TensorFlow (二): 单变量线性回归

- 日理万妓 2022-03-15 11:54 180阅读 0赞

### TensorFlow 单变量线性回归 ###

*  代码
 *  效果图

# 代码 #

import tensorflow as tf 
    import numpy as np 
    import matplotlib.pyplot as plt 
    
    # generate data
    
    data_size = 100
    x_data = np.random.rand(data_size) * 0.6 + 0.2
    noise = np.random.rand(data_size)
    noise = (noise - noise.mean()) * 0.2
    w = 1.5
    b = 0.5
    y_data = x_data * w + b + noise
    
    train_test_boundary = int(0.7 * data_size)
    
    x_train = x_data[:train_test_boundary]
    y_train = y_data[:train_test_boundary]
    x_test = x_data[train_test_boundary:]
    y_test = y_data[train_test_boundary:]
    
    # my model
    
    train_loss_history = []
    test_loss_history = []
    nb_epoch = 100
    my_w = 0
    my_b = 0
    learning_rate = 0.1
    
    with tf.Graph().as_default():
    	x = tf.placeholder(dtype = tf.float32, shape = [None])
    	y_ = tf.placeholder(dtype = tf.float32, shape = [None])
    	w = tf.Variable(0, dtype = tf.float32)
    	b = tf.Variable(0, dtype = tf.float32)
    	y = x * w + b
    	loss = tf.reduce_mean((y - y_) ** 2)
    	train = tf.train.AdamOptimizer(learning_rate).minimize(loss)
    
    	with tf.Session() as sess:
    		sess.run(tf.global_variables_initializer())
    		for i in range(nb_epoch):
    			sess.run(train, feed_dict = { x: x_train, y_: y_train})
    			l = sess.run(loss, feed_dict = { x: x_train, y_:y_train})
    			train_loss_history.append(l)
    			l = sess.run(loss, feed_dict = { x: x_test, y_: y_test})
    			test_loss_history.append(l)
    
    		my_w = w.eval()
    		my_b = b.eval()
    
    
    # display
    
    plt.figure(figsize=(8, 4))
    plt.subplot(1, 2, 1)
    plt.xlim([0, 1])
    plt.ylim([0, 3])
    plt.plot(np.linspace(0, 1, 100), my_w * np.linspace(0, 1, 100) + my_b, 'r--')
    plt.scatter(x_train, y_train, 1.0, 'b', marker='o')
    plt.title('dataset & hypothesis function')
    
    plt.subplot(1, 2, 2)
    plt.title('training & testing loss')
    plt.plot(train_loss_history, 'b--')
    plt.plot(test_loss_history, 'r--')
    plt.legend(['trian', 'test'])
    
    plt.show()

# 效果图 #

![0069McTXly1g0ohg9tiotj318g0m8q4o.jpg][]

[0069McTXly1g0ohg9tiotj318g0m8q4o.jpg]: /images/20220315/98b028ab2c7940b0ae0cd87495017250.png