research/gam/gam/trainer/trainer_classification.py [154:193]:
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        model=model,
        abs_loss_chg_tol=abs_loss_chg_tol,
        rel_loss_chg_tol=rel_loss_chg_tol,
        loss_chg_iter_below_tol=loss_chg_iter_below_tol)
    self.data = data
    self.trainer_agr = trainer_agr
    self.batch_size = batch_size
    self.min_num_iter = min_num_iter
    self.max_num_iter = max_num_iter
    self.num_iter_after_best_val = num_iter_after_best_val
    self.max_num_iter_cotrain = max_num_iter_cotrain
    self.enable_summaries = enable_summaries
    self.summary_step = summary_step
    self.summary_dir = summary_dir
    self.warm_start = warm_start
    self.gradient_clip = gradient_clip
    self.logging_step = logging_step
    self.eval_step = eval_step
    self.checkpoint_path = (
        os.path.join(checkpoints_dir, 'classif_best.ckpt')
        if checkpoints_dir is not None else None)
    self.weight_decay_initial = weight_decay
    self.weight_decay_schedule = weight_decay_schedule
    self.num_pairs_reg = num_pairs_reg
    self.reg_weight_ll = reg_weight_ll
    self.reg_weight_lu = reg_weight_lu
    self.reg_weight_uu = reg_weight_uu
    self.reg_weight_vat = reg_weight_vat
    self.use_ent_min = use_ent_min
    self.penalize_neg_agr = penalize_neg_agr
    self.use_l2_classif = use_l2_classif
    self.first_iter_original = first_iter_original
    self.iter_cotrain = iter_cotrain
    self.lr_initial = lr_initial
    self.lr_decay_steps = lr_decay_steps
    self.lr_decay_rate = lr_decay_rate
    self.use_graph = use_graph

    # Build TensorFlow graph.
    logging.info('Building classification TensorFlow graph...')
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research/gam/gam/trainer/trainer_classification_gcn.py [154:193]:
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        model=model,
        abs_loss_chg_tol=abs_loss_chg_tol,
        rel_loss_chg_tol=rel_loss_chg_tol,
        loss_chg_iter_below_tol=loss_chg_iter_below_tol)
    self.data = data
    self.trainer_agr = trainer_agr
    self.batch_size = batch_size
    self.min_num_iter = min_num_iter
    self.max_num_iter = max_num_iter
    self.num_iter_after_best_val = num_iter_after_best_val
    self.max_num_iter_cotrain = max_num_iter_cotrain
    self.enable_summaries = enable_summaries
    self.summary_step = summary_step
    self.summary_dir = summary_dir
    self.warm_start = warm_start
    self.gradient_clip = gradient_clip
    self.logging_step = logging_step
    self.eval_step = eval_step
    self.checkpoint_path = (
        os.path.join(checkpoints_dir, 'classif_best.ckpt')
        if checkpoints_dir is not None else None)
    self.weight_decay_initial = weight_decay
    self.weight_decay_schedule = weight_decay_schedule
    self.num_pairs_reg = num_pairs_reg
    self.reg_weight_ll = reg_weight_ll
    self.reg_weight_lu = reg_weight_lu
    self.reg_weight_uu = reg_weight_uu
    self.reg_weight_vat = reg_weight_vat
    self.use_ent_min = use_ent_min
    self.penalize_neg_agr = penalize_neg_agr
    self.use_l2_classif = use_l2_classif
    self.first_iter_original = first_iter_original
    self.iter_cotrain = iter_cotrain
    self.lr_initial = lr_initial
    self.lr_decay_steps = lr_decay_steps
    self.lr_decay_rate = lr_decay_rate
    self.use_graph = use_graph

    # Build TensorFlow graph.
    logging.info('Building classification TensorFlow graph...')
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