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

Before Change

    // Check for errors with batch size != 1
    // We need to specify the batch shape as one for the GraphConvolutional logic to work
    x_t = Input(batch_shape=(2,) + features.shape)
    output_indices_t = Input(batch_shape=(2, None), dtype="int32")
    with pytest.raises(ValueError):
        out = GraphConvolution(2)([x_t, A_t, output_indices_t])

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])
    model = keras.Model(inputs=[x_t, A_t], outputs=out)

Instances