tf.contrib.distributions.InverseGamma.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.

virtual Status tensorflow::WritableFile::Sync()=0

tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.loss(sample_loss) Returns the term to add to the surrogate loss. This method is called by surrogate_loss. The input sample_loss should have already had stop_gradient applied to it. This is because the surrogate_loss usually provides a Monte Carlo sample term of the form differentiable_surrogate * sample_loss where sample_loss is considered constant with respect to the input for purposes of the gradient. Args: sample_loss: Tensor, sam

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.loss(final_loss, name='Loss')

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.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.MultivariateNormalFull.name Name prepended to all ops created by this Distribution.

tf.contrib.distributions.Exponential.log_survival_function(value, name='log_survival_function') Log survival function. Given random variable X, the survival function is defined: log_survival_function(x) = Log[ P[X > x] ] = Log[ 1 - P[X <= x] ] = Log[ 1 - cdf(x) ] Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1. Args: value: float or double

tf.contrib.distributions.Beta.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.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.dtype

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