張量(Tensor)🤖

在PyTorch中，儲存資料的單位稱為tensor。Tensor相當於NumPy的array，兩者之間的差別在於tensor可以在GPU上運算，而array不行。

這裡示範透過不同方式建立tensor：

import torch
import numpy as np

# 從list轉換成tensor
data = [[1, 2], [3, 4]]
x_data = torch.tensor(data)
print(x_data)

# 從 NumPy array轉換成tensor
np_array = np.array(data)
x_np = torch.from_numpy(np_array)
print(x_np)

tensor可以儲存不同維度的資料型態，如果讀者們對於線性代數有一點印象，就知道向量(vector)或矩陣(matrix)可以進行運算並表達不同形式的資料。例如：圖片、語音、影像等，都是多維度的資料。要分析這些維度的資料，就可以透過tensor將這些多維度資料轉換成可以運算的型態，然後再進行訓練和分析。

實戰演練 🔥

程式碼如下：

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

# 下載訓練數據集
training_data = datasets.FashionMNIST(
    root="data",
    train=True,
    download=True,
    transform=ToTensor(),
)

# 下載測試數據集
test_data = datasets.FashionMNIST(
    root="data",
    train=False,
    download=True,
    transform=ToTensor(),
)

batch_size = 64

# 建立數據加載器
train_dataloader = DataLoader(training_data, batch_size=batch_size)
test_dataloader = DataLoader(test_data, batch_size=batch_size)

# 確認數據加載器的輸出
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

# 設定訓練設備
device = (
    "cuda"
    if torch.cuda.is_available()
    else "mps"
    if torch.backends.mps.is_available()
    else "cpu"
)
print(f"Using {device} device")

# 定義神經網路模型
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)

# 設定損失函數和優化器
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)

# 定義訓練和測試函數
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)

        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")

# 訓練和測試模型
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!")

# 儲存和載入模型
torch.save(model.state_dict(), "model.pth")
print("Saved PyTorch Model State to model.pth")

model = NeuralNetwork().to(device)
model.load_state_dict(torch.load("model.pth"))

# 使用模型進行預測
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}"')

以上是透過PyTorch 框架訓練並評估神經網絡模型來分類 Fashion-MNIST dataset的範例。明天會針對此程式碼的詳細內容做介紹。

題外話😂

作為一個剛進入職場的新鮮人，昨天簽了一張賣身契哈哈(實習轉正合約)，一想到接下來要去當兵，可能再唸個研究所，剩下的時間基本上就被工作填滿😱，我每天出門上班的時間基本上都差不多，連騎車時哪些紅綠燈一定會過哪些一定會停下來都一模一樣，感覺很不可思議超級玄。

參考資料💯

PyTorch Tutorials