机器学习（python+numpy+tensorflow）实战样例

Myth丶恋晨 2022-02-03 00:13 240阅读 0赞

import numpy as np
    import tensorflow as tf
    import matplotlib.pyplot as plt
    ses = tf.Session()
    # 构建数据集
    np.random.seed(5)
    x_data = np.linspace(-1, 1, 100)
    y_data = 2 * x_data + 1.0 + np.random.randn(x_data.shape[0]) * 0.4 # np.random.randn 用于增加噪声
    # 画图
    plt.scatter(x_data, y_data)
    plt.plot(x_data, 1.0 + 2 * x_data, color="red", linewidth=3)
    plt.show()
    
    # 构建模型
    def model(x, w, b):
        return tf.multiply(x, w) + b
    x = tf.placeholder("float", name="x")
    y = tf.placeholder("float", name="y")
    w = tf.Variable(1.0, name="w")
    b = tf.Variable(0.0, name="b")
    pred = model(x, w, b)
    
    # 训练模型
    train_epochs = 10
    learning_rate = 0.05
    # 均方差
    loss_function = tf.reduce_mean(tf.square(y-pred))
    # 梯度下降优化器
    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss_function)
    ses.run(tf.global_variables_initializer())
    for _ in range(10):
        for xs, ys in zip(x_data, y_data):
            loss, _ = ses.run([loss_function, optimizer], feed_dict={x: xs, y: ys})
        btmp = b.eval(ses)
        wtmp = w.eval(ses)
        print("w=", wtmp, ", b=", btmp, "loss=", loss)
    
    ses.close()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hc3RlclNoYXc_size_16_color_FFFFFF_t_70][]

步骤：

1. 生成人工数据集及可视化（matplotlib）

2. 构建线性模型

3. 定义损失函数

4. 定义优化器，最小化损失函数

5. 训练结果的可视化

6. 利用学习到的模型进行预测

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L21hc3RlclNoYXc_size_16_color_FFFFFF_t_70]: /images/20220203/2c68e6e77260463d86be70bf029444aa.png