# Using customized GNN¶

Sometimes you would like to design your own GNN module or use GNN for other purposes. In this chapter, we introduce how to use GNN layer in CogDL to write your own GNN model and how to write a GNN layer from scratch.

## GNN layers in CogDL to Define model¶

CogDL has implemented popular GNN layers in `cogdl.layers`

, and they can serve as modules to help design new GNNs.
Here is how we implement Jumping Knowledge Network (JKNet) with `GCNLayer`

in CogDL.

JKNet collects the output of all layers and concatenate them together to get the result:

```
import torch
from cogdl.models import BaseModel
class JKNet(BaseModel):
def __init__(self, in_feats, out_feats, hidden_size, num_layers):
super(JKNet, self).__init__()
shapes = [in_feats] + [hidden_size] * num_layers
#
self.layers = nn.ModuleList([
GCNLayer(shape[i], shape[i+1])
for i in range(num_layers)
])
self.fc = nn.Linear(hidden_size * num_layers, out_feats)
def forward(self, graph):
graph.add_remaining_self_loops()
graph.sym_norm()
h = graph.x
out = []
for layer in self.layers:
h = layer(x)
out.append(h)
out = torch.cat(out, dim=1)
return self.fc(out)
```

## Define your GNN Module¶

In most cases, you may build a layer module with new message propagation and aggragation scheme. Here the code snippet
shows how to implement a GCNLayer using `Graph`

and efficient sparse matrix operators in CogDL.

```
import torch
from cogdl.utils import spmm
class GCNLayer(torch.nn.Module):
"""
Args:
in_feats: int
Input feature size
out_feats: int
Output feature size
"""
def __init__(self, in_feats, out_feats):
super(GCNLayer, self).__init__()
self.fc = torch.nn.Linear(in_feats, out_feats)
def forward(self, graph, x):
# symmetric normalization of adjacency matrix
graph.sym_norm()
h = self.fc(x)
h = spmm(graph, h)
return h
```

`spmm`

is sparse matrix multiplication operation frequently used in GNNs.

Sparse matrix is stored in `Graph`

and will be called automatically. Message-passing in spatial space is equivalent to
matrix operations. CogDL also supports other efficient operators like `edge_softmax`

and `multi_head_spmm`

, you can refer
to this page for usage.

## Use Custom models with CogDL¶

Now that you have defined your own GNN, you can use dataset/task in CogDL to immediately train and evaluate the performance of your model.

```
data = dataset.data
# Use the JKNet model as defined above
model = JKNet(data.num_features, data.num_classes, 32, 4)
experiment(model=model, dataset="cora", mw="node_classification_mw", dw="node_classification_dw")
```