Question

因此，我尝试使用以下代码从3个顺序子模型中创建一个整体模型：

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)

我得到这个模型：

__________________________________________________________________________________________________
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]                    
==================================================================================================

所有子模型如下：

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

这是我提供数据的方式：

def fit_lstm(train, batch_size, nb_epoch, nb_neurons):
    X, y = train[:, 0:-1], train[:, -1]
    X = X.reshape(X.shape[0], 1, X.shape[1])
    model = Sequential()
    model.add(LSTM(nb_neurons, batch_input_shape=(batch_size, X.shape[1], X.shape[2]), stateful=True))
    model.add(Dense(1))
    model.compile(loss='mean_squared_error', optimizer='adam')
    for i in range(nb_epoch):
        model.fit(X, y, epochs=1, batch_size=batch_size, verbose=0, shuffle=False)
        model.reset_states()
    return model

我正在尝试为集成模型提供与子模型相同的数据，但是标题出现错误。我怎么了？

Answer 1

由于您正在使用Sequestial api，因此无法正常工作

model = Sequential()
model.add(Dense(32, input_dim=784))
model.add(Activation('relu'))

为了将模型合并在一起，您需要使用Functional api：

a = Input(shape=(32,))
b = Dense(32)(a)
model = Model(inputs=a, outputs=b)

您必须使用dtype float和shape [1,1,1]输入占位符张量'lstm_3_input'的值

1 个答案: