tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor.input_dict

tf.Session.as_default() Returns a context manager that makes this object the default session. Use with the with keyword to specify that calls to Operation.run() or Tensor.eval() should be executed in this session. c = tf.constant(..) sess = tf.Session() with sess.as_default(): assert tf.get_default_session() is sess print(c.eval()) To get the current default session, use tf.get_default_session(). N.B. The as_default context manager does not close the session when you exit the context, an

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.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.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.Gamma.survival_function(value, name='survival_function') Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x] = 1 - P[X <= x] = 1 - cdf(x). Args: value: float or double Tensor. name: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.

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.