PyTorch 快速入门

最新推荐文章于 2024-09-03 10:36:30 发布

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#pytorch #深度学习 #python

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1. 加载数据

PyTorch 有两个处理数据的原语： torch.utils.data.DataLoader和torch.utils.data.Dataset. Dataset存储样本及其对应的标签，并使用DataLoader加载Dataset.

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

PyTorch 提供特定领域的库，例如TorchText、 TorchVision和TorchAudio，所有这些库都包含数据集。在本教程中，我们将使用 TorchVision 数据集。

在torchvision.datasets模块中包含了许多开放的Dataset，如 CIFAR、COCO（此处为完整列表）。在本教程中，我们使用 FashionMNIST 数据集。每个 TorchVision 都包含两个参数：transform和 target_transform分别用于修改样本和标签。

# 下载训练数据集
training_data = datasets.FashionMNIST(
    root="data",
    train=True,
    download=True,
    transform=ToTensor(),
)

# 下载测试数据集
test_data = datasets.FashionMNIST(
    root="data",
    train=False,
    download=True,
    transform=ToTensor(),
)

Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz

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Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw

Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz

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Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw

Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz

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Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw

Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz

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Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw

我们将Dataset作为参数传递给DataLoader。DataLoader包装了一个对Dataset遍历的迭代器，并支持自动批处理、采样、混洗和多进程数据加载。这里我们定义了batch size为64，即dataloader 在每次迭代时都会返回 64个数据和其对应的标签。

# 设置批次大小为64
batch_size = 64

# Create data loaders.
# 加载训练集
train_dataloader = DataLoader(training_data, batch_size=batch_size)
# 加载测试集
test_dataloader = DataLoader(test_data, batch_size=batch_size)

for X, y in test_dataloader:
    print(f"Shape of X [N, C, H, W]: {X.shape}")
    print(f"Shape of y: {y.shape} {y.dtype}")
    break

Shape of X [N, C, H, W]: torch.Size([64, 1, 28, 28])
Shape of y: torch.Size([64]) torch.int64

阅读有关在 PyTorch 中加载数据的更多信息。

2.创建模型

为了在 PyTorch 中定义神经网络，我们创建了一个继承自nn.Module的类。我们在__init__函数中定义网络层，并在forward函数中指定数据将如何通过网络。为了加速神经网络中的操作，我们将其移至 GPU（如果可用）。

# Get cpu or gpu device for training.
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using {device} device")

# 定义模型NeuralNetwork，该模型继承于nn.Module
class NeuralNetwork(nn.Module):
    def __init__(self):
        super().__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)
        )
        
    # 覆盖重写前向传播函数
    def forward(self, x):
        x = self.flatten(x)
        logits = self.linear_relu_stack(x)
        return logits
        
# 把模型传送到gpu
model = NeuralNetwork().to(device)
print(model)

Using cuda device
NeuralNetwork(
  (flatten): Flatten(start_dim=1, end_dim=-1)
  (linear_relu_stack): Sequential(
    (0): Linear(in_features=784, out_features=512, bias=True)
    (1): ReLU()
    (2): Linear(in_features=512, out_features=512, bias=True)
    (3): ReLU()
    (4): Linear(in_features=512, out_features=10, bias=True)
  )
)

在在 PyTorch 中构建神经网络的阅读有关更多信息。

3.优化模型参数

为了训练一个模型，我们需要一个损失函数和一个优化器。

# 损失函数
loss_fn = nn.CrossEntropyLoss()
# 优化器为 随机梯度下降SGG, 学习率为lr=1e-3
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)

在单个训练循环中，模型对训练数据集进行预测（分批输入），并反向传播预测误差以调整模型的参数。

# 训练函数
def train(dataloader, model, loss_fn, optimizer):
    # 获取数据集总大小
    size = len(dataloader.dataset)
    
    # 准备模型进行前向传播训练
    #  model.train()启用 batch normalization 和 dropout 。
    # 如果模型中有BN层（Batch Normalization）和 Dropout ，需要在 训练时 添加 
    # model.train() 是保证 BN 层能够用到 每一批数据 的均值和方差。对于 Dropout，     # model.train() 是 随机取一部分 网络连接来训练更新参数
    
    model.train()
    
    # 分批次把数据传入模型
    for batch, (X, y) in enumerate(dataloader):
        # 把数据加载到GPU
        X, y = X.to(device), y.to(device)
   
        # 预测结果
        pred = model(X)
        # 计算损失
        loss = loss_fn(pred, y)

        # 梯度归零
        optimizer.zero_grad()
        # 反向传播
        loss.backward()
        # 更新权重
        optimizer.step()

        if batch % 100 == 0:
            loss, current = loss.item(), batch * len(X)
            print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")

