tf.contrib.learn.extract_pandas_matrix(data) Extracts numpy matrix from pandas DataFrame. Args:

tf.contrib.bayesflow.stochastic_tensor.BetaTensor.dtype

tf.contrib.distributions.QuantizedDistribution.__init__(base_dist_cls, lower_cutoff=None, upper_cutoff=None, name='QuantizedDistribution', **base_dist_args)

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

tf.nn.rnn_cell.OutputProjectionWrapper.__call__(inputs, state, scope=None) Run the cell and output projection on inputs, starting

tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor.value(name=None)

tf.contrib.distributions.WishartFull.log_normalizing_constant(name='log_normalizing_constant') Computes the log normalizing constant

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

tf.contrib.rnn.GRUBlockCell.zero_state(batch_size, dtype) Return zero-filled state tensor(s). Args:

class tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagPlusVDVTTensor MultivariateNormalDiagPlusVDVTTensor