刘二大人循环神经网络代码

循环神经网络的代码实现可以使用 Python 编程语言来完成。以下是一个简单的循环神经网络的 Python 代码示例，可以供您参考：

import numpy as np

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

# 定义循环神经网络类
class RNN:
    def __init__(self, input_size, output_size, hidden_size):
        self.input_size = input_size
        self.output_size = output_size
        self.hidden_size = hidden_size

        # 定义网络权重
        self.Wxh = np.random.randn(hidden_size, input_size) * 0.01
        self.Whh = np.random.randn(hidden_size, hidden_size) * 0.01
        self.Why = np.random.randn(output_size, hidden_size) * 0.01

        # 定义网络偏置
        self.bh = np.zeros((hidden_size, 1))
        self.by = np.zeros((output_size, 1))

    # 定义前向传播函数
    def forward(self, inputs):
        h = np.zeros((self.hidden_size, 1))
        self.last_inputs = inputs
        self.last_hs = {0: h}

        # 循环计算每个时间步的隐藏状态和输出
        for i, x in enumerate(inputs):
            h = sigmoid(np.dot(self.Wxh, x) + np.dot(self.Whh, h) + self.bh)
            self.last_hs[i+1] = h

        y = np.dot(self.Why, h) + self.by
        return y

    # 定义反向传播函数
    def backward(self, dy):
        n = len(self.last_inputs)

        # 计算输出层的权重和偏置的梯度
        dWhy = np.dot(dy, self.last_hs[n].T)
        dby = dy

        # 初始化隐藏层的梯度
        dh = np.dot(self.Why.T, dy)
        dhraw = (1 - self.last_hs[n] ** 2) * dh

        # 计算隐藏层和输入层的权重和偏置的梯度
        dWxh = np.zeros_like(self.Wxh)
        dWhh = np.zeros_like(self.Whh)
        dbh = np.zeros_like(self.bh)
        for t in reversed(range(n)):
            x = self.last_inputs[t]
            dhraw_t = dhraw * (1 - self.last_hs[t+1] ** 2)
            dWxh += np.dot(dhraw_t, x.T)
            dWhh += np.dot(dhraw_t, self.last_hs[t].T)
            dbh += dhraw_t
            dhraw = np.dot(self.Whh.T, dhraw_t)

        return dWxh, dWhh, dWhy, dbh, dby

    # 定义更新网络参数的函数
    def update(self, dWxh, dWhh, dWhy, dbh, dby, learning_rate):
        self.Wxh -= learning_rate * dWxh
        self.Whh -= learning_rate * dWhh
        self.Why -= learning_rate * dWhy
        self.bh -= learning_rate * dbh
        self.by -= learning_rate * dby

    # 定义训练函数
    def train(self, inputs, targets, learning_rate):
        # 前向传播
        output = self.forward(inputs)

        # 计算输出误差
        dy = output - targets

        # 反向传播
        dWxh, dWhh, dWhy, dbh, dby = self.backward(dy)

        # 更新网络参数
        self.update(dWxh, dWhh, dWhy, dbh, dby, learning_rate)

        return np.sum(dy ** 2)

    # 定义预测函数
    def predict(self, inputs):
        output = self.forward(inputs)
        return np.argmax(output)

在实际使用过程中，您需要根据具体的问题和数据集进行修改和调整。