tf.contrib.distributions.Normal.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.

tf.TensorArray.grad(source, flow=None, name=None)

tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.entropy(name='entropy')

tf.errors.InternalError.__init__(node_def, op, message) Creates an InternalError.

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

tf.contrib.learn.monitors.ValidationMonitor.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.learn.monitors.ValidationMonitor.best_value Returns the best early stopping metric value found so far.

tf.contrib.distributions.Chi2.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.Laplace.scale Distribution parameter for scale.

tf.contrib.distributions.MultivariateNormalDiag.is_reparameterized