How to feed ensemble model same input data as its sub models?

Question

I'm trying to create an ensemble model that gets the same input as the sub models.

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)

When I load my ensemble model and feed it same data as I did for seperate sub models, I got the following error.

You must feed a value for placeholder tensor 'lstm_2_input' with dtype float and shape [1,1,1] [[{{node lstm_2_input}}]]

For the three sub models they have:

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

and for the ensemble model:

__________________________________________________________________________________________________
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)

can you add the summary of each model, something like https://stackoverflow.com/a/49527269/5094841 and the shape of your data — VnC, Apr 26 '19 at 13:21

score 0 · Answer 1 · answered Apr 26 '19 at 14:34

See this example taken from here

# 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

First input model

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)

Second input model

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)

Merge input models

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())

Model summary

____________________________________________________________________________________________________
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

Plot graph

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

I tried this but it's a different model architecture in my opinion — mobelahcen, Apr 26 '19 at 14:42

How to feed ensemble model same input data as its sub models?

1 Answers1