tf.contrib.learn.Estimator.evaluate(x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None) See

tf.nn.rnn_cell.BasicRNNCell.__init__(num_units, input_size=None, activation=tanh)

tf.contrib.distributions.NormalWithSoftplusSigma.mean(name='mean') Mean.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.is_reparameterized

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

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.variance(name='variance') Variance. Additional

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.validate_args Python boolean indicated possibly expensive checks

tf.contrib.distributions.Poisson.get_batch_shape() Shape of a single sample from a single event index as a TensorShape

tf.contrib.distributions.Binomial.validate_args Python boolean indicated possibly expensive checks are enabled.

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.distribution