def test_monitor_spherical(): f = lambda th, ph: 0. g = lambda th, ph: 0. conditions = [DirichletBVPSpherical(r_0=0., f=f, r_1=1., g=g)] nets = [FCNN(3, 1)] monitor = MonitorSpherical(0.0, 1.0, check_every=1) loss_history = { 'train': list(np.random.rand(10)), 'valid': list(np.random.rand(10)), } analytic_mse_history = { 'train': list(np.random.rand(10)), 'valid': list(np.random.rand(10)), } history = { 'train_loss': list(np.random.rand(10)), 'valid_loss': list(np.random.rand(10)), 'train_foo': list(np.random.rand(10)), 'valid_foo': list(np.random.rand(10)), 'train_bar': list(np.random.rand(10)), 'valid_bar': list(np.random.rand(10)), } with pytest.warns(FutureWarning): monitor.check(nets, conditions, history=history, analytic_mse_history=analytic_mse_history) with pytest.warns(FutureWarning): monitor.check(nets, conditions, history=loss_history) with pytest.warns(FutureWarning): monitor.check(nets, conditions, history=loss_history, analytic_mse_history=analytic_mse_history) with pytest.raises(ValueError): monitor.check(nets, conditions, history={ 'train_foo': [], 'valid_foo': [] }) monitor.check(nets, conditions, history=history)
def test_monitor_spherical(): f = lambda th, ph: 0. g = lambda th, ph: 0. conditions = [DirichletBVPSpherical(r_0=0., f=f, r_1=1., g=g)] nets = [FCNN(3, 1)] monitor = MonitorSpherical(0.0, 1.0, check_every=1) loss_history = { 'train': list(np.random.rand(10)), 'valid': list(np.random.rand(10)), } analytic_mse_history = { 'train': list(np.random.rand(10)), 'valid': list(np.random.rand(10)), } monitor.check( nets, conditions, loss_history=loss_history, analytic_mse_history=analytic_mse_history, )