class tf.contrib.distributions.LaplaceWithSoftplusScale Laplace with softplus applied to scale.

tf.contrib.distributions.NormalWithSoftplusSigma.is_reparameterized

tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.__init__(n=1, stop_gradient=False) Sample n times and reshape the outer 2 axes so rank does not change. Args: n: A python integer or int32 tensor. The number of samples to take. stop_gradient: If True, StochasticTensors' values are wrapped in stop_gradient, to avoid backpropagation through.

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

tf.contrib.distributions.Chi2WithAbsDf.alpha Shape parameter.

tf.ReaderBase.serialize_state(name=None) Produce a string tensor that encodes the state of a reader. Not all Readers support being serialized, so this can produce an Unimplemented error. Args: name: A name for the operation (optional). Returns: A string Tensor.

TTypes<T>::UnalignedFlat tensorflow::Tensor::unaligned_flat()

tf.contrib.distributions.InverseGamma.beta Scale parameter.

tf.contrib.learn.monitors.ExportMonitor.every_n_step_begin(step) Callback before every n'th step begins. Args: step: int, the current value of the global step. Returns: A list of tensors that will be evaluated at this step.

tf.contrib.distributions.WishartFull.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.