Python构建一个BP神经网络的代码

以下是Python构建一个BP神经网络的代码：

import numpy as np

# 构建 BP 神经网络类
class BPNeuralNetwork(object):
    def __init__(self, input_size, hidden_size, output_size):
        # 初始化权重和偏置
        self.input_size = input_size  # 输入层节点数
        self.hidden_size = hidden_size  # 隐藏层节点数
        self.output_size = output_size  # 输出层节点数
        self.weights_input_hidden = np.random.randn(self.input_size, self.hidden_size) * 0.01
        self.bias_hidden = np.zeros((1, self.hidden_size))
        self.weights_hidden_output = np.random.randn(self.hidden_size, self.output_size) * 0.01
        self.bias_output = np.zeros((1, self.output_size))

    # 定义 sigmoid 函数
    def sigmoid(self, x):
        return 1.0 / (1.0 + np.exp(-x))

    # 定义 sigmoid 的导数函数
    def sigmoid_derivative(self, x):
        return x * (1.0 - x)

    def train(self, X, y, epochs, learning_rate):
        for i in range(epochs):  # 迭代次数
            # 正向传播
            hidden_layer_activation = np.dot(X, self.weights_input_hidden) + self.bias_hidden
            hidden_layer_output = self.sigmoid(hidden_layer_activation)
            output_layer_activation = np.dot(hidden_layer_output, self.weights_hidden_output) + self.bias_output
            predicted_output = self.sigmoid(output_layer_activation)

            # 反向传播
            error = y - predicted_output
            d_predicted_output = error * self.sigmoid_derivative(predicted_output)

            error_hidden_layer = d_predicted_output.dot(self.weights_hidden_output.T)
            d_hidden_layer = error_hidden_layer * self.sigmoid_derivative(hidden_layer_output)

            # 更新权重和偏置
            self.weights_hidden_output += hidden_layer_output.T.dot(d_predicted_output) * learning_rate
            self.bias_output += np.sum(d_predicted_output, axis=0, keepdims=True) * learning_rate
            self.weights_input_hidden += X.T.dot(d_hidden_layer) * learning_rate
            self.bias_hidden += np.sum(d_hidden_layer, axis=0, keepdims=True) * learning_rate

    # 预测
    def predict(self, X):
        hidden_layer_activation = np.dot(X, self.weights_input_hidden) + self.bias_hidden
        hidden_layer_output = self.sigmoid(hidden_layer_activation)
        output_layer_activation = np.dot(hidden_layer_output, self.weights_hidden_output) + self.bias_output
        predicted_output = self.sigmoid(output_layer_activation)
        return predicted_output

以上是构建 BP 神经网络的 Python 代码，可以通过该类进行训练和预测。