# Lab 2 Linear Regression

import tensorflow as tf

x_train = [1, 2, 3]

y_train = [1, 2, 3]

# Try to find values for W and b to compute y_data = W * x_data + b

# We know that W should be 1 and b should be 0

# But let's use Tensorflow to figure it out

W = tf.Variable(tf.random_uniform(

shape=[1], minval=-1.0, maxval=1.0, dtype=tf.float32))

b = tf.Variable(tf.random_uniform(

shape=[1], minval=-1.0, maxval=1.0, dtype=tf.float32))

X = tf.placeholder(tf.float32)

Y = tf.placeholder(tf.float32)

# Now we can use X and Y in place of x_data and y_data

# Our hypothesis

hypothesis = X * W + b

# Simplified cost function

cost = tf.reduce_mean(tf.square(hypothesis - Y))

# Minimize

optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)

train = optimizer.minimize(cost)

# Initialize all variables before we start

init = tf.global_variables_initializer()

# Launch graph

sess = tf.Session()

sess.run(init)

# Fit the line

for step in range(2001):

sess.run(train, feed_dict={X: x_train, Y: y_train})

if step % 20 == 0:

print(step, sess.run(cost, feed_dict={

X: x_train, Y: y_train}), sess.run(W), sess.run(b))

# Learns best fit W:[ 1.], b:[ 0]

# Testing out model

print(sess.run(hypothesis, feed_dict={X: 5}))

print(sess.run(hypothesis, feed_dict={X: 2.5}))