tf.contrib.distributions.LaplaceWithSoftplusScale.entropy(name='entropy') Shanon entropy in nats.

tf.contrib.util.constant_value(tensor) Returns the constant value of the given tensor, if efficiently calculable. This function attempts to partially evaluate the given tensor, and returns its value as a numpy ndarray if this succeeds. TODO(mrry): Consider whether this function should use a registration mechanism like gradients and ShapeFunctions, so that it is easily extensible. NOTE: If constant_value(tensor) returns a non-None result, it will no longer be possible to feed a different value

class tf.contrib.distributions.Chi2WithAbsDf Chi2 with parameter transform df = floor(abs(df)).

tf.contrib.distributions.Laplace.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.

class tf.contrib.bayesflow.stochastic_tensor.BinomialTensor BinomialTensor is a StochasticTensor backed by the distribution Binomial.

Status tensorflow::Env::NewWritableFile(const string &fname, std::unique_ptr< WritableFile > *result) Creates an object that writes to a new file with the specified name. Deletes any existing file with the same name and creates a new file. On success, stores a pointer to the new file in *result and returns OK. On failure stores NULL in *result and returns non-OK. The returned file will only be accessed by one thread at a time. The ownership of the returned WritableFile is passed to t

tf.contrib.graph_editor.SubGraphView.is_passthrough(t) Check whether a tensor is passthrough.

tf.contrib.util.ops_used_by_graph_def(graph_def) Collect the list of ops used by a graph. Does not validate that the ops are all registered. Args: graph_def: A GraphDef proto, as from graph.as_graph_def(). Returns: A list of strings, each naming an op used by the graph.

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

tf.contrib.distributions.LaplaceWithSoftplusScale.log_pdf(value, name='log_pdf') Log probability density function. 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. Raises: TypeError: if not is_continuous.