6、Linear Layers - Linear
参考文档：https://pytorch.org/docs/stable/generated/torch.nn.Linear.html#torch.nn.Linear
import torch import torchvision from torch import nn from torch.nn import Linear from torch.utils.data import DataLoader dataset = torchvision.datasets.CIFAR10("../data", train=False, transform=torchvision.transforms.ToTensor(), download=True) dataloader = DataLoader(dataset, batch_size=64) class Tudui(nn.Module): def __init__(self): super(Tudui, self).__init__() self.liner1 = Linear(in_features=196608, out_features=10) def forward(self, input): output = self.liner1(input) return output tudui = Tudui() for data in dataloader: imgs, targets = data print(imgs.shape) # torch.Size([64, 3, 32, 32]) output = torch.reshape(imgs, (1, 1, 1, -1)) print(output.shape) # torch.Size([1, 1, 1, 196608]) output = tudui(output) print(output.shape) # torch.Size([1, 1, 1, 10])
Files already downloaded and verified torch.Size([64, 3, 32, 32]) torch.Size([1, 1, 1, 196608]) torch.Size([1, 1, 1, 10]) ...
6.1 flatten
参考文档：https://pytorch.org/docs/stable/generated/torch.flatten.html?highlight=flatten#torch.flatten
output = torch.reshape(imgs, (1, 1, 1, -1)) print(output.shape) # torch.Size([1, 1, 1, 196608]) 改为 output = torch.flatten(imgs) print(output.shape) # torch.Size([196608]) output --> torch.Size([10])
7、CIFAR 10 Model and Sequential
CIFAR 10：https://www.cs.toronto.edu/~kriz/cifar.html
import torch from torch import nn from torch.nn import Conv2d, MaxPool2d, Flatten, Linear class Tudui(nn.Module): def __init__(self): super(Tudui, self).__init__() self.conv1 = Conv2d(in_channels=3, out_channels=32, kernel_size=5, padding=2) self.maxpool1 = MaxPool2d(kernel_size=2) self.conv2 = Conv2d(in_channels=32, out_channels=32, kernel_size=5, padding=2) self.maxpool2 = MaxPool2d(kernel_size=2) self.conv3 = Conv2d(in_channels=32, out_channels=64, kernel_size=5, padding=2) self.maxpool3 = MaxPool2d(kernel_size=2) self.flatten = Flatten() self.linear1 = Linear(1024, 64) self.linear2 = Linear(64, 10) def forward(self, x): x = self.conv1(x) x = self.maxpool1(x) x = self.conv2(x) x = self.maxpool2(x) x = self.conv3(x) x = self.maxpool3(x) x = self.flatten(x) x = self.linear1(x) x = self.linear2(x) return x tudui = Tudui() print(tudui) input = torch.ones((64, 3, 32, 32)) output = tudui(input) print(output.shape)
Tudui( (conv1): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2)) (maxpool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (conv2): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2)) (maxpool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (conv3): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2)) (maxpool3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (flatten): Flatten(start_dim=1, end_dim=-1) (linear1): Linear(in_features=1024, out_features=64, bias=True) (linear2): Linear(in_features=64, out_features=10, bias=True) ) torch.Size([64, 10])
Sequential：https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html#torch.nn.Sequential
import torch from torch import nn from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential from torch.utils.tensorboard import SummaryWriter class Tudui(nn.Module): def __init__(self): super(Tudui, self).__init__() self.model1 = Sequential( Conv2d(3, 32, 5, padding=2), MaxPool2d(2), Conv2d(32, 32, 5, padding=2), MaxPool2d(2), Conv2d(32, 64, 5, padding=2), MaxPool2d(2), Flatten(), Linear(1024, 64), Linear(64, 10) ) def forward(self, x): x = self.model1(x) return x tudui = Tudui() print(tudui) input = torch.ones((64, 3, 32, 32)) output = tudui(input) print(output.shape) writer = SummaryWriter("../logs") writer.add_graph(tudui, input) writer.close()
Tudui( (model1): Sequential( (0): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2)) (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (2): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2)) (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (4): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2)) (5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) (6): Flatten(start_dim=1, end_dim=-1) (7): Linear(in_features=1024, out_features=64, bias=True) (8): Linear(in_features=64, out_features=10, bias=True) ) ) torch.Size([64, 10])
8、Loss Functions
8.1 L1Loss
参考文档：https://pytorch.org/docs/stable/generated/torch.nn.L1Loss.html#torch.nn.L1Loss
import torch from torch.nn import L1Loss input = torch.tensor([1, 2, 3], dtype=torch.float32) # torch.Size([3]) target = torch.tensor([1, 2, 5], dtype=torch.float32) # torch.Size([3]) input = torch.reshape(input, (1, 1, 1, 3)) # torch.Size([1, 1, 1, 3]) target = torch.reshape(target, (1, 1, 1, 3)) # torch.Size([1, 1, 1, 3]) loss1 = L1Loss(reduction='mean') # 默认为mean result = loss1(input, target) print(result) loss2 = L1Loss(reduction='sum') # sum result = loss2(input, target) print(result)
tensor(0.6667) tensor(2.)
