tensorflow的第一个练习：线性回归

代码和运行结果

找了个例子，把代码敲进去，先贴代码吧：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

def moving_average(a, w = 10):
    if len(a) < w:
        return a[:]
    return [val if idx < w else sum(a[(idx - w): idx])/w for idx, val in enumerate(a)]

train_X = np.linspace(-1, 1, 100)
train_Y = 2 * train_X + np.random.randn(*train_X.shape) * 0.3 #y = 2x
plt.plot(train_X, train_Y,'ro', label = 'Original data')
plt.legend()
plt.show()

#创建模型
#占位符
X = tf.placeholder("float")
Y = tf.placeholder("float")
#模型参数
W = tf.Variable(tf.random_normal([1]), name = "weight")
b = tf.Variable(tf.Variable(tf.zeros([1])), name = "bias")
#向前结构
z = tf.multiply(X, W) + b
#反向优化
cost = tf.reduce_mean(tf.square(Y - z))
learning_rate = 0.01
optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate).minimize(cost)
#初始化所有变量
init = tf.global_variables_initializer()
#定义参数
train_epochs = 20
display_step = 1

#启动session
with tf.Session() as sess:
    sess.run(init)
    plotdata = {"batchsize": [], "loss":[]}
    #向模型中输入数据
    for epoch in range(train_epochs):
        for (x, y) in zip(train_X, train_Y):
            sess.run(optimizer, feed_dict = {X: x, Y: y})
        
        #显示训练中的详细信息
        if epoch % display_step == 0:
            loss = sess.run(cost, feed_dict = {X: train_X, Y: train_Y})
            print("Epoch:", epoch + 1, " cost = ", loss, " W = ", sess.run(W), "b = ", sess.run(b))
            if not (loss == "NA"):
                plotdata["batchsize"].append(epoch)
                plotdata["loss"].append(loss)
    
    print("Finished!")
    print("cost = ", sess.run(cost, feed_dict = {X: train_X, Y: train_Y}), "W = ", sess.run(W), "b = ", sess.run(b))    
    #图形显示
    plt.plot(train_X, train_Y, 'ro', label = 'Original data')
    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label = 'Fittedline')
    plt.legend()
    plt.show()
    
    plotdata["avgloss"] = moving_average(plotdata["loss"])
    plt.figure(1)
    plt.subplot(211)
    plt.plot(plotdata["batchsize"], plotdata["avgloss"], 'b--')
    plt.xlabel("Minibatch number")
    plt.ylabel("Loss")
    plt.title("Minibatch run vs. Training loss")
    
    plt.show()    
    print("x = 0.2, z = ", sess.run(z, feed_dict = {X: 0.2}))

运行结果如下：

• 训练样本图示：
  

• 训练过程：

Epoch: 1  cost =  0.3932809  W =  [1.0674796] b =  [0.28588685]
Epoch: 2  cost =  0.18038075  W =  [1.4702674] b =  [0.19911513]
Epoch: 3  cost =  0.112091534  W =  [1.6866398] b =  [0.12487796]
Epoch: 4  cost =  0.09329908  W =  [1.7981633] b =  [0.08322224]
Epoch: 5  cost =  0.088284045  W =  [1.8550674] b =  [0.06153361]
Epoch: 6  cost =  0.086936906  W =  [1.8840283] b =  [0.05043918]
Epoch: 7  cost =  0.08656489  W =  [1.898758] b =  [0.04478921]
Epoch: 8  cost =  0.08645678  W =  [1.9062486] b =  [0.04191512]
Epoch: 9  cost =  0.08642279  W =  [1.9100573] b =  [0.04045354]
Epoch: 10  cost =  0.08641093  W =  [1.9119941] b =  [0.03971026]
Epoch: 11  cost =  0.0864063  W =  [1.912979] b =  [0.0393323]
Epoch: 12  cost =  0.0864043  W =  [1.9134799] b =  [0.03914016]
Epoch: 13  cost =  0.08640339  W =  [1.9137341] b =  [0.03904257]
Epoch: 14  cost =  0.08640294  W =  [1.9138632] b =  [0.03899306]
Epoch: 15  cost =  0.08640273  W =  [1.9139293] b =  [0.03896773]
Epoch: 16  cost =  0.08640262  W =  [1.9139631] b =  [0.03895474]
Epoch: 17  cost =  0.08640256  W =  [1.91398] b =  [0.03894821]
Epoch: 18  cost =  0.08640253  W =  [1.9139886] b =  [0.0389449]
Epoch: 19  cost =  0.08640251  W =  [1.9139936] b =  [0.03894305]
Epoch: 20  cost =  0.08640251  W =  [1.9139961] b =  [0.03894206]
Finished!
cost =  0.08640251 W =  [1.9139961] b =  [0.03894206]

• 训练结果：

• 训练过程中损失函数结果的变化，以及 x=0.2 时预测的 z 值：
  

遇到的问题

• 在 anaconda 下用 pip install tensorflow==1.15.0rc3 命令安装的 CPU 版本运行正常。
• 在 anaconda 下用 pip install tensorflow-gpu==1.15.0rc3 命令安装的 GPU 版本出现问题，错误提示说是某个函数设备不支持。更换成1.13.0rc1版本运行正常。
• 在 anaconda 下用 pip install tensorflow-gpu 命令默认安装 GPU 最新版本2.0.0出现问题，错误提示非法的数据。估计GPU驱动或者CUDA的版本不匹配。以后再考虑2.0版本。

进一步的讨论话题

• 得认真学习一下 Python 语言了，不然程序根本看不懂。
• Numpy 是个什么鬼？估计得花时间学习一下。