问ValueError:应该在输入列表上调用合并层。Add()

EN

# import the necessary packages
import keras
from keras.initializers import glorot_uniform
from keras.layers import AveragePooling2D, Input, Add
from keras.models import Model
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers.core import Activation
from keras.layers.core import Flatten
from keras.layers.core import Dropout
from keras.layers.core import Dense


class SmallerVGGNet:
    @staticmethod
    def build(width, height, depth, classes, finalact):

        X1 = Input(shape=(height, width, depth))

        # # CONV => RELU => POOL
        X = Conv2D(16, (3, 3), padding="same", strides=(1, 1), name="con_layer1")(X1)
        X = BatchNormalization(axis=3)(X)
        X = Activation("relu")(X)
        X = MaxPooling2D(pool_size=(3, 3), strides=(1, 1))(X)

        X = Conv2D(32, (3, 3), padding="same", strides=(2, 2), name="con_layer2")(X)
        X = BatchNormalization(axis=3)(X)
        X = Activation("relu")(X)

        X = Conv2D(32, (3, 3), padding="same", strides=(1, 1), name="con_layer3")(X)
        X = Activation("relu")(X)
        X = BatchNormalization(axis=3)(X)

        X = MaxPooling2D(pool_size=(3, 3), strides=(1, 1))(X)

        # First component
        X0 = Conv2D(256, (5, 5), strides=(1, 1), padding='same', kernel_initializer=glorot_uniform(seed=0))(X)
        X0 = BatchNormalization(axis=3)(X0)
        X0 = Activation("relu")(X0)

        # (CONV => RELU) * 2 => POOL
        X = Conv2D(64, (3, 3), padding="same", strides=(2, 2), name="con_layer4")(X0)
        X = BatchNormalization(axis=3)(X)
        X = Activation("relu")(X)

        X = Conv2D(64, (3, 3), padding="same", strides=(1, 1), name="con_layer5")(X)
        X = BatchNormalization(axis=3)(X)
        X = Activation("relu")(X)

        X = AveragePooling2D(pool_size=(3, 3), strides=(1, 1))(X)

        # Second Component
        X0 = Conv2D(512, (5, 5), strides=(1, 1), padding='valid', kernel_initializer=glorot_uniform(seed=0))(X)
        X0 = BatchNormalization(axis=3)(X0)
        X0 = Activation("relu")(X0)

        # (CONV => RELU) * 2 => POOL
        X = Conv2D(128, (3, 3), padding="same", strides=(2, 2), name="con_layer6")(X0)
        X = BatchNormalization(axis=3)(X)
        X = Activation("relu")(X)

        X = Conv2D(128, (3, 3), padding="same", strides=(1, 1), name="con_layer7")(X)
        X = BatchNormalization(axis=3)(X)
        X = Activation("relu")(X)

        X = MaxPooling2D(pool_size=(3, 3), strides=(1, 1))(X)

        # Third Component
        X0 = Conv2D(1024, (7, 7), strides=(2, 2), padding='valid', kernel_initializer=glorot_uniform(seed=0))(X)
        X0 = BatchNormalization(axis=3)(X0)
        X0 = Dense(128, activation="relu")(X0)
        X0 = Activation("relu")(X0)

        X = Add()([X0])
        X = Flatten()(X1)
        X = BatchNormalization()(X)
        X = Dropout(0.5)(X)
        output = Dense(classes, activation=finalact)(X)

        model = Model(inputs=[X1], outputs=output)

        print(model.summary())
        return model