如何解决如何在keras中拟合两个串联LSTM的模型？
嗨，我是喀拉喀什的新手，我在喀拉拉邦连接了两个LSTM。数据集是一个单变量时间序列，由方法滑动窗口拆分。然后，我将其重塑为[样本，特征，时间步长]的原型。但是，当我尝试拟合模型时，会出现以下错误。
TypeError：用户代码中：
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function *
return step_function(self,iterator)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py:796 step_function **
outputs = model.distribute_strategy.run(run_step,args=(data,))
/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn,args=args,kwargs=kwargs)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn,args,kwargs)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
return fn(*args,**kwargs)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py:789 run_step **
outputs = model.train_step(data)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/training.py:747 train_step
y_pred = self(x,training=True)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer.py:985 __call__
outputs = call_fn(inputs,*args,**kwargs)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/sequential.py:386 call
outputs = layer(inputs,**kwargs)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer.py:982 __call__
self._maybe_build(inputs)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer.py:2643 _maybe_build
self.build(input_shapes) # pylint:disable=not-callable
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/utils/tf_utils.py:323 wrapper
output_shape = fn(instance,input_shape)
/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/merge.py:500 build
del reduced_inputs_shapes[i][self.axis]
TypeError: list indices must be integers or slices,not ListWrapper
我应该如何通过trainX和testX？我想做这张照片中的东西：
代码如下
trainX = numpy.reshape(trainX,(trainX.shape[0],1,trainX.shape[1]))
testX = numpy.reshape(testX,(testX.shape[0],testX.shape[1]))
model1= Sequential()
model1.add(LSTM(6,input_shape=(1,look_back)))
model2 = Sequential()
model2.add(LSTM(6,look_back)))
model = Sequential()
model.add(Concatenate([model1,model1]))
model.add(Dense(1))
model.compile(loss='mean_squared_error',optimizer='adam',metrics = ['mse'])
model.fit([trainX,trainX],trainY,epochs=50,batch_size=1,verbose=1)
解决方法
尝试这样写：
...
model1= Sequential()
model1.add(LSTM(6,input_shape=(1,look_back)))
model2 = Sequential()
model2.add(LSTM(6,look_back)))
concatenated_models = concatenate([model1,model2])
out = Dense(1,activation='softmax',name='output')(concatenated_models)
added_model = Model([model1,model2],out)
added_model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
added_model.fit([trainX,trainX],trainY,epochs=50,batch_size=1,verbose=1)
这可能会更好。不确定100％（在这里很晚；-））
,您需要使用Functional API in Keras。
您使用Functional API的模型：
from tensorflow.keras.layers import Dense,Concatenate,LSTM,Input,Flatten
from tensorflow.keras.models import Model
look_back = 3200 # just for running the code I used this number
# Model architecture
inputs = Input(shape=(1,look_back),name='Input_1')
lstm1 = LSTM(6,name='LSTM_1')(inputs)
lstm2 = LSTM(6,name='LSTM_2')(inputs)
concatenated = Concatenate( name='Concatenate_1')([lstm1,lstm2])
output1 = Dense(1,name='Dense_1')(concatenated)
model = Model(inputs=inputs,outputs=output1)
现在让我们看一下架构：
model.summary()
输出：
Model: "functional_13"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
Input_1 (InputLayer) [(None,1,3200)] 0
__________________________________________________________________________________________________
LSTM_1 (LSTM) (None,6) 76968 Input_1[0][0]
__________________________________________________________________________________________________
LSTM_2 (LSTM) (None,6) 76968 Input_1[0][0]
__________________________________________________________________________________________________
Concatenate_1 (Concatenate) (None,12) 0 LSTM_1[0][0]
LSTM_2[0][0]
__________________________________________________________________________________________________
Dense_1 (Dense) (None,1) 13 Concatenate_1[0][0]
==================================================================================================
Total params: 153,949
Trainable params: 153,949
Non-trainable params: 0
__________________________________________________________________________________________________
此外，通过检查模型图，我们可以更好地了解图层及其连接
模型图：
from tensorflow.keras.utils import plot_model
plot_model(model)
输出：
现在让我们训练模型：
我用sklearn创建了一些虚拟数据来训练模型，一切正常。
训练模型：
from sklearn.datasets import make_blobs
train_x,train_y = make_blobs(n_samples=1000,centers=2,n_features=look_back,random_state=0)
train_x = train_x.reshape(train_x.shape[0],train_x.shape[1])
model.compile(loss='mean_squared_error',metrics = ['mse'])
model.fit(train_x,train_y,epochs=5,verbose=1)
输出：
Epoch 1/5
1000/1000 [==============================] - 2s 2ms/step - loss: 0.0133 - mse: 0.0133
Epoch 2/5
1000/1000 [==============================] - 2s 2ms/step - loss: 1.4628e-13 - mse: 1.4628e-13
Epoch 3/5
1000/1000 [==============================] - 2s 2ms/step - loss: 2.2808e-14 - mse: 2.2808e-14
Epoch 4/5
1000/1000 [==============================] - 2s 2ms/step - loss: 5.2458e-15 - mse: 5.2458e-15
Epoch 5/5
1000/1000 [==============================] - 2s 2ms/step - loss: 1.1384e-15 - mse: 1.1384e-15
<tensorflow.python.keras.callbacks.History at 0x7f5fe4ce9f28>
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