class tf.contrib.distributions.Distribution A generic probability distribution base class. Distribution is a base class for constructing and organizing properties (e.g., mean, variance) of random variables (e.g, Bernoulli, Gaussian).

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

tf.contrib.distributions.BetaWithSoftplusAB.a_b_sum Sum of parameters.

tf.contrib.distributions.Beta.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.OpError.message The error message that describes the error.

tf.contrib.learn.monitors.LoggingTrainable.step_end(step, output) Overrides BaseMonitor.step_end. When overriding this method, you must call the super implementation. Args: step: int, the current value of the global step. output: dict mapping string values representing tensor names to the value resulted from running these tensors. Values may be either scalars, for scalar tensors, or Numpy array, for non-scalar tensors. Returns: bool, the result of every_n_step_end, if that was called this

tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.mean(name='mean')

tf.SparseTensor.indices The indices of non-zero values in the represented dense tensor. Returns: A 2-D Tensor of int64 with shape [N, ndims], where N is the number of non-zero values in the tensor, and ndims is the rank.

string tensorflow::TensorShapeUtils::ShapeListString(const gtl::ArraySlice< TensorShape > &shapes)

tf.contrib.learn.monitors.CaptureVariable.epoch_begin(epoch) Begin epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've already begun an epoch, or epoch < 0.