我們用Tensorflow實(shí)現(xiàn)線性回歸（linear regression learning）算法：
首先踩娘，導(dǎo)入需要的包：
import tensorflow as tf
import numpy
import matplotlib.pyplot as plt
rng = numpy.random
設(shè)置訓(xùn)練的參數(shù)：
# Parameters
learning_rate = 0.01
training_epochs = 1000
display_step = 50
其中鲁捏，learning_rate是梯度下降的速率，training_epochs是訓(xùn)練時(shí)的最大迭代次數(shù)曹动，display_step用于將訓(xùn)練的信息打印出來(lái)隔嫡，打印前50步的信息。
然后是讀取訓(xùn)練集數(shù)據(jù)
# Training Data
train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,
7.042,10.791,5.313,7.997,5.654,9.27,3.1])
train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,
2.827,3.465,1.65,2.904,2.42,2.94,1.3])
n_samples = train_X.shape[0]
# tf Graph Input
X = tf.placeholder("float")
Y = tf.placeholder("float")
# Set model weights
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")
構(gòu)建線性模型：
# Construct a linear model
pred = tf.add(tf.multiply(X, W), b)
以Mean squared error作為訓(xùn)練的目標(biāo)函數(shù)：
# Mean squared error
cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)
# Gradient descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
初始化變量
# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()
開(kāi)始訓(xùn)練：
# Start training
with tf.Session() as sess:
sess.run(init)
# Fit all training data
for epoch in range(training_epochs):
for (x, y) in zip(train_X, train_Y):
sess.run(optimizer, feed_dict={X: x, Y: y})
#Display logs per epoch step
if (epoch+1) % display_step == 0:
c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})
print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \
"W=", sess.run(W), "b=", sess.run(b)
print "Optimization Finished!"
training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
print "Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n'
#Graphic display
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
plt.legend()
plt.show()
程序的輸出：
Epoch: 0050 cost= 0.195095107 W= 0.441748 b= -0.580876
Epoch: 0100 cost= 0.181448311 W= 0.430319 b= -0.498661
Epoch: 0150 cost= 0.169377610 W= 0.419571 b= -0.421336
Epoch: 0200 cost= 0.158700854 W= 0.409461 b= -0.348611
Epoch: 0250 cost= 0.149257123 W= 0.399953 b= -0.28021
Epoch: 0300 cost= 0.140904188 W= 0.391011 b= -0.215878
Epoch: 0350 cost= 0.133515999 W= 0.3826 b= -0.155372
Epoch: 0400 cost= 0.126981199 W= 0.374689 b= -0.0984639
Epoch: 0450 cost= 0.121201262 W= 0.367249 b= -0.0449408
Epoch: 0500 cost= 0.116088994 W= 0.360252 b= 0.00539905
Epoch: 0550 cost= 0.111567356 W= 0.35367 b= 0.052745
Epoch: 0600 cost= 0.107568085 W= 0.34748 b= 0.0972751
Epoch: 0650 cost= 0.104030922 W= 0.341659 b= 0.139157
Epoch: 0700 cost= 0.100902475 W= 0.336183 b= 0.178547
Epoch: 0750 cost= 0.098135538 W= 0.331033 b= 0.215595
Epoch: 0800 cost= 0.095688373 W= 0.32619 b= 0.25044
Epoch: 0850 cost= 0.093524046 W= 0.321634 b= 0.283212
Epoch: 0900 cost= 0.091609895 W= 0.317349 b= 0.314035
Epoch: 0950 cost= 0.089917004 W= 0.31332 b= 0.343025
Epoch: 1000 cost= 0.088419855 W= 0.30953 b= 0.370291
Optimization Finished!
Training cost= 0.0884199 W= 0.30953 b= 0.370291
最終訓(xùn)練結(jié)果：
image.png
