python - scikit-learn中的神经网络时代和max_iter

scikit-learn中的神经网络（NN）时期与max_iter参数之间有什么区别或关系？

例如，从代码中可以看出，从1到10000评估max_iter的NN模型，并为每次迭代评估平均绝对误差可以看作时代？请参见下面的图片/链接！



非常感谢你！

for i in range(1,10000,10):

    clf = MLPRegressor(max_iter=i, solver='lbfgs', alpha=1e-6, activation='relu', # melhorou e muito o treino com relu
                       hidden_layer_sizes=hidden_layer_sizes, random_state=1)

     clf.fit(X_train_scaled, y_train)

     mae_B = cross_val_score(clf, X_train_scaled, y_train, scoring="neg_mean_absolute_error", cv=10)

     print i, float(-mae_B.mean()), clf.score(X_train_scaled, y_train), clf.score(X_test_scaled, y_test)

最佳答案

max_iter等于要训练模型的最大时期数。之所以称为最大值是因为学习也可以在达到最大迭代次数之前根据其他终止条件-n_iter_no_change停止。因此，请勿循环使用不同的max_iterations，如果要避免过度拟合，请尝试调整tol和n_iter_no_change。

尝试以下操作，并在max_iter中设置足够足够的纪元，然后使用n_iter_no_change和tol播放。参考Doc

clf = MLPRegressor(max_iter=50, solver='lbfgs', alpha=1e-6, activation='relu',
                     hidden_layer_sizes=hidden_layer_sizes, random_state=1,
                     tol=1e-3, n_iter_no_change = 5)

clf.fit(X_train_scaled, y_train)

mae_B = cross_val_score(clf, X_train_scaled, y_train, scoring="neg_mean_absolute_error", cv=10)

print i, float(-mae_B.mean()), clf.score(X_train_scaled, y_train), clf.score(X_test_scaled, y_test)