tf.contrib.learn.monitors.PrintTensor.every_n_step_begin(step)

tf.contrib.distributions.BernoulliWithSigmoidP.parameters Dictionary of parameters used by this Distribution.

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

class tf.contrib.learn.monitors.NanLoss NaN Loss monitor. Monitors loss and stops training if loss is NaN. Can either fail with exception or just stop training.

tf.contrib.distributions.NormalWithSoftplusSigma.std(name='std') Standard deviation.

class tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor NormalWithSoftplusSigmaTensor is a StochasticTensor backed by the distribution NormalWithSoftplusSigma.

tf.contrib.distributions.Gamma.__init__(alpha, beta, validate_args=False, allow_nan_stats=True, name='Gamma') Construct Gamma 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 inverse scale params of the distribution(s). beta

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.value(name='value')

tf.contrib.distributions.NormalWithSoftplusSigma.mu Distribution parameter for the mean.

tf.contrib.distributions.WishartCholesky.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.