tf.exp(x, name=None) Computes exponential of x element-wise. \(y = e^x\). Args: x: A Tensor. Must be one of the following types: half, float32, float64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.FixedLenSequenceFeature.shape Alias for field number 0

tf.contrib.distributions.InverseGamma.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.SparseTensor.shape A 1-D Tensor of int64 representing the shape of the dense tensor.

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

class tf.contrib.learn.ModeKeys Standard names for model modes. The following standard keys are defined: TRAIN: training mode. EVAL: evaluation mode. INFER: inference mode.

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

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

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.value_type

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)