机器学习-神经网络

一、

import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.neural_network import MLPClassifier
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
import numpy as np
import matplotlib.pyplot as plt

line = np.linspace(-5,5,200)
wine = load_wine()
X = wine.data[:,:2]
y = wine.target
X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=0)

cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
cmap_bold = ListedColormap(['#FF0000', '#00ff00', '#0000FF'])
x_min, x_max = X_train[:, 0].min() -1, X_train[:, 0].max() + 1
y_min, y_max = X_train[:, 1].min() -1, X_train[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, .02),
np.arange(y_min, y_max, .02))

mlp_20=MLPClassifier(solver='lbfgs', hidden_layer_sizes=[10])
mlp_20.fit(X_train, y_train)
Z1 = mlp_20.predict(np.c_[xx.ravel(), yy.ravel()])

Z1 = Z1.reshape(xx.shape)
plt.figure()
plt.pcolormesh(xx, yy, Z1, cmap=cmap_light)
plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', s=60)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.show()

二、使用MNIST数据集训练MLP神经网络

from sklearn.datasets import fetch_mldata　　　　#导入MNIST数据集获取工具
from sklearn.model_selection import train_test_split　　#数据集随机切分
from sklearn.neural_network import MLPClassifier　　　　#导入神经网络包

mnist = fetch_mldata("MNIST original", data_home='MNIST_data/')　　　　#使用工具获取MNIST数据集
X = mnist.data/255.　　　　#建立数据集并把特征向量的值全部除以255，这样全部数值就会在0和1之间
y = mnist.target　　　　　　#建立测试数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size= 5000, test_size=1000,random_state=62)　　　#选取5000个训练数据集，1000个作为测试数据集，随机数设置62
mlp_hw = MLPClassifier(solver='lbfgs',hidden_layer_sizes=[100,100],activation='relu', alpha = 1e-5,random_state=62)　　#设置100个隐藏层，使用lbfgs的优化器
mlp_hw.fit(X_train,y_train)　　　　#训练数据
print('测试数据集得分: {:.2f}'.format(mlp_hw.score(X_test,y_test)*100))