您需要在Keras中使用函数API。

您的带有功能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)

现在让我们看一下体系结构：

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，我们可以通过检查模型图来更好地查看层及其连接

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。

培训模型：

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], 1, train_x.shape[1])

model.compile(loss='mean_squared_error', optimizer='adam', metrics = ['mse'])
model.fit(train_x, train_y, epochs=5, batch_size=1, 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>

试着这样写：

...

model1= Sequential()
model1.add(LSTM(6, input_shape=(1,look_back)))

model2 = Sequential()
model2.add(LSTM(6, input_shape=(1,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)

这样可能会更好。不是100%确定（这里很晚；-））