给我搭建一个简单的efficientnet的模型，说明每一步的原理

EfficientNet是一种基于深度神经网络的图像分类模型，它通过使用自适应网络结构和深度缩放来提高模型的性能和效率。

以下是搭建一个简单的EfficientNet的模型步骤及原理：

1.导入必要的库：

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

2.定义EfficientNet的基础结构：

def EfficientNet(input_shape, num_classes):
    inputs = tf.keras.Input(shape=input_shape)
    # Stem
    x = Conv2D(filters=32, kernel_size=3, strides=2, padding='same', use_bias=False)(inputs)
    x = BatchNormalization()(x)
    x = tf.keras.layers.Activation('swish')(x)
    # Blocks
    x = Block(x, filters=16, n_layers=1, strides=1)
    x = Block(x, filters=24, n_layers=2, strides=2)
    x = Block(x, filters=40, n_layers=2, strides=2)
    x = Block(x, filters=80, n_layers=3, strides=2)
    x = Block(x, filters=112, n_layers=3, strides=1)
    x = Block(x, filters=192, n_layers=4, strides=2)
    x = Block(x, filters=320, n_layers=1, strides=1)
    # Head
    x = Conv2D(filters=1280, kernel_size=1, strides=1, padding='same', use_bias=False)(x)
    x = BatchNormalization()(x)
    x = tf.keras.layers.Activation('swish')(x)
    x = GlobalAveragePooling2D()(x)
    outputs = Dense(units=num_classes, activation='softmax')(x)
    # Model
    model = Model(inputs, outputs)

    return model

3.定义EfficientNet中的Block结构：

def Block(inputs, filters, n_layers, strides):
    x = MBConv(inputs, filters, strides)
    for _ in range(1, n_layers):
        x = MBConv(x, filters, 1)
    return x

4.定义EfficientNet中的MBConv结构：

def MBConv(inputs, filters, strides):
    # Expansion
    x = Conv2D(filters=inputs.shape[-1]*6, kernel_size=1, strides=1, padding='same', use_bias=False)(inputs)
    x = BatchNormalization()(x)
    x = tf.keras.layers.Activation('swish')(x)
    # Depthwise Convolution
    x = tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=strides, padding='same', use_bias=False)(x)
    x = BatchNormalization()(x)
    x = tf.keras.layers.Activation('swish')(x)
    # Squeeze-and-Excitation
    x = SqueezeExcite(x)
    # Projection
    x = Conv2D(filters=filters, kernel_size=1, strides=1, padding='same', use_bias=False)(x)
    x = BatchNormalization()(x)
    if strides == 1 and inputs.shape[-1] == filters:
        x = tf.keras.layers.Add()([inputs, x])
    return x

5.定义EfficientNet中的Squeeze-and-Excitation结构：

def SqueezeExcite(inputs):
    filters = inputs.shape[-1]
    x = GlobalAveragePooling2D()(inputs)
    x = Dense(units=filters//4, activation='swish')(x)
    x = Dense(units=filters, activation='sigmoid')(x)
    x = tf.reshape(x, [-1, 1, 1, filters])
    x = tf.multiply(inputs, x)
    return x

在EfficientNet中，主要使用了以下技术：

1.自适应网络结构：通过自动缩放网络深度、宽度和分辨率来适应不同的任务和硬件设备。

2.深度缩放：通过使用不同的深度和宽度比例来提高模型的性能和效率。

3.MBConv结构：一种轻量级的卷积结构，使用了深度可分离卷积和Squeeze-and-Excite模块来提高模型的性能和效率。

4.Squeeze-and-Excite结构：一种轻量级的注意力机制，通过自适应地调整通道的重要性来提高模型的性能和效率。

通过以上步骤，我们可以搭建一个简单的EfficientNet模型，用于图像分类任务。