tf.train.shuffle_batch_join(tensors_list, batch_size, capacity, min_after_dequeue, seed=None, enqueue_many=False, shapes=None, allow_smaller_final_batch=False, shared_name=None

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

tf.contrib.distributions.MultivariateNormalDiag.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified

tf.nn.rnn_cell.LSTMCell.output_size

tf.contrib.bayesflow.stochastic_tensor.DirichletMultinomialTensor.mean(name='mean')

class tf.contrib.distributions.Exponential The Exponential distribution with rate parameter lam. The

tf.contrib.util.make_ndarray(tensor) Create a numpy ndarray from a tensor. Create a numpy ndarray

tf.contrib.bayesflow.stochastic_tensor.GammaTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

class tf.contrib.distributions.Categorical Categorical distribution. The categorical distribution

tf.contrib.distributions.Distribution.log_pmf(value, name='log_pmf') Log probability mass function. Args: