AlexNet网络模型代码

以下是AlexNet网络模型的代码：

import tensorflow as tf

def alexnet(input_shape, num_classes):
    model = tf.keras.Sequential([
        tf.keras.layers.Conv2D(filters=96, kernel_size=(11,11), strides=(4,4), activation='relu', input_shape=input_shape),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.MaxPooling2D(pool_size=(3,3), strides=(2,2)),
        tf.keras.layers.Conv2D(filters=256, kernel_size=(5,5), strides=(1,1), activation='relu', padding="same"),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.MaxPooling2D(pool_size=(3,3), strides=(2,2)),
        tf.keras.layers.Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), activation='relu', padding="same"),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), activation='relu', padding="same"),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), activation='relu', padding="same"),
        tf.keras.layers.BatchNormalization(),
        tf.keras.layers.MaxPooling2D(pool_size=(3,3), strides=(2,2)),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(units=4096, activation='relu'),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Dense(units=4096, activation='relu'),
        tf.keras.layers.Dropout(0.5),
        tf.keras.layers.Dense(units=num_classes, activation='softmax')
    ])
    
    return model

该模型包括13个层，其中包括5个卷积层、3个池化层和5个全连接层。它的输入是一个形状为（224,224,3）的图像，输出是一个长度为num_classes的向量，表示每个类别的概率。该模型使用了ReLU激活函数和批归一化技术，以及Dropout层来防止过拟合。