tf.contrib.distributions.Beta.a_b_sum Sum of parameters.

tf.contrib.bayesflow.stochastic_tensor.QuantizedDistributionTensor.input_dict

tf.contrib.distributions.StudentT.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.beta Scale parameter.

tf.contrib.learn.monitors.NanLoss.every_n_step_begin(step)

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.dtype

tf.contrib.distributions.Distribution.std(name='std') Standard deviation.

tf.contrib.learn.LinearRegressor.get_variable_names() Returns list of all variable names in this model. Returns: List of names.

tf.contrib.distributions.MultivariateNormalCholesky.get_batch_shape() Shape of a single sample from a single event index as a TensorShape. Same meaning as batch_shape. May be only partially defined. Returns: batch_shape: TensorShape, possibly unknown.

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