minimizing_cost_graph.py

import tkinter
import tensorflow as tf
import matplotlib.pyplot as plt

x = [1,2,3]
y = [1,2,3]

W = tf.placeholder(tf.float32)

hypothesis = x * W

cost = tf.reduce_mean(tf.square(hypothesis - y))

sess = tf.Session()

sess.run(tf.global_variables_initializer())

W_val = []

cost_val = []

for i in range(-30, 50):
	feed_W = i * 0.1
	curr_cost, curr_W = sess.run([cost, W], feed_dict={W: feed_W})
	W_val.append(curr_W)
	cost_val.append(curr_cost)

plt.plot(W_val, cost_val)

plt.show()