def test_starnet2017(self):
# Data preparation, keep the data size large (>800 data points to prevent issues)
random_xdata = np.random.normal(0, 1, (200, 7514))
random_ydata = np.random.normal(0, 1, (200, 25))
# StarNet2017
print("======StarNet2017======")
starnet2017 = StarNet2017()
starnet2017.max_epochs = 1
starnet2017.callbacks = ErrorOnNaN()
starnet2017.train(random_xdata, random_ydata)
prediction = starnet2017.test(random_xdata)
np.testing.assert_array_equal(prediction.shape, random_ydata.shape)
starnet2017.save(name='starnet2017')
def test_starnet2017(self):
# StarNet2017
print("======StarNet2017======")
starnet2017 = StarNet2017()
starnet2017.max_epochs = 1
starnet2017.callbacks = ErrorOnNaN()
starnet2017.train(random_xdata, random_ydata)
prediction = starnet2017.test(random_xdata)
jacobian = starnet2017.jacobian(random_xdata[:10])
np.testing.assert_array_equal(prediction.shape, random_ydata.shape)
np.testing.assert_array_equal(jacobian.shape, [random_xdata[:10].shape[0], random_ydata.shape[1],
random_xdata.shape[1]])
starnet2017.save(name='starnet2017')
starnet2017_loaded = load_folder("starnet2017")
prediction_loaded = starnet2017_loaded.test(random_xdata)
# StarNet2017 is deterministic
np.testing.assert_array_equal(prediction, prediction_loaded)
# Fine-tuning test
starnet2017_loaded.max_epochs = 1
starnet2017.callbacks = ErrorOnNaN()
starnet2017_loaded.train(random_xdata, random_ydata)