PyTorch ​

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

# Download training data from open datasets.
training_data = datasets.FashionMNIST(
  root="data",
  train=True,
  download=True,
  transform=ToTensor(),
)

# Download test data from open datasets.
test_data = datasets.FashionMNIST(
  root="data",
  train=False,
  download=True,
  transform=ToTensor(),
)

batch_size = 64

# Create data loaders.
train_dataloader = DataLoader(training_data, batch_size=batch_size)
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

device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else "cpu"
print(f"Using {device} device")

# Define model
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)
    )

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

model = NeuralNetwork().to(device)
print(model)

loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)

def train(dataloader, model, loss_fn, optimizer):
  size = len(dataloader.dataset)
  model.train()
  for batch, (X, y) in enumerate(dataloader):
    X, y = X.to(device), y.to(device)

    # Compute prediction error
    pred = model(X)
    loss = loss_fn(pred, y)

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

    if batch % 100 == 0:
      loss, current = loss.item(), (batch + 1) * len(X)
      print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")

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

epochs = 5
for t in range(epochs):
  print(f"Epoch {t+1}\n-------------------------------")
  train(train_dataloader, model, loss_fn, optimizer)
  test(test_dataloader, model, loss_fn)
print("Done!")

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

# 重载
model = NeuralNetwork().to(device)
model.load_state_dict(torch.load("model.pth", weights_only=True))

classes = [
    "T-shirt/top",
    "Trouser",
    "Pullover",
    "Dress",
    "Coat",
    "Sandal",
    "Shirt",
    "Sneaker",
    "Bag",
    "Ankle boot",
]

model.eval()
x, y = test_data[0][0], test_data[0][1]
with torch.no_grad():
  x = x.to(device)
  pred = model(x)
  predicted, actual = classes[pred[0].argmax(0)], classes[y]
  print(f'Predicted: "{predicted}", Actual: "{actual}"')

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

import os
import pandas as pd
from torch.utils.data import Dataset
from torchvision.io import decode_image

class CustomImageDataset(Dataset):
  # 实例化、初始化
  def __init__(self, annotations_file, img_dir, transform=None, target_transform=None):
    self.img_labels = pd.read_csv(annotations_file)
    self.img_dir = img_dir
    self.transform = transform
    self.target_transform = target_transform

  # 获取长度方法
  def __len__(self):
    return len(self.img_labels)

  # 获取单个数据方法
  def __getitem__(self, idx):
    img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0])
    image = decode_image(img_path)
    label = self.img_labels.iloc[idx, 1]
    if self.transform:
        image = self.transform(image)
    if self.target_transform:
        label = self.target_transform(label)
    return image, label

from torch.utils.data import DataLoader

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

# Display image and label.
train_features, train_labels = next(iter(train_dataloader))
print(f"Feature batch shape: {train_features.size()}")
print(f"Labels batch shape: {train_labels.size()}")
img = train_features[0].squeeze()
label = train_labels[0]
plt.imshow(img, cmap="gray")
plt.show()
print(f"Label: {label}")