迭代次数问题（BP）

F:\develop\pythonidle\lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet. warnings.warn( F:\develop\pythonidle\lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet. warnings.warn( F:\develop\pythonidle\lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet. warnings.warn( F:\develop\pythonidle\lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet. warnings.warn( F:\develop\pythonidle\lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet. warnings.warn(

 

您遇到的警告信息表明，在训练BP神经网络时，优化器在达到最大迭代次数（300次）之前没有收敛。这意味着模型可能还没有找到最佳的权重和偏置参数，因此性能可能不是最优的。

 增加最大迭代次数

增加`max_iter`参数的值可以给优化器更多的迭代机会来找到更好的解。例如，将其增加到1000次迭代：

 
mlp = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', solver='adam', random_state=42, max_iter=1000)

这里我的1000次还不够最后加到5000就不报错了