tf.contrib.bayesflow.stochastic_tensor.MixtureTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.clone(name=None, **dist_args)

tf.contrib.distributions.WishartCholesky.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.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.distribution

tf.contrib.learn.monitors.NanLoss.step_end(step, output) Overrides BaseMonitor.step_end. When overriding this method, you must call the super implementation. Args: step: int, the current value of the global step. output: dict mapping string values representing tensor names to the value resulted from running these tensors. Values may be either scalars, for scalar tensors, or Numpy array, for non-scalar tensors. Returns: bool, the result of every_n_step_end, if that was called this step, or

tf.contrib.framework.get_unique_variable(var_op_name) Gets the variable uniquely identified by that var_op_name. Args: var_op_name: the full name of the variable op, including the scope. Returns: a tensorflow variable. Raises: ValueError: if no variable uniquely identified by the name exists.

tf.contrib.distributions.Beta.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Additional documentation from Beta: Note that the argument x must be a non-negative floating point tensor whose shape can be broadcast with self.a and self.b. For fixed leading dimensions, the last dimension represents counts for the corresponding Beta distribution in self.a and self.b. x is only legal if 0 < x < 1. Args: value: float or double Tensor. name: The name t

tf.contrib.distributions.QuantizedDistribution.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). Additional documentation from QuantizedDistribution: For whole numbers y, survival_function(y) := P[Y > y] = 0, if y >= upper_cutoff, = 1, if y < lower_cutoff

tf.contrib.distributions.ExponentialWithSoftplusLam.is_continuous

tf.contrib.distributions.Uniform.dtype The DType of Tensors handled by this Distribution.