pytorch的FashionMNIST

目录

pytorch的FashionMNIST项目从加载数据到训练模型评估到模型保存模型加载及预测

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

training_data = datasets.FashionMNIST(root = "data", train = True, download = True, transform = ToTensor(),)
test_data = datasets.FashionMNIST(root = "data", train = False, download = True, transform = ToTensor(),)

batch_size = 64

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


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)
        
        pred = model(X)
        loss = loss_fn(pred, y)
        
        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")
    
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!")

torch.save(model.state_dict(), "model.pth")
print("Saved PyTorch Model State to model.pth")
model = NeuralNetwork()
model.load_state_dict(torch.load("model.pth"))
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():
    pred = model(x)
    predicted, actual = classes[pred[0].argmax(0)], classes[y]
    print(f'Predicted: "{predicted}", Actual: "{actual}"')