相关疑难解决方法(0)

class Model:
    def __init__(self):    
        self.model = Sequential()
        self.model.add(Conv2D(24, 3, 2, 'valid', input_shape=(75, 75, 3)))
        self.model.add(BatchNormalization())
        self.model.add(Conv2D(24, 3, 2))
        self.model.add(BatchNormalization())
        self.model.add(Conv2D(24, 3, 2))
        self.model.add(BatchNormalization())
        self.model.add(Conv2D(24, 3, 2))
        self.model.add(BatchNormalization())
        self.model.add(Flatten())

    def get_model(self):
        return self.model

class CNN_MLP:
    def __init__(self):
        model = Model()
        self.model = model.get_model()
        self.optimizer = optimizers

    def get_model(self): 
        self.model = self.extend(self.model)
        return self.model

    def extend(self, model):
        self.model = model
        self.sequence = Input(shape=(75, 75, 3), name='Sequence')
        self.features = Input(shape=(11, ), name='Features')
        conv_sequence = self.model(self.sequence)

         merged_features = concatenate([conv_sequence, self.features])
         fc1 = Dense(256, activation='relu')(merged_features) …