AttributeError：“MLPClassifier”对象没有属性“_label_binarizer”答案

【问题标题】：AttributeError: 'MLPClassifier' object has no attribute '_label_binarizer'AttributeError：“MLPClassifier”对象没有属性“_label_binarizer”
【发布时间】：2021-08-01 17:56:05
【问题描述】：

我正在尝试使用 sklearn 的 MLPClassifier 利用 partial_fit() 函数来实现批量训练，但出现以下错误：

AttributeError：“MLPClassifier”对象没有属性“_label_binarizer”。

我已经咨询了一些与此相关的问题 (partial_fit Sklearn's MLPClassifier)。这是我用来重现错误的一段代码（来自所附参考）：

from __future__ import division 
import numpy as np
from sklearn.datasets import make_classification
from sklearn.neural_network import MLPClassifier

#Creating an imaginary dataset
input, output = make_classification(1000, 30, n_informative=10, n_classes=2)
input= input / input.max(axis=0)
N = input.shape[0]
train_input = input[0:500,:]
train_target = output[0:500]

test_input= input[500:N,:]
test_target = output[500:N]

#Creating and training the Neural Net 
# 1. Disable verbose (verbose is annoying with partial_fit)

clf = MLPClassifier(activation='tanh', learning_rate='constant',
 alpha=1e-4, hidden_layer_sizes=(15,), random_state=1, batch_size=1,verbose= False,
 max_iter=1, warm_start=False)

# 2. Set what the classes are
clf.classes_ = [0,1]

for j in range(0,100):
    for i in range(0,train_input.shape[0]):
       input_inst = train_input[[i]]
       target_inst= train_target[[i]]
       clf=clf.partial_fit(input_inst,target_inst)
    # 3. Monitor progress
    print("Score on training set: %0.8f" % clf.score(train_input, train_target))
#Testing the Neural Net
y_pred = clf.predict(test_input)
print(y_pred)

# 4. Compute score on testing set
print(clf.score(test_input, test_target))

我还修改了第 895 行的 multilayer_perceptron.py 代码以替换它，如 here 所述：

self.label_binarizer_.fit(y)

有了这个：

if not incremental:
    self.label_binarizer_.fit(y)

else:
    self.label_binarizer_.fit(self.classes_)

而且还是不行。非常感谢任何帮助。

谢谢！

【问题讨论】：

标签： python scikit-learn neural-network classification

【解决方案1】：

这可行：

from __future__ import division 
import numpy as np
from sklearn.datasets import make_classification
from sklearn.neural_network import MLPClassifier

#Creating an imaginary dataset
input, output = make_classification(1000, 30, n_informative=10, n_classes=2)
input= input / input.max(axis=0)
N = input.shape[0]
train_input = input[0:500,:]
train_target = output[0:500]

test_input= input[500:N,:]
test_target = output[500:N]

#Creating and training the Neural Net 
# 1. Disable verbose (verbose is annoying with partial_fit)

clf = MLPClassifier(activation='tanh', learning_rate='constant',
 alpha=1e-4, hidden_layer_sizes=(15,), random_state=1, batch_size=1,verbose= False,
 max_iter=1, warm_start=False)


for j in range(0,100):
    for i in range(0,train_input.shape[0]):
       input_inst = train_input[[i]]
       target_inst= train_target[[i]]
       clf.partial_fit(input_inst,target_inst,[0,1])
    # 3. Monitor progress
    print("Score on training set: %0.8f" % clf.score(train_input, train_target))
#Testing the Neural Net
y_pred = clf.predict(test_input)
print(y_pred)

# 4. Compute score on testing set
print(clf.score(test_input, test_target))

此行导致错误：

# 2. Set what the classes are
clf.classes_ = [0,1]

而且你必须在这里通过课程：

clf.partial_fit(input_inst,target_inst,[0,1])

【讨论】：