def main()

in example_zoo/tensorflow/probability/latent_dirichlet_allocation_distributions/trainer/latent_dirichlet_allocation_distributions.py [0:0]

37 lines of code
10 McCabe index (conditional complexity)


def main(argv):
  del argv  # unused

  params = FLAGS.flag_values_dict()
  params["layer_sizes"] = [int(units) for units in params["layer_sizes"]]
  params["activation"] = getattr(tf.nn, params["activation"])
  if FLAGS.delete_existing and tf.io.gfile.exists(FLAGS.model_dir):
    tf.compat.v1.logging.warn("Deleting old log directory at {}".format(
        FLAGS.model_dir))
    tf.io.gfile.rmtree(FLAGS.model_dir)
  tf.io.gfile.makedirs(FLAGS.model_dir)

  if FLAGS.fake_data:
    train_input_fn, eval_input_fn, vocabulary = build_fake_input_fns(
        FLAGS.batch_size)
  else:
    train_input_fn, eval_input_fn, vocabulary = build_input_fns(
        FLAGS.data_dir, FLAGS.batch_size)
  params["vocabulary"] = vocabulary

  estimator = tf.estimator.Estimator(
      model_fn,
      params=params,
      config=tf.estimator.RunConfig(
          model_dir=FLAGS.model_dir,
          save_checkpoints_steps=FLAGS.viz_steps,
      ),
  )

  for _ in range(FLAGS.max_steps // FLAGS.viz_steps):
    estimator.train(train_input_fn, steps=FLAGS.viz_steps)
    eval_results = estimator.evaluate(eval_input_fn)
    # Print the evaluation results. The keys are strings specified in
    # eval_metric_ops, and the values are NumPy scalars/arrays.
    for key, value in eval_results.items():
      print(key)
      if key == "topics":
        # Topics description is a np.array which prints better row-by-row.
        for s in value:
          print(s)
      else:
        print(str(value))
      print("")
    print("")