pytorch快速入门


title: Pytorch学习笔记-Pytorch快速入门

学习笔记和实现代码详见如下:

"""
pytorch官网:
https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
QUICKSTART
DATAT: FashionMNIST
@Author Yuzzz
"""

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

# Working with data
# 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

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


# Define model
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().to(device)
print(model)

# Optimizing the Model Parameters
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
        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(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")


# # 预测模型
# 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!")

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

# Loading Models
model = NeuralNetwork()
model.load_state_dict(torch.load("model.pth"))

# 结果测试
classes = [  # 重新设置标签信息
    "T-shirt/top",  # 0
    "Trouser",  # 1
    "Pullover",  # 2
    "Dress",  # 3
    "Coat",  # 4
    "Sandal",  # 5
    "Shirt",  # 6
    "Sneaker",  # 7
    "Bag",  # 8
    "Ankle boot",  # 9
]

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

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