机器学习实践之：tensorflow与sklearn实现线性回归对比

#!/usr/bin/env python
#-*- coding=utf-8 -*-

import numpy as np
import pandas as pd
import sklearn as sk
import tensorflow as tf

x_data = pd.read_csv()
y_data = pd.read_csv()


reg = sk.liner_model.LinearRegression()
reg.fit(x_data.reshape(-1, 1), y_data)


learning_rate   = 0.1

w = tf.get_variable("weight", [-1, 1], tf.float32, tf.random_uniform([1], -1.0, 1.0))
b = tf.get_variable("bias",[-1, 1],  tf. float32, tf.zeros([1]))

y = w*x_data +b

loss = tf.reduce_mean(tf.square(y - y_data)) / 2
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
train = optimizer.minimize(loss)

sess = tf.Session()

sess.run(tf.initialize_all_variables())


for step in range(1000):
    sess.run(train)