VGG網路

5.7.1 VGG塊

• VGG引入了Block的概念 作為模型的基礎模組

import time
import torch
from torch import nn, optim
import pytorch_deep as pyd
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

def vgg_block(num_convs, in_channels, out_channels):
    blk = []
    for i in range(num_convs):
        if i == 0:
            blk.append(nn.Conv2d(in_channels, out_channels,kernel_size=3, padding=1))
        else:
            blk.append(nn.Conv2d(out_channels, out_channels,kernel_size=3, padding=1))
        blk.append(nn.ReLU())
    blk.append(nn.MaxPool2d(kernel_size=2, stride=2)) # 這⾥會使寬⾼減半
    return nn.Sequential(*blk)
 

實現VGG_11網路

• 8個卷積層和3個全連線

def vgg_11(conv_arch, fc_features, fc_hidden_units=4096):
    net = nn.Sequential()
    # 卷積層部分
    for i, (num_convs, in_channels, out_channels) in enumerate(conv_arch):
        # 每經過⼀個vgg_block都會使寬⾼減半
        net.add_module("vgg_block_" + str(i+1),vgg_block(num_convs, in_channels, out_channels))
    # 全連線層部分
    net.add_module("fc", nn.Sequential(
                    pyd.FlattenLayer(),
                    nn.Linear(fc_features,fc_hidden_units),
                    nn.ReLU(),
                    nn.Dropout(0.5),
                    nn.Linear(fc_hidden_units,fc_hidden_units),
                    nn.ReLU(),
                    nn.Dropout(0.5),
                    nn.Linear(fc_hidden_units, 10)
                    ))
    return net
 

ratio = 8
small_conv_arch = [(1, 1, 64//ratio), (1, 64//ratio, 128//ratio),(2, 128//ratio, 256//ratio),(2, 256//ratio, 512//ratio), (2, 512//ratio,512//ratio)]
fc_features = 512 * 7 * 7 # c *
fc_hidden_units = 4096 # 任意
net = vgg_11(small_conv_arch, fc_features // ratio, fc_hidden_units //ratio)
print(net)

Sequential(
  (vgg_block_1): Sequential(
    (0): Conv2d(1, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU()
    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (vgg_block_2): Sequential(
    (0): Conv2d(8, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU()
    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (vgg_block_3): Sequential(
    (0): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU()
    (2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU()
    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (vgg_block_4): Sequential(
    (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU()
    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU()
    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (vgg_block_5): Sequential(
    (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (1): ReLU()
    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (3): ReLU()
    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (fc): Sequential(
    (0): FlattenLayer()
    (1): Linear(in_features=3136, out_features=512, bias=True)
    (2): ReLU()
    (3): Dropout(p=0.5)
    (4): Linear(in_features=512, out_features=512, bias=True)
    (5): ReLU()
    (6): Dropout(p=0.5)
    (7): Linear(in_features=512, out_features=10, bias=True)
  )
)
 

訓練資料

batch_size = 32
# 如出現“out of memory”的報錯資訊，可減⼩batch_size或resize
train_iter, test_iter = pyd.load_data_fashion_mnist(batch_size,resize=224)
lr, num_epochs = 0.001, 5
optimizer = torch.optim.Adam(net.parameters(), lr=lr)
pyd.train_ch5(net, train_iter, test_iter, batch_size, optimizer,device, num_epochs)

training on  cuda
epoch 1, loss 0.5166, train acc 0.810, test acc 0.872,time 57.6 sec
epoch 2, loss 0.1557, train acc 0.887, test acc 0.902,time 57.9 sec
epoch 3, loss 0.0916, train acc 0.900, test acc 0.907,time 57.7 sec
epoch 4, loss 0.0609, train acc 0.912, test acc 0.915,time 57.6 sec
epoch 5, loss 0.0449, train acc 0.919, test acc 0.914,time 57.4 sec