11-优化器的使用

torch.optim

To use torch.optim you have to construct an optimizer object that will hold the current state and will update the parameters based on the computed gradients.

Constructing it

To construct an Optimizer you have to give it an iterable containing the parameters (all should be s) to optimize. Then, you can specify optimizer-specific options such as the learning rate, weight decay, etc.

optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
optimizer = optim.Adam([var1, var2], lr=0.0001)

Taking an optimization step

All optimizers implement a step() method, that updates the parameters. It can be used in two ways:

This is a simplified version supported by most optimizers. The function can be called once the gradients are computed using e.g. .

for input, target in dataset:
 ? ?optimizer.zero_grad()
 ? ?output = model(input)
 ? ?loss = loss_fn(output, target)
 ? ?loss.backward()
 ? ?optimizer.step()

Some optimization algorithms such as Conjugate Gradient and LBFGS need to reevaluate the function multiple times, so you have to pass in a closure that allows them to recompute your model. The closure should clear the gradients, compute the loss, and return it.

for input, target in dataset:
 ? ?def closure():
 ? ? ? ?optimizer.zero_grad() ?# 重置上一步中的梯度值
 ? ? ? ?output = model(input)
 ? ? ? ?loss = loss_fn(output, target)
 ? ? ? ?loss.backward()
 ? ? ? ?return loss
 ? ?optimizer.step(closure)

CLASS torch.optim.Optimizer(params, defaults)[SOURCE]

Base class for all optimizers.

Optimizer.add_param_group	Add a param group to the Optimizer s param_groups.
Optimizer.load_state_dict	Loads the optimizer state.
Optimizer.state_dict	Returns the state of the optimizer as a dict.
Optimizer.step	Performs a single optimization step (parameter update).
Optimizer.zero_grad	Resets the gradients of all optimized torch.Tensor s.

Adadelta	Implements Adadelta algorithm.
Adagrad	Implements Adagrad algorithm.
Adam	Implements Adam algorithm.
AdamW	Implements AdamW algorithm.
SparseAdam	SparseAdam implements a masked version of the Adam algorithm suitable for sparse gradients.
Adamax	Implements Adamax algorithm (a variant of Adam based on infinity norm).
ASGD	Implements Averaged Stochastic Gradient Descent.
LBFGS	Implements L-BFGS algorithm, heavily inspired by minFunc.
NAdam	Implements NAdam algorithm.
RAdam	Implements RAdam algorithm.
RMSprop	Implements RMSprop algorithm.
Rprop	Implements the resilient backpropagation algorithm.
SGD	Implements stochastic gradient descent (optionally with momentum).

ADADELTA

CLASS torch.optim.Adadelta(params, lr=1.0, rho=0.9, eps=1e-06, weight_decay=0, foreach=None, ***, maximize=False, differentiable=False)[SOURCE]

初学的话，只用设置params和lr即可，其他参数可用默认值，需要时再做学习

import torch
import torchvision
from torch import nn
from torch.nn import Sequential
from torch.utils.data import DataLoader
?
dataset = torchvision.datasets.CIFAR10(root="https://blog.csdn.net/m0_64711692/article/details/dataset", train=True, download=False,
 ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? transform=torchvision.transforms.ToTensor())
dataloader = DataLoader(dataset, batch_size=64, shuffle=True)
?
class XiaoMo(nn.Module):
 ? ?def __init__(self):
 ? ? ? ?super(XiaoMo, self).__init__()
?
 ? ? ? ?self.model1 = Sequential(
 ? ? ? ? ? ?nn.Conv2d(3, 32, 5, 1, 2),
 ? ? ? ? ? ?nn.MaxPool2d(2),
 ? ? ? ? ? ?nn.Conv2d(32, 32, 5, 1, 2),
 ? ? ? ? ? ?nn.MaxPool2d(2),
 ? ? ? ? ? ?nn.Conv2d(32, 64, 5, 1, 2),
 ? ? ? ? ? ?nn.MaxPool2d(2),
 ? ? ? ? ? ?nn.Flatten(),
 ? ? ? ? ? ?nn.Linear(64 * 4 * 4, 64),
 ? ? ? ? ? ?nn.Linear(64, 10)
 ? ? ?  )
?
 ? ?def forward(self, x):
 ? ? ? ?x = self.model1(x)
?
 ? ? ? ?return x
?
loss = nn.CrossEntropyLoss()
?
xiaomo = XiaoMo()
?
optim = torch.optim.SGD(xiaomo.parameters(), lr=0.01) ?# 创建优化器
?
for epoch in range(20): ?# 重复20轮
 ? ?loss_running = 0.0
 ? ?for imgs, target in dataloader: ?# 对数据进行一轮学习
 ? ? ? ?outputs = xiaomo(imgs)
 ? ? ? ?loss_res = loss(outputs, target)
?
 ? ? ? ?optim.zero_grad()
 ? ? ? ?loss_res.backward() ?# 计算梯度
?
 ? ? ? ?optim.step()
 ? ? ? ?loss_running += loss_res
?
 ? ?print(loss_running)