tf.contrib.learn.DNNClassifier.get_variable_value(name) Returns value of the variable given by name. Args: name: string, name of the tensor. Returns: Tensor object.

virtual void tensorflow::Env::SchedClosureAfter(int64 micros, std::function< void()> closure)=0

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagTensor.dtype

tf.contrib.distributions.WishartCholesky.validate_args Python boolean indicated possibly expensive checks are enabled.

tf.contrib.bayesflow.entropy.renyi_ratio(log_p, q, alpha, z=None, n=None, seed=None, name='renyi_ratio') Monte Carlo estimate of the ratio appearing in Renyi divergence. This can be used to compute the Renyi (alpha) divergence, or a log evidence approximation based on Renyi divergence.

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

tf.scan(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, swap_memory=False, infer_shape=True, name=None) scan on the list of tensors unpacked from elems on dimension 0. The simplest version of scan repeatedly applies the callable fn to a sequence of elements from first to last. The elements are made of the tensors unpacked from elems on dimension 0. The callable fn takes two tensors as arguments. The first argument is the accumulated value computed from the preceding invoca

tf.contrib.distributions.Mixture.mode(name='mode') Mode.

tf.contrib.distributions.Exponential.beta Inverse scale parameter.

tf.contrib.distributions.ExponentialWithSoftplusLam.validate_args Python boolean indicated possibly expensive checks are enabled.