初始化LSTM隐藏状态Tensorflow / Keras

Can someone explain how can I initialize hidden state of LSTM in tensorflow? I am trying to build LSTM recurrent auto-encoder, so after i have that model trained i want to transfer learned hidden state of unsupervised model to hidden state of supervised model. Is that even possible with current API? This is paper I am trying to recreate:

有人可以解释如何在tensorflow中初始化LSTM的隐藏状态？我正在尝试构建LSTM循环自动编码器，所以在我训练了该模型后，我想将学习到的无监督模型的隐藏状态转移到监督模型的隐藏状态。这是否可以使用当前的API？这是我试图重新创建的论文：

http://papers.nips.cc/paper/5949-semi-supervised-sequence-learning.pdf

1 个解决方案

#1

Yes - this is possible but truly cumbersome. Let's go through an example.

是的 - 这是可能的，但真的很麻烦。我们来看一个例子吧。

Defining a model:

定义模型：
```
from keras.layers import LSTM, Input
from keras.models import Model

input = Input(batch_shape=(32, 10, 1))
lstm_layer = LSTM(10, stateful=True)(input)

model = Model(input, lstm_layer)
model.compile(optimizer="adam", loss="mse")
```
It's important to build and compile model first as in compilation the initial states are reset. Moreover - you need to specify a batch_shape where batch_size is specified as in this scenario our network should be stateful (which is done by setting a stateful=True mode.

首先构建和编译模型非常重要，因为在编译时初始状态被重置。此外 - 您需要指定batch_shape，其中指定了batch_size，因为在此场景中我们的网络应该是有状态的（通过设置有状态= True模式来完成）。
Now we could set the values of initial states:

现在我们可以设置初始状态的值：
```
import numpy
import keras.backend as K

hidden_states = K.variable(value=numpy.random.normal(size=(32, 10)))
cell_states = K.variable(value=numpy.random.normal(size=(32, 10)))

model.layers[1].states[0] = hidden_states
model.layers[1].states[1] = cell_states 
```
Note that you need to provide states as a keras variables. states[0] holds hidden states and states[1] holds cell states.

请注意，您需要提供状态作为keras变量。 states [0]保持隐藏状态，状态[1]保存单元状态。

Hope that helps.

希望有所帮助。

#1