tf.contrib.learn.TensorFlowEstimator.get_variable_value(name) Returns value of the variable given by name. Args: name: string, name of the tensor. Returns: Numpy array - value of the tensor.

tf.contrib.learn.DNNClassifier.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None) See trainable.Trainable.

tf.contrib.distributions.WishartCholesky.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler(f, log_p, sampling_dist_q, z=None, n=None, seed=None, name='expectation_importance_sampler') Monte Carlo estimate of E_p[f(Z)] = E_q[f(Z) p(Z) / q(Z)]. With p(z) := exp{log_p(z)}, this Op returns n^{-1} sum_{i=1}^n [ f(z_i) p(z_i) / q(z_i) ], z_i ~ q, \approx E_q[ f(Z) p(Z) / q(Z) ] = E_p[f(Z)] This integral is done in log-space with max-subtraction to better handle the often extreme values that f(z) p(z) / q(z) can take o

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

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

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)