Update

    92

    >>> cgcnn_dgl_graph = cgcnn_graph.to_dgl_graph()

    >>> model = GCN(in_node_dim=92, hidden_node_dim=92, num_gnn_layers=2)

    >>> # Call model.eval as batch norm is implemented

    >>> model.eval()

    >>> model(cgcnn_dgl_graph)

    References

    Examples

    --------

    # Todo

    >>> import deepchem as dc

    >>> from deepchem.models import GCNModel

    >>> dataset_config = {"reload": False, "featurizer": dc.feat.CGCNNFeaturizer, "transformers": []}

    >>> tasks, datasets, transformers = dc.molnet.load_perovskite(**dataset_config)

    >>> train, valid, test = datasets

    >>> model = dc.models.CGCNNModel(mode='regression', batch_size=32, learning_rate=0.001)

    >>> model.fit(train, nb_epoch=50)

    References

    ----------