old_contents: ################################################################################
  # Copyright 2019 DeepMind Technologies Limited
  #
  #     Licensed under the Apache License, Version 2.0 (the "License");
  #     you may not use this file except in compliance with the License.
  #     You may obtain a copy of the License at
  #
  #         https://www.apache.org/licenses/LICENSE-2.0
  #
  #     Unless required by applicable law or agreed to in writing, software
  #     distributed under the License is distributed on an "AS IS" BASIS,
  #     WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  #     See the License for the specific language governing permissions and
  #     limitations under the License.
  ################################################################################
  """Runs the unsupervised i.i.d benchmark experiments in the paper."""
  
  from absl import app
  from absl import flags
  
  from curl import training
  
  flags.DEFINE_enum('dataset', 'mnist', ['mnist', 'omniglot'], 'Dataset.')
  
  FLAGS = flags.FLAGS
  
  
  def main(unused_argv):
    if FLAGS.dataset == 'mnist':
      n_y = 25
      n_y_active = 25
      n_z = 50
    else:  # omniglot
      n_y = 100
      n_y_active = 100
      n_z = 100
  
    training.run_training(
        dataset=FLAGS.dataset,
        n_y=n_y,
        n_y_active=n_y_active,
        n_z=n_z,
        output_type='bernoulli',
        training_data_type='iid',
        n_concurrent_classes=1,
        lr_init=5e-4,
        lr_factor=1.,
        lr_schedule=[1],
        blend_classes=False,
        train_supervised=False,
        n_steps=100000,
        report_interval=10000,
        knn_values=[3, 5, 10],
        random_seed=1,
        encoder_kwargs={
            'encoder_type': 'multi',
            'n_enc': [500, 500],
            'enc_strides': [1],
        },
        decoder_kwargs={
            'decoder_type': 'single',
            'n_dec': [500],
            'dec_up_strides': None,
        },
        dynamic_expansion=False,
        ll_thresh=-0.0,
        classify_with_samples=True,
        gen_replay_type=None,
        use_supervised_replay=False,
        )
  
  if __name__ == '__main__':
    app.run(main)
  
new_contents: ################################################################################
  # Copyright 2019 DeepMind Technologies Limited
  #
  #     Licensed under the Apache License, Version 2.0 (the "License");
  #     you may not use this file except in compliance with the License.
  #     You may obtain a copy of the License at
  #
  #         https://www.apache.org/licenses/LICENSE-2.0
  #
  #     Unless required by applicable law or agreed to in writing, software
  #     distributed under the License is distributed on an "AS IS" BASIS,
  #     WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  #     See the License for the specific language governing permissions and
  #     limitations under the License.
  ################################################################################
  """Runs the unsupervised i.i.d benchmark experiments in the paper."""
  
  from absl import app
  from absl import flags
  
  from curl import training
  
  flags.DEFINE_enum('dataset', 'mnist', ['mnist', 'omniglot'], 'Dataset.')
  
  FLAGS = flags.FLAGS
  
  
  def main(unused_argv):
    if FLAGS.dataset == 'mnist':
      n_y = 25
      n_y_active = 1
      n_z = 50
    else:  # omniglot
      n_y = 100
      n_y_active = 1
      n_z = 100
  
    training.run_training(
        dataset=FLAGS.dataset,
        n_y=n_y,
        n_y_active=n_y_active,
        n_z=n_z,
        output_type='bernoulli',
        training_data_type='iid',
        n_concurrent_classes=1,
        lr_init=5e-4,
        lr_factor=1.,
        lr_schedule=[1],
        blend_classes=False,
        train_supervised=False,
        n_steps=100000,
        report_interval=10000,
        knn_values=[3],
        random_seed=1,
        encoder_kwargs={
            'encoder_type': 'multi',
            'n_enc': [500, 500],
            'enc_strides': [1],
        },
        decoder_kwargs={
            'decoder_type': 'single',
            'n_dec': [500],
            'dec_up_strides': None,
        },
        dynamic_expansion=True,
        ll_thresh=-200.0,
        classify_with_samples=True,
        gen_replay_type=None,
        use_supervised_replay=False,
        )
  
  if __name__ == '__main__':
    app.run(main)
  
subject: Fix default config parameters.