圆月山庄资源网

利用PyTorch实现VGG16教程
脚本专栏 2024/12/27 佚名
3 2
圆月山庄资源网 Design By www.vgjia.com

我就废话不多说了，大家还是直接看代码吧~

import torch
import torch.nn as nn
import torch.nn.functional as F
class VGG16(nn.Module):
 
 def __init__(self):
  super(VGG16, self).__init__()
  
  # 3 * 224 * 224
  self.conv1_1 = nn.Conv2d(3, 64, 3) # 64 * 222 * 222
  self.conv1_2 = nn.Conv2d(64, 64, 3, padding=(1, 1)) # 64 * 222* 222
  self.maxpool1 = nn.MaxPool2d((2, 2), padding=(1, 1)) # pooling 64 * 112 * 112
  
  self.conv2_1 = nn.Conv2d(64, 128, 3) # 128 * 110 * 110
  self.conv2_2 = nn.Conv2d(128, 128, 3, padding=(1, 1)) # 128 * 110 * 110
  self.maxpool2 = nn.MaxPool2d((2, 2), padding=(1, 1)) # pooling 128 * 56 * 56
  
  self.conv3_1 = nn.Conv2d(128, 256, 3) # 256 * 54 * 54
  self.conv3_2 = nn.Conv2d(256, 256, 3, padding=(1, 1)) # 256 * 54 * 54
  self.conv3_3 = nn.Conv2d(256, 256, 3, padding=(1, 1)) # 256 * 54 * 54
  self.maxpool3 = nn.MaxPool2d((2, 2), padding=(1, 1)) # pooling 256 * 28 * 28
  
  self.conv4_1 = nn.Conv2d(256, 512, 3) # 512 * 26 * 26
  self.conv4_2 = nn.Conv2d(512, 512, 3, padding=(1, 1)) # 512 * 26 * 26
  self.conv4_3 = nn.Conv2d(512, 512, 3, padding=(1, 1)) # 512 * 26 * 26
  self.maxpool4 = nn.MaxPool2d((2, 2), padding=(1, 1)) # pooling 512 * 14 * 14
  
  self.conv5_1 = nn.Conv2d(512, 512, 3) # 512 * 12 * 12
  self.conv5_2 = nn.Conv2d(512, 512, 3, padding=(1, 1)) # 512 * 12 * 12
  self.conv5_3 = nn.Conv2d(512, 512, 3, padding=(1, 1)) # 512 * 12 * 12
  self.maxpool5 = nn.MaxPool2d((2, 2), padding=(1, 1)) # pooling 512 * 7 * 7
  # view
  
  self.fc1 = nn.Linear(512 * 7 * 7, 4096)
  self.fc2 = nn.Linear(4096, 4096)
  self.fc3 = nn.Linear(4096, 1000)
  # softmax 1 * 1 * 1000
  
 def forward(self, x):
  
  # x.size(0)即为batch_size
  in_size = x.size(0)
  
  out = self.conv1_1(x) # 222
  out = F.relu(out)
  out = self.conv1_2(out) # 222
  out = F.relu(out)
  out = self.maxpool1(out) # 112
  
  out = self.conv2_1(out) # 110
  out = F.relu(out)
  out = self.conv2_2(out) # 110
  out = F.relu(out)
  out = self.maxpool2(out) # 56
  
  out = self.conv3_1(out) # 54
  out = F.relu(out)
  out = self.conv3_2(out) # 54
  out = F.relu(out)
  out = self.conv3_3(out) # 54
  out = F.relu(out)
  out = self.maxpool3(out) # 28
  
  out = self.conv4_1(out) # 26
  out = F.relu(out)
  out = self.conv4_2(out) # 26
  out = F.relu(out)
  out = self.conv4_3(out) # 26
  out = F.relu(out)
  out = self.maxpool4(out) # 14
  
  out = self.conv5_1(out) # 12
  out = F.relu(out)
  out = self.conv5_2(out) # 12
  out = F.relu(out)
  out = self.conv5_3(out) # 12
  out = F.relu(out)
  out = self.maxpool5(out) # 7
  
  # 展平
  out = out.view(in_size, -1)
  
  out = self.fc1(out)
  out = F.relu(out)
  out = self.fc2(out)
  out = F.relu(out)
  out = self.fc3(out)
  
  out = F.log_softmax(out, dim=1)
  return out

补充知识：Pytorch实现VGG（GPU版）

看代码吧~

import torch
from torch import nn
from torch import optim
from PIL import Image
import numpy as np

print(torch.cuda.is_available())
device = torch.device('cuda:0')
path="/content/drive/My Drive/Colab Notebooks/data/dog_vs_cat/"

train_X=np.empty((2000,224,224,3),dtype="float32")
train_Y=np.empty((2000,),dtype="int")
train_XX=np.empty((2000,3,224,224),dtype="float32")

for i in range(1000):
 file_path=path+"cat."+str(i)+".jpg"
 image=Image.open(file_path)
 resized_image = image.resize((224, 224), Image.ANTIALIAS)
 img=np.array(resized_image)
 train_X[i,:,:,:]=img
 train_Y[i]=0

for i in range(1000):
 file_path=path+"dog."+str(i)+".jpg"
 image = Image.open(file_path)
 resized_image = image.resize((224, 224), Image.ANTIALIAS)
 img = np.array(resized_image)
 train_X[i+1000, :, :, :] = img
 train_Y[i+1000] = 1

train_X /= 255

index = np.arange(2000)
np.random.shuffle(index)

