【笔记】PyTorch快速入门： 训练，保存和加载模型

优化模型参数

有了模型，接下来要进行训练、验证和测试。

前置代码

首先要加载数据，建立模型

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()

超参数

定义三个超参数

• Epochs数：数据集迭代次数
• Batch size：单次训练样本数
• Learning Rate：学习速度

优化循环

接下来，我们进行多轮的优化，每轮叫一个epoch

每个epoch包含两部分，训练loop和验证/测试loop

Loss Function

PyTorch提供常见的Loss Functions

• nn.MSELoss (Mean Square Error)
• nn.NLLLoss (Negative Log Likelihood)
• nn.CrossEntropyLoss (交叉熵)

我们使用交叉熵，把原始结果logits放进去

loss_fn = nn.CrossEntropyLoss()

Optimizer

初始化优化器，给它需要优化的参数，和超参数Learning Rate

optimizer = torch.optim.SGC(model.parameters(),lr = learning_rate)

优化器在每个epoch里做三件事

• optimizer.zero_grad()将梯度清零
• loss.backward()进行反向传播
• optimizer.step()根据梯度调整参数

完整实现

在train_loop里训练，test_loop里测试

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()

learning_rate = 1e-3
batch_size = 64
epochs = 5

# Initialize the loss function
loss_fn = nn.CrossEntropyLoss()

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

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)

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

        if batch % 100 == 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).item()
            correct += (pred.argmax(1) == y).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 = 10
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!")

保存和加载模型

如何保存和加载训好的模型？

import torch
import torchvision.models as models

保存和加载模型权重

通过torch.save方法，可以将模型保存到state_dict类型的字典里。

model = models.vgg16(pretrained=True)
torch.save(model.state_dict(), 'model_weights.pth')

而要加载的话，需要先构造相同类型的模型，然后把参数加载进去

model = models.vgg16() # we do not specify pretrained=True, i.e. do not load default weights
model.load_state_dict(torch.load('model_weights.pth'))
model.eval()

注意，一定要调一下model.eval()，防止后续出错

保存和加载模型

上一种方法里，需要先实例化模型，再导入权值

有没有办法直接保存和加载整个模型呢？

我们用不传mode.state_dict()参数，改为model

保存方式：

torch.save(model,'model.pth')

加载方式：

model = torch.load('model.pth')