8.2 MSELoss
参考文档：https://pytorch.org/docs/stable/generated/torch.nn.MSELoss.html#torch.nn.MSELoss
import torch from torch.nn import MSELoss input = torch.tensor([1, 2, 3], dtype=torch.float32) target = torch.tensor([1, 2, 5], dtype=torch.float32) input = torch.reshape(input, (1, 1, 1, 3)) target = torch.reshape(target, (1, 1, 1, 3)) loss = MSELoss() result = loss(input, target) print(result)
tensor(1.3333)
8.3 CrossEntropyLoss
参考文档：https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss
计算公式：
import torch from torch.nn import CrossEntropyLoss input = torch.tensor([0.1, 0.2, 0.3]) target = torch.tensor([1]) input = torch.reshape(input, (1, 3)) cross = CrossEntropyLoss() result = cross(input, target) print(result)
tensor(1.1019)
8.4 Sequential
import torchvision from torch import nn from torch.nn import Sequential, Conv2d, MaxPool2d, Flatten, Linear from torch.utils.data import DataLoader dataset = torchvision.datasets.CIFAR10("../data", train=False, transform=torchvision.transforms.ToTensor(), download=True) dataloader = DataLoader(dataset, batch_size=1) class Tudui(nn.Module): def __init__(self): super(Tudui, self).__init__() self.model1 = Sequential( Conv2d(3, 32, 5, padding=2), MaxPool2d(2), Conv2d(32, 32, 5, padding=2), MaxPool2d(2), Conv2d(32, 64, 5, padding=2), MaxPool2d(2), Flatten(), Linear(1024, 64), Linear(64, 10) ) def forward(self, x): x = self.model1(x) return x tudui = Tudui() for data in dataloader: imgs, targets = data output = tudui(imgs) print(output) print(targets)
Files already downloaded and verified tensor([[-0.0715, 0.0221, -0.0562, -0.0901, 0.0627, -0.0606, 0.0137, 0.0783, -0.0951, -0.1070]], grad_fn=<AddmmBackward0>) tensor([3]) tensor([[-0.0715, 0.0304, -0.0729, -0.0767, 0.0554, -0.0834, -0.0089, 0.0624, -0.0777, -0.0848]], grad_fn=<AddmmBackward0>) tensor([8]) ...
加入Loss Functions：
import torchvision from torch import nn from torch.nn import Sequential, Conv2d, MaxPool2d, Flatten, Linear, CrossEntropyLoss from torch.utils.data import DataLoader dataset = torchvision.datasets.CIFAR10("../data", train=False, transform=torchvision.transforms.ToTensor(), download=True) dataloader = DataLoader(dataset, batch_size=1) class Tudui(nn.Module): def __init__(self): super(Tudui, self).__init__() self.model1 = Sequential( Conv2d(3, 32, 5, padding=2), MaxPool2d(2), Conv2d(32, 32, 5, padding=2), MaxPool2d(2), Conv2d(32, 64, 5, padding=2), MaxPool2d(2), Flatten(), Linear(1024, 64), Linear(64, 10) ) def forward(self, x): x = self.model1(x) return x loss = CrossEntropyLoss() tudui = Tudui() for data in dataloader: imgs, targets = data output = tudui(imgs) result_loss = loss(output, targets) print(result_loss)
Files already downloaded and verified tensor(2.3437, grad_fn=<NllLossBackward0>) tensor(2.3600, grad_fn=<NllLossBackward0>) tensor(2.3680, grad_fn=<NllLossBackward0>) ...
8.5 backward
import torchvision from torch import nn from torch.nn import Sequential, Conv2d, MaxPool2d, Flatten, Linear, CrossEntropyLoss from torch.utils.data import DataLoader dataset = torchvision.datasets.CIFAR10("../data", train=False, transform=torchvision.transforms.ToTensor(), download=True) dataloader = DataLoader(dataset, batch_size=1) class Tudui(nn.Module): def __init__(self): super(Tudui, self).__init__() self.model1 = Sequential( Conv2d(3, 32, 5, padding=2), MaxPool2d(2), Conv2d(32, 32, 5, padding=2), MaxPool2d(2), Conv2d(32, 64, 5, padding=2), MaxPool2d(2), Flatten(), Linear(1024, 64), Linear(64, 10) ) def forward(self, x): x = self.model1(x) return x loss = CrossEntropyLoss() tudui = Tudui() for data in dataloader: imgs, targets = data output = tudui(imgs) result_loss = loss(output, targets) result_loss.backward() # 反向传播 print("OK")
