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,
)