用python写bp神经网络主函数

你好，我是 C 知道，可以回答您的问题。以下是用 Python 写 BP 神经网络主函数的示例代码：

import numpy as np

def sigmoid(x):
    return 1 / (1 + np.exp(-x))

def bp_neural_network(X, y, hidden_layers, alpha, epochs):
    # 初始化权重和偏置
    input_layer_size = X.shape[1]
    output_layer_size = y.shape[1]
    weights = []
    biases = []
    for i in range(len(hidden_layers) + 1):
        if i == :
            weights.append(np.random.randn(input_layer_size, hidden_layers[i]))
            biases.append(np.zeros((1, hidden_layers[i])))
        elif i == len(hidden_layers):
            weights.append(np.random.randn(hidden_layers[i-1], output_layer_size))
            biases.append(np.zeros((1, output_layer_size)))
        else:
            weights.append(np.random.randn(hidden_layers[i-1], hidden_layers[i]))
            biases.append(np.zeros((1, hidden_layers[i])))

    # 训练网络
    for epoch in range(epochs):
        # 前向传播
        a = [X]
        for i in range(len(hidden_layers) + 1):
            z = np.dot(a[i], weights[i]) + biases[i]
            a.append(sigmoid(z))

        # 反向传播
        delta = [None] * (len(hidden_layers) + 1)
        delta[-1] = (a[-1] - y) * a[-1] * (1 - a[-1])
        for i in range(len(hidden_layers), , -1):
            delta[i-1] = np.dot(delta[i], weights[i].T) * a[i-1] * (1 - a[i-1])

        # 更新权重和偏置
        for i in range(len(hidden_layers) + 1):
            weights[i] -= alpha * np.dot(a[i].T, delta[i])
            biases[i] -= alpha * np.sum(delta[i], axis=, keepdims=True)

    # 返回训练好的网络
    return weights, biases

希望能对您有所帮助。