code

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

x = np.array([0,1,2,3,4,5,6,7,8,9,10])
y = np.array([1,3,5,7,9,11,13,15,17,19,21])

plt.plot(x,y, "k")
plt.show()

inputt = tf.placeholder(dtype = tf.float64, shape = None)
output = tf.placeholder(dtype = tf.float64, shape = None)

slope = tf.Variable(4, dtype=tf.float64)
b = tf.Variable(5 , dtype=tf.float64)

model_operation = slope * inputt + b
error = output - model_operation

squared = error ** 2

loss = tf.reduce_mean(squared)



opt = tf.train.GradientDescentOptimizer(0.0005)

train = opt.minimize(loss)

init = tf.global_variables_initializer()

with tf.Session()as sess:
    sess.run(init)
    for i in range(20000):
        sess.run(train, feed_dict={inputt:x, output:y})
        if i % 10 ==0:
            print(sess.run([slope, b]))
            plt.plot(x, sess.run(model_operation, feed_dict={inputt:x}))

    plt.plot(x,y, "k")