tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.entropy(name='entropy') Shanon entropy in nats. Additional documentation from InverseGamma: This is defined to be entropy = alpha - log(beta) + log(Gamma(alpha)) + (1-alpha)digamma(alpha) where digamma(alpha) is the digamma function.

tf.contrib.learn.BaseEstimator.__repr__()

tf.WholeFileReader.num_work_units_completed(name=None) Returns the number of work units this reader has finished processing. Args: name: A name for the operation (optional). Returns: An int64 Tensor.

tf.contrib.distributions.Distribution.is_continuous

tf.contrib.distributions.Categorical.name Name prepended to all ops created by this Distribution.

tf.contrib.distributions.MultivariateNormalDiag.sigma Dense (batch) covariance matrix, if available.

tf.contrib.distributions.Bernoulli.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.

tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.dtype

tf.contrib.learn.TensorFlowRNNRegressor.save(path) Saves checkpoints and graph to given path. Args: path: Folder to save model to.

tf.contrib.graph_editor.OpMatcher.__call__(op) Evaluate if the op matches or not.