machine-learning - multilayer_perceptron : ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet. 警告？

Question

我已经编写了一个基本程序来了解 MLP 分类器中发生的情况？

from sklearn.neural_network import MLPClassifier

数据:标记为男性或女性的 body 指标(高度、宽度和鞋号)数据集:

X = [[181, 80, 44], [177, 70, 43], [160, 60, 38], [154, 54, 37], [166, 65, 40],
     [190, 90, 47], [175, 64, 39],
     [177, 70, 40], [159, 55, 37], [171, 75, 42], [181, 85, 43]]
y = ['male', 'male', 'female', 'female', 'male', 'male', 'female', 'female',
     'female', 'male', 'male']

准备模型:

 clf= MLPClassifier(hidden_layer_sizes=(3,), activation='logistic',
                       solver='adam', alpha=0.0001,learning_rate='constant', 
                      learning_rate_init=0.001)

火车

clf= clf.fit(X, y)

学习分类器的属性:

print('current loss computed with the loss function: ',clf.loss_)
print('coefs: ', clf.coefs_)
print('intercepts: ',clf.intercepts_)
print(' number of iterations the solver: ', clf.n_iter_)
print('num of layers: ', clf.n_layers_)
print('Num of o/p: ', clf.n_outputs_)

测试

print('prediction: ', clf.predict([  [179, 69, 40],[175, 72, 45] ]))

计算。准确率

print( 'accuracy: ',clf.score( [ [179, 69, 40],[175, 72, 45] ], ['female','male'], sample_weight=None ))

运行1

current loss computed with the loss function:  0.617580287851
coefs:  [array([[ 0.17222046, -0.02541928,  0.02743722],
       [-0.19425909,  0.14586716,  0.17447281],
       [-0.4063903 ,  0.148889  ,  0.02523247]]), array([[-0.66332919],
       [ 0.04249613],
       [-0.10474769]])]
intercepts:  [array([-0.05611057,  0.32634023,  0.51251098]), array([ 0.17996649])]
 number of iterations the solver:  200
num of layers:  3
Num of o/p:  1
prediction:  ['female' 'male']
accuracy:  1.0
/home/anubhav/anaconda3/envs/mytf/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.
  % (), ConvergenceWarning)

运行2

current loss computed with the loss function:  0.639478303643
coefs:  [array([[ 0.02300866,  0.21547873, -0.1272455 ],
       [-0.2859666 ,  0.40159542,  0.55881399],
       [ 0.39902066, -0.02792529, -0.04498812]]), array([[-0.64446013],
       [ 0.60580985],
       [-0.22001532]])]
intercepts:  [array([-0.10482234,  0.0281211 , -0.16791644]), array([-0.19614561])]
 number of iterations the solver:  39
num of layers:  3
Num of o/p:  1
prediction:  ['female' 'female']
accuracy:  0.5

运行3

current loss computed with the loss function:  0.691966937074
coefs:  [array([[ 0.21882191, -0.48037975, -0.11774392],
       [-0.15890357,  0.06887471, -0.03684797],
       [-0.28321762,  0.48392007,  0.34104955]]), array([[ 0.08672174],
       [ 0.1071615 ],
       [-0.46085333]])]
intercepts:  [array([-0.36606747,  0.21969636,  0.10138625]), array([-0.05670653])]
 number of iterations the solver:  4
num of layers:  3
Num of o/p:  1
prediction:  ['male' 'male']
accuracy:  0.5

运行4:

current loss computed with the loss function:  0.697102567593
coefs:  [array([[ 0.32489731, -0.18529689, -0.08712877],
       [-0.35425908,  0.04214241,  0.41249622],
       [-0.19993622, -0.38873908, -0.33057999]]), array([[ 0.43304555],
       [ 0.37959392],
       [ 0.55998979]])]
intercepts:  [array([ 0.11555407, -0.3473817 , -0.16852093]), array([ 0.31326347])]
 number of iterations the solver:  158
num of layers:  3
Num of o/p:  1
prediction:  ['male' 'male']
accuracy:  0.5

------------------------------------------------------------ ------------------

我有以下问题:

1.Why in the RUN1 the optimizer did not converge?
2.Why in RUN3 the number of iteration were suddenly becomes so low and in the RUN4 so high?
3.What else can be done to increase the accuracy which I get in RUN1.? 

Answer 1

1:您的 MLP 未收敛:该算法通过逐步收敛到最小值进行优化，但在运行 1 中未找到最小值。

2 运行差异:您的 MLP 有一些随机起始值，因此您不会得到与数据中看到的相同的结果。看来您在第四次运行中的起点非常接近最低值。您可以将 MLP 的 random_state 参数更改为常量，例如random_state=0 一遍又一遍地获得相同的结果。

3是最难的一点。您可以通过以下方式优化参数

from sklearn.model_selection import GridSearchCV

Gridsearch 将测试集分成大小相等的部分，使用一部分作为测试数据，其余部分作为训练数据。因此，它会优化与您将数据拆分成的部分一样多的分类器。

您需要指定(您的数据很小，所以我建议 2 或 3)您分割的部分数量、分类器(您的 MLP)以及您想要优化的参数网格，如下所示:

param_grid = [
        {
            'activation' : ['identity', 'logistic', 'tanh', 'relu'],
            'solver' : ['lbfgs', 'sgd', 'adam'],
            'hidden_layer_sizes': [
             (1,),(2,),(3,),(4,),(5,),(6,),(7,),(8,),(9,),(10,),(11,), (12,),(13,),(14,),(15,),(16,),(17,),(18,),(19,),(20,),(21,)
             ]
        }
       ]

因为您曾经通过三个神经元的隐藏层获得了 100% 的准确率，所以您可以尝试优化学习率和动量等参数，而不是隐藏层。

像这样使用 Gridsearch:

clf = GridSearchCV(MLPClassifier(), param_grid, cv=3,
                           scoring='accuracy')
clf.fit(X,y)


print("Best parameters set found on development set:")
print(clf.best_params_)