인공 신경망 코드로 구현해서 다중 분류해보기 (2)

2024년 03월 17일 12시 02분 55초에 업로드 된 글입니다.

작성자: 재형이

오늘 저녁 약속이 있는데 잘 하고 올게요~

오늘은 지난번에 했던 데이터 세트와 다른 데이터로 복습할겸 인공신경망을 구성해서 다시 학습을 해볼 것이다
추가로 학습이 완료된 모델을 저장하는 방법도 실습해볼 것이다
- 모델을 저장하는 방법에는 두가지가 있다
  1. 모델의 파라미터만 저장하는 방법
    - 이 방법은 모델의 계층 구조를 알고 있어야만 나중에 다시 해당 모델을 사용할 수 있음
  2. 모델 전체를 저장하는 방법
사용할 데이터 세트 : Fashion MNIST

torchvision.datasets — Torchvision master documentation

torchvision.datasets All datasets are subclasses of torch.utils.data.Dataset i.e, they have __getitem__ and __len__ methods implemented. Hence, they can all be passed to a torch.utils.data.DataLoader which can load multiple samples in parallel using torch.

pytorch.org

참고
- https://tutorials.pytorch.kr/beginner/basics/buildmodel_tutorial.html
  
  신경망 모델 구성하기
  
  파이토치(PyTorch) 기본 익히기|| 빠른 시작|| 텐서(Tensor)|| Dataset과 Dataloader|| 변형(Transform)|| 신경망 모델 구성하기|| Autograd|| 최적화(Optimization)|| 모델 저장하고 불러오기 신경망은 데이터에 대한 연
  
  tutorials.pytorch.kr
- https://tutorials.pytorch.kr/beginner/basics/data_tutorial.html
  
  Dataset과 DataLoader
  
  파이토치(PyTorch) 기본 익히기|| 빠른 시작|| 텐서(Tensor)|| Dataset과 DataLoader|| 변형(Transform)|| 신경망 모델 구성하기|| Autograd|| 최적화(Optimization)|| 모델 저장하고 불러오기 데이터 샘플을 처리하는 코
  
  tutorials.pytorch.kr

복습

TensorDataset과 DataLoader
- 입력 데이터를 쉽게 처리하고, 배치 단위로 잘러서 학습할 수 있게 도와주는 모듈
- Dataset : 학습시 사용하는 feature와 target의 pair로 이루어짐
- DataLoader: 학습 시 각 인스턴스에 쉽게 접근할 수 있도록 순회 가능한 객체(iterable)를 생성
- DataLoader가 하는 역할
  - shuffling
  - batch ...
Device 설정
- 일반적으로 인공신경망의 학습은 (가능하다면) GPU를 사용하는 것이 바람직함
- GPU를 사용하여 학습을 진행하도록 명시적으로 작성 필요
- 연산 유형에 따라 GPU에서 수행이 불가능한 경우도 존재하는데, 그럴 경우도 마찬가지로 명시적으로 어떤 프로세서에서 연산을 수행해야하는지 코드로 작성해야함
신경망 생성
- torch.nn 패키지는 신경망 생성 및 학습 시 설정해야하는 다양한 기능을 제공
- 신경망을 nn.Module을 상속받아 정의
  - __ init __(): 신경망에서 사용할 layer를 초기화하는 부분
  - forward(): feed foward 연산 수행 시, 각 layer의 입출력이 어떻게 연결되는지를 지정
Model compile
- 학습 시 필요한 정보들(loss function, optimizer)을 선언
- 일반적으로 loss와 optimizer는 아래와 같이 변수로 선언하고, 변수를 train/test 시 참고할 수 있도록 매개변수로 지정해줌
Train
- 신경망의 학습과정을 별도의 함수로 구성하는 것이 일반적
  - feed forward → loss → error back propagation → print(진행상황) 또는 로깅 → (반복)
Test
- 학습과정과 비슷하나 error back propagate하는 부분이 없음
  - feed forward → loss → print(진행상황) 또는 로깅 → (반복)
Iteration
- 신경망 학습은 여러 epochs을 반복해서 수행하면서 모델을 구성하는 최적의 파라미터를 찾음
- 지정한 epochs 수만큼 학습과정과 평가과정을 반복하면서, 모델의 성능(loss, accuracy 등)을 체크함

