Pytorch实现VGG（GPU版）

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 过拟合了