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blake · 发表于 2017-9-18 07:42:46

#!/usr/bin/env python
# -*- coding=utf-8 -*-
# @author: 陈水平
# @date: 2016-12-30
# @description: compare scikit-learn and tensorflow, using linear regression data from deep learning course by Andrew Ng.
# @ref: http://openclassroom.stanford.edu/MainFolder/DocumentPage.php?course=DeepLearning&doc=exercises/ex2/ex2.html
import tensorflow as tf
import numpy as np
from sklearn import linear_model
# Read x and y
x_data = np.loadtxt("ex2x.dat")
y_data = np.loadtxt("ex2y.dat")
# We use scikit-learn first to get a sense of the coefficients
reg = linear_model.LinearRegression()
reg.fit(x_data.reshape(-1, 1), y_data)
print "Coefficient of scikit-learn linear regression: k=%f, b=%f" % (reg.coef_, reg.intercept_)
# Then we apply tensorflow to achieve the similar results
# The structure of tensorflow code can be divided into two parts:
# First part: set up computation graph
W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
b = tf.Variable(tf.zeros([1]))
y = W * x_data + b
loss = tf.reduce_mean(tf.square(y - y_data)) / 2
optimizer = tf.train.GradientDescentOptimizer(0.07) # Try 0.1 and you will see unconvergency
train = optimizer.minimize(loss)
init = tf.initialize_all_variables()
# Second part: launch the graph
sess = tf.Session()
sess.run(init)
for step in range(1500):
sess.run(train)
if step % 100 == 0:
print step, sess.run(W), sess.run(b)
print "Coeeficient of tensorflow linear regression: k=%f, b=%f" % (sess.run(W), sess.run(b))

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