今儿个用pytorch写了一个识别证件照性别的神经网络,一开始用sgd,死活收敛不到一半,还不如蒙呢,蒙还有50%的准确率,后来用adam,一下子就收敛到接近100%了,可以商用了。我发现别放男明星的,不太准啊,这个明星娘化看来被人工智能发现了。。。
来,先上图,看看成果。
该程序用了ImageLoader加载数据,省去了自己定义数据模型的功夫。
本地图片应该如下图一样放置
test文件夹一样的结构,就不上图了。
训练代码我贴一下把,完整代码可以加我微信好友获取。我的微信是A1354164181。
```python
if __name__ == '__main__':
# transform = transforms.Compose(
# [transforms.ToTensor(),
# transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
#
# trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
# trainloader = torch.utils.data.DataLoader(trainset, batch_size=4, shuffle=True, num_workers=2)
#
# testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
# testloader = torch.utils.data.DataLoader(testset, batch_size=4, shuffle=False, num_workers=2)
data_transform = transforms.Compose([
transforms.Resize(22),
transforms.CenterCrop(20),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
hymenoptera_dataset = datasets.ImageFolder(root='MaleAndFemale',
transform=data_transform)
test_dataset = datasets.ImageFolder(root='Test',
transform=data_transform)
trainloader = torch.utils.data.DataLoader(hymenoptera_dataset,
batch_size=4, shuffle=False, num_workers=2)
testloader = torch.utils.data.DataLoader(test_dataset,
batch_size=4, shuffle=False, num_workers=2)
classes = ('男人', '女人')
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)
for epoch in range(20): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# get the inputs
inputs, labels = data
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
# if i % 4 == 19: # print every 2000 mini-batches
# print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 20))
# running_loss = 0.0
print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, loss.item()))
print('Finished Training')
PATH = './cifar_net.pth'
# torch.save(net.state_dict(), PATH)
dataiter = iter(testloader)
images, labels = dataiter.next()
print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))
# 输出图片
imshow(torchvision.utils.make_grid(images))
net.load_state_dict(torch.load(PATH))
outputs = net(images)
print(outputs)
_, predicted = torch.max(outputs, 1)
print('predicted',predicted)
print('Predicted: ', ' '.join('%5s' % classes[predicted[j]] for j in range(4)))