if nb_trg_labels > 0:
train_auc_list.append(train_target_AUC)
tC_loss_list.append(tC_loss)
np.savetxt(os.path.join(DA_model_folder, 'trg_train_auc.txt'),
train_auc_list)
np.savetxt(os.path.join(DA_model_folder, 'trg_clf_loss.txt'),
tC_loss_list)
print_green(
'AUC: T-test {0:.4f}, T-valid {1:.4f}, T-train {2:.4f}, S-test: {3:.4f}'
.format(test_target_AUC, val_target_AUC, train_target_AUC,
test_source_AUC))
print_yellow(
'Loss: D:{:.4f}, S:{:.4f}, T:{:.4f}, Iter:{:}'.format(
M_loss, sC_loss, tC_loss, iteration))
plot_LOSS(
DA_model_folder + '/loss_{}.png'.format(DA_model_name),
M_loss_list, sC_loss_list, tC_loss_list)
plot_loss(
DA_model_folder, M_loss_list, M_loss_list,
DA_model_folder + '/MMD_loss_{}.png'.format(DA_model_name))
plot_src_trg_AUCs(
DA_model_folder + '/AUC_src_{}.png'.format(DA_model_name),
train_auc_list, val_auc_list, test_auc_list, src_test_list)
plot_AUCs(
DA_model_folder + '/AUC_trg_{}.png'.format(DA_model_name),
train_auc_list, val_auc_list, test_auc_list)
else:
print_green(
'AUC: T-test {0:.4f}, T-valid {1:.4f}, S-test: {2:.4f}'.
format(test_target_AUC, val_target_AUC, test_source_AUC))
print_yellow('Loss: D:{:.4f}, S:{:.4f}, Iter:{:}'.format(
# save model
if iteration % 10000 == 0:
saver.save(sess,
model_folder + '/model',
global_step=iteration)
print_red('Update model')
# save results
loss_trn_list, loss_val_list, loss_norm_list, loss_anomaly_list, auc_list =\
np.append(loss_trn_list, loss_trn), np.append(loss_val_list, loss_val),\
np.append(loss_norm_list, loss_norm), np.append(loss_anomaly_list, loss_anomaly), np.append(auc_list, AE_auc)
np.savetxt(model_folder + '/train_loss.txt', loss_trn_list)
np.savetxt(model_folder + '/val_loss.txt', loss_val_list)
np.savetxt(model_folder + '/norm_loss.txt', loss_norm_list)
np.savetxt(model_folder + '/anomaly_loss.txt', loss_anomaly_list)
plot_LOSS(model_folder + '/loss-{}.png'.format(model_name), 0,
loss_trn_list, loss_val_list, loss_norm_list,
loss_anomaly_list)
np.savetxt(model_folder + '/AE_auc.txt', auc_list)
plot_AUC(model_folder + '/auc-{}.png'.format(model_name), auc_list)
if best_loss_val > loss_val:
best_loss_val = loss_val
saver.save(sess, model_folder + '/best')
print_red('update best:{}'.format(model_name))
np.savetxt(model_folder + '/AE_stat.txt', recon_errs)
np.savetxt(model_folder + '/best_auc.txt', [AE_auc, MP_auc])
plot_hist(model_folder + '/hist-{}.png'.format(model_name),
recon_errs[:int(len(recon_errs) / 2)],
recon_errs[int(len(recon_errs) / 2):])
save_recon_images(model_folder +
'/recon-{}.png'.format(model_name),