Fashion MNIST Classifier

Fashion MNIST 데이터셋을 사용하여 옷의 품목을 구분하는 분류기를 신경망을 사용하여 구현

[Step1] Load libraries & Datasets

torch.nn : 신경망을 생성하기 위한 기본 재료들을 제공(Modules, Sequential, Layer, Activations, Loss, Dropout...)
torchvision.datasets : torchvision.transforms를 사용해 변형이 가능한 형태, feature와 label을 반환
torchvision.transforms
- ToTensor() : ndarray를 FloatTensor로 변환하고 이미지 픽셀 크기를 [0., 1.]범위로 조정(scale)

import numpy as np
import matplotlib.pyplot as plt

import torch
from torch.utils.data import DataLoader
from torch import nn

from torchvision import datasets
from torchvision.transforms import ToTensor

# FashionMNIST 데이터 불러오기
training_data = datasets.FashionMNIST(
    root = 'data',
    train = True,
    download = True,
    transform = ToTensor(),
)

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

Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz
100%|██████████| 26421880/26421880 [00:02<00:00, 9298111.12it/s]
Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw

Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz
100%|██████████| 29515/29515 [00:00<00:00, 163915.28it/s]
Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw

Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
100%|██████████| 4422102/4422102 [00:01<00:00, 3134603.50it/s]
Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw

Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
100%|██████████| 5148/5148 [00:00<00:00, 19700982.66it/s]
Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw

[Step2] Create DataLoader

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

# Device 설정
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f'device = {device}')

device = cuda

EDA(Exploratory Data Analysis, 탐색적 데이터 분석)

print(training_data, '\n------------------\n', test_data)

training_data[0]

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, label = training_data[0]
plt.imshow(img.squeeze(), cmap='gray')
print(f'label={label}')

labels_map = {
    0: "T-Shirt",
    1: "Trouser",
    2: "Pullover",
    3: "Dress",
    4: "Coat",
    5: "Sandal",
    6: "Shirt",
    7: "Sneaker",
    8: "Bag",
    9: "Ankle Boot",
}

figure = plt.figure(figsize = (20, 8))
cols, rows = 5, 2

for i in range(1, cols * rows +1):
    sample_idx = torch.randint(len(training_data), size=(1,)).item()
    img, label = training_data[sample_idx]
    figure.add_subplot(rows, cols, i)
    plt.title(labels_map[label])
    print(labels_map[label])
    plt.axis('off')
    plt.imshow(img.squeeze(), cmap='gray')
plt.show()

Dataset FashionMNIST
    Number of datapoints: 60000
    Root location: data
    Split: Train
    StandardTransform
Transform: ToTensor()
------------------
Dataset FashionMNIST
    Number of datapoints: 10000
    Root location: data
    Split: Test
    StandardTransform
Transform: ToTensor()
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9)
Feature batch shape: torch.Size([64, 1, 28, 28])
Labels batch shape: torch.Size([64])

[Step3] Set Network Structure

class NeuralNetwork(nn.Module):
    def __init__(self):
        super(NeuralNetwork, self).__init__()
        self.flatten = nn.Flatten()
        self.classifier = nn.Sequential(
            nn.Linear(28*28, 128),
            nn.ReLU(),
            nn.Dropout(0.2),
            nn.Linear(128, 10),
        )

    def forward(self, x):
        x = self.flatten(x)
        output = self.classifier(x)
        return output

[Step4] Create Model instance

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

NeuralNetwork(
  (flatten): Flatten(start_dim=1, end_dim=-1)
  (classifier): Sequential(
    (0): Linear(in_features=784, out_features=128, bias=True)
    (1): ReLU()
    (2): Dropout(p=0.2, inplace=False)
    (3): Linear(in_features=128, out_features=10, bias=True)
  )
)

Model 테스트

X = torch.rand(1, 28, 28, device = device)
output = model(X)
print(f'모델 출력 결과: {output}\n')
pred_probab = nn.Softmax(dim=1)(output)
print(f'Softmax 결과: {pred_probab}\n')
y_pred = pred_probab.argmax()
print(y_pred)

모델 출력 결과: tensor([[ 0.3374, -0.2491,  0.2523, -0.0858, -0.0171, -0.0398, -0.0953, -0.1535,
         -0.0450,  0.0389]], device='cuda:0', grad_fn=<AddmmBackward0>)

Softmax 결과: tensor([[0.1388, 0.0772, 0.1275, 0.0909, 0.0974, 0.0952, 0.0901, 0.0850, 0.0947,
         0.1030]], device='cuda:0', grad_fn=<SoftmaxBackward0>)

tensor(0, device='cuda:0')

