LSTM基本模型的代码
时间: 2023-04-02 10:02:49 浏览: 85
LSTM基本模型的代码如下:
```python
import numpy as np
class LSTM:
def __init__(self, input_size, hidden_size, output_size):
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
# 初始化权重和偏置
self.Wf = np.random.randn(hidden_size, input_size + hidden_size)
self.bf = np.zeros((hidden_size, 1))
self.Wi = np.random.randn(hidden_size, input_size + hidden_size)
self.bi = np.zeros((hidden_size, 1))
self.Wc = np.random.randn(hidden_size, input_size + hidden_size)
self.bc = np.zeros((hidden_size, 1))
self.Wo = np.random.randn(hidden_size, input_size + hidden_size)
self.bo = np.zeros((hidden_size, 1))
self.Wy = np.random.randn(output_size, hidden_size)
self.by = np.zeros((output_size, 1))
def sigmoid(self, x):
return 1 / (1 + np.exp(-x))
def tanh(self, x):
return np.tanh(x)
def forward(self, x, h_prev, c_prev):
# 合并输入和上一个时刻的隐藏状态
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)
# 计算候选记忆细胞
c_bar = self.tanh(np.dot(self.Wc, concat) + self.bc)
# 计算当前记忆细胞
c = f * c_prev + i * c_bar
# 计算输出门
o = self.sigmoid(np.dot(self.Wo, concat) + self.bo)
# 计算当前隐藏状态
h = o * self.tanh(c)
# 计算输出
y = np.dot(self.Wy, h) + self.by
# 保存当前状态,用于下一时刻的计算
self.x = x
self.h_prev = h
self.c_prev = c
return y, h, c
def backward(self, dy, dh_next, dc_next):
# 计算输出层的梯度
dWy = np.dot(dy, self.h_prev.T)
dby = dy
# 计算当前时刻的隐藏状态的梯度
dh = np.dot(self.Wy.T, dy) + dh_next
# 计算当前时刻的输出门的梯度
do = dh * self.tanh(self.c_prev)
do = do * (1 - self.sigmoid(np.dot(self.Wo, np.vstack((self.h_prev, self.x))) + self.bo))
# 计算当前时刻的记忆细胞的梯度
dc = np.copy(dc_next)
dc += dh * self.sigmoid(np.dot(self.Wo, np.vstack((self.h_prev, self.x))) + self.bo) * (1 - self.tanh(self.c_prev) ** 2)
dc_bar = dc * self.sigmoid(np.dot(self.Wi, np.vstack((self.h_prev, self.x))) + self.bi)
dc_bar = dc_bar * (1 - self.tanh(np.dot(self.Wc, np.vstack((self.h_prev, self.x))) + self.bc) ** 2)
# 计算当前时刻的输入门的梯度
di = dc * self.tanh(np.dot(self.Wc, np.vstack((self.h_prev, self.x))) + self.bc)
di = di * (1 - self.sigmoid(np.dot(self.Wi, np.vstack((self.h_prev, self.x))) + self.bi))
# 计算当前时刻的遗忘门的梯度
df = dc * self.c_prev
df = df * (1 - self.sigmoid(np.dot(self.Wf, np.vstack((self.h_prev, self.x))) + self.bf))
# 计算当前时刻的输入的梯度
dconcat = np.dot(self.Wf.T, df) + np.dot(self.Wi.T, di) + np.dot(self.Wc.T, dc_bar) + np.dot(self.Wo.T, do)
dx = dconcat[self.hidden_size:, :]
dh_prev = dconcat[:self.hidden_size, :]
# 保存当前梯度,用于下一时刻的计算
self.dh_next = dh_prev
self.dc_next = dc
# 更新权重和偏置
self.Wf -= self.learning_rate * df * np.vstack((self.h_prev, self.x)).T
self.bf -= self.learning_rate * df
self.Wi -= self.learning_rate * di * np.vstack((self.h_prev, self.x)).T
self.bi -= self.learning_rate * di
self.Wc -= self.learning_rate * dc_bar * np.vstack((self.h_prev, self.x)).T
self.bc -= self.learning_rate * dc_bar
self.Wo -= self.learning_rate * do * np.vstack((self.h_prev, self.x)).T
self.bo -= self.learning_rate * do
self.Wy -= self.learning_rate * dWy
self.by -= self.learning_rate * dby
return dx, dh_prev, dc
```
希望能够帮到你!