b站小土堆pytorch教程学习笔记
根据loss更新模型参数1.计算实际输出与目标之间的差距
2.为我们更新输出提供一定的依据（反向传播）
1 MSEloss
python
import torch
from torch.nn import L1Loss
from torch import nn
inputs=torch.tensor([1,2,3],dtype=torch.float32)
targets=torch.tensor([1,2,5],dtype=torch.float32)
inputs=torch.reshape(inputs,(-1,1,1,3))
targets=torch.reshape(targets,(-1,1,1,3))
loss=L1Loss()
result=loss(inputs,targets)
loss_mse=nn.MSELoss()
result_mse=loss_mse(inputs,targets)
print(result)
print(result_mse)tensor(0.6667)
tensor(1.3333)
2 Cross EntropyLoss
python
x=torch.tensor([0.1,0.2,0.3])#需要reshape为要求的(batch_size,class)
y=torch.tensor([1])#target已经为要求的batch_size无需reshape
x=torch.reshape(x,(-1,3))
loss_cross=nn.CrossEntropyLoss()
result_cross=loss_cross(x,y)
print(result_cross)tensor(1.1019)
3 在具体的神经网络中使用loss
python
import torch
import torchvision.datasets
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
dataset=torchvision.datasets.CIFAR10('dataset',train=False,
transform=torchvision.transforms.ToTensor(),
download=True)
dataloader=DataLoader(dataset,batch_size=1)
class Han(nn.Module):
def __init__(self):
super(Han, 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=nn.CrossEntropyLoss()
han=Han()
for data in dataloader:
imgs,target=data
output=han(imgs)
# print(target)
# print(output)
result_loss=loss(output,target)
print(result_loss)*tensor([7])
tensor([[ 0.0057, -0.0201, -0.0796, 0.0556, -0.0625, 0.0125, -0.0413, -0.0056,
0.0624, -0.1072]], grad_fn=)...
tensor(2.2664, grad_fn=)...
4 反向传播 优化器
- 定义优化器
- 将待更新的每个参数梯度清零
- 调用损失函数的反向传播函数求出每个节点的梯度
- 使用step函数对模型的每个参数调优
python
import torch
import torchvision.datasets
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
dataset=torchvision.datasets.CIFAR10('dataset',train=False,
transform=torchvision.transforms.ToTensor(),
download=True)
dataloader=DataLoader(dataset,batch_size=64)
class Han(nn.Module):
def __init__(self):
super(Han, 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=nn.CrossEntropyLoss()
han=Han()
optim=torch.optim.SGD(han.parameters(),lr=0.01)
for epoch in range(5):
running_loss=0.0#一个epoch结束的loss和
for data in dataloader:
imgs,target=data
output=han(imgs)
result_loss=loss(output,target)#每次迭代的loss
optim.zero_grad()#将网络中每个可调节参数对应的梯度调为0
result_loss.backward()#优化器需要每个参数的梯度，使用反向传播获得
optim.step()#对每个参数调优
running_loss=running_loss+result_loss
print(running_loss)Files already downloaded and verified
tensor(361.0316, grad_fn=)
tensor(357.6938, grad_fn=)
tensor(343.0560, grad_fn=)
tensor(321.8132, grad_fn=)
tensor(313.3173, grad_fn=)