tf.contrib.distributions.StudentT.sample_n(n, seed=None, name='sample_n') Generate n samples.

tf.contrib.bayesflow.stochastic_tensor.UniformTensor.value_type

tf.contrib.distributions.Mixture.__init__(cat, components, validate_args=False, allow_nan_stats=True, name='Mixture') Initialize

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

tf.contrib.graph_editor.get_walks_union_ops(forward_seed_ops, backward_seed_ops, forward_inclusive=True, backward_inclusive=True, within_ops=None, control_inputs=False

tf.contrib.learn.LinearRegressor.get_variable_value(name) Returns value of the variable given by name.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.variance(name='variance') Variance.

TensorShapeIter tensorflow::TensorShape::end() const

tf.contrib.distributions.Categorical.sample_n(n, seed=None, name='sample_n') Generate n samples.

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