research/gam/gam/experiments/run_train_gam.py [204:240]:
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    'Evaluate the agreement model every this number of iterations.')
flags.DEFINE_bool(
    'warm_start_cls', False,
    'Whether to reinitialize the parameters of the classification model before '
    'retraining (if False), or use the ones from the previous cotrain'
    ' iteration.')
flags.DEFINE_bool(
    'warm_start_agr', False,
    'Whether to reinitialize the parameters of the agreement model before '
    'retraining (if False), or use the ones from the previous cotrain '
    'iteration.')
flags.DEFINE_bool('use_perfect_agreement', False,
                  'Whether to use perfect agreement.')
flags.DEFINE_bool('use_perfect_classifier', False,
                  'Whether to use perfect classifier.')
flags.DEFINE_float('reg_weight_ll', 0.00,
                   'Regularization weight for labeled-labeled edges.')
flags.DEFINE_float('reg_weight_lu', 0.1,
                   'Regularization weight for labeled-unlabeled edges.')
flags.DEFINE_float('reg_weight_uu', 0.05,
                   'Regularization weight for unlabeled-unlabeled edges.')
flags.DEFINE_integer(
    'num_pairs_reg', 128,
    'Number of pairs of nodes to use in the agreement loss term of the '
    'classification model.')
flags.DEFINE_float(
    'reg_weight_vat', 0.0,
    'Regularization weight for the virtual adversarial training (VAT) loss.')
flags.DEFINE_bool(
    'use_ent_min', False,
    'A boolean specifying whether to add entropy minimization to VAT.')
flags.DEFINE_string(
    'aggregation_agr_inputs', 'dist',
    'Operation to apply on the pair of nodes in the agreement model. '
    'Available options are `add`, `dist`, `concat`, `project_add`,'
    '`project_dist`, `project_concat` and None.')
flags.DEFINE_bool(
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research/gam/gam/experiments/run_train_gam_graph.py [182:218]:
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    'Evaluate the agreement model every this number of iterations.')
flags.DEFINE_bool(
    'warm_start_cls', False,
    'Whether to reinitialize the parameters of the classification model before '
    'retraining (if False), or use the ones from the previous cotrain'
    ' iteration.')
flags.DEFINE_bool(
    'warm_start_agr', False,
    'Whether to reinitialize the parameters of the agreement model before '
    'retraining (if False), or use the ones from the previous cotrain '
    'iteration.')
flags.DEFINE_bool('use_perfect_agreement', False,
                  'Whether to use perfect agreement.')
flags.DEFINE_bool('use_perfect_classifier', False,
                  'Whether to use perfect classifier.')
flags.DEFINE_float('reg_weight_ll', 0.00,
                   'Regularization weight for labeled-labeled edges.')
flags.DEFINE_float('reg_weight_lu', 0.1,
                   'Regularization weight for labeled-unlabeled edges.')
flags.DEFINE_float('reg_weight_uu', 0.05,
                   'Regularization weight for unlabeled-unlabeled edges.')
flags.DEFINE_integer(
    'num_pairs_reg', 128,
    'Number of pairs of nodes to use in the agreement loss term of the '
    'classification model.')
flags.DEFINE_float(
    'reg_weight_vat', 0.0,
    'Regularization weight for the virtual adversarial training (VAT) loss.')
flags.DEFINE_bool(
    'use_ent_min', False,
    'A boolean specifying whether to add entropy minimization to VAT.')
flags.DEFINE_string(
    'aggregation_agr_inputs', 'dist',
    'Operation to apply on the pair of nodes in the agreement model. '
    'Available options are `add`, `dist`, `concat`, `project_add`,'
    '`project_dist`, `project_concat` and None.')
flags.DEFINE_bool(
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