线性单元和梯度下降--代码实现

最新推荐文章于 2022-06-16 17:38:44 发布

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最新推荐文章于 2022-06-16 17:38:44 发布

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分类专栏：机器学习文章标签：线性单元梯度下降算法代码实现深度学习机器学习

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本文介绍了一个使用Python实现的简单感知机模型，并基于此构建了线性单元预测器。该预测器通过训练数据集来调整权重和偏置，以预测不同工作年限对应的月薪水平。此外，还展示了如何可视化训练结果。

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import numpy as np
from functools import reduce

class Perceptron(object):
    def __init__(self, input_num, activator):
        self.activator = activator
        self.weights = [0.0 for _ in range(input_num)]
        self.bias = 0.0

    def __str__(self):
        return 'weights\t:%s\nbias\t:%f\n' % (self.weights, self.bias)


    def predict(self, input_vec):
        return self.activator(
            reduce(lambda a, b: a + b, [x_w[0] * x_w[1] for x_w in zip(input_vec, self.weights)], 0.0) + self.bias)

    def train(self, input_vecs, labels, iteration, rate):
        for i in range(iteration):
            self._one_iteration(input_vecs, labels, rate)

    def _one_iteration(self, input_vecs, labels, rate):
[(input_vec, label), ...]
        samples = list(zip(input_vecs, labels))
        for (input_vec, label) in samples:
            output = self.predict(input_vec)
            self._update_weights(input_vec, output, label, rate)

    def _update_weights(self, input_vec, output, label, rate):
        delta = label - output
        self.weights = [x_w1[1] + rate * delta * x_w1[0] for x_w1 in zip(input_vec, self.weights)]
        self.bias += rate * delta


def f(x):
    return 1 if x > 0 else 0

f = lambda x: x

class LinearUnit(Perceptron):
    def __init__(self, input_num):
        Perceptron.__init__(self, input_num, f)


def get_training_dataset():
    input_vecs = [[5], [3], [8], [1.4], [10.1]]

    labels = [5500, 2300, 7600, 1800, 11400]
    return input_vecs, labels    


def train_linear_unit():

    lu = LinearUnit(1)
    input_vecs, labels = get_training_dataset()
    lu.train(input_vecs, labels, 10, 0.01)
    return lu


def plot(linear_unit):
    import matplotlib.pyplot as plt
    input_vecs, labels = get_training_dataset()
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.scatter([x[0] for x in input_vecs], labels)
    weights = linear_unit.weights
    bias = linear_unit.bias
    x = list(range(0,12,1))
    y = [weights[0] * x + bias for x in x]
    ax.plot(x, y)
    plt.show()


if __name__ == '__main__': 
    linear_unit = train_linear_unit()
    print (linear_unit)
    print ('Work 3.4 years, monthly salary = %.2f' % linear_unit.predict([3.4]))
    print ('Work 15 years, monthly salary = %.2f' % linear_unit.predict([15]))
    print ('Work 1.5 years, monthly salary = %.2f' % linear_unit.predict([1.5]))
    print ('Work 6.3 years, monthly salary = %.2f' % linear_unit.predict([6.3]))
    plot(linear_unit)