Machine Learning Series-tensorflow-03-linear regression Linear Regression

Use tensorflow to implement linear regression of data

Import related libraries

import tensorflow as tfimport numpyimport matplotlib.pyplot as pltrng = numpy.random

Parameter settings

learning_rate = 0.01training_epochs = 1000display_step = 50

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 Image Input

X = tf.placeholder("float")Y = tf.placeholder("float")

Set weight and offset

W = tf.Variable(rng.randn(), name="weight")b = tf.Variable(rng.randn(), name="bias")

Build a Linear Model

pred = tf.add(tf.multiply(X, W), b)

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)

Initialize variable

init = tf.global_variables_initializer()

Start Training

With TF. session () as sess: sess. run (init) # suitable for 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 the logs of each 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 finis Hed! ") 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') # drawing displays 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 ()

Result Display

EPOCH: 0050 cost = 0.183995649 W = 0.43250677 B =-0.5143978
EPOCH: 0100 cost = 0.171630666 W = 0.42162812 B =-0.43613702
EPOCH: 0150 cost = 0.160693780 W = 0.41139638 B =-0.36253116
EPOCH: 0200 cost = 0.151019916 W = 0.40177315 B =-0.2933027
EPOCH: 0250 cost = 0.142463341 W = 0.39272234 B =-0.22819161
EPOCH: 0300 cost = 0.134895071 W = 0.3842099 B =-0.16695316
EPOCH: 0350 cost = 0.128200993 W = 0.37620357 B =-0.10935676
EPOCH: 0400 cost = 0.122280121 W = 0.36867347 B =-0.055185713
EPOCH: 0450 cost = 0.117043234 W = 0.36159125 B =-0.004236537
EPOCH: 0500 cost = 0.112411365 W = 0.3549302 B = 0.04368245
EPOCH: 0550 cost = 0.108314596 W = 0.34866524 B = 0.08875148
EPOCH: 0600 cost = 0.104691163 W = 0.34277305 B = 0.13114017
EPOCH: 0650 cost = 0.101486407 W = 0.33723122 B = 0.17100765
EPOCH: 0700 cost = 0.098651998 W = 0.33201888 B = 0.20850417
EPOCH: 0750 cost = 0.096145160 W = 0.32711673 B = 0.24377018
EPOCH: 0800 cost = 0.093927994 W = 0.32250607 B = 0.27693948
EPOCH: 0850 cost = 0.091967128 W = 0.31816947 B = 0.308136
EPOCH: 0900 cost = 0.090232961 W = 0.31409115 B = 0.33747625
EPOCH: 0950 cost = 0.088699281 W = 0.31025505 B = 0.36507198
EPOCH: 1000 cost = 0.087342896 W = 0.30664718 B = 0.39102668
Optimization finished!
Training cost = 0.087342896 W = 0.30664718 B = 0.39102668

Refer:
Author: Aymeric damien
Project: https://github.com/aymericdamien/TensorFlow-Examples/

Machine Learning Series-tensorflow-03-linear regression Linear Regression