VGG
我们已经见过了 L e N e t LeNet LeNet 和 A l e x N e t AlexNet AlexNet，一个是 3 3 3 层，一个是 7 7 7 层，那么如果我们想实现一些更多层的神经网络要怎么办呢，还是手动开每一层吗？
显然不能这么原始，所以我们想到了一件事：为社么不试试for循环呢qwq
这就是 v g g vgg vgg 的本质了
这玩意儿虽然名字叫very deep network 但是现在看来这玩意儿也就那么几层 其实挺浅的哈哈qwq
vgg_block
一个 v g g _ b l o c k vgg\_block vgg_block 就是一个小的 n e t net net，其中包含多个 c o n v o l u t i o n l a y e r convolution \;\; layer convolutionlayer 和一个 m a x p o o l i n g maxpooling maxpooling，就像这样：
python
from mxnet.gluon import nn
def vgg_block(num_convs, channels):
out = nn.Sequential()
with out.name_scope():
for _ in range(num_convs):
out.add(nn.Conv2D(channels = channels, kernel_size = 3, padding = 1, activation = 'relu'))
out.add(nn.MaxPool2D(pool_size = 2, strides = 2))
return out然后我们可以定义一个block来试试效果：
python
blk = vgg_block(2, 128)
blk.initialize()
x = nd.random.uniform(shape = (2, 3, 16, 16)) # (batch_size, channels, height, width)
print(blk(x).shape)输出的shape就是：
(2, 128, 8, 8)vgg_stack
我们先定义一个 a r c h i t e c t u r e architecture architecture，它由很多个元组组成：
python
architecture = ((1, 64), (1, 128), (2, 256), (2, 512), (2, 512))每一个元组代表一个 v g g _ b l o c k vgg\_block vgg_block，就像这样：
python
def vgg_stack(architecture):
out = nn.Sequential()
with out.name_scope():
for (num_convs, channels) in architecture:
out.add(vgg_block(num_convs, channels))
return outvgg net
最后我们的 n e t net net 就是这样的：
python
net = nn.Sequential()
with net.name_scope():
net.add(vgg_stack(architecture))
net.add(nn.Flatten())
net.add(nn.Dense(4096, activation = 'relu'))
net.add(nn.Dropout(.5))
net.add(nn.Dense(4096, activation = 'relu'))
net.add(nn.Dropout(.5))
net.add(nn.Dense(10))
print(net)输出：
Sequential(
(0): Sequential(
(0): Sequential(
(0): Conv2D(None -> 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))
(1): MaxPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)
)
(1): Sequential(
(0): Conv2D(None -> 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))
(1): MaxPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)
)
(2): Sequential(
(0): Conv2D(None -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))
(1): Conv2D(None -> 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))
(2): MaxPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)
)
(3): Sequential(
(0): Conv2D(None -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))
(1): Conv2D(None -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))
(2): MaxPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)
)
(4): Sequential(
(0): Conv2D(None -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))
(1): Conv2D(None -> 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), Activation(relu))
(2): MaxPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)
)
)
(1): Flatten
(2): Dense(None -> 4096, Activation(relu))
(3): Dropout(p = 0.5, axes=())
(4): Dense(None -> 4096, Activation(relu))
(5): Dropout(p = 0.5, axes=())
(6): Dense(None -> 10, linear)
)其他的vgg
我们刚才构造的其实准确的来说应该叫 v g g 11 vgg11 vgg11，因为我们一共有 8 8 8 个卷积层加上 3 3 3 个全连接。但是更常用的应该是 v g g 16 vgg16 vgg16 和 v g g 19 vgg19 vgg19
根据上表你可以看到其他的 v g g vgg vgg 是怎么定义的，也可以试着自己构造一下qwq
训练
训练和 A l e x N e t AlexNet AlexNet 和 L e N e t LeNet LeNet 都是一样的，这里因为太慢了，所以把reszie的224改成了96[合十]：
python
batch_size, num_epoch, lr = 128, 5, 0.01
train_data, test_data = d2l.load_data_fashion_mnist(batch_size, resize = 96) # resize from 28 * 28 to 96 * 96
ctx = d2l.try_gpu()
net.initialize(ctx = ctx, init = init.Xavier())
softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate' : lr})
def accuracy(output, label):
return nd.mean(output.argmax(axis = 1) == label.astype('float32')).asscalar()
def evaluate_accuracy(data_itetator, net, context):
acc = 0.
for data, label in data_itetator:
output = net(data)
acc += accuracy(output, label)
return acc / len(data_itetator)
import time
for epoch in range(num_epoch):
start_time = time.time()
train_loss, train_acc = 0., 0.
for data, label in train_data:
label = label.as_in_context(ctx)
data = data.as_in_context(ctx)
with autograd.record():
out = net(data)
loss = softmax_cross_entropy(out, label)
loss.backward()
trainer.step(batch_size)
train_loss += nd.mean(loss).asscalar()
train_acc += accuracy(out, label)
test_acc = evaluate_accuracy(test_data, net, ctx)
end_time = time.time()
print("Epoch %d. Loss : %f, Train acc : %f, Test acc : %f, Used time : %f min" %
(epoch, train_loss / len(train_data), train_acc / len(train_data), test_acc, (end_time - start_time) / 60))
if(epoch != num_epoch - 1):
time.sleep(300)
print("CPU has rested 5 min")输出（一个epoch跑10min 真是服了：
Epoch 0. Loss : 2.139392, Train acc : 0.270794, Test acc : 0.585443, Used time : 11.752733 min
CPU has rested 5 min
Epoch 1. Loss : 0.861443, Train acc : 0.669210, Test acc : 0.757021, Used time : 10.107111 min
CPU has rested 5 min
Epoch 2. Loss : 0.650519, Train acc : 0.758362, Test acc : 0.796974, Used time : 10.013898 min
CPU has rested 5 min
Epoch 3. Loss : 0.552376, Train acc : 0.795109, Test acc : 0.832081, Used time : 9.947225 min
CPU has rested 5 min
Epoch 4. Loss : 0.488786, Train acc : 0.819946, Test acc : 0.847310, Used time : 9.911732 min