如何使用for循环创建Keras多LSTM层? [英] how to create Keras multi LSTM layer using for loop?

问题描述

我正在尝试使用 for循环和

I'm trying to implement a multi layer LSTM in Keras using for loop and this tutorial to be able to optimize the number of layers, which is obviously a hyper-parameter. In the tutorial, the author used skopt for hyper-parameter optimization. I used Functional API to create my model. For simplicity, I changed input_tensor's shape to arbitrary values. My model is:

from keras.layers.core import Dense
from keras.layers import LSTM, Input
from keras.models import Model

from keras.optimizers import RMSprop
from keras.initializers import glorot_uniform, glorot_normal, RandomUniform


input_tensor = Input(shape=(10, 20))

def create_model(learning_rate, num_lstm_layers, num_lstm_units, activation):
    init = glorot_normal(seed=None)
    init1 = RandomUniform(minval=-0.05, maxval=0.05)

    x = Input(shape=(10, 20))


    for i in range(num_lstm_layers):

        name = 'layer_lstm_{0}'.format(i+1)

        if( (i==0) and (num_lstm_layers==1) ):
            x = LSTM(units=num_lstm_units, dropout=0.2, recurrent_dropout=0.2, 
                    return_sequences=False, kernel_initializer=init, 
                    activation=activation, name=name)(x)
        elif(i != (num_lstm_layers-1) ):
            x = LSTM(units=num_lstm_units, dropout=0.2, recurrent_dropout=0.2,  
                    return_sequences=True, kernel_initializer=init, 
                    activation=activation, name=name)(x)
        else:
            x = LSTM(units=num_lstm_units, dropout=0.2, recurrent_dropout=0.2,  
                    return_sequences=False, kernel_initializer=init, 
                    activation=activation, name=name)(x)
    x = Dense(1, activation='linear', kernel_initializer= init1)(x)

    model = Model(input_tensor, x)

    optimizer = RMSprop(lr=learning_rate, rho=0.9, epsilon=None, decay=0.0)
    model.compile(loss='mean_squared_error', optimizer=optimizer, metrics=['mse'] )

    return model      

每当我尝试使模型适合数据时，都会遇到此错误:

Whenever I try to fit the model to the data, I encounter this error :

ValueError:变量layer_lstm_1_14/kernel/的初始化程序来自 在控制流构造(例如循环或条件)中.什么时候 在循环或条件内创建变量，请使用lambda作为 初始化程序.

ValueError: Initializer for variable layer_lstm_1_14/kernel/ is from inside a control-flow construct, such as a loop or conditional. When creating a variable inside a loop or conditional, use a lambda as the initializer.

到目前为止，我知道应该在某个地方添加lambda函数或keras Lambda layer. 另外，我在单独的python脚本中测试了该模型，如下所示:

So far, I've known that somewhere I should add a lambda function or keras Lambda layer. Also, I tested the model in a separate python script, like below:

model = create_model(learning_rate=1e-3, num_lstm_layers=3, num_lstm_units=64, activation='linear')

但是它再次给了我这个错误:

But again it gives me this error:

ValueError:图形已断开:无法获取张量的值 层上的Tensor("input_2:0"，shape =(?, 10，20)，dtype = float32) "input_2".可以访问以下先前的层，而不会出现问题: []

ValueError: Graph disconnected: cannot obtain value for tensor Tensor("input_2:0", shape=(?, 10, 20), dtype=float32) at layer "input_2". The following previous layers were accessed without issue: []

我还尝试创建模型的Sequential版本.但是遇到了同样的错误.

I Also tried to create Sequential version of the model. But encountered the same error.

编辑了从if( i==0):到if( (i==0) and (num_lstm_layers==1) ):的if语句，然后按照André的建议进行了更改，便可以使用for循环创建LSTM模型.

Edited if statement from if( i==0): to if( (i==0) and (num_lstm_layers==1) ): By doing so and making the changes André suggested, you are able to create LSTM models using for loop.

推荐答案

我并不担心您的for循环，而是担心输入.我不确定100％，但是认为您应该尝试删除

As I said in the comments, I'm not worried about your for-loop, but rather the input. I'm not 100% sure, but think that you should try to delete

input_tensor = Input(shape=(10, 20)) 

...位于create_model(...)函数之前，并如下编辑其中一个的创建:

... which comes before the create_model(...) function and edit the creation of the one inside as follows:

input_tensor = x = Input(shape=(10, 20))

继续阅读，您说您得到了Graph disconnected: cannot obtain value for tensor.听起来肯定好像您的输入未连接.我建议所做的更改应连接您的输入和输出(分别为Model(...)的第一个和第二个参数).

Reading on, you say you get Graph disconnected: cannot obtain value for tensor. That definitely sounds like you're input isn't connected. The change I suggest should connect your input and output (respectively the first and second argument of Model(...)).

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