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# 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

我想在最后一个激活函数中添加第三个组件，为此我创建了一个添加函数来添加所有 X0 值。但是在添加此错误时会发生此错误。添加 ADD 功能时会发生这种情况。

raise ValueError('一个合并层应该被称为 ' ValueError: 一个合并层应该在一个输入列表上被调用。

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1 回答 1

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Add()通常在列表中取 2 个值。你只给了一个。

于 2021-08-24T18:25:17.880 回答