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.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.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.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.TensorFlowEstimator.save(path) Saves checkpoints and graph to given path. Args: path: Folder to save model to.

bool tensorflow::TensorShape::IsSameSize(const TensorShape &b) const Returns true if *this and b have the same sizes. Ignores dimension names.

Status tensorflow::Env::Stat(const string &fname, FileStatistics *stat) Obtains statistics for the given path.

tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.dtype

tf.contrib.graph_editor.filter_ts_from_regex(ops, regex) Get all the tensors linked to ops that match the given regex. Args: ops: an object convertible to a list of tf.Operation. regex: a regular expression matching the tensors' name. For example, "^foo(/.*)?:\d+$" will match all the tensors in the "foo" scope. Returns: A list of tf.Tensor. Raises: TypeError: if ops cannot be converted to a list of tf.Operation.

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