linear_regression_full.py

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

def generate_dataset():
	x_batch = np.linspace(0, 2, 100)
	y_batch = 1.5 * x_batch + np.random.randn(*x_batch.shape) * 0.2 + 0.5
	return x_batch, y_batch

def linear_regression():
	x = tf.placeholder(tf.float32, shape=(None, ), name='x')
	y = tf.placeholder(tf.float32, shape=(None, ), name='y')

	with tf.variable_scope('lreg') as scope:
		w = tf.Variable(np.random.normal(), name='W')
		b = tf.Variable(np.random.normal(), name='b')
		
		y_pred = tf.add(tf.multiply(w, x), b)

		loss = tf.reduce_mean(tf.square(y_pred - y))

	return x, y, y_pred, loss

def run():
	x_batch, y_batch = generate_dataset()

	x, y, y_pred, loss = linear_regression()

	optimizer = tf.train.GradientDescentOptimizer(0.1)
	train_op = optimizer.minimize(loss)

	with tf.Session() as session:
		session.run(tf.global_variables_initializer())

		feed_dict = {x: x_batch, y: y_batch}
		
		for i in range(30):
			_ = session.run(train_op, feed_dict)
			print(i, "loss:", loss.eval(feed_dict))

		print('Predicting')
		y_pred_batch = session.run(y_pred, {x : x_batch})

	plt.scatter(x_batch, y_batch)
	plt.plot(x_batch, y_pred_batch, color='red')
	plt.xlim(0, 2)
	plt.ylim(0, 2)
	plt.savefig('plot.png')

if __name__ == "__main__":
	run()

Hello Derek,
Thank you for the detailed explanation. Can you please let me know how to extract the Slope and Bias from the Best Fit Line (Red Line in your plot). Thanks!