Python 属性错误:';MLP分类器';对象没有属性'_标签二值化器&x27;
我试图使用sklearn的MLPClassizer利用partial_fit()函数实现批处理训练,但我得到以下错误:Python 属性错误:';MLP分类器';对象没有属性'_标签二值化器&x27;,python,scikit-learn,neural-network,classification,Python,Scikit Learn,Neural Network,Classification,我试图使用sklearn的MLPClassizer利用partial_fit()函数实现批处理训练,但我得到以下错误: # 2. Set what the classes are clf.classes_ = [0,1] AttributeError:“MLPClassizer”对象没有属性“\u label\u binarizer” 我已经咨询了一些与此相关的问题()。这是我用来重现错误的代码(来自所附的参考): 我还修改了第895行的multilayer_perceptron.py代码以替
# 2. Set what the classes are
clf.classes_ = [0,1]
AttributeError:“MLPClassizer”对象没有属性“\u label\u binarizer”
我已经咨询了一些与此相关的问题()。这是我用来重现错误的代码(来自所附的参考):
我还修改了第895行的multilayer_perceptron.py
代码以替换此代码,如前所述:
为此:
if not incremental:
self.label_binarizer_.fit(y)
else:
self.label_binarizer_.fit(self.classes_)
但仍然不起作用。非常感谢您的帮助
谢谢 这将起作用:
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]
你必须在这里通过课程:
clf.partial_fit(input_inst,target_inst,[0,1])
clf.partial_fit(input_inst,target_inst,[0,1])