gscalar = get_scalar_csv(settings["valid_feat_file"])
escalar = get_scalar_csv(settings["valid_engy_file"])
dtype, device = locate(settings['dtype']), settings['device']
M2, M3 = settings['M2'], settings['M3']
num_feat, num_engy = M2 + M3 ** 3, 1
mlp = [num_feat, *settings['hidden_layers'], num_engy]
Rc = settings['Router']
# initialize weights and biases if they are not provided
weights = list(np2torch(*weights))
biases = list(np2torch(*biases))
for (w, b) in zip(weights, biases): w.requires_grad, b.requires_grad = True, True
# convert gscalar and escale to torch tensors
gscalar, escalar = list(np2torch(*gscalar, dtype=dtype)), list(np2torch(*escalar, dtype=dtype))
start = time.time()
g, g_dldl, g_dpdl, idxNb = get_dG_dR(Rcart, lattice,
basis='cosine', M2=M2, M3=M3, Rinner=0, Router=Rc)
g, g_dldl, g_dpdl = np2torch(g, g_dldl, g_dpdl, dtype=dtype)
feat_scaled = gscalar[0] * g + gscalar[1]
nn_out2, d_nn_out2 = d_mlnn(feat_scaled, weights, biases, activation="sigmoid")
energies = nn_out2
forces = get_forces(g_dldl, g_dpdl, d_nn_out2, idxNb, gscalar[0])
for iepoch in range(settings['epoch']):
for step, (Rcart, lattice, atom_types, F, Ei) in enumerate(pwmat_mmt):
g, g_dldl, g_dpdl, idxNb = ct_ft.get_dG_dR(Rcart,
lattice,
basis='cosine',
M2=M2,
M3=M3,
Rinner=0,
Router=Rc)
g, g_dldl, g_dpdl = ct_nn.np2torch(g, g_dldl, g_dpdl, dtype=dtype)
feat_scaled = feat_a * g + feat_b
nn_out2, d_nn_out2 = ct_nn.d_mlnn(feat_scaled,
weights,
biases,
activation="sigmoid")
forces = ct_nn.get_forces(g_dldl, g_dpdl, d_nn_out2, idxNb, feat_a)
z = (nn_out2 - engy_b) / engy_a
dz = forces / engy_a
y, dy = ct_nn.np2torch(Ei[:, None], F, dtype=dtype)
F_mse = torch.mean((dz - dy)**2)
E_mse = torch.mean((z - y)**2)
loss = E_mse + F_mse
ct_nn.mlnn_optimize(loss, optimizer)
optimizer.step()
print(step, *ct_nn.torch2np(torch.sqrt(E_mse), torch.sqrt(F_mse)),
time.time() - start)