tensorflow1.py

import numpy as np
import tensorflow as tf

print(tf.__version__) # 1.15.0

w = tf.Variable(0, dtype = tf.float32) # defining the parameter "w"
#cost = tf.add(tf.add(w**2, tf.multiply(-10., w)), 25) # defining the coost function "J"
cost = w**2 -10*w + 25
train = tf.train.GradientDescentOptimizer(0.01).minimize(cost) # learning rate is 0.01 to minimize the cost

init = tf.global_variables_initializer()
session = tf.Session()
session.run(init)

# Now let's run 1000 iteration of Gradient Descent to minimize this cost and see the optimal value of "w"
for i in range(1000):
    session.run(train)
    
print(session.run(w))