de190ef8f5ed51aa288b877f8dda855a2d19b086,deepchem/molnet/run_benchmark_models.py,,benchmark_classification,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,21

Before Change

    max_atoms_train = max([mol.get_num_atoms() for mol in train_dataset.X])
    max_atoms_valid = max([mol.get_num_atoms() for mol in valid_dataset.X])
    max_atoms_test = max([mol.get_num_atoms() for mol in test_dataset.X])
    max_atoms = max([max_atoms_train, max_atoms_valid, max_atoms_test])

    graph_model = deepchem.nn.SequentialWeaveGraph(
        max_atoms=max_atoms, n_atom_feat=n_features, n_pair_feat=n_pair_feat)
    graph_model.add(deepchem.nn.WeaveLayer(max_atoms, 75, 14))

After Change

        batch_size, n_atom_feat=n_features, n_pair_feat=n_pair_feat, max_atoms=120)
    graph_model.add(deepchem.nn.WeaveLayer(75, 14))
    graph_model.add(deepchem.nn.WeaveLayer(50, 50))
    graph_model.add(deepchem.nn.Dense(n_graph_feat, 50, activation="tanh"))
    graph_model.add(deepchem.nn.BatchNormalization(epsilon=1e-5, mode=1))
    graph_model.add(
        deepchem.nn.WeaveGather(

Instances