bfs_params.append(pht.rbfweights.detach().clone().numpy())
pht.train()
train_indices_batch = train_indices[b * batch_size:(b + 1) *
batch_size]
optimizer_filter.zero_grad()
loss = pht([data[i] for i in train_indices_batch])
loss.backward()
optimizer_filter.step()
lss += float(loss)
loss_func.append(float(lss) / len(train_indices))
pht.eval()
test_lss = pht([data[i] for i in test_indices])
test_loss.append(float(test_lss) / len(test_indices))
run_fold_index = str(run) + '_' + str(fold)
pickle.dump(
test_loss,
open(result_dump + 'test_loss_' + run_fold_index + '.pkl', 'wb'))
pickle.dump(
loss_func,
open(result_dump + 'train_loss_' + run_fold_index + '.pkl', 'wb'))
pickle.dump(
bfs_params,
open(result_dump + 'theta_' + run_fold_index + '.pkl', 'wb'))
del bfs_params, test_lss, loss_func, pht