由5个卷积层块（2个单卷积层，3个双卷积层），3个全连接层组成——VGG-11

from mxnet import gluon,init,nd,autogradfrom mxnet.gluon import nn,lossdef vgg_block(num_convs, num_channels):    blk = nn.Sequential()    for _ in range(num_convs):        blk.add(nn.Conv2D(num_channels,kernel_size=3,padding=1,activation='relu'))    blk.add(nn.MaxPool2D(pool_size=2,strides=2))    return blkconv_arch = ((1,64),(1,128),(2,256),(2,512),(2,512))def vgg(conv_arch):    net = nn.Sequential()    # 卷积层    for (num_convs,num_channels) in conv_arch:        net.add(vgg_block(num_channels,num_channels))    # 全连接层    net.add(nn.Dense(4096,activation='relu'),nn.Dropout(0.5),            nn.Dense(4096,activation='relu'),nn.Dropout(0.5),            nn.Dense(10)            )    return netnet = vgg(conv_arch)net.initialize()X = nd.random.uniform(shape=(1,1,224,224))for blk in net:    X = blk(X)    print(blk.name,'output shape:\t',X.shape)

输出形状。