tf.contrib.bayesflow.stochastic_tensor.GammaTensor.entropy(name='entropy')

tf.contrib.distributions.InverseGamma.__init__(alpha, beta, validate_args=False, allow_nan_stats=True, name='InverseGamma') Construct InverseGamma distributions with parameters alpha and beta. The parameters alpha and beta must be shaped in a way that supports broadcasting (e.g. alpha + beta is a valid operation). Args: alpha: Floating point tensor, the shape params of the distribution(s). alpha must contain only positive values. beta: Floating point tensor, the scale params of the distribut

tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.graph

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.entropy(name='entropy')

tf.contrib.distributions.StudentT.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.name

tf.contrib.distributions.Bernoulli.p

void tensorflow::EnvWrapper::SchedClosureAfter(int64 micros, std::function< void()> closure) override

Status tensorflow::Env::DeleteFile(const string &fname) Deletes the named file.

tf.SparseTensor.eval(feed_dict=None, session=None) Evaluates this sparse tensor in a Session. Calling this method will execute all preceding operations that produce the inputs needed for the operation that produces this tensor. N.B. Before invoking SparseTensor.eval(), its graph must have been launched in a session, and either a default session must be available, or session must be specified explicitly. Args: feed_dict: A dictionary that maps Tensor objects to feed values. See Session.run() f