crop_yield_train_cross_location.py [27:54]:
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                                    n_tsteps, train_years=None):
    batch_size = 64
    test_batch_size = 128
    n_triplets_per_file = 1
    epochs = 50
    attention_nhead = 8
    adam_lr = 0.001
    adam_betas = (0.9, 0.999)
    n_experiment = 2

    neighborhood_radius = args.neighborhood_radius
    distant_radius = args.distant_radius
    weight_decay = args.weight_decay
    tilenet_margin = args.tilenet_margin
    tilenet_l2 = args.tilenet_l2
    tilenet_ltn = args.tilenet_ltn
    tilenet_zdim = args.tilenet_zdim
    attention_layer = args.attention_layer
    attention_dff = args.attention_dff
    sentence_embedding = args.sentence_embedding
    dropout = args.dropout
    unsup_weight = args.unsup_weight
    patience = args.patience if args.patience != 9999 else None
    feature = args.feature
    feature_len = args.feature_len
    query_type = args.query_type

    assert tilenet_zdim % attention_nhead == 0
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crop_yield_train_semi_transformer.py [27:54]:
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                                      n_tsteps, train_years=None):
    batch_size = 64
    test_batch_size = 128
    n_triplets_per_file = 1
    epochs = 50
    attention_nhead = 8
    adam_lr = 0.001
    adam_betas = (0.9, 0.999)
    n_experiment = 2

    neighborhood_radius = args.neighborhood_radius
    distant_radius = args.distant_radius
    weight_decay = args.weight_decay
    tilenet_margin = args.tilenet_margin
    tilenet_l2 = args.tilenet_l2
    tilenet_ltn = args.tilenet_ltn
    tilenet_zdim = args.tilenet_zdim
    attention_layer = args.attention_layer
    attention_dff = args.attention_dff
    sentence_embedding = args.sentence_embedding
    dropout = args.dropout
    unsup_weight = args.unsup_weight
    patience = args.patience if args.patience != 9999 else None
    feature = args.feature
    feature_len = args.feature_len
    query_type = args.query_type

    assert tilenet_zdim % attention_nhead == 0
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