tf.contrib.distributions.ExponentialWithSoftplusLam.is_reparameterized

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

tf.SparseTensor.op The Operation that produces values as an output.

tf.contrib.distributions.WishartCholesky.survival_function(value, name='survival_function') Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x] = 1 - P[X <= x] = 1 - cdf(x). Args: value: float or double Tensor. name: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.

tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.input_dict

tf.contrib.distributions.ExponentialWithSoftplusLam.lam

Status tensorflow::EnvWrapper::LoadLibrary(const char *library_filename, void **handle) override

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.