TensorFlow - 线性回归（1）

TensorFlow - 线性回归（1）

flyfish

摘自 《面向机器智能TensorFlow实践》

import tensorflow as tf

W = tf.Variable(tf.zeros([2, 1]), name="weights")
b = tf.Variable(0., name="bias")

def inference(X):
    return tf.matmul(X, W) + b

def loss(X, Y):
    Y_predicted = inference(X)
    return tf.reduce_sum(tf.squared_difference(Y, Y_predicted))

def inputs():
    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], [63, 30]]

    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, 244]

    return tf.to_float(weight_age), tf.to_float(blood_fat_content)

def train(total_loss):
    learning_rate = 0.0000001
    return tf.train.GradientDescentOptimizer(learning_rate).minimize(total_loss)


def evaluate(sess, X, Y):
    print(sess.run(inference([[80., 25.]])), sess.run(inference([[65., 25.]])))  # [[320.6497]] [[267.78183]]

with tf.Session() as sess:
    tf.global_variables_initializer().run()
    X, Y = inputs()
    train_op = train(loss(X, Y))

    for step in range(1000):
        sess.run([train_op])

    evaluate(sess, X, Y)
    sess.close()