1. Deep Learning
딥러닝의 구분
- AI (인공지능)
- Mimic human intelligence, 사람의 지능을 모방
- ML (기계학습)
- Data-driven approach, 데이터를 기반으로 무언가를 (기계)학습
- DL (딥러닝)
- Neural Networks, 그 안에서 뉴럴 네트워크를 사용하여 데이터를 가지고 무언가를 학습하는 세부 분야
딥러닝의 Key Components
- data
- the model can learn from
- model
- how to transform the data
- loss function
- quantifies the bbadness of the model
- algorithm
- to adjust the parameters to minimize the loss
- to adjust the parameters to minimize the loss
기타
- loss function을 줄이기만 하는 것이 목적이 아니라, 모델이 학습하지 않았던 테스트 데이터/실제 사용 환경에서 잘 동작하는 것이 목적..! -> 해당 parameter 값(weight, bias)을 찾는 것이 중요할 것
- 모델의 layer를 깊게 쌓으면 문제가 생기는 이유 또한, train error는 적게 나오지만, test data에서 성능이 잘 안 나오기 때문 (+ 이는 ResNet 등장 후 보완이 됨)
Neural Networks
- 인간의 뇌 구조를 모방한 구조
- 뉴럴 네트워크는 함수를 근사하는 모델(
Function Approximator) - 비선형 변환의 연속
Neural networks are function approximators that stack affine transformations followed by nonlinear transformations.
MLP(다층퍼셉트론) PyTorch로 구현해보기
- Evaluate Function 정의
Eval과정은with torch.no_grad():설정해줘야 함- 전체 Dataset에서 정의한
iterator통해, 돌면서Batch Size만큼 뽑아내며eval과정 처리 .to(device)이용해Batch Size만큼의 X 데이터/y 데이터로부터 예측 값/정답 값 정의view()함수 : 필요 시, reshape
def func_eval(model,data_iter,device):
with torch.no_grad():
model.eval() # evaluate (affects DropOut and BN)
n_total,n_correct = 0,0
for batch_in,batch_out in data_iter:
y_trgt = batch_out.to(device)
model_pred = model(batch_in.view(-1, 28*28).to(device))
_,y_pred = torch.max(model_pred.data,1)
n_correct += (y_pred == y_trgt).sum().item()
n_total += batch_in.size(0)
val_accr = (n_correct/n_total)
model.train() # back to train mode
return val_accr
print ("Done")- Train 코드
-
전체 Dataset에서 정의한
iterator통해, 돌면서Batch Size만큼 뽑아내며train과정 처리 -
[1] 포워드 과정
- (1)위에서 정의한
CustomModel의forward(self, x)함수와 (2)선택한loss함수(크로스엔트로피 등)에 배치를 돌면서피드포워드
- (1)위에서 정의한
-
[2] 옵티마이저 및 로스 업데이트 과정 (
backpropagation)- (1)
옵티마이저.zero_grad()로 값 리셋, (2)출력로스.backward()로 미분값 백워드, (3)옵티마이저.step()로 값 업데이트
- (1)
- code
print ("Start training.")
M.init_param() # initialize parameters
M.train()
EPOCHS,print_every = 10,1
for epoch in range(EPOCHS):
loss_val_sum = 0
for batch_in,batch_out in train_iter:
# Forward path
y_pred = M.forward(batch_in.view(-1, 28*28).to(device))
loss_out = loss(y_pred,batch_out.to(device))
# Update
optm.zero_grad() # reset gradient
loss_out.backward() # backpropagate
optm.step() # optimizer update
loss_val_sum += loss_out
loss_val_avg = loss_val_sum/len(train_iter)
# Print
if ((epoch%print_every)==0) or (epoch==(EPOCHS-1)):
train_accr = func_eval(M,train_iter,device)
test_accr = func_eval(M,test_iter,device)
print ("epoch:[%d] loss:[%.3f] train_accr:[%.3f] test_accr:[%.3f]."%
(epoch,loss_val_avg,train_accr,test_accr))
print ("Done") 2. Optimization
-
Generalization
- 일반화 성능을 높이자
- 학습 데이터 - 테스트 데이터 간 성능의 차이(Generalization Gap)가 적을 때를 지향
- underfitting vs. overfitting 용어와 관련
-
Cross-validations
- 학습 데이터 / 테스트 데이터를 k개로 나누어서, (k-1)개로 training, 1개로 validation 일부만 학습에 사용
- Cross-validations을 이용해 최적의 하이퍼 파라미터 값을 찾고, 이 하이퍼 파라미터 값을 가지고 모델을 학습하는 방법
-
Bias와 Variance
- 편향과 분산
- bias와 variance는 Trade-off 관계를 갖는다.
- Cost를 최소화하는 문제는, 즉, bias, variance, noise 세 가지를 최소화하는 것
- Bootstrapping
- 학습 데이터가 고정되어 있을 때, 그 안에서 subsampling을 통해서 학습 데이터를 여러 개로 만들고, 이를 통해 만든 여러 개의 모델/metric을 통해, 전체 모델의 일치성/불확실성을 파악하는 접근법
선형회귀문제(음성신호 데이터) PyTorch로 optimizer 비교해보기
- optimizers 정의
torch.optim라이브러리 활용해 선언momentum옵티마이저는momentum=(확률값)옵션 추가
- code
### 임포트 : import torch.optim as optim
LEARNING_RATE = 1e-2
# Instantiate models
model_sgd = Model(name='mlp_sgd',xdim=1,hdims=[64,64],ydim=1).to(device)
model_momentum = Model(name='mlp_momentum',xdim=1,hdims=[64,64],ydim=1).to(device)
model_adam = Model(name='mlp_adam',xdim=1,hdims=[64,64],ydim=1).to(device)
# Optimizers
loss = nn.MSELoss()
optm_sgd = optim.SGD(model_sgd.parameters(), lr=LEARNING_RATE)
optm_momentum = optim.SGD(model_momentum.parameters(), lr=LEARNING_RATE, momentum=0.9)
optm_adam = optim.Adam(model_adam.parameters(), lr=LEARNING_RATE)
print ("Done.")- 💡 인사이트
-
Adam
Adam은 짧은 학습 시간부터 성능(정확도)이 확보됨Adam에는adaptive learning개념이 반영된 접근법. (adaptive learning: 어떠한 파라미터에 대해서는 lr을 높이고, 다른 어떠한 파라미터에 대해서는 lr을 줄여나감)- 따라서, 똑같은 lr을 선언해주어도 더 빠르게 성능이 확보될 수 있다..! ✨
- Momentum
Momentum은 학습 시간이 조금 길어지면 성능 확보됨. 반면, SGD는 같은 시간까지도 성능이 확보되지 않음Momentum은 이전 배치의gradient정보를 활용해 현재 배치 턴에서 사용하겠다는 접근법- 이러한 점에서 미니배치일 때,
SGD보다 좋다..! ✨
- SGD
SGD는 데이터에서 큰 특징이 되는 파트 위주로 학습이 잘되고, 세부적으로는 잘 놓치는 모양새를 보임- 하지만,
SGD는 오랜 학습 시간이 흘렀을 때에는 더 좋은 성능을 보일 순 있다.
- 결론:
Adam/rAdam을 초기에 사용하면 어느정도의 성능을 짧은 시간에 효율적으로 확보할 수 있다..! ✨
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