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# Write a program using tensorflow to calculate :
\$\$y=mx+c\$\$

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Part 1

1. Read 2 arrays x,y containing floating point values
2. Calculate mean of x & y
3. Calculate variance for x \$\$variance(x)=sum((x-mean(x))^2)\$\$
4. Calculate covariance of x & y \$\$covariance = sum((x(i) - mean(x)) * (y(i) - mean(y)))\$\$
5. Calculate value of c \$\$c = covariance(x,y)/variance(x)\$\$
6. Calculate value of m \$\$m = mean(y) -c* mean(x)\$\$
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In [68]:

import tensorflow as tf

with tf.name_scope("var"):
with tf.name_scope("mean_x"):
a=tf.constant([5.0,7.0,20.2,17.32],shape=[1,4],name='a')
b=tf.constant([7.0,9.0,19.0,18.0],shape=[1,4],name='b')
x=tf.reduce_mean(a)
sess=tf.Session()
print("mean",sess.run(x))

#mean x

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mean 12.38

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In [69]:

#mean of y

with tf.name_scope("mean_y"):
y=tf.reduce_mean(b)
sess=tf.Session()
print("mean",sess.run(y))

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mean 13.25

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In [70]:

#variance of x
d=tf.subtract(a,x)
sess=tf.Session()
print(sess.run(d ))

e=tf.square(d)
f=tf.reduce_sum(e)
sess=tf.Session()
print(sess.run(f))

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[[-7.38000011 -5.38000011  7.82000065  4.93999958]]
168.965

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In [71]:

#covariance
with tf.name_scope("covariance"):
g=tf.subtract(b,y)
sess=tf.Session()
g=tf.multiply(d,g)
h=tf.reduce_sum(g)
print(sess.run(h))

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137.42

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In [72]:

#value of c
with tf.name_scope("value_of_c"):
j=tf.divide(h,f)
print(sess.run(j))

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0.813305

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In [73]:

#m value
with tf.name_scope("value_m"):
i=tf.multiply(j,x)
k=tf.subtract(y,i)
print(sess.run(j))

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0.813305

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Part 2

1. Plot graph for actual values against predicted value
2. Calculate root mean square error.
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In [76]:

with tf.Session() as lb:
Writer =tf.summary.FileWriter("/tmp/tboard/output3",lb.graph)
Writer.close()

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