<filename>linear_regression.py
#!/usr/bin/env python

import numpy as np
import tensorflow as tf

# Prepare train data
train_X = np.linspace(-1, 1, 100)
train_Y = 2 * train_X + np.random.randn(*train_X.shape) * 0.33 + 10

# Define the model
X = tf.placeholder("float")
Y = tf.placeholder("float")
w = tf.Variable(0.0, name="weight")
b = tf.Variable(0.0, name="bias")
loss = tf.square(Y - X * w - b)
train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

# Create session to run
with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())

    epoch = 1
    for i in range(10):
        for (x, y) in zip(train_X, train_Y):
            _, w_value, b_value = sess.run([train_op, w, b],
                                           feed_dict={X: x,
                                                      Y: y})
        print("Epoch: {}, w: {}, b: {}".format(epoch, w_value, b_value))
        epoch += 1

<filename>linear_regression.py
#!/usr/bin/env python

import numpy as np
import tensorflow as tf

# Prepare train data
train_X = np.linspace(-1, 1, 100)
train_Y = 2 * train_X + np.random.randn(*train_X.shape) * 0.33 + 10

# Define the model
X = tf.placeholder("float")
Y = tf.placeholder("float")
w = tf.Variable(0.0, name="weight")
b = tf.Variable(0.0, name="bias")
loss = tf.square(Y - X * w - b)
train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

# Create session to run
with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())

    epoch = 1
    for i in range(10):
        for (x, y) in zip(train_X, train_Y):
            _, w_value, b_value = sess.run([train_op, w, b],
                                           feed_dict={X: x,
                                                      Y: y})
        print("Epoch: {}, w: {}, b: {}".format(epoch, w_value, b_value))
        epoch += 1

#!/usr/bin/env python # Prepare train data # Define the model # Create session to run