batch_size=4096, n_epochs=n_epochs, x=x, y=y, x0=x0, x1=x1, scale_inputs=True, #early_stopping = True, #early_stopping_patience = 10 ) loss_out_path = model_out_path + "/loss" directory = os.path.dirname(loss_out_path) if not os.path.exists(loss_out_path): os.makedirs(loss_out_path) np.savetxt(loss_out_path + "/train_loss.csv", train_loss, delimiter=",") np.savetxt(loss_out_path + "/val_loss.csv", val_loss, delimiter=",") # model_out_path = model_out_path + '/carl/' print('all done, saving model to:', model_out_path) estimator.save(model_out_path, x=x, export_model=True) # also store patch, if it was provided: if (args.patch != ""): patch_out_path = model_out_path + "/patch/" if not os.path.exists(patch_out_path): os.makedirs(patch_out_path) copy2(args.patch, patch_out_path)
TreeName=treename, randomize=False, save=True, correlation=True, preprocessing=True, nentries=n, pathA=p + nominal + ".root", pathB=p + variation + ".root", ) logger.info(" Loaded new datasets ") ####################################### ####################################### # Estimate the likelihood ratio estimator = RatioEstimator(n_hidden=(10, 10, 10), activation="relu") estimator.train( method='carl', batch_size=1024, n_epochs=100, x=x, y=y, x0=x0, x1=x1, scale_inputs=True, ) estimator.save('models/' + global_name + '_carl_' + str(n), x, metaData, export_model=True) ########################################
else: x, y, x0, x1, metaData = loading.loading( folder='./data/', plot=True, var=var, do=sample, randomize=False, save=True, correlation=True, preprocessing=True, nentries=n, path=p, ) logger.info(" Loaded new datasets ") estimator = RatioEstimator(n_hidden=(10, 10, 10), activation="relu") estimator.train( method='carl', batch_size=1024, n_epochs=100, x=x, y=y, x0=x0, x1=x1, scale_inputs=True, ) estimator.save('models/' + sample + '/' + var + '_carl_' + str(n), x, metaData, export_model=True)