写一个完整的LSTM的代码
时间: 2024-06-10 12:09:02 浏览: 3
好的,以下是一个简单的 LSTM 的 Python 代码:
```
import numpy as np
# 定义 LSTM 类
class LSTM:
def __init__(self, input_size, hidden_size, output_size):
# 初始化权重
self.Wf = np.random.randn(hidden_size, input_size + hidden_size)
self.Wi = np.random.randn(hidden_size, input_size + hidden_size)
self.Wc = np.random.randn(hidden_size, input_size + hidden_size)
self.Wo = np.random.randn(hidden_size, input_size + hidden_size)
self.Wy = np.random.randn(output_size, hidden_size)
# 初始化偏置
self.bf = np.zeros((hidden_size, 1))
self.bi = np.zeros((hidden_size, 1))
self.bc = np.zeros((hidden_size, 1))
self.bo = np.zeros((hidden_size, 1))
self.by = np.zeros((output_size, 1))
# 定义 sigmoid 函数
def sigmoid(self, x):
return 1 / (1 + np.exp(-x))
# 定义 tanh 函数
def tanh(self, x):
return np.tanh(x)
# 定义前向传播
def forward(self, x, h_prev, c_prev):
# 计算合并后的输入
concat = np.vstack((h_prev, x))
# 计算遗忘门
f = self.sigmoid(np.dot(self.Wf, concat) + self.bf)
# 计算输入门
i = self.sigmoid(np.dot(self.Wi, concat) + self.bi)
# 计算候选值
c_bar = self.tanh(np.dot(self.Wc, concat) + self.bc)
# 更新细胞状态
c = f * c_prev + i * c_bar
# 计算输出门
o = self.sigmoid(np.dot(self.Wo, concat) + self.bo)
# 计算隐藏状态
h = o * self.tanh(c)
# 计算输出值
y = np.dot(self.Wy, h) + self.by
# 返回隐藏状态、细胞状态和输出值
return h, c, y
# 定义训练函数
def train(self, X, Y, learning_rate, epochs):
# 初始化隐藏状态和细胞状态
h_prev = np.zeros((self.Wf.shape[0], 1))
c_prev = np.zeros((self.Wf.shape[0], 1))
# 迭代训练
for epoch in range(epochs):
# 初始化梯度
dWf = np.zeros_like(self.Wf)
dWi = np.zeros_like(self.Wi)
dWc = np.zeros_like(self.Wc)
dWo = np.zeros_like(self.Wo)
dWy = np.zeros_like(self.Wy)
dbf = np.zeros_like(self.bf)
dbi = np.zeros_like(self.bi)
dbc = np.zeros_like(self.bc)
dbo = np.zeros_like(self.bo)
dby = np.zeros_like(self.by)
dh_next = np.zeros_like(h_prev)
dc_next = np.zeros_like(c_prev)
# 迭代训练样本
for t in range(len(X)):
# 前向传播
x = X[t].reshape(-1, 1)
y = Y[t].reshape(-1, 1)
h_prev, c_prev, y_pred = self.forward(x, h_prev, c_prev)
# 计算损失和梯度
loss = np.sum((y - y_pred)**2)
dy = 2 * (y_pred - y)
dWy += np.dot(dy, h_prev.T)
dby += dy
dh = np.dot(self.Wy.T, dy) + dh_next
do = dh * self.tanh(c_prev)
do = do * (1 - self.sigmoid(np.dot(self.Wo, np.vstack((h_prev, x))) + self.bo))
dWo += np.dot(do, np.vstack((h_prev, x)).T)
dbo += do
dc = dh * self.sigmoid(np.dot(self.Wo, np.vstack((h_prev, x))) + self.bo) * self.tanh(self.tanh(c_prev))
dc = dc + dc_next
dc_bar = dc * self.sigmoid(np.dot(self.Wi, np.vstack((h_prev, x))) + self.bi)
dWc += np.dot(dc_bar, np.vstack((h_prev, x)).T)
dbc += dc_bar
di = dc * self.tanh(np.dot(self.Wc, np.vstack((h_prev, x))) + self.bc)
di = di * self.sigmoid(np.dot(self.Wi, np.vstack((h_prev, x))) + self.bi)
dWi += np.dot(di, np.vstack((h_prev, x)).T)
dbi += di
df = dc * c_prev
df = df * self.sigmoid(np.dot(self.Wf, np.vstack((h_prev, x))) + self.bf)
dWf += np.dot(df, np.vstack((h_prev, x)).T)
dbf += df
dh_next = np.dot(self.Wf.T, df) + np.dot(self.Wo.T, do) + np.dot(self.Wc.T, dc_bar) + np.dot(self.Wi.T, di)
dc_next = dc * self.sigmoid(np.dot(self.Wf, np.vstack((h_prev, x))) + self.bf)
# 更新权重和偏置
self.Wy -= learning_rate * dWy / len(X)
self.by -= learning_rate * dby / len(X)
self.Wo -= learning_rate * dWo / len(X)
self.bo -= learning_rate * dbo / len(X)
self.Wc -= learning_rate * dWc / len(X)
self.bc -= learning_rate * dbc / len(X)
self.Wi -= learning_rate * dWi / len(X)
self.bi -= learning_rate * dbi / len(X)
self.Wf -= learning_rate * dWf / len(X)
self.bf -= learning_rate * dbf / len(X)
# 打印损失
if epoch % 100 == 0:
print("Epoch", epoch, "Loss", loss)
```
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