Example#1-1.py

import tensorflow as tf

# X and Y data
# x_train = [1,2,3]
# y_train = [1,2,3]

# Now we can use X and Y in place of x_data and y_data
# # placeholders for a tensor that will be always fed using feed_dict
# See http://stackoverflow.com/questions/36683740/
X = tf.placeholder(tf.float32, shape=[None])
Y = tf.placeholder(tf.float32, shape=[None])

W = tf.Variable(tf.random_normal([1]), name='weight')
b = tf.Variable(tf.random_normal([1]), name='bias')

# Our hypothesis XW+b
hypothesis = X * W + b

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

# minimize
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
train = optimizer.minimize(cost)

# Launch the graph in a session
sess = tf.Session()

# Initializes global variables in the graph.
sess.run(tf.global_variables_initializer())

# Fit the line
for step in range(2001):
    cost_val, W_val, b_val, _ = sess.run([cost, W, b, train],
        feed_dict={X: [1, 2, 3, 4, 5],
                   Y: [2.1, 3.1, 4.1, 5.1, 6.1]})
    if step % 20 == 0:
        print(step, cost_val, W_val, b_val)