train_X = train_X[index, :, :, :]
train_Y = train_Y[index]

for i in range(3):
 train_XX[:,i,:,:]=train_X[:,:,:,i]
# 创建网络

class Net(nn.Module):

 def __init__(self):
  super(Net, self).__init__()
  self.conv1 = nn.Sequential(
   nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1),
   nn.ReLU(),
   nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1),
   nn.ReLU(),
   nn.BatchNorm2d(num_features=64, eps=1e-05, momentum=0.1, affine=True),
   nn.MaxPool2d(kernel_size=2,stride=2)
  )
  self.conv2 = nn.Sequential(
   nn.Conv2d(in_channels=64,out_channels=128,kernel_size=3,stride=1,padding=1),
   nn.ReLU(),
   nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1),
   nn.ReLU(),
   nn.BatchNorm2d(128,eps=1e-5,momentum=0.1,affine=True),
   nn.MaxPool2d(kernel_size=2,stride=2)
  )
  self.conv3 = nn.Sequential(
   nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1),
   nn.ReLU(),
   nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),
   nn.ReLU(),
   nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),
   nn.ReLU(),
   nn.BatchNorm2d(256,eps=1e-5, momentum=0.1, affine=True),
   nn.MaxPool2d(kernel_size=2, stride=2)
  )
  self.conv4 = nn.Sequential(
   nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=1, padding=1),
   nn.ReLU(),
   nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
   nn.ReLU(),
   nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
   nn.ReLU(),
   nn.BatchNorm2d(512, eps=1e-5, momentum=0.1, affine=True),
   nn.MaxPool2d(kernel_size=2, stride=2)
  )
  self.conv5 = nn.Sequential(
   nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
   nn.ReLU(),
   nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
   nn.ReLU(),
   nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, stride=1, padding=1),
   nn.ReLU(),
   nn.BatchNorm2d(512, eps=1e-5, momentum=0.1, affine=True),
   nn.MaxPool2d(kernel_size=2, stride=2)
  )
  self.dense1 = nn.Sequential(
   nn.Linear(7*7*512,4096),
   nn.ReLU(),
   nn.Linear(4096,4096),
   nn.ReLU(),
   nn.Linear(4096,2)
  )

 def forward(self, x):
  x=self.conv1(x)
  x=self.conv2(x)
  x=self.conv3(x)
  x=self.conv4(x)
  x=self.conv5(x)
  x=x.view(-1,7*7*512)
  x=self.dense1(x)
  return x

batch_size=16
net = Net().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.0005)

train_loss = []
for epoch in range(10):

 for i in range(2000//batch_size):
  x=train_XX[i*batch_size:i*batch_size+batch_size]
  y=train_Y[i*batch_size:i*batch_size+batch_size]

  x = torch.from_numpy(x)  #(batch_size,input_feature_shape)
  y = torch.from_numpy(y)  #(batch_size,label_onehot_shape)
  x = x.cuda()
  y = y.long().cuda()

  out = net(x)

  loss = criterion(out, y)   # 计算两者的误差
  optimizer.zero_grad()    # 清空上一步的残余更新参数值
  loss.backward()     # 误差反向传播, 计算参数更新值
  optimizer.step()     # 将参数更新值施加到 net 的 parameters 上
  train_loss.append(loss.item())

  print(epoch, i*batch_size, np.mean(train_loss))
  train_loss=[]

total_correct = 0
for i in range(2000):
 x = train_XX[i].reshape(1,3,224,224)
 y = train_Y[i]
 x = torch.from_numpy(x)

 x = x.cuda()
 out = net(x).cpu()
 out = out.detach().numpy()
 pred=np.argmax(out)
 if pred==y:
  total_correct += 1
 print(total_correct)

acc = total_correct / 2000.0
print('test acc:', acc)

torch.cuda.empty_cache()

将上面代码中batch_size改为32，训练次数改为100轮，得到如下准确率

利用PyTorch实现VGG16教程

过拟合了~

以上这篇利用PyTorch实现VGG16教程就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。

标签：
PyTorch,VGG16
圆月山庄资源网 Design By www.vgjia.com
广告合作：本站广告合作请联系QQ：858582 申请时备注：广告合作（否则不回）
免责声明：本站文章均来自网站采集或用户投稿，网站不提供任何软件下载或自行开发的软件！ 如有用户或公司发现本站内容信息存在侵权行为，请邮件告知！ 858582#qq.com
圆月山庄资源网 Design By www.vgjia.com
www.vgjia.com 圆月山庄资源网
139,976互联网资源
144,792高清电影
21,817无损音乐
631,128技术资源

更新日志

友情链接

杰晶网络 DDR爱好者之家 桃源资源网 杰网资源 富贵资源网 南强小屋 铁雪资源网 幽灵资源网 万梅资源网 狼山资源网 白云岛资源网 昆仑资源网 相思资源网 明霞山资源网 内蒙古资源网 黑松山资源网 茶园资源网 饿虎岗资源网 大旗谷资源网 常春岛资源网 岱庙资源网 兴国资源网 快活林资源网 蝙蝠岛资源网 帝王谷资源网 白云城资源网 伏龙阁资源网 清风细雨楼 天枫庄资源网 圆月山庄资源网 无争山庄资源网 神水资源网 移花宫资源网 神剑山庄资源网 无为清净楼资源网 金钱帮资源网 丐帮资源网 华山资源网 极乐门资源网 小李飞刀资源网 凤求凰客栈 风云阁资源网 金狮镖局 鸳鸯亭资源网 千金楼资源网 更多链接