m3_linear_regression.py

import tensorflow as tf

# Model Params
W = tf.Variable([.3], dtype=tf.float32)
b = tf.Variable([-.3], dtype=tf.float32)

# Model input and output

x = tf.placeholder(tf.float32)
linear_model = W * x + b

y = tf.placeholder(tf.float32)

# loss

loss = tf.reduce_sum(tf.square(linear_model - y))

# optimizer
optimizer = tf.train.GradientDescentOptimizer(0.01)

train = optimizer.minimize(loss)

# training data
x_train = [1, 2, 3, 4]
y_train = [0, -1, -2, -3]

init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)

    for i in range(3000):
        sess.run(train, {x: x_train, y: y_train})

    # evaluate training accuracy
    curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x: x_train, y: y_train})

    print('W: %s b: %s, loss: %s'%(curr_W, curr_b, curr_loss))