"hidden": [25], "output": 10, } } print('') #0 0.3155 99.87 23.8 48.38 11.42 for s in struct: Options["structure"]["hidden"] = s Options['regularization'] = 1.0 info = str() tp = Perceptron(train_set['y'], train_set['x'], Options) print('\nStructure: ' + str(s) + '\tparams: ' + str(len(tp.params))) print('idx cost acc acc norm time') print('-----------------------------------------') for i in range(0, test): mpl = Perceptron(train_set['y'], train_set['x'], Options) lb = lambda: mpl.lbfgs(ite_table[3]) time = tm.timeit(lb, number=1) h1 = mpl.predict(train_set['x'], mpl.params) h2 = mpl.predict(valid_set['x'], mpl.params) info = str(i) + '\t' + \