我们还根据测试数据集检查模型的性能，以确保它正在学习。

# 测试
def test(dataloader, model, loss_fn):
    size = len(dataloader.dataset)
    num_batches = len(dataloader)
    
    # model.eval()不启用 BatchNormalization 和 Dropout
    # 训练完 train 样本后，生成的模型 model 要用来测试样本。在 model(test) 之
    # 前，需要加上model.eval()，否则只要有输入数据，即使不训练，model 也会改变权
    # 值。这是model中含有的 batch normalization 层所带来的的性质
    
    model.eval()
    
    test_loss, correct = 0, 0
    
    # torch.no_grad在测试时不更新梯度
    with torch.no_grad():
        for X, y in dataloader:
            X, y = X.to(device), y.to(device)
            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")

训练过程在多次迭代（epochs）中进行。在每个epoch，模型都会学习参数以做出更好的预测。我们在每个epoch打印模型的准确性和损失；我们希望看到每个 epoch 的准确率增加和损失减少。

# 设置默认的迭代次数为5
epochs = 5
for t in range(epochs):
    print(f"Epoch {t+1}\n-------------------------------")
    # 训练
    train(train_dataloader, model, loss_fn, optimizer)
    # 测试
    test(test_dataloader, model, loss_fn)
print("Done!")

Epoch 1
-------------------------------
loss: 2.314893  [    0/60000]
loss: 2.295206  [ 6400/60000]
loss: 2.278248  [12800/60000]
loss: 2.261804  [19200/60000]
loss: 2.259621  [25600/60000]
loss: 2.220173  [32000/60000]
loss: 2.232810  [38400/60000]
loss: 2.199674  [44800/60000]
loss: 2.190488  [51200/60000]
loss: 2.160208  [57600/60000]
Test Error:
 Accuracy: 34.4%, Avg loss: 2.153365

Epoch 2
-------------------------------
loss: 2.172394  [    0/60000]
loss: 2.158403  [ 6400/60000]
loss: 2.104490  [12800/60000]
loss: 2.118272  [19200/60000]
loss: 2.084654  [25600/60000]
loss: 2.008146  [32000/60000]
loss: 2.046550  [38400/60000]
loss: 1.967219  [44800/60000]
loss: 1.970731  [51200/60000]
loss: 1.904694  [57600/60000]
Test Error:
 Accuracy: 56.2%, Avg loss: 1.898913

Epoch 3
-------------------------------
loss: 1.931435  [    0/60000]
loss: 1.901530  [ 6400/60000]
loss: 1.791456  [12800/60000]
loss: 1.836366  [19200/60000]
loss: 1.731514  [25600/60000]
loss: 1.669595  [32000/60000]
loss: 1.696603  [38400/60000]
loss: 1.593150  [44800/60000]
loss: 1.619061  [51200/60000]
loss: 1.514459  [57600/60000]
Test Error:
 Accuracy: 61.3%, Avg loss: 1.526715

Epoch 4
-------------------------------
loss: 1.593652  [    0/60000]
loss: 1.553579  [ 6400/60000]
loss: 1.409360  [12800/60000]
loss: 1.483904  [19200/60000]
loss: 1.365681  [25600/60000]
loss: 1.352317  [32000/60000]
loss: 1.362342  [38400/60000]
loss: 1.286407  [44800/60000]
loss: 1.323951  [51200/60000]
loss: 1.222849  [57600/60000]
Test Error:
 Accuracy: 63.8%, Avg loss: 1.248915

Epoch 5
-------------------------------
loss: 1.327686  [    0/60000]
loss: 1.306169  [ 6400/60000]
loss: 1.145820  [12800/60000]
loss: 1.253144  [19200/60000]
loss: 1.131874  [25600/60000]
loss: 1.150164  [32000/60000]
loss: 1.162306  [38400/60000]
loss: 1.102028  [44800/60000]
loss: 1.143301  [51200/60000]
loss: 1.062239  [57600/60000]
Test Error:
 Accuracy: 65.4%, Avg loss: 1.083091

Done!

阅读有关训练模型的更多信息。

4.保存模型

保存模型的常用方法是序列化内部状态字典（包含模型参数）。

torch.save(model.state_dict(), "model.pth")
print("Saved PyTorch Model State to model.pth")

Saved PyTorch Model State to model.pth

5.加载模型

加载模型的过程包括重新创建模型结构并将状态字典加载到其中。

# 重新创建模型
model = NeuralNetwork()
# 加载权重参数
model.load_state_dict(torch.load("model.pth"))

<All keys matched successfully>

该模型现在可用于进行预测。

# 预测类的标签
classes = [
    "T-shirt/top",
    "Trouser",
    "Pullover",
    "Dress",
    "Coat",
    "Sandal",
    "Shirt",
    "Sneaker",
    "Bag",
    "Ankle boot",
]
# 初始化模型
model.eval()
# 准备预测数据
x, y = test_data[0][0], test_data[0][1]

# 在预测时不更新梯度
with torch.no_grad():
    # 调用模型执行预测
    pred = model(x)
    predicted, actual = classes[pred[0].argmax(0)], classes[y]
    print(f'Predicted: "{predicted}", Actual: "{actual}"')

Predicted: "Ankle boot", Actual: "Ankle boot"