python实现神经元计算过程

python代码：

import numpy as np

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

# 定义神经元类
class Neuron:
    def __init__(self, weights: np.ndarray, bias: float):
        self.weights = weights
        self.bias = bias

    def feedforward(self, inputs: np.ndarray) -> np.ndarray:
        # 计算权重和输入的点积，并加上偏差
        total = np.dot(self.weights, inputs) + self.bias
        return sigmoid(total)

# 初始化权重和偏差
weights = np.array([0, 1])  # 例如两个输入权重
bias = 4  # 偏差值

# 创建神经元实例
n = Neuron(weights, bias)

# 输入数据
x = np.array([2, 3])

# 输出结果
print(n.feedforward(x))