Logistic 回归(mnist数据集)

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from numpy import *
import numpy as np
import time
import struct


#读取图片
def read_image(file_name):
    #先用二进制方式把文件都读进来
    file_handle=open(file_name,"rb")  #以二进制打开文档
    file_content=file_handle.read()   #读取到缓冲区中
    offset=0
    head = struct.unpack_from('>IIII', file_content, offset)  # 取前4个整数，返回一个元组
    offset += struct.calcsize('>IIII')
    imgNum = head[1]  #图片数
    rows = head[2]   #宽度
    cols = head[3]  #高度

    images=np.empty((imgNum , 784))#empty，是它所常见的数组内的所有元素均为空，没有实际意义，它是创建数组最快的方法
    image_size=rows*cols#单个图片的大小
    fmt='>' + str(image_size) + 'B'#单个图片的format

    for i in range(imgNum):
        images[i] = np.array(struct.unpack_from(fmt, file_content, offset))
        # images[i] = np.array(struct.unpack_from(fmt, file_content, offset)).reshape((rows, cols))
        offset += struct.calcsize(fmt)
    return images

#读取标签
def read_label(file_name):
    file_handle = open(file_name, "rb")  # 以二进制打开文档
    file_content = file_handle.read()  # 读取到缓冲区中

    head = struct.unpack_from('>II', file_content, 0)  # 取前2个整数，返回一个元组
    offset = struct.calcsize('>II')

    labelNum = head[1]  # label数
    # print(labelNum)
    bitsString = '>' + str(labelNum) + 'B'  # fmt格式：'>47040000B'
    label = struct.unpack_from(bitsString, file_content, offset)  # 取data数据，返回一个元组
    return np.array(label)

def loadDataSet():
    train_x_filename="/Users/didi/work/git_repo/demo_proj/src/logist_test/train-images-idx3-ubyte"
    train_y_filename="/Users/didi/work/git_repo/demo_proj/src/logist_test/train-labels-idx1-ubyte"
    test_x_filename="/Users/didi/work/git_repo/demo_proj/src/logist_test/t10k-images-idx3-ubyte"
    test_y_filename="/Users/didi/work/git_repo/demo_proj/src/logist_test/t10k-labels-idx1-ubyte"
    train_x=read_image(train_x_filename)
    train_y=read_label(train_y_filename)
    test_x=read_image(test_x_filename)
    test_y=read_label(test_y_filename)

    return train_x, test_x, train_y, test_y


if __name__=='__main__':
    print("Start reading data...")
    time1=time.time()
    train_x, test_x, train_y, test_y = loadDataSet()

    clf = LogisticRegression()
    clf.fit(train_x, train_y)
    y_pred = clf.predict(test_x)
    print('准确率:'+accuracy_score(test_y, y_pred))