构建Alextenet 解决 Cifar100分类问题（1024）

素颜马尾好姑娘i 2023-10-02 12:48 23阅读 0赞

import tensorflow as tf
    
    # TODO  '注意'  如果下载数据失败则加入下面注释的两行代码
    # import ssl
    # ssl._create_default_https_context = ssl._create_unverified_context
    
    from tensorflow import keras
    
    cifar100 =keras.datasets.cifar100
    (train_images, train_labels), _ = cifar100.load_data()   #下载数据
    train_images = train_images/255.0       #归一化
    train_images = tf.image.resize(train_images[:5000],(224,224))   #取前5000张照片并强制转换为（224*224*3）
    train_labels = train_labels[:5000]  #取前5000张训练照片标签
    
    
    # TODO.# ALEXNET解决Cifar100分类问题
    #  TODO 1.conv1:11*11,96,4,valid   例：11*11（卷积核） ，stride=4(步长)
    #   Maxpooling：3*3，2
    
    # TODO 2.conv2：5*5，256，1，same
    #  Maxpooling:3*3,2,valid
    
    # TODO 3.  conv3：3*3，384，1，same
    
    # TODO 4.  conv4：3*3，384，1，same
    # TODO 5.  conv5：3*3，256，1，same
    #  Maxpooling(最大池化):3*3,2,valid
    
    #TODO 6.全连接层（打平） fc1:4096,dropout=0.5
    # fc2:4096,dropout=0.5
    # output:100（分为100类）
    alexNet = keras.Sequential(layers=[
    
        #Conv1
        keras.layers.Conv2D(96,11,strides=(4,4),padding='valid',activation='relu'),
        keras.layers.BatchNormalization(),
        keras.layers.MaxPool2D(pool_size=(3,3),strides=(2,2),padding='valid'),
        #Conv2
        keras.layers.Conv2D(256,5,strides=(1,1),padding='same',activation='relu'),
        keras.layers.BatchNormalization(),
        keras.layers.MaxPool2D(pool_size=(3,3),strides=(2,2),padding='valid'),
        #conv3
        keras.layers.Conv2D(384,3,strides=(1*1),padding='same',activation='relu'),
        #Conv4
        keras.layers.Conv2D(384,3,strides=(1,1),padding='same',activation='relu'),
        #Conv5
        keras.layers.Conv2D(256,3,strides=(1,1),padding='same',activation='relu'),
        keras.layers.MaxPool2D(pool_size=(3,3),strides=(2,2),padding='valid'),
        #Flatten
        keras.layers.Flatten(),
        #FC1
        keras.layers.Dense(4096,activation='relu'),
        keras.layers.Dropout(rate=0.5),
        #fc2
        keras.layers.Dense(4096, activation='relu'),
        keras.layers.Dropout(rate=0.5),
        keras.layers.Dense(100)
    ])
    #配置模型
    alexNet.compile(optimizer='adam',
                    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                    metrics=['accuracy']
                    )
    
    #训练模型
    alexNet.fit(x=train_images,y=train_labels,batch_size=16,epochs=10)

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