tensorflow49 《面向机器智能的TensorFlow实战》笔记-04-01 线性回归

# 《面向机器智能的Tensor Flow实战》04 机器学习基础# win10 Tensorflow-gpu1.1.0 python3.5.3# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1# 原书代码(tensorflow0.8)：https://github.com/backstopmedia/tensorflowbook# tensorflow不同版本api变化:https://github.com/tensorflow/tensorflow/releases# filename:tfmi04.01.py # 线性回归import tensorflow as tfW = tf.Variable(tf.zeros([2, 1]), name="weights")b = tf.Variable(0., name="bias")def inference(X):    return tf.matmul(X, W) + bdef loss(X, Y):    Y_predicted = inference(X)    return tf.reduce_sum(tf.squared_difference(Y, Y_predicted))def inputs():    # Data from http://people.sc.fsu.edu/~jburkardt/datasets/regression/x09.txt    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("inference([[80., 25.]]):", sess.run(inference([[80., 25.]])))    print("inference([[65., 25.]]):", sess.run(inference([[65., 25.]])))with tf.Session() as sess:    tf.global_variables_initializer().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)    training_steps = 1000    for step in range(training_steps):        sess.run([train_op])        if step % 100 == 0:            print("loss: ", sess.run([total_loss]))    evaluate(sess, X, Y)    coord.request_stop()    coord.join(threads)    sess.close()'''loss:  [7608773.0]loss:  [5341925.0]loss:  [5339993.0]loss:  [5338747.0]loss:  [5337539.0]loss:  [5336333.5]loss:  [5335129.5]loss:  [5333926.0]loss:  [5332724.5]loss:  [5331523.0]inference([[80., 25.]]): [[ 320.64968872]]inference([[65., 25.]]): [[ 267.78182983]]'''