[Step5] Model compile

# Loss
loss = nn.CrossEntropyLoss()
# Optimizer
learning_rate = 1e-3  #0.001
optimizer = torch.optim.Adam(model.parameters(), lr = learning_rate)

[Step6] Set train loop

def train_loop(train_loader, model, loss_fn, optimizer):
    size = len(train_loader.dataset)

    for batch, (X, y) in enumerate(train_loader):
        X, y = X.to(device), y.to(device)
        pred = model(X)

        # 손실 계산
        loss = loss_fn(pred, y)

        # 역전파
        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}')

[Step7] Set test loop

def test_loop(test_loader, model, loss_fn):
    size = len(test_loader.dataset)
    num_batches = len(test_loader)
    test_loss, correct = 0, 0

    with torch.no_grad():
        for X, y in test_loader:
            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")

[Step8] Run model

epochs = 10

for i in range(epochs) :
    print(f"Epoch {i+1} \n------------------------")
    train_loop(train_dataloader, model, loss, optimizer)
    test_loop(test_dataloader, model, loss)
print("Done!")

Epoch 1
------------------------
loss: 2.324765  [    0]/60000
loss: 0.691231  [ 6400]/60000
loss: 0.471458  [12800]/60000
loss: 0.613083  [19200]/60000
loss: 0.444306  [25600]/60000
loss: 0.549569  [32000]/60000
loss: 0.454276  [38400]/60000
loss: 0.423303  [44800]/60000
loss: 0.461990  [51200]/60000
loss: 0.667872  [57600]/60000
Test Error:
Accuracy: 81.8%, Avg loss: 0.497612

Epoch 2
------------------------
loss: 0.371661  [    0]/60000
loss: 0.409686  [ 6400]/60000
loss: 0.383233  [12800]/60000
loss: 0.420215  [19200]/60000
loss: 0.388781  [25600]/60000
loss: 0.447195  [32000]/60000
loss: 0.476783  [38400]/60000
loss: 0.300698  [44800]/60000
loss: 0.307176  [51200]/60000
loss: 0.498841  [57600]/60000
Test Error:
Accuracy: 84.8%, Avg loss: 0.427579

• • •

Epoch 9
------------------------
loss: 0.353967  [    0]/60000
loss: 0.383568  [ 6400]/60000
loss: 0.510100  [12800]/60000
loss: 0.307564  [19200]/60000
loss: 0.241812  [25600]/60000
loss: 0.327063  [32000]/60000
loss: 0.312592  [38400]/60000
loss: 0.401675  [44800]/60000
loss: 0.229188  [51200]/60000
loss: 0.273675  [57600]/60000
Test Error:
Accuracy: 87.4%, Avg loss: 0.362324

Epoch 10
------------------------
loss: 0.293197  [    0]/60000
loss: 0.239528  [ 6400]/60000
loss: 0.118810  [12800]/60000
loss: 0.316047  [19200]/60000
loss: 0.220012  [25600]/60000
loss: 0.263426  [32000]/60000
loss: 0.263754  [38400]/60000
loss: 0.349957  [44800]/60000
loss: 0.210180  [51200]/60000
loss: 0.347154  [57600]/60000
Test Error:
Accuracy: 86.8%, Avg loss: 0.367648

Done!

[Step9] Save & load model

parameter만 저장하고 불러오기

torch.save(model.state_dict(), 'model_weights.pth')

model2 = NeuralNetwork().to(device)
print(model2)

model2.load_state_dict(torch.load('model_weights.pth'))

model2.eval()
test_loop(test_dataloader, model2, loss)

NeuralNetwork(
  (flatten): Flatten(start_dim=1, end_dim=-1)
  (classifier): Sequential(
    (0): Linear(in_features=784, out_features=128, bias=True)
    (1): ReLU()
    (2): Dropout(p=0.2, inplace=False)
    (3): Linear(in_features=128, out_features=10, bias=True)
  )
)
<All keys matched successfully>
Test Error:
Accuracy: 88.0%, Avg loss: 0.338681

Model 전체를 저장하고 불러오기

torch.save(model, 'model.pth')

model3 = torch.load('model.pth')

model3.eval()
test_loop(test_dataloader, model3, loss)

Test Error:
Accuracy: 88.0%, Avg loss: 0.338681

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