tf-linear-regression

import tensorflow as tf

# initialize variables/model parameters
W = tf.Variable(tf.zeros([2,1]), name="weights")
b = tf.Variable(0., name="bias")

# define the training loop operations
def inference(X):
    # compute inference model over data X and return the result
    # pass
    return tf.matmul(X, W) + b

def loss(X, Y):
    # compute loss over training data X and expected output Y
    # pass
    Y_predicted = inference(X)
    return tf.reduce_sum(tf.squared_difference(Y, Y_predicted))

def inputs():
    # read/generate input training data X and expected output Y
    # pass
    weight_age = [[84,46], [73,20], [65,52], [70,30], [76,57], [69,25], [63,28], [72,36],   [79,57], [75,44], [27,24], [89,31], [65,52], [57,23], [59,60], [69,48], [60,34], [79,51], [75,50], [82,34], [59,46], [67,23], [85,37], [55,40]]
    blood_fat_content = [354, 190, 405, 263, 451, 302, 288, 385, 402, 365, 209, 290, 346,   254, 395, 434, 220, 374, 308, 220, 311, 181, 274, 303]
    return tf.to_float(weight_age), tf.to_float(blood_fat_content)

def train(total_loss):
    # train/adjust model parameters according to computed total loss
    # pass
    learning_rate = 0.0000001
    return tf.train.GradientDescentOptimizer(learning_rate).minimize(total_loss)

def evaluate(sess, X, Y):
    # evaluate the resulting trained model
    # pass
    print(sess.run(inference([[80.,25.]]))) #~303
    print(sess.run(inference([[63.,30.]]))) #~256

# launch the graph in a session, setup boilerplate
with tf.Session() as sess:

    tf.initialize_all_variables().run()

    X, Y = inputs()

    total_loss = loss(X, Y)
    train_op = train(total_loss)

    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)

    # actual training loop
    training_steps = 1000
    for step in range(training_steps):
        sess.run([train_op])
        # for debugging or learning purposes
        if step%10 == 0:
            print("loss: ", sess.run([total_loss]))

    evaluate(sess, X, Y)
    coord.request_stop()
    coord.join(threads)
    sess.close()