tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.sample_n(n, seed=None, name='sample_n') Generate n

tf.nn.rnn_cell.MultiRNNCell.__init__(cells, state_is_tuple=True) Create a RNN cell composed sequentially of a number of RNNCells

tf.contrib.learn.monitors.CheckpointSaver.set_estimator(estimator) A setter called automatically by the target estimator.

tf.get_session_handle(data, name=None) Return the handle of data. This is EXPERIMENTAL

tf.contrib.distributions.ExponentialWithSoftplusLam.log_cdf(value, name='log_cdf') Log cumulative distribution function.

tf.contrib.learn.monitors.get_default_monitors(loss_op=None, summary_op=None, save_summary_steps=100, output_dir=None, summary_writer=None)

tf.contrib.learn.monitors.LoggingTrainable.every_n_post_step(step, session) Callback after a step is finished or end()

tf.contrib.learn.monitors.LoggingTrainable.every_n_step_end(step, outputs)

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

tf.contrib.distributions.LaplaceWithSoftplusScale.sample_n(n, seed=None, name='sample_n') Generate n samples.