tensorflow笔记1 平面拟合

这段很短的 Python 程序生成了一些三维数据,然后用一个平面拟合它.

    import tensorflow as tf
    import numpy as np
    
    # 使用 NumPy 生成假数据(phony data), 总共 100 个点.
    x_data = np.float32(np.random.rand(2, 100)) # 随机输入
    y_data = np.dot([0.100, 0.200], x_data) + 0.300
    
    # 构造一个线性模型
    b = tf.Variable(tf.zeros([1]))
    W = tf.Variable(tf.random_uniform([1, 2], -1.0, 1.0))
    y = tf.matmul(W, x_data) + b
    
    # 最小化方差
    loss = tf.reduce_mean(tf.square(y - y_data))
    optimizer = tf.train.GradientDescentOptimizer(0.5)
    train = optimizer.minimize(loss)
    
    # 初始化变量
    init = tf.initialize_all_variables()
    
    # 启动图 (graph)
    sess = tf.Session()
    sess.run(init)
    
    # 拟合平面
    for step in xrange(0, 201):
        sess.run(train)
        if step % 20 == 0:
            print step, sess.run(W), sess.run(b)
    
    # 得到最佳拟合结果 W: [[0.100  0.200]], b: [0.300]

0 [[ 0.82516074 -0.41683942]] [0.50299114]
20 [[0.19490492 0.07856247]] [0.31059143]
40 [[0.11309221 0.1780714 ]] [0.30416673]
60 [[0.10130052 0.1956378 ]] [0.30155557]
80 [[0.09991585 0.1990025 ]] [0.30056834]
100 [[0.09988044 0.19973396]] [0.30020574]
120 [[0.09994256 0.19991934]] [0.30007416]
140 [[0.09997701 0.19997342]] [0.3000267]
160 [[0.09999137 0.19999085]] [0.30000958]
180 [[0.09999685 0.19999678]] [0.30000344]
200 [[0.09999886 0.19999886]] [0.30000123]