机器学习人工神经网络算法代码

以下是一个简单的神经网络算法的 Python 代码示例：

import numpy as np

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

# 定义神经网络类
class NeuralNetwork:
    def __init__(self, input_dim, hidden_dim, output_dim):
        # 初始化权重矩阵
        self.weights1 = np.random.randn(input_dim, hidden_dim)
        self.weights2 = np.random.randn(hidden_dim, output_dim)

    def forward(self, X):
        # 前向传播
        self.z2 = np.dot(X, self.weights1)
        self.a2 = sigmoid(self.z2)
        self.z3 = np.dot(self.a2, self.weights2)
        y_hat = sigmoid(self.z3)
        return y_hat

    def backward(self, X, y, y_hat, learning_rate):
        # 反向传播
        delta3 = (y_hat - y) * sigmoid(self.z3) * (1 - sigmoid(self.z3))
        d_weights2 = np.dot(self.a2.T, delta3)
        delta2 = np.dot(delta3, self.weights2.T) * sigmoid(self.z2) * (1 - sigmoid(self.z2))
        d_weights1 = np.dot(X.T, delta2)
        # 更新权重矩阵
        self.weights2 -= learning_rate * d_weights2
        self.weights1 -= learning_rate * d_weights1

    def train(self, X, y, learning_rate, epochs):
        for i in range(epochs):
            y_hat = self.forward(X)
            self.backward(X, y, y_hat, learning_rate)

# 测试
X = np.array([[0,0],[0,1],[1,0],[1,1]])
y = np.array([[0],[1],[1],[0]])
nn = NeuralNetwork(input_dim=2, hidden_dim=4, output_dim=1)
nn.train(X, y, learning_rate=0.1, epochs=10000)

# 预测
y_pred = nn.forward(X)
print(y_pred)

这是一个简单的三层神经网络，使用 sigmoid 作为激活函数，采用随机梯度下降法更新权重矩阵。你可以根据具体需求修改代码，例如更改激活函数、优化算法等等。