n_fit = int(t_fit // Ts)
input_data = u[0:n_fit]
state_data = x_noise[0:n_fit]
u_torch = torch.from_numpy(input_data)
x_true_torch = torch.from_numpy(state_data)
# Setup neural model structure
ss_model = NeuralStateSpaceModel(n_x=2, n_u=1, n_feat=64, init_small=False)
nn_solution = NeuralStateSpaceSimulator(ss_model)
# Setup optimizer
optimizer = optim.Adam(nn_solution.ss_model.parameters(), lr=lr)
# Scale loss with respect to the initial one
with torch.no_grad():
x_est_torch = nn_solution.f_onestep(x_true_torch, u_torch)
err_init = x_est_torch - x_true_torch
scale_error = torch.sqrt(torch.mean(err_init**2, dim=0))
# Training loop
LOSS = []
start_time = time.time()
for itr in range(0, num_iter):
optimizer.zero_grad()
x_est_torch = nn_solution.f_onestep(x_true_torch, u_torch)
err = x_est_torch - x_true_torch
err_scaled = err / scale_error
loss_sc = torch.mean(
(err_scaled)**
2) #torch.mean(torch.sq(batch_x[:,1:,:] - batch_x_pred[:,1:,:]))
# Fit data to pytorch tensors #
n_fit = int(len_fit//Ts)
u_fit = u[0:n_fit]
x_fit = x_noise[0:n_fit]
t_fit = t[0:n_fit]
u_fit_torch = torch.from_numpy(u_fit)
x_meas_fit_torch = torch.from_numpy(x_fit)
# Setup optimizer
params = list(nn_solution.ss_model.parameters())
optimizer = optim.Adam(params, lr=lr)
end = time.time()
# Scale loss with respect to the initial one
with torch.no_grad():
x_est_torch = nn_solution.f_onestep(x_meas_fit_torch, u_fit_torch)
err_init = x_est_torch - x_meas_fit_torch
scale_error = torch.sqrt(torch.mean((err_init)**2, dim=0))
LOSS = []
start_time = time.time()
# Training loop
for itr in range(1, num_iter + 1):
optimizer.zero_grad()
# Perform one-step ahead prediction
x_pred_torch = nn_solution.f_onestep(x_meas_fit_torch, u_fit_torch)
# Compute fit loss
err = x_pred_torch - x_meas_fit_torch
