神经网络实验——MLP
目录
1 目的
2 方法
3 源代码
4 结果
1 目的
①熟悉 Python 的输入输出流;
②学会使用 matplotlib进行图像可视化;
③掌握神经网络的基本原理，学会使用 sklearn 库中的 MLPClassifier 函数构建基础的多层感知机神经网络分类器;
④学会使用网格查找进行超参数优化。
2 方法
①读取并解压 mnist.gz文件，并区分好训练集与测试集;
②查看数据结构，对手写字符进行可视化展示;
③构建多层感知机神经网络模型，并使用网格查找出最优参数;
④输出模型的最优参数以及模型的预测精度。
3 源代码
①启动 Spyder，新建.py 文件，加载试验所需模块
# 导入相关模块
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import GridSearchCV
import numpy as np
import pickle
import gzip
import matplotlib. pyplot as plt②加载数据，数据文件保存在 mnist.gz 安装包中，因此需要对文件进行解压后对文件进行读取，且区分训练集、测试集与验证集
#解压数据并进行读取
with gzip.open(r"D:\大二下\数据挖掘\神经网络\mnist.gz") as fp:
training_data, valid_data, test_data= pickle.load(fp,encoding='bytes')
#区分训练集与测试集
X_training_data,y_training_data= training_data
X_valid_data,y_valid_data= valid_data
X_test_data, y_test_data= test_data③查看数据的结构，为后续建模做准备:
#定义函数show_data_struct 用于展示数据的结构
def show_data_struct():
print(X_training_data.shape,y_training_data.shape)
print(X_valid_data.shape,y_valid_data.shape)
print(X_test_data.shape,y_test_data.shape)
print(X_training_data[0])
print(y_training_data[0])
#使用show_data_struct 函数进行数据展示
show_data_struct()④为了更好地了解数据的形态，对手写字符进行可视化展示
#定义函数用于可视化字符的原有图像
def show_image():
plt.figure(1)
for i in range(10):
image=X_training_data[i]
pixels=image.reshape((28,28))
plt.subplot(5,2,i+1)
plt.imshow(pixels,cmap='gray')
plt.title(y_training_data[i])
plt.axis('off')
plt.subplots_adjust(top=0.92,bottom=0.08,left=0.10,right=0.95, hspace=0.45,wspace=0.85)
plt.show()
#使用show_image函数进行图像展示
show_image()⑤构建参数字典，用于后续使用网格查找进行超参数优化
#字典中用于存放的 MLPClassifier 函数的参数列表
mlp_clf__tuned_parameters= {"hidden_layer_sizes":[(100,),(100,30)],
"solver":[' adam', 'sgd', 'bfgs'],
"max_iter":[20],
"verbose":[True]
}⑥使用MLPClassifier 丽数构建多层感知机神经网络，并使用GridSearchCV 网格查找进行超参数优化，找出最合适的参数
#构建多层感知机分类器
mlp=MLPClassifier()
#通过网格查找出最优参数
estimator= GridSearchCV(mlp,mlp_clf__tuned_parameters,n_jobs=6)
#拟合模型
estimator.fit(X_training_data, y_training_data)
#输出最优参数
print(estimator.best_params_)
#输出模型的预测精度
print(estimator.score(X_test_data, y_test_data))4 结果
(50000, 784) (50000,)
(10000, 784) (10000,)
(10000, 784) (10000,)
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5
通过观察数据结构可知，数据由 10000个样本组成，其中每一个样本是由784(28*28)个像素组成的图像，像素黑白用 0/1 进行表示，对应的label目标变量的每个字符图像的真实标签。
由图可知，MNIST数据由手写字符图像和标签组成。
通过对 MLP分类器的学习可见，模型经过 20次迭代，loss 不断减少0.2320587后达到拟合状态。
由输出结果可见，通过 GridSearchCV 网格查到的最优参数为:隐藏层数为(100，30)，最大池化层为 20，激活函数为sgd;且此时多层感知机神经网络MNIST手写字符识别的准确率达到了 0.9347。