56e6adb2773497506875c7cbd7a1fe6b5c59fb4c,tests/layer/test_gcn.py,,test_GraphConvolution_dense,#,60

Before Change

    x = features[None, :, :]

    // For dense matrix, remove batch dimension
    A_mat = Lambda(lambda A: K.squeeze(A, 0))(A_t)

    out = GraphConvolution(2)([x_t, A_mat])
    model = keras.Model(inputs=[x_t, A_t], outputs=out)
    preds = model.predict([x, adj], batch_size=1)

After Change

    // batch dimension > 1 should work with a dense matrix
    x_t = Input(batch_shape=(10,) + features.shape)
    A_t = Input(batch_shape=(10, 3, 3))
    input_data = [np.broadcast_to(x, x_t.shape), np.broadcast_to(adj, A_t.shape)]

    out = GraphConvolution(2)([x_t, A_t])

Instances