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

tensorflow::Session::Session()

TTypes<T>::Vec tensorflow::Tensor::vec() Return the tensor data as an Eigen::Tensor with the type and sizes of this Tensor. Use these methods when you know the data type and the number of dimensions of the Tensor and you want an Eigen::Tensor automatically sized to the Tensor sizes. The implementation check fails if either type or sizes mismatch. Example: Tensor my_mat(...built with Shape{rows: 3, cols: 5}...); auto mat = my_mat.matrix<T>(); // 2D Eigen::Tensor, 3 x 5. auto mat

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

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.distribution

tf.contrib.learn.TensorFlowEstimator.model_dir

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

tf.contrib.distributions.Poisson.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.errors.DeadlineExceededError.__init__(node_def, op, message) Creates a DeadlineExceededError.

tf.contrib.distributions.NormalWithSoftplusSigma.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.