slstm代码keras
时间: 2023-09-27 18:11:07 浏览: 76
以下是使用Keras实现SLSTM的示例代码:
```python
from keras.layers import Layer, LSTM, Multiply, Add
class SLSTM(Layer):
def __init__(self, units, **kwargs):
self.units = units
super(SLSTM, self).__init__(**kwargs)
def build(self, input_shape):
self.lstm = LSTM(self.units, return_sequences=True)
self.wf = self.add_weight(name='forget_weight', shape=(self.units,), initializer='uniform', trainable=True)
self.wi = self.add_weight(name='input_weight', shape=(self.units,), initializer='uniform', trainable=True)
self.wo = self.add_weight(name='output_weight', shape=(self.units,), initializer='uniform', trainable=True)
self.wc = self.add_weight(name='candidate_weight', shape=(self.units,), initializer='uniform', trainable=True)
super(SLSTM, self).build(input_shape)
def call(self, inputs):
x, h, c = inputs
lstm_out = self.lstm(x)
f = Multiply()([lstm_out, self.wf])
i = Multiply()([lstm_out, self.wi])
o = Multiply()([lstm_out, self.wo])
c = Add()([Multiply()([c, self.wf]), Multiply()([i, self.wc])])
h = Multiply()([o, c])
return [h, c]
def compute_output_shape(self, input_shape):
return [(input_shape[0][0], input_shape[0][1], self.units), (input_shape[0][0], self.units)]
def get_config(self):
config = super(SLSTM, self).get_config()
config.update({'units': self.units})
return config
```
在该代码中,我们首先定义一个`SLSTM`类,它继承了Keras的`Layer`类。在`__init__`方法中,我们传入`units`参数来指定SLSTM层中LSTM层的输出维度。在`build`方法中,我们首先创建一个LSTM层,并初始化了一些权重。其中,`wf`、`wi`、`wo`和`wc`分别代表了遗忘门、输入门、输出门和候选记忆单元的权重。在`call`方法中,我们首先将输入`x`通过LSTM层进行处理,然后分别使用上述4个权重计算出遗忘门、输入门、输出门和候选记忆单元。接着,我们使用这些门和候选记忆单元更新当前时刻的记忆单元`c`和隐藏状态`h`。最后,在`compute_output_shape`方法中,我们指定了输出的形状。