Как передать ансамблевой модели те же входные данные, что и ее подмоделям?

Question

Я пытаюсь создать модель ансамбля, которая получает те же данные, что и подмодели.

models = list()
nb_models = 3

#load all sub models
for i in range(nb_models):
    model_tmp = load_model("lstm_model"+str(i+1)+".h5")
    model_tmp.name = "model_"+str(i+1)
    models.append(model_tmp)

def create_ensemble(models,model_input):

    # take-in all outputs fro all models
    outModels = [model(model_input) for model in models]

    # calculate average of all results
    outAvg = layers.average(outModels)

    # merge into one model

    modelMerge = Model(inputs=model_input,outputs=outAvg,name='ensemble')

    return modelMerge

model_input = Input(shape=models[0].input_shape[1:])
modelEns = create_ensemble(models,model_input)

Когда я загружаю свою модель ансамбля и передаю ей те же данные, что и для отдельных подмоделей,Я получил следующую ошибку.

Вы должны передать значение для тензора-заполнителя 'lstm_2_input' с плавающей запятой dtype и формой [1,1,1] [[{{node lstm_2_input}}]]

Для трех подмоделей они имеют:

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_1 (LSTM)                (1, 1)                    12        
_________________________________________________________________
dense_1 (Dense)              (1, 1)                    2         
=================================================================

и для модели ансамбля:

__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            (None, 1, 1)         0                                            
__________________________________________________________________________________________________
model_1 (Sequential)            multiple             14          input_1[0][0]                    
__________________________________________________________________________________________________
model_2 (Sequential)            multiple             14          input_1[0][0]                    
__________________________________________________________________________________________________
model_3 (Sequential)            multiple             14          input_1[0][0]                    
__________________________________________________________________________________________________
average_1 (Average)             (None, 1)            0           model_1[1][0]                    
                                                                 model_2[1][0]                    
                                                                 model_3[1][0]                    
==================================================================================================

test_reshaped.shape() 
(28, 1, 1)

Answer 1

См. Этот пример, взятый из здесь

# Multiple Inputs
from keras.utils import plot_model
from keras.models import Model
from keras.layers import Input
from keras.layers import Dense
from keras.layers import Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.pooling import MaxPooling2D
from keras.layers.merge import concatenate

Первая модель ввода

visible1 = Input(shape=(64,64,1))
conv11 = Conv2D(32, kernel_size=4, activation='relu')(visible1)
pool11 = MaxPooling2D(pool_size=(2, 2))(conv11)
conv12 = Conv2D(16, kernel_size=4, activation='relu')(pool11)
pool12 = MaxPooling2D(pool_size=(2, 2))(conv12)
flat1 = Flatten()(pool12)

Вторая модель ввода

visible2 = Input(shape=(32,32,3))
conv21 = Conv2D(32, kernel_size=4, activation='relu')(visible2)
pool21 = MaxPooling2D(pool_size=(2, 2))(conv21)
conv22 = Conv2D(16, kernel_size=4, activation='relu')(pool21)
pool22 = MaxPooling2D(pool_size=(2, 2))(conv22)
flat2 = Flatten()(pool22)

Объединение моделей ввода

merge = concatenate([flat1, flat2])
# interpretation model
hidden1 = Dense(10, activation='relu')(merge)
hidden2 = Dense(10, activation='relu')(hidden1)
output = Dense(1, activation='sigmoid')(hidden2)
model = Model(inputs=[visible1, visible2], outputs=output)
# summarize layers
print(model.summary())

Сводка модели

____________________________________________________________________________________________________
Layer (type)                     Output Shape          Param #     Connected to
====================================================================================================
input_1 (InputLayer)             (None, 64, 64, 1)     0
____________________________________________________________________________________________________
input_2 (InputLayer)             (None, 32, 32, 3)     0
____________________________________________________________________________________________________
conv2d_1 (Conv2D)                (None, 61, 61, 32)    544         input_1[0][0]
____________________________________________________________________________________________________
conv2d_3 (Conv2D)                (None, 29, 29, 32)    1568        input_2[0][0]
____________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D)   (None, 30, 30, 32)    0           conv2d_1[0][0]
____________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D)   (None, 14, 14, 32)    0           conv2d_3[0][0]
____________________________________________________________________________________________________
conv2d_2 (Conv2D)                (None, 27, 27, 16)    8208        max_pooling2d_1[0][0]
____________________________________________________________________________________________________
conv2d_4 (Conv2D)                (None, 11, 11, 16)    8208        max_pooling2d_3[0][0]
____________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D)   (None, 13, 13, 16)    0           conv2d_2[0][0]
____________________________________________________________________________________________________
max_pooling2d_4 (MaxPooling2D)   (None, 5, 5, 16)      0           conv2d_4[0][0]
____________________________________________________________________________________________________
flatten_1 (Flatten)              (None, 2704)          0           max_pooling2d_2[0][0]
____________________________________________________________________________________________________
flatten_2 (Flatten)              (None, 400)           0           max_pooling2d_4[0][0]
____________________________________________________________________________________________________
concatenate_1 (Concatenate)      (None, 3104)          0           flatten_1[0][0]
                                                                   flatten_2[0][0]
____________________________________________________________________________________________________
dense_1 (Dense)                  (None, 10)            31050       concatenate_1[0][0]
____________________________________________________________________________________________________
dense_2 (Dense)                  (None, 10)            110         dense_1[0][0]
____________________________________________________________________________________________________
dense_3 (Dense)                  (None, 1)             11          dense_2[0][0]
====================================================================================================
Total params: 49,699
Trainable params: 49,699
Non-trainable params: 0

График графика

plot_model(model, to_file='multiple_inputs.png')