站长素材音效网站建设投入产出分析
9.1 优化器
① 损失函数调用backward方法，就可以调用损失函数的反向传播方法，就可以求出我们需要调节的梯度，我们就可以利用我们的优化器就可以根据梯度对参数进行调整，达到整体误差降低的目的。
② 梯度要清零，如果梯度不清零会导致梯度累加。
9.2 神经网络优化一轮
import torch
import torchvision
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriterdataset = torchvision.datasets.CIFAR10("./dataset",train=False,transform=torchvision.transforms.ToTensor(),download=True)
dataloader = DataLoader(dataset, batch_size=64,drop_last=True)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 xloss = nn.CrossEntropyLoss() # 交叉熵
tudui = Tudui()
optim = torch.optim.SGD(tudui.parameters(),lr=0.01) # 随机梯度下降优化器
for data in dataloader:imgs, targets = dataoutputs = tudui(imgs)result_loss = loss(outputs, targets) # 计算实际输出与目标输出的差距optim.zero_grad() # 梯度清零result_loss.backward() # 反向传播，计算损失函数的梯度optim.step() # 根据梯度，对网络的参数进行调优print(result_loss) # 对数据只看了一遍，只看了一轮，所以loss下降不大结果：
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9.3 神经网络优化多轮
import torch
import torchvision
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriterdataset = torchvision.datasets.CIFAR10("./dataset",train=False,transform=torchvision.transforms.ToTensor(),download=True)
dataloader = DataLoader(dataset, batch_size=64,drop_last=True)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 xloss = nn.CrossEntropyLoss() # 交叉熵
tudui = Tudui()
optim = torch.optim.SGD(tudui.parameters(),lr=0.01) # 随机梯度下降优化器
for epoch in range(20):running_loss = 0.0for data in dataloader:imgs, targets = dataoutputs = tudui(imgs)result_loss = loss(outputs, targets) # 计算实际输出与目标输出的差距optim.zero_grad() # 梯度清零result_loss.backward() # 反向传播，计算损失函数的梯度optim.step() # 根据梯度，对网络的参数进行调优running_loss = running_loss + result_lossprint(running_loss) # 对这一轮所有误差的总和结果：
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9.4 神经网络学习率优化
import torch
import torchvision
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriterdataset = torchvision.datasets.CIFAR10("./dataset",train=False,transform=torchvision.transforms.ToTensor(),download=True)
dataloader = DataLoader(dataset, batch_size=64,drop_last=True)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 xloss = nn.CrossEntropyLoss() # 交叉熵
tudui = Tudui()
optim = torch.optim.SGD(tudui.parameters(),lr=0.01) # 随机梯度下降优化器
scheduler = torch.optim.lr_scheduler.StepLR(optim, step_size=5, gamma=0.1) # 每过 step_size 更新一次优化器，更新是学习率为原来的学习率的的 0.1 倍
for epoch in range(20):running_loss = 0.0for data in dataloader:imgs, targets = dataoutputs = tudui(imgs)result_loss = loss(outputs, targets) # 计算实际输出与目标输出的差距optim.zero_grad() # 梯度清零result_loss.backward() # 反向传播，计算损失函数的梯度optim.step() # 根据梯度，对网络的参数进行调优scheduler.step() # 学习率太小了，所以20个轮次后，相当于没走多少running_loss = running_loss + result_lossprint(running_loss) # 对这一轮所有误差的总和结果：
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