sklearn學習筆記之神經網路
阿新 • • 發佈:2018-12-15
# -*- coding: utf-8 -*- import sklearn from sklearn.neural_network import MLPClassifier import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn import datasets import pandas as pd import numpy def getData_1(): iris = datasets.load_iris() X = iris.data #樣本特徵矩陣,150*4矩陣,每行一個樣本,每個樣本維度是4y = iris.target #樣本類別矩陣,150維行向量,每個元素代表一個樣本的類別 df1=pd.DataFrame(X, columns =['SepalLengthCm','SepalWidthCm','PetalLengthCm','PetalWidthCm']) df1['target']=y return df1 df=getData_1() X_train, X_test, y_train, y_test = train_test_split(df.iloc[:,0:3],df['target'], test_size=0.3, random_state=42) print X_train, X_test, y_train, y_test model = MLPClassifier(activation='relu', solver='adam', alpha=0.0001,max_iter=10000) # 神經網路 """引數 --- n_neighbors: 使用鄰居的數目 n_jobs:並行任務數 """ model.fit(X_train,y_train) predict=model.predict(X_test) print predict print y_test.values print '神經網路分類:{:.3f}'.format(model.score(X_test, y_test))結果:
當沒有設定 max_iter=10000,預設迭代次數為200,會出現
ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200)
reached and the optimization hasn't converged yet. % self.max_iter,
ConvergenceWarning)
放開迭代次數後,最終結果
Name: target, dtype: int32
[1 0 2 1 1 0 1 1 1 1 1 0 0 0 0 1 2 1 1 2 0 2 0 2 2 2 2 2 0 0 0 0 1 0 0 2 10 0 0 2 1 1 0 0]
[1 0 2 1 1 0 1 2 1 1 2 0 0 0 0 1 2 1 1 2 0 2 0 2 2 2 2 2 0 0 0 0 1 0 0 2 1
0 0 0 2 1 1 0 0]
神經網路分類準確度 :0.956