用卷积搭建五分类的EfficientNet代码

以下是使用卷积构建五分类EfficientNet的代码示例：

import tensorflow as tf
from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, GlobalAveragePooling2D, Dense
from tensorflow.keras import Model


class EfficientNet(Model):
    def __init__(self, num_classes=5):
        super(EfficientNet, self).__init__()

        self.conv1 = Conv2D(filters=32, kernel_size=3, strides=2, padding='same')
        self.bn1 = BatchNormalization()
        self.act1 = Activation('swish')

        self.conv2 = Conv2D(filters=64, kernel_size=3, strides=1, padding='same')
        self.bn2 = BatchNormalization()
        self.act2 = Activation('swish')

        self.conv3 = Conv2D(filters=128, kernel_size=3, strides=2, padding='same')
        self.bn3 = BatchNormalization()
        self.act3 = Activation('swish')

        self.conv4 = Conv2D(filters=256, kernel_size=3, strides=1, padding='same')
        self.bn4 = BatchNormalization()
        self.act4 = Activation('swish')

        self.conv5 = Conv2D(filters=512, kernel_size=3, strides=2, padding='same')
        self.bn5 = BatchNormalization()
        self.act5 = Activation('swish')

        self.avgpool = GlobalAveragePooling2D()
        self.fc = Dense(units=num_classes, activation='softmax')

    def call(self, inputs):
        x = self.conv1(inputs)
        x = self.bn1(x)
        x = self.act1(x)

        x = self.conv2(x)
        x = self.bn2(x)
        x = self.act2(x)

        x = self.conv3(x)
        x = self.bn3(x)
        x = self.act3(x)

        x = self.conv4(x)
        x = self.bn4(x)
        x = self.act4(x)

        x = self.conv5(x)
        x = self.bn5(x)
        x = self.act5(x)

        x = self.avgpool(x)
        x = self.fc(x)

        return x

这是一个简单的EfficientNet模型，其中包括五个卷积层、BatchNormalization层、Swish激活函数和全局平均池化层。最后，我们在密集层中使用softmax激活函数来输出五个类别的概率。您可以在需要时添加更多卷积层和其他层。