写一段能运行的RNN代码
时间: 2024-02-12 09:07:06 浏览: 27
好的,以下是一个简单的RNN代码示例,可以用于文本生成任务:
```python
import numpy as np
# 初始化参数
input_size = 10
hidden_size = 20
output_size = 10
# 随机生成输入数据和目标数据
inputs = np.random.randn(5, input_size)
targets = np.random.randn(5, output_size)
# 初始化权重和偏置
Wxh = np.random.randn(input_size, hidden_size)
Whh = np.random.randn(hidden_size, hidden_size)
Why = np.random.randn(hidden_size, output_size)
bh = np.zeros((1, hidden_size))
by = np.zeros((1, output_size))
# 定义激活函数
def sigmoid(x):
return 1 / (1 + np.exp(-x))
# 前向传播过程
h = np.zeros((1, hidden_size))
for i in range(len(inputs)):
x = inputs[i]
h = sigmoid(np.dot(x, Wxh) + np.dot(h, Whh) + bh)
y = np.dot(h, Why) + by
# 计算误差
loss = np.square(y - targets[i]).sum()
# 反向传播过程
dWxh = np.zeros_like(Wxh)
dWhh = np.zeros_like(Whh)
dWhy = np.zeros_like(Why)
dbh = np.zeros_like(bh)
dby = np.zeros_like(by)
dhnext = np.zeros_like(h)
for i in reversed(range(len(inputs))):
x = inputs[i]
h = sigmoid(np.dot(x, Wxh) + np.dot(h, Whh) + bh)
y = np.dot(h, Why) + by
# 计算误差
loss = np.square(y - targets[i]).sum()
# 反向传播误差
dy = 2 * (y - targets[i])
dWhy += np.dot(h.T, dy)
dby += dy
dh = np.dot(dy, Why.T) + dhnext
dhraw = h * (1 - h) * dh
dbh += dhraw
dWxh += np.dot(x.T, dhraw)
dWhh += np.dot(h.T, dhraw)
dhnext = np.dot(dhraw, Whh.T)
# 更新权重和偏置
Wxh -= 0.1 * dWxh
Whh -= 0.1 * dWhh
Why -= 0.1 * dWhy
bh -= 0.1 * dbh
by -= 0.1 * dby
```
以上代码实现了一个简单的RNN网络,它可以处理一个长度为5的输入序列,并输出相应的目标序列。在这个示例中,我们使用了sigmoid作为激活函数,均方误差作为损失函数,以及随机梯度下降算法来训练网络。