tensorflow-for-deep-learning-py/TensorFlow/intro_linear_model.py

import numpy as np
import tensorflow as tf

# Model linear regression y = Wx + b
x = tf.placeholder(tf.float32, [None, 1])
W = tf.Variable(tf.zeros([1,1]))
b = tf.Variable(tf.zeros([1]))
product = tf.matmul(x,W)
y = product + b
y_ = tf.placeholder(tf.float32, [None, 1])

# Cost function sum((y_-y)**2)
cost = tf.reduce_mean(tf.square(y_-y))

# Training using Gradient Descent to minimize cost
train_step = tf.train.GradientDescentOptimizer(0.0000001).minimize(cost)

sess = tf.Session()
init = tf.initialize_all_variables()
sess.run(init)
steps = 1000
for i in range(steps):

    # Create fake data for y = W.x + b where W = 2, b = 0
    xs = np.array([[i]])
    ys = np.array([[2*i]])

    # Train
    feed = { x: xs, y_: ys }
    sess.run(train_step, feed_dict=feed)

    print("After %d iteration:" % i)
    print("W: %f" % sess.run(W))
    print("b: %f" % sess.run(b))

    print("cost: %f" % sess.run(cost, feed_dict=feed))