用python写出RNN的代码
时间: 2023-03-05 21:41:28 浏览: 71
好的,以下是使用Python编写基本RNN模型的示例代码:
```python
import numpy as np
# 定义RNN类
class RNN:
def __init__(self, input_size, hidden_size, output_size):
# 初始化权重参数
self.Whh = np.random.randn(hidden_size, hidden_size) * 0.01
self.Wxh = np.random.randn(hidden_size, input_size) * 0.01
self.Why = np.random.randn(output_size, hidden_size) * 0.01
self.bh = np.zeros((hidden_size, 1))
self.by = np.zeros((output_size, 1))
def forward(self, inputs):
# 存储中间状态值
self.hidden_states = []
# 初始化前一个隐藏状态
h_t = np.zeros((self.Whh.shape[0], 1))
# 对输入序列进行迭代
for x_t in inputs:
# 计算当前隐藏状态
h_t = np.tanh(np.dot(self.Wxh, x_t) + np.dot(self.Whh, h_t) + self.bh)
self.hidden_states.append(h_t)
# 计算输出序列
y = np.dot(self.Why, h_t) + self.by
return y, self.hidden_states
def backward(self, inputs, targets, learning_rate=0.1):
# 进行前向传播
y_pred, hidden_states = self.forward(inputs)
# 初始化梯度值
dWhh = np.zeros_like(self.Whh)
dWxh = np.zeros_like(self.Wxh)
dWhy = np.zeros_like(self.Why)
dbh = np.zeros_like(self.bh)
dby = np.zeros_like(self.by)
dh_next = np.zeros_like(hidden_states[0])
# 对输出序列进行迭代
for t in reversed(range(len(targets))):
# 计算输出误差
dy = y_pred - targets[t]
dWhy += np.dot(dy, hidden_states[t].T)
dby += dy
# 计算当前时刻的梯度
dh = np.dot(self.Why.T, dy) + dh_next
dh_raw = (1 - hidden_states[t] * hidden_states[t]) * dh
dbh += dh_raw
dWxh += np.dot(dh_raw, inputs[t].T)
dWhh += np.dot(dh_raw, hidden_states[t-1].T)
dh_next = np.dot(self.Whh.T, dh_raw)
# 对参数进行更新
self.Whh -= learning_rate * dWhh
self.Wxh -= learning_rate * dWxh
self.Why -= learning_rate * dWhy
self.bh -= learning_rate * dbh
self.by -= learning_rate * dby
```
这里的代码实现了一个基本的RNN类,可以用于序列数据的训练和预测。在初始化时,我们随机初始化了权重参数,并定义了前向传播和反向传播的函数。前向传播函数用于计算输入序列的输出序列和中间隐藏状态,而反向传播函数用于根据输出误差更新权重参数。可以使用这个类来构建一个R