137a2a616f0d253b4ed246f612bef8613cc6046e,examples/delaney/delaney_graph_conv.py,,,#,9

Before Change

n_feat = 75
// Batch size of models
batch_size = 128
graph_model = dc.nn.SequentialGraph(n_feat)
graph_model.add(dc.nn.GraphConv(128, n_feat, activation="relu"))
graph_model.add(dc.nn.BatchNormalization(epsilon=1e-5, mode=1))
graph_model.add(dc.nn.GraphPool())
graph_model.add(dc.nn.GraphConv(128, 128, activation="relu"))
graph_model.add(dc.nn.BatchNormalization(epsilon=1e-5, mode=1))
graph_model.add(dc.nn.GraphPool())
// Gather Projection
graph_model.add(dc.nn.Dense(256, 128, activation="relu"))
graph_model.add(dc.nn.BatchNormalization(epsilon=1e-5, mode=1))
graph_model.add(dc.nn.GraphGather(batch_size, activation="tanh"))
// Dense post-processing layer

model = dc.models.MultitaskGraphRegressor(
    graph_model,
    len(delaney_tasks),
    n_feat,

After Change

n_feat = 75
// Batch size of models
batch_size = 128
model = GraphConvTensorGraph(len(delaney_tasks), batch_size=batch_size, mode="regression")

// Fit trained model
model.fit(train_dataset, nb_epoch=20)

Instances