MachineLP的Github(欢迎follow):https://github.com/MachineLP
本小节使用torch搭建CNN模型,训练和测试:
(1)定义模型超参数:输出、迭代次数、批量大小、学习率。
(2)定义训练数据,加餐部分是使用自己的数据集:(可参考:https://blog.csdn.net/u014365862/article/details/80506147)
(3)定义模型(定义卷积神经网络)。
(4)定义损失函数,选用适合的损失函数。
(5)定义优化算法(SGD、Adam等)。
(6)保存模型。
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代码部分:
# coding=utf-8 import torch import torch.nn as nn import torchvision import torchvision.transforms as transforms # 判定GPU是否存在 device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') # 定义超参数 num_epochs = 5 num_classes = 10 batch_size = 100 learning_rate = 0.001 # 手写体数据,(数据+标签) train_dataset = torchvision.datasets.MNIST(root='./data/', train=True, transform=transforms.ToTensor(), download=True) test_dataset = torchvision.datasets.MNIST(root='./data/', train=False, transform=transforms.ToTensor()) # # 构建数据管道, 使用自己的数据集请参考:https://blog.csdn.net/u014365862/article/details/80506147 train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True) test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False) # 定义自己的卷积神经网络 class ConvNet(nn.Module): def __init__(self, num_classes=10): super(ConvNet, self).__init__() self.layer1 = nn.Sequential( nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2), nn.BatchNorm2d(16), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2)) self.layer2 = nn.Sequential( nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2), nn.BatchNorm2d(32), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2)) self.layer3 = nn.Sequential( nn.Conv2d(32, 64, kernel_size=5, stride=1, padding=2), nn.BatchNorm2d(64), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2)) self.fc = nn.Linear(3*3*64, num_classes) def forward(self, x): out = self.layer1(x) out = self.layer2(out) out = self.layer3(out) out = out.reshape(out.size(0), -1) out = self.fc(out) return out # 定义模型 model = ConvNet(num_classes).to(device) # 定义损失函数+优化算法 criterion = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) # 训练模型 total_step = len(train_loader) for epoch in range(num_epochs): for i, (images, labels) in enumerate(train_loader): images = images.to(device) labels = labels.to(device) # 前向传播+计算loss outputs = model(images) loss = criterion(outputs, labels) # 后向传播+调整参数 optimizer.zero_grad() loss.backward() optimizer.step() # 每100个batch打印一次数据 if (i+1) % 100 == 0: print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' .format(epoch+1, num_epochs, i+1, total_step, loss.item())) # 模型测试部分 # 测试阶段不需要计算梯度,注意 model.eval() # eval mode (batchnorm uses moving mean/variance instead of mini-batch mean/variance) with torch.no_grad(): correct = 0 total = 0 for images, labels in test_loader: images = images.to(device) labels = labels.to(device) outputs = model(images) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total)) # 保存模型参数 torch.save(model.state_dict(), 'model.ckpt')
加餐:在自己数据集上使用:
其中,train.txt中的数据格式:
gender/0male/0(2).jpg 1
gender/0male/0(3).jpeg 1
gender/0male/0(1).jpg 0
test.txt中的数据格式如下:
gender/0male/0(3).jpeg 1
gender/0male/0(1).jpg 0
gender/1female/1(6).jpg 1
代码部分:
# coding=utf-8 import torch import torch.nn as nn import torchvision from torch.utils.data import Dataset, DataLoader from torchvision import transforms, utils from PIL import Image # 判定GPU是否存在 device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') # 定义超参数 num_epochs = 5 num_classes = 10 batch_size = 16 learning_rate = 0.001 def default_loader(path): # 注意要保证每个batch的tensor大小时候一样的。 return Image.open(path).convert('RGB') class MyDataset(Dataset): def __init__(self, txt, transform=None, target_transform=None, loader=default_loader): fh = open(txt, 'r') imgs = [] for line in fh: line = line.strip('\n') # line = line.rstrip() words = line.split(' ') imgs.append((words[0],int(words[1]))) self.imgs = imgs self.transform = transform self.target_transform = target_transform self.loader = loader def __getitem__(self, index): fn, label = self.imgs[index] img = self.loader(fn) if self.transform is not None: img = self.transform(img) return img,label def __len__(self): return len(self.imgs) def get_loader(dataset='train.txt', crop_size=128, image_size=28, batch_size=2, mode='train', num_workers=1): """Build and return a data loader.""" transform = [] if mode == 'train': transform.append(transforms.RandomHorizontalFlip()) transform.append(transforms.CenterCrop(crop_size)) transform.append(transforms.Resize(image_size)) transform.append(transforms.ToTensor()) transform.append(transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))) transform = transforms.Compose(transform) train_data=MyDataset(txt=dataset, transform=transform) data_loader = DataLoader(dataset=train_data, batch_size=batch_size, shuffle=(mode=='train'), num_workers=num_workers) return data_loader # 注意要保证每个batch的tensor大小时候一样的。 # data_loader = DataLoader(train_data, batch_size=2,shuffle=True) train_loader = get_loader('train.txt', batch_size=batch_size) print(len(train_loader)) test_loader = get_loader('test.txt', batch_size=batch_size) print(len(test_loader)) # 定义自己的卷积神经网络 class ConvNet(nn.Module): def __init__(self, num_classes=10): super(ConvNet, self).__init__() self.layer1 = nn.Sequential( nn.Conv2d(3, 16, kernel_size=5, stride=1, padding=2), nn.BatchNorm2d(16), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2)) self.layer2 = nn.Sequential( nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2), nn.BatchNorm2d(32), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2)) self.layer3 = nn.Sequential( nn.Conv2d(32, 64, kernel_size=5, stride=1, padding=2), nn.BatchNorm2d(64), nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2)) self.fc = nn.Linear(3*3*64, num_classes) def forward(self, x): out = self.layer1(x) out = self.layer2(out) out = self.layer3(out) out = out.reshape(out.size(0), -1) out = self.fc(out) return out # 定义模型 model = ConvNet(num_classes).to(device) # 定义损失函数+优化算法 criterion = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) # 训练模型 total_step = len(train_loader) for epoch in range(num_epochs): for i, (images, labels) in enumerate(train_loader): images = images.to(device) labels = labels.to(device) # 前向传播+计算loss outputs = model(images) loss = criterion(outputs, labels) # 后向传播+调整参数 optimizer.zero_grad() loss.backward() optimizer.step() # 每100个batch打印一次数据 if (i+1) % 100 == 0: print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' .format(epoch+1, num_epochs, i+1, total_step, loss.item())) # 模型测试部分 # 测试阶段不需要计算梯度,注意 model.eval() # eval mode (batchnorm uses moving mean/variance instead of mini-batch mean/variance) with torch.no_grad(): correct = 0 total = 0 for images, labels in test_loader: images = images.to(device) labels = labels.to(device) outputs = model(images) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total)) # 保存模型参数 torch.save(model.state_dict(), 'model.ckpt')
总结:
本节使用CNN训练MNIST识别、自己数据的识别。
上面加餐部分需要生成自己的txt文件(数据+标签),可以参考这个,自己以前调试用的:https://github.com/MachineLP/py_workSpace/blob/master/g_img_path.py
torch系列:
2. torch02:logistic regression--MNIST识别
4. torch04:全连接神经网络--MNIST识别和自己数据集