tensorflow v2.0入门教程——03线性回归

浅浅的花香味﹌ 2023-08-17 17:33 130阅读 0赞

[个人博客][Link 1]  
本教程在理解[线性回归原理][Link 2]的基础上。

## 线性回归 ##

import tensorflow as tf
    import numpy as np
    
    # 一些参数
    learning_rate = 0.01  # 学习率
    training_steps = 1000  # 训练次数
    display_step = 50  # 训练50次输出一次
    
    # 训练数据
    X = np.array([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,
                  7.042,10.791,5.313,7.997,5.654,9.27,3.1])
    Y = np.array([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,
                  2.827,3.465,1.65,2.904,2.42,2.94,1.3])
    n_samples = X.shape[0]
    
    # 随机初始化权重和偏置
    W = tf.Variable(np.random.randn(), name="weight")
    b = tf.Variable(np.random.randn(), name="bias")
    
    # 线性回归函数
    def linear_regression(x):
        return W*x + b
    
    # 损失函数
    def mean_square(y_pred, y_true):
        return tf.reduce_sum(tf.pow(y_pred-y_true, 2)) / (2 * n_samples)
    
    # 优化器采用随机梯度下降(SGD)
    optimizer = tf.optimizers.SGD(learning_rate)
    
    # 计算梯度，更新参数
    def run_optimization():
        # tf.GradientTape()梯度带，可以查看每一次epoch的参数值
        with tf.GradientTape() as g:
            pred = linear_regression(X)
            loss = mean_square(pred, Y)
        # 计算梯度
        gradients = g.gradient(loss, [W, b])
        # 更新W，b
        optimizer.apply_gradients(zip(gradients, [W, b]))
    
    # 开始训练
    for step in range(1, training_steps+1):
        run_optimization()
        if step % display_step == 0:
            pred = linear_regression(X)
            loss = mean_square(pred, Y)
            print("step: %i, loss: %f, W: %f, b: %f" % (step, loss, W.numpy(), b.numpy()))

## 输出结果 ##

step: 50, loss: 0.122252, W: 0.372333, b: -0.056897
    step: 100, loss: 0.117069, W: 0.365222, b: -0.006478
    step: 150, loss: 0.112478, W: 0.358529, b: 0.040970
    step: 200, loss: 0.108412, W: 0.352231, b: 0.085622
    step: 250, loss: 0.104811, W: 0.346304, b: 0.127643
    step: 300, loss: 0.101622, W: 0.340726, b: 0.167189
    step: 350, loss: 0.098798, W: 0.335476, b: 0.204404
    step: 400, loss: 0.096297, W: 0.330536, b: 0.239427
    step: 450, loss: 0.094082, W: 0.325887, b: 0.272386
    step: 500, loss: 0.092120, W: 0.321512, b: 0.303402
    step: 550, loss: 0.090383, W: 0.317395, b: 0.332592
    step: 600, loss: 0.088844, W: 0.313520, b: 0.360061
    step: 650, loss: 0.087481, W: 0.309874, b: 0.385912
    step: 700, loss: 0.086274, W: 0.306442, b: 0.410240
    step: 750, loss: 0.085205, W: 0.303213, b: 0.433135
    step: 800, loss: 0.084259, W: 0.300174, b: 0.454680
    step: 850, loss: 0.083421, W: 0.297314, b: 0.474956
    step: 900, loss: 0.082678, W: 0.294623, b: 0.494037
    step: 950, loss: 0.082021, W: 0.292090, b: 0.511994
    step: 1000, loss: 0.081438, W: 0.289706, b: 0.528893

## 可视化查看 ##

import matplotlib.pyplot as plt
    
    plt.plot(X, Y, 'ro', label='Original data')
    plt.plot(X, np.array(W * X + b), label='Fitted line')
    plt.legend()
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhcmRzZXB0ZW1iZXI_size_16_color_FFFFFF_t_70]  
[个人博客][Link 1]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhcmRzZXB0ZW1iZXI_size_16_color_FFFFFF_t_70 1]

[Link 1]: http://www.wardseptember.top/
[Link 2]: https://blog.csdn.net/alw_123/article/details/82193535
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhcmRzZXB0ZW1iZXI_size_16_color_FFFFFF_t_70]: /images/20230808/3c242c5e587f4b5785684672ef727c21.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhcmRzZXB0ZW1iZXI_size_16_color_FFFFFF_t_70 1]: /images/20230808/77995e5239954ef29abc7bf8c98fcc89.png