Tensorflow - Simplest Gradient Descent using GradientDescentOptimizer

Below is the code to find the minimum possible value of weight (w) such that the outcome of equation is closest to 0, using Gradient Descent method.

These weights are multiplied with input (x) to compute cost, thus minimizing it.

The equation is below...

w2-10w+25 ---------------------- (i)
i.e.
(w - 5)2

Thus, the coefficients from (i) would be 1, -10 and 25

import numpy as np

import tensorflow as tf

coefficients = np.array([[1], [-10], [25]]) # try 1, -20, 100 -- line 4

w = tf.Variable([0], dtype=tf.float32)

x = tf.placeholder(tf.float32, [3,1])

cost = (x[0][0] * w ** 2) + (x[1][0] * w) + x[2][0] # (w-5)^2

train = tf.train.GradientDescentOptimizer(0.01).minimize(cost)

init = tf.global_variables_initializer()

with tf.Session() as session:

session.run(init)

print(session.run(w))

for i in range(1000):

session.run(train, feed_dict={x:coefficients})

print(session.run(w))

The output would be 4.9999886, i.e. closest to 5

Try changing the line 4 to 1, -20, 100. Then the outcome would be 9.999977 and equation would be
(w - 10)2