In [1]:

    

import tensorflow as tf
import matplotlib.pyplot as plt

a = tf.constant([15.0,16.0,17.0,12.0,11.0,10.0,9.0],name ='x')
b = tf.constant([7.0,1.0,2.0,5.0,4.0,9.0,6.0],name='y')
training_epoch = 500
learning_rate = 0.005
t1=0.0
t0=0.0
for epoch in range(training_epoch):
    h0=tf.divide(1.0,tf.add(1.0,tf.exp(-tf.add(t0,tf.reduce_sum(tf.multiply(t1,a))))))
    tn1 = tf.subtract(t1,tf.multiply(learning_rate,tf.divide(h0,tf.cast(tf.size(a),tf.float32))))
    t1=tn1
    model=tf.global_variables_initializer()
sess = tf.Session() 
print("h0 values:",sess.run(h0))
c=tf.cond(h0<0.5,lambda:0.00,lambda:1.00)
print(sess.run(c))


    

    




h0 values: 0.0324959
0.0

In [2]:

    

print(sess.run(c))


    

    




0.0

In [3]:

    

g=tf.log(100.0)
sess.run(g)


    

    
Out[3]:





4.6051702

In [4]:

    

#cost function
v=tf.multiply(b,tf.log(c))
n=tf.subtract(1.0,b)
m=tf.log(tf.subtract(1.0,c))
m1=tf.multiply(n,m)
m2=tf.reduce_sum(tf.subtract(-v,m1))
sess.run(m2)


    

    
Out[4]:





inf

In [ ]: