작업 내용을 조금 바꿔서 약간 더 복잡해졌으므로 더 오랫동안 학습을 진행해야 합니다. 그리
고 적어도 가끔은 좋은 결과를 얻을 수 있었습니다. 수차례 실행해보면, 실행마다 결과가 상당
히 다르다는 점을 알 수 있습니다. 매우 깊은 신경망이 사용되었기 때문에, 매우 크거나 매우
작은 그레이디언트가 만들어질 수 있기 때문입니다. 이를 처리하는 방법은 다음 부분에서 살펴
봅니다.
이제 더 나은 모델을 얻을 확실한 방법은 더 깊은 구조를 쌓는 것입니다. 기본
RNN
에는 은닉
상태, 출력 활성, 이 둘 사이의
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