# Linear Regression in TensorFlow¶

## Updated for Python 3.6+¶

Credits: Forked from TensorFlow-Examples by Aymeric Damien

## Setup¶

Refer to the setup instructions

In [1]:
import tensorflow as tf
import numpy
import matplotlib.pyplot as plt
rng = numpy.random

In [2]:
# Parameters
learning_rate = 0.01
training_epochs = 2000
display_step = 50

In [3]:
# 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]

In [4]:
# tf Graph Input
X = tf.placeholder("float")
Y = tf.placeholder("float")

In [5]:
# Create Model

# Set model weights
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")

In [7]:
# Construct a linear model
activation = tf.add(tf.multiply(X, W), b)

In [8]:
# Minimize the squared errors
cost = tf.reduce_sum(tf.pow(activation-Y, 2))/(2*n_samples) #L2 loss

In [9]:
# Initializing the variables
init = tf.initialize_all_variables()

WARNING:tensorflow:From /Users/tarrysingh/anaconda/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py:175: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use tf.global_variables_initializer instead.

In [11]:
# Launch the graph
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 % display_step == 0:
print ("Epoch:", '%04d' % (epoch+1), "cost=", \
"{:.9f}".format(sess.run(cost, feed_dict={X: train_X, Y:train_Y})), \
"W=", sess.run(W), "b=", sess.run(b))

print ("Optimization Finished!")
print ("cost=", sess.run(cost, feed_dict={X: train_X, Y: train_Y}), \
"W=", sess.run(W), "b=", sess.run(b))

#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()

Epoch: 0001 cost= 4.036506653 W= -0.129287 b= 0.502527
Epoch: 0051 cost= 0.080105469 W= 0.280994 b= 0.575571
Epoch: 0101 cost= 0.079743326 W= 0.279127 b= 0.589008
Epoch: 0151 cost= 0.079423159 W= 0.27737 b= 0.601645
Epoch: 0201 cost= 0.079140082 W= 0.275718 b= 0.613531
Epoch: 0251 cost= 0.078889869 W= 0.274164 b= 0.624707
Epoch: 0301 cost= 0.078668654 W= 0.272703 b= 0.635219
Epoch: 0351 cost= 0.078473099 W= 0.271329 b= 0.645105
Epoch: 0401 cost= 0.078300200 W= 0.270036 b= 0.654404
Epoch: 0451 cost= 0.078147389 W= 0.26882 b= 0.663149
Epoch: 0501 cost= 0.078012303 W= 0.267677 b= 0.671376
Epoch: 0551 cost= 0.077892922 W= 0.266602 b= 0.679112
Epoch: 0601 cost= 0.077787377 W= 0.26559 b= 0.686389
Epoch: 0651 cost= 0.077694103 W= 0.264639 b= 0.693233
Epoch: 0701 cost= 0.077611685 W= 0.263744 b= 0.69967
Epoch: 0751 cost= 0.077538833 W= 0.262902 b= 0.705724
Epoch: 0801 cost= 0.077474467 W= 0.262111 b= 0.711419
Epoch: 0851 cost= 0.077417582 W= 0.261366 b= 0.716774
Epoch: 0901 cost= 0.077367350 W= 0.260666 b= 0.721811
Epoch: 0951 cost= 0.077322975 W= 0.260007 b= 0.72655
Epoch: 1001 cost= 0.077283740 W= 0.259388 b= 0.731006
Epoch: 1051 cost= 0.077249110 W= 0.258805 b= 0.735197
Epoch: 1101 cost= 0.077218518 W= 0.258257 b= 0.739139
Epoch: 1151 cost= 0.077191517 W= 0.257742 b= 0.742846
Epoch: 1201 cost= 0.077167653 W= 0.257257 b= 0.746334
Epoch: 1251 cost= 0.077146590 W= 0.256802 b= 0.749614
Epoch: 1301 cost= 0.077128001 W= 0.256373 b= 0.752699
Epoch: 1351 cost= 0.077111594 W= 0.255969 b= 0.7556
Epoch: 1401 cost= 0.077097103 W= 0.25559 b= 0.758329
Epoch: 1451 cost= 0.077084318 W= 0.255233 b= 0.760896
Epoch: 1501 cost= 0.077073045 W= 0.254898 b= 0.76331
Epoch: 1551 cost= 0.077063099 W= 0.254582 b= 0.765581
Epoch: 1601 cost= 0.077054359 W= 0.254285 b= 0.767716
Epoch: 1651 cost= 0.077046596 W= 0.254006 b= 0.769724
Epoch: 1701 cost= 0.077039793 W= 0.253743 b= 0.771615
Epoch: 1751 cost= 0.077033766 W= 0.253496 b= 0.773391
Epoch: 1801 cost= 0.077028476 W= 0.253264 b= 0.775061
Epoch: 1851 cost= 0.077023804 W= 0.253046 b= 0.776633
Epoch: 1901 cost= 0.077019729 W= 0.25284 b= 0.778113
Epoch: 1951 cost= 0.077016093 W= 0.252647 b= 0.779503
Optimization Finished!
cost= 0.077013 W= 0.252469 b= 0.780784

In [ ]:
from IPython.display import Image
Image(filename='linearreg.png')