Linear Regression Neural Network made with Tensor Flow

neuralnetwork.py

import tensorflow as tf
import matplotlib.pyplot as plt
tf.reset_default_graph()
input_node = tf.placeholder(dtype=tf.float32, shape=None)
output_node = tf.placeholder(dtype=tf.float32, shape=None)
slope = tf.Variable(5.0, dtype=tf.float32)
y_intercept = tf.Variable(1.0, dtype=tf.float32)
conclusion_operation = slope * input_node + y_intercept
error_squared = tf.square(conclusion_operation - output_node)
loss = tf.reduce_mean(error_squared)
init = tf.global_variables_initializer()

x_values = [2, 4, 6, 8, 10]
y_values = [1, 3, 5, 7, 9]

optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
train = optimizer.minimize(loss)

with tf.Session() as sess:
    sess.run(init)
    for i in range(2500):
        sess.run(train, feed_dict={input_node: x_values, output_node: y_values})
        if i % 100 == 0:
            print(sess.run([slope, y_intercept]))
            plt.plot(x_values, sess.run(conclusion_operation, feed_dict={input_node: x_values}))
    print(sess.run(loss, feed_dict={input_node: x_values, output_node: y_values}))
    plt.plot(x_values, y_values, 'ro', 'Expected Output')
    plt.plot(x_values, sess.run(conclusion_operation, feed_dict={input_node: x_values}))
    plt.show()