使用tensorflow的线性回归的例子（二）

#List3-30

拟合y=2x

%matplotlib inline

import tensorflow as tf

import numpy as np

import matplotlib.pyplot as plt

x_train = np.linspace(-1, 1, 101)

y_train = 2 * x_train + np.random.randn(*x_train.shape) * 0.33

plt.scatter(x_train, y_train)

w = tf.Variable(np.random.randn(),tf.float32)

b = tf.Variable(np.random.randn(),tf.float32)

#设置迭代次数和学习率

learning_rate = 0.01

training_epochs = 100

loss = []

count = 0

display_count = 10 #控制显示粒度的参数,每训练10个样本输出一次损失值

#定义模型函数

def model(x,w,b):

    return tf.multiply(x,w)+b

def loss_fun(x,y,w,b):

    err = model(x,w,b)-y

    squared_err = tf.square(err)

    return tf.reduce_mean(squared_err)

def grad(x,y,w,b):

    with tf.GradientTape() as tape:

        loss_ = loss_fun(x,y,w,b)

    return tape.gradient(loss_,[w,b])

#optimizer= tf.keras.optimizers.Adam(learning_rate=0.01)

for epoch in range(training_epochs):

    for (x, y) in zip(x_train, y_train):

        loss_ =loss_fun(x,y,w,b)

        loss.append(loss_)

        #计算当前[w,b]的梯度

        delta_w,delta_b = grad(x,y,w,b)

        change_w = delta_w * learning_rate

        change_b = delta_b * learning_rate

        w.assign_sub(change_w)

        b.assign_sub(change_b)

        #训练步数加1

        count = count +1

        if count % display_count == 0:

            print('train epoch : ','%02d'%(epoch+1),'step:%03d' % (count),'loss= ','{:.9f}'.format(loss_))

plt.scatter(x_train, y_train)

y_learned = x_train*w.numpy()+b.numpy()

plt.plot(x_train, y_learned, 'r')

plt.show()