous

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.__init__()

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.contrib.distributions.Multinomial.n

tf.contrib.distributions.Multinomial.n Number of trials.