tf.contrib.distributions.Chi2.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.distributions.WishartFull.allow_nan_stats Python boolean describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1

tf.contrib.distributions.NormalWithSoftplusSigma.mean(name='mean') Mean.

tf.contrib.distributions.NormalWithSoftplusSigma.mode(name='mode') Mode.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.mode(name='mode') Mode.

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

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

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

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