}
n_runs = 5
for run_idx in np.arange(n_runs):
X_train, y_train, rand_idx = dataset.create_dataset(x_grid.reshape(-1, 1),
t_grid.reshape(-1, 1),
n_samples=1000,
noise=0.1,
random=True,
return_idx=True)
theta = dataset.library(x_grid.reshape(-1, 1),
t_grid.reshape(-1, 1),
poly_order=2,
deriv_order=3)[rand_idx, :]
dt = dataset.time_deriv(x_grid.reshape(-1, 1),
t_grid.reshape(-1, 1))[rand_idx, :]
model = DeepMod(**config)
optimizer = torch.optim.Adam(model.parameters(),
betas=(0.99, 0.999),
amsgrad=True)
train(model,
X_train,
y_train,
optimizer,
20000,
loss_func_args={
'library': torch.tensor(theta),
'time_deriv': torch.tensor(dt)
},
log_dir=f'runs_new/deepmod_run_{run_idx}')
t = np.linspace(0.5, 5.0, 25)
x_grid, t_grid = np.meshgrid(x, t, indexing='ij')
# Making data
dataset = Dataset(BurgersDelta, v=v, A=A)
X_train, y_train = dataset.create_dataset(x_grid.reshape(-1, 1),
t_grid.reshape(-1, 1),
n_samples=0,
noise=0.1,
random=False)
theta = dataset.library(x_grid.reshape(-1, 1),
t_grid.reshape(-1, 1),
poly_order=2,
deriv_order=3)
dt = dataset.time_deriv(x_grid.reshape(-1, 1), t_grid.reshape(-1, 1))
# Running deepmod
config = {
'n_in': 2,
'hidden_dims': [30, 30, 30, 30, 30],
'n_out': 1,
'library_function': library_1D_in,
'library_args': {
'poly_order': 2,
'diff_order': 3
}
}
model = DeepMod(**config)
optimizer = torch.optim.Adam(model.parameters(),
