lab-03-1-minimizing_cost_show_graph.py

# Lab 3 Minimizing Cost
import tensorflow as tf
import matplotlib.pyplot as plt
tf.set_random_seed(777)  # for reproducibility

X = [1, 2, 3]
Y = [1, 2, 3]

W = tf.placeholder(tf.float32)

# Our hypothesis for linear model X * W
hypothesis = X * W

# cost/loss function
cost = tf.reduce_mean(tf.square(hypothesis - Y))

# Launch the graph in a session.
sess = tf.Session()
# Initializes global variables in the graph.
sess.run(tf.global_variables_initializer())

# Variables for plotting cost function
W_vals = []
cost_vals = []

for i in range(-30, 50):
    curr_W = i * 0.1
    curr_cost = sess.run(cost, feed_dict={W: curr_W})
    W_vals.append(curr_W)
    cost_vals.append(curr_cost)

# Show the cost function
plt.plot(W_vals, cost_vals)
plt.show()