Tensorflow tf.keras.layers.LSTM
参数
参数 | 描述 |
---|---|
units | 输出空间的维度 |
input_shape | (timestep, input_dim),timestep可以设置为None,由输入决定,input_dime根据具体情况 |
activation | 激活函数,默认tanh |
recurrent_activation | |
use_bias | |
kernel_initializer | |
recurrent_initializer | |
bias_initializer | |
unit_forget_bias | |
kernel_regularizer | |
recurrent_regularizer | |
bias_regularizer | |
activity_regularizer | |
kernel_constraint | |
recurrent_constraint | |
bias_constraint | |
dropout | |
recurrent_dropout | |
implementation | |
return_sequences | |
return_state | |
go_backwards | |
stateful | |
unroll |
例子
keras.layers.LSTM(units=200,input_shape=(None,1),return_sequences=True)
init
__init__(
units,
activation='tanh',
recurrent_activation='sigmoid',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
unit_forget_bias=True,
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.0,
recurrent_dropout=0.0,
implementation=2,
return_sequences=False,
return_state=False,
go_backwards=False,
stateful=False,
time_major=False,
unroll=False,
**kwargs
)
原理
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\hat{c}^{<t>}
c^<t>是记忆状态,对应矩阵形状(
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units*features+units*units+bias
units∗features+units∗units+bias)
Γ u \Gamma_u Γu为更新门(update),形状是 ( u n i t s ∗ f e a t u r e s + u n i t s ∗ u n i t s + b i a s ) (units*features+units*units+bias) (units∗features+units∗units+bias)
Γ f \Gamma_f Γf为更新门(forget),形状是 ( u n i t s ∗ f e a t u r e s + u n i t s ∗ u n i t s + b i a s ) (units*features+units*units+bias) (units∗features+units∗units+bias)
Γ o \Gamma_o Γo为输出门(out),形状是 ( u n i t s ∗ f e a t u r e s + u n i t s ∗ u n i t s + b i a s ) (units*features+units*units+bias) (units∗features+units∗units+bias)
所以LSTM的参数个数是: ( u n i t s ∗ f e a t u r e s + u n i t s ∗ u n i t s + u n i t s ) ∗ 4 (units*features+units*units+units)*4 (units∗features+units∗units+units)∗4
参考:
官网
https://blog.csdn.net/jiangpeng59/article/details/77646186
https://www.zhihu.com/question/41949741?sort=created
https://stackoverflow.com/questions/38080035/how-to-calculate-the-number-of-parameters-of-an-lstm-network/56614978#56614978
https://stackoverflow.com/questions/46584171/why-does-the-first-lstm-in-a-keras-model-have-more-params-than-the-subsequent-on
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