Python(31)- 데이터 로더

*이 글을 읽기전에 작성자 개인의견이 있으니, 다른 블로그와 교차로 읽는것을 권장합니다.*

1. 데이터 로더(Data Loader)

• 데이터의 양이 많을 때 배치 단위로 학습하는 방법을 제공

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2. 손글씨 인식 모델 만들기

import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split

# 런타임 유형 변경 -> GPU 로 변경
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(device)

digits = load_digits()

x_data = digits['data']
y_data = digits['target']

print(x_data.shape)
print(y_data.shape)

fig, axes = plt.subplots(nrows=2, ncols=5, figsize=(14,8))

for i, ax in enumerate(axes.flatten()):
    ax.imshow(x_data[i].reshape((8,8)), cmap='gray')
    ax.set_title(y_data[i])
    ax.axis('off')

x_data = torch.FloatTensor(x_data)
y_data = torch.LongTensor(y_data)

print(x_data.shape)
print(y_data.shape)

x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.2, random_state=2024)
print(x_train.shape, y_train.shape)
print(x_test.shape, y_test.shape)

loader = torch.utils.data.DataLoader(
    dataset=list(zip(x_train, y_train)),
    batch_size=64,
    shuffle=True,
    drop_last=False
)

imgs, labels = next(iter(loader))
fig, axes = plt.subplots(nrows=8, ncols=9, figsize=(14,14))

for ax, img, label in zip(axes.flatten(), imgs, labels):
    ax.imshow(img.reshape((8,8)), cmap='gray')
    ax.set_title(str(label))
    ax.axis('off')

model = nn.Sequential(
    nn.Linear(64, 10)
)

optimizer = optim.Adam(model.parameters(), lr=0.01)
epochs = 50

for epoch in range(epochs + 1):
    sum_losses = 0
    sum_accs = 0

    for x_batch, y_batch in loader:
        y_pred = model(x_batch)
        loss = nn.CrossEntropyLoss()(y_pred, y_batch)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        sum_losses = sum_losses + loss
        y_prob = nn.Softmax(1)(y_pred)
        y_pred_index = torch.argmax(y_prob, axis=1)
        acc = (y_batch == y_pred_index).float().sum() / len(y_batch) * 100
        sum_accs = sum_accs + acc

    avg_loss = sum_losses / len(loader)
    avg_acc = sum_accs / len(loader)
    print(f'Epoch {epoch:4d}/{epochs} Loss: {avg_loss:.6f} Accuracy: {avg_acc:.2f}%')

plt.imshow(x_test[10].reshape((8,8)), cmap='gray')
print(y_test[10])

y_pred = model(x_test)
y_pred[10]

y_prob = nn.Softmax(1)(y_pred)
y_prob[10]

for i in range(10):
    print(f'숫자 {i}일 확률: {y_prob[10][i]:.2f}')
# 머신러닝 모델은 정답이 아니라 확률을 내보내기에, 사용자가 직접 확률을 보고 후처리 필요

y_pred_index = torch.argmax(y_prob, axis=1)
accuracy = (y_test == y_pred_index).float().sum() / len(y_test) * 100
print(f'테스트 정확도는 {accuracy:.2f}%입니다')