spyder 写BP神经网络代码

下面是使用Spyder编写BP神经网络代码的示例：

import numpy as np

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

# 定义BP神经网络类
class BPNeuralNetwork(object):
    def __init__(self, input_size, hidden_size, output_size):
        # 输入层、隐藏层、输出层的大小
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        
        # 初始化权重和偏置
        self.weights1 = np.random.randn(self.input_size, self.hidden_size)
        self.bias1 = np.random.randn(self.hidden_size)
        self.weights2 = np.random.randn(self.hidden_size, self.output_size)
        self.bias2 = np.random.randn(self.output_size)
        
    # 前向传播
    def forward(self, X):
        self.z1 = np.dot(X, self.weights1) + self.bias1
        self.a1 = sigmoid(self.z1)
        self.z2 = np.dot(self.a1, self.weights2) + self.bias2
        self.output = sigmoid(self.z2)
        return self.output
    
    # 反向传播
    def backward(self, X, y, output, learning_rate):
        delta_output = (output - y) * output * (1 - output)
        delta_hidden = np.dot(delta_output, self.weights2.T) * self.a1 * (1 - self.a1)
        self.weights2 -= learning_rate * np.dot(self.a1.T, delta_output)
        self.bias2 -= learning_rate * np.sum(delta_output, axis=0)
        self.weights1 -= learning_rate * np.dot(X.T, delta_hidden)
        self.bias1 -= learning_rate * np.sum(delta_hidden, axis=0)
        
    # 训练函数
    def train(self, X, y, epochs, learning_rate):
        for i in range(epochs):
            output = self.forward(X)
            self.backward(X, y, output, learning_rate)
            
    # 预测函数
    def predict(self, X):
        output = self.forward(X)
        return np.round(output)

使用示例：

# 创建一个BP神经网络实例
nn = BPNeuralNetwork(2, 3, 1)

# 训练数据
X_train = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y_train = np.array([[0], [1], [1], [0]])

# 训练BP神经网络
nn.train(X_train, y_train, epochs=10000, learning_rate=0.1)

# 预测
X_test = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y_pred = nn.predict(X_test)
print(y_pred)