用卷积搭建三分类的EfficientNet代码

以下是使用卷积搭建三分类的EfficientNet代码：

import tensorflow as tf
from tensorflow.keras.layers import Conv2D, BatchNormalization, Activation, Dropout, Dense, GlobalAveragePooling2D
from tensorflow.keras.models import Model
from tensorflow.keras.regularizers import l2

class EfficientNet():

    def __init__(self, input_shape, num_classes):
        self.input_shape = input_shape
        self.num_classes = num_classes

    def _conv_block(self, inputs, filters, kernel_size, strides):
        x = Conv2D(filters, kernel_size, strides=strides, padding='same', kernel_regularizer=l2(0.01))(inputs)
        x = BatchNormalization()(x)
        x = Activation('swish')(x)
        return x

    def _bottleneck_block(self, inputs, filters, kernel_size, t, s, r=False):
        """MobileNetV2-like block"""
        tchannel = inputs.shape[-1] * t

        x = self._conv_block(inputs, tchannel, kernel_size=(1, 1), strides=(1, 1))

        x = Conv2D(tchannel, kernel_size=(3, 3), strides=(s, s), padding='same', use_bias=False)(x)
        x = BatchNormalization()(x)
        x = Activation('swish')(x)

        x = Conv2D(filters, kernel_size=(1, 1), strides=(1, 1), padding='same', use_bias=False)(x)
        x = BatchNormalization()(x)

        if r:
            x = tf.keras.layers.add([x, inputs])
        return x

    def build_model(self):
        inputs = tf.keras.Input(shape=self.input_shape)

        x = self._conv_block(inputs, 32, kernel_size=(3, 3), strides=(2, 2))
        x = self._bottleneck_block(x, 16, kernel_size=(3, 3), t=1, s=1)
        x = self._bottleneck_block(x, 24, kernel_size=(3, 3), t=6, s=2)
        x = self._bottleneck_block(x, 40, kernel_size=(5, 5), t=6, s=2)
        x = self._bottleneck_block(x, 80, kernel_size=(3, 3), t=6, s=2)
        x = self._bottleneck_block(x, 112, kernel_size=(5, 5), t=6, s=1)
        x = self._bottleneck_block(x, 192, kernel_size=(5, 5), t=6, s=2)
        x = self._bottleneck_block(x, 320, kernel_size=(3, 3), t=6, s=1)
        x = self._conv_block(x, 1280, kernel_size=(1, 1), strides=(1, 1))
        x = GlobalAveragePooling2D()(x)

        x = Dropout(0.3)(x)
        x = Dense(256, activation='swish')(x)
        x = Dropout(0.3)(x)
        x = Dense(self.num_classes, activation='softmax')(x)

        model = Model(inputs, x)
        return model

该代码使用了EfficientNet的基本架构，并添加了Dropout和全连接层进行分类。请注意，该代码只是一个示例，您可以根据自己的需要进行修改。