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Dushyant Rao
80 lines
2.6 KiB
Python
80 lines
2.6 KiB
Python
################################################################################
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# Copyright 2019 DeepMind Technologies Limited
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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################################################################################
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"""Training file to run most of the experiments in the paper.
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The default parameters corresponding to the first set of experiments in Section
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4.2.
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For the expansion ablation, run with different ll_thresh values as in the paper.
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Note that n_y_active represents the number of *active* components at the
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start, and should be set to 1, while n_y represents the maximum number of
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components allowed, and should be set sufficiently high (eg. n_y = 100).
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For the MGR ablation, setting use_sup_replay = True switches to using SMGR,
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and the gen_replay_type flag can switch between fixed and dynamic replay. The
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generative snapshot period is set automatically in the train_curl.py file based
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on these settings (ie. the data_period variable), so the 0.1T runs can be
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reproduced by dividing this value by 10.
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"""
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from absl import app
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from absl import flags
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from curl import training
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flags.DEFINE_enum('dataset', 'mnist', ['mnist', 'omniglot'], 'Dataset.')
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FLAGS = flags.FLAGS
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def main(unused_argv):
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training.run_training(
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dataset=FLAGS.dataset,
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output_type='bernoulli',
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n_y=30,
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n_y_active=1,
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training_data_type='sequential',
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n_concurrent_classes=1,
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lr_init=1e-3,
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lr_factor=1.,
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lr_schedule=[1],
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blend_classes=False,
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train_supervised=False,
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n_steps=100000,
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report_interval=10000,
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knn_values=[10],
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random_seed=1,
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encoder_kwargs={
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'encoder_type': 'multi',
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'n_enc': [1200, 600, 300, 150],
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'enc_strides': [1],
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},
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decoder_kwargs={
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'decoder_type': 'single',
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'n_dec': [500, 500],
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'dec_up_strides': None,
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},
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n_z=32,
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dynamic_expansion=True,
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ll_thresh=-200.0,
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classify_with_samples=False,
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gen_replay_type='fixed',
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use_supervised_replay=False,
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)
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if __name__ == '__main__':
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app.run(main)
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