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
# 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"