已完结…
记录PyTorch 官方入门教程中的大部分代码和对代码的解释注释

暂时内容包括：优化模型参数，保存并加载模型参数

参考网站：https://pytorch.org/tutorials/beginner/basics/optimization_tutorial.html

优化模型参数

import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor, Lambda

training_data = datasets.FashionMNIST(
    root="data",
    train=True,
    download=True,
    transform=ToTensor()
)

test_data = datasets.FashionMNIST(
    root="data",
    train=False,
    download=True,
    transform=ToTensor()
)

train_dataloader = DataLoader(training_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)

class NeuralNetwork(nn.Module):
    def __init__(self):
        super(NeuralNetwork, self).__init__()
        self.flatten = nn.Flatten()
        self.linear_relu_stack = nn.Sequential(
            nn.Linear(28*28, 512),
            nn.ReLU(),
            nn.Linear(512, 512),
            nn.ReLU(),
            nn.Linear(512, 10),
        )

    def forward(self, x):
        x = self.flatten(x)
        logits = self.linear_relu_stack(x)
        return logits

model = NeuralNetwork()

与之前介绍的相似，前面是设置训练集和测试集（引入的是现成的给的datasets数据集）
之后设置线性全连接神经网络，打包为model

learning_rate = 1e-1
batch_size = 64
epochs = 10

这部分是超参数，分别控制着学习率，批处理的数据大小，训练集中数据被训练的次数
(在本文的代码中实际上没有传入batch_size,我现在还不知道到底在哪里设置这个batch_size，但是猜测应该是在生成数据集时进行设置)

def train_loop(dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)
    for batch, (X, y) in enumerate(dataloader):
        # Compute prediction and loss
        pred = model(X)
        #该函数有些传入的参数的定义如下
        #optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
        #loss_fn = nn.CrossEntropyLoss()
        loss = loss_fn(pred, y.to(device))

        # Backpropagation
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if batch % 100 == 0:
        	#这里即是每批64个，训练100批共6400个后将loss和准确率进行打印
            loss, current = loss.item(), batch * len(X)
            print(f"loss: {
      loss:>7f} [{
      current:>5d}/{
      size:>5d}]")

在这里设置循环训练函数，传入dataloader（来自于导入的库）model，loss_fn和optimizer
之后用enumerate迭代器进行迭代，把数据集一个个传入写的神经网络中
之后用优化器torch.optim.SGD存储参数然后根据梯度更新优化参数

再接着在下面三行进行反向传播，具体实现和背后原理参考下文链接

（后面的print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]")，具体用法参考python里的输出格式）

函数参考：enumerate
torch.optim.SGD
Backpropagation
输出格式

def test_loop(dataloader, model, loss_fn):
    size = len(dataloader.dataset)
    num_batches = len(dataloader)
    test_loss, correct = 0, 0

    with torch.no_grad():
        for X, y in dataloader:
            pred = model(X)
            test_loss += loss_fn(pred, y.to(device)).item()
            correct += (pred.argmax(1) == y.to(device)).type(torch.float).sum().item()

    test_loss /= num_batches
    correct /= size
    print(f"Test Error: \n Accuracy: {
      (100*correct):>0.1f}%, Avg loss: {
      test_loss:>8f} \n")

这里是测试循环函数的编写
y对应着准确值，pred是我们之前的神经网络对这个X的预测值
然后计算交叉熵损失loss和准确预测的次数correct，得出平均损失和准确率
tips:这里设置不用反向传播是因为在这里并不需要求梯度，所以为了省时间，设置torch.no_grad

epochs = 2
for t in range(epochs):
    print(f"Epoch {
      t+1}\n-------------------------------")
    train_loop(train_dataloader, model, loss_fn, optimizer)
    test_loop(test_dataloader, model, loss_fn)
print("Done!")

然后循环给定次数

保存并加载模型参数

本章节比较简单，直接给参考
参考网站：保存并加载模型参数

下面是在上文训练模型参数的基础上，将其保存，再在下次加载时进行读取

import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor, Lambda

if torch.cuda.is_available:
    device = "cuda"
    print("cuda加速已开启")
else:
    device = "cpu"

training_data = datasets.FashionMNIST(
    root="data",
    train=True,
    download=True,
    transform=ToTensor()
)

test_data = datasets.FashionMNIST(
    root="data",
    train=False,
    download=True,
    transform=ToTensor()
)

train_dataloader = DataLoader(training_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)

class NeuralNetwork(nn.Module):
    def __init__(self):
        super(NeuralNetwork, self).__init__()
        self.flatten = nn.Flatten()
        self.linear_relu_stack = nn.Sequential(
            nn.Linear(28*28, 512),
            nn.ReLU(),
            nn.Linear(512, 512),
            nn.ReLU(),
            nn.Linear(512,256),
            nn.ReLU(),
            nn.Linear(256, 128),
            nn.ReLU(),
            nn.Linear(128,64),
            nn.ReLU(), 
            nn.Linear(64,32),
            nn.ReLU(), 
            nn.Linear(32,20),
        )

    def forward(self, x):
        x = self.flatten(x).to(device)
        logits = self.linear_relu_stack(x)
        return logits

model = NeuralNetwork().to(device)
model.load_state_dict(torch.load('data_1'))
model.eval()
#

learning_rate = 1e-1
batch_size = 64
epochs = 10
# 
# 
# 
def train_loop(dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)
    for batch, (X, y) in enumerate(dataloader):
        # Compute prediction and loss
        pred = model(X)
        loss = loss_fn(pred, y.to(device))

        # Backpropagation
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if batch % 500 == 0:
            loss, current = loss.item(), batch * len(X)
            print(f"loss: {
      loss:>7f} [{
      current:>5d}/{
      size:>5d}]")


def test_loop(dataloader, model, loss_fn):
    size = len(dataloader.dataset)
    num_batches = len(dataloader)
    test_loss, correct = 0, 0

    with torch.no_grad():
        for X, y in dataloader:
            pred = model(X)
            test_loss += loss_fn(pred, y.to(device)).item()
            correct += (pred.argmax(1) == y.to(device)).type(torch.float).sum().item()

    test_loss /= num_batches
    correct /= size
    print(f"Test Error: \n Accuracy: {
      (100*correct):>0.1f}%, Avg loss: {
      test_loss:>8f} \n")

loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

epochs = 2
for t in range(epochs):
    print(f"Epoch {
      t+1}\n-------------------------------")
    train_loop(train_dataloader, model, loss_fn, optimizer)
    test_loop(test_dataloader, model, loss_fn)
torch.save(model.state_dict(),'data_1')
print("Done!")

在这段代码中
保存模型参数：torch.save(model.state_dict(),‘data_1’)
注意后面存的是文件路径
读取模型参数：
要加载模型权重，需要先创建同一模型的实例，然后使用方法加载参数
model = NeuralNetwork().to(device)
model.load_state_dict(torch.load(‘data_1’))
model.eval()

其他方法：
torch.save(model, ‘data_1’)
model = torch.load(‘data_1’)