使用tensorflow实现logistic回归

最新推荐文章于 2025-01-27 14:46:17 发布

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最新推荐文章于 2025-01-27 14:46:17 发布 · 783 阅读

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逻辑回归原理很简单，这里不再赘述，我使用tensorflow的思路和前面一样，还是利用Supervisor模块（这个确实好用啊）,argparser和logging日志模块。实现代码如下：

import sys
reload(sys)
sys.setdefaultencoding('utf-8')

import tensorflow as tf
import logging
import argparse
import os
import matplotlib.pyplot as plt
import numpy as np
logging.basicConfig(format="[%(process)d] %(levelname)s %(filename)s:%(lineno)s | %(message)s")
log = logging.getLogger('train')
log.setLevel(logging.INFO)

data = np.mat([[0.697,0.460,1],
        [0.774,0.376,1],
        [0.634,0.264,1],
        [0.608,0.318,1],
        [0.556,0.215,1],
        [0.403,0.237,1],
        [0.481,0.149,1],
        [0.437,0.211,1],
        [