The global minimum of the virtual training criterion $C(G)$ is achieved if and only if $p_g=g+{data}.$ At that point, $C(G)$ achieves the value $-log(4)$.
$\mathbb{E}_{x \sim p_{data}} \left[ -log(2)\right] + \mathbb{E}_{x \sim p_{g}} \left[ -log(2)\right]$
뭐 이런 식으로?
그럼 어떻게 해야 하는 지 확인해 보자;
- blogger에 로그인 하고
- 템플릿 설정에 들어간 후
- 맞춤 설정을 클릭하고
- 사이드 바에서 고급을 클릭
- CSS 추가로 들어가서 아래 내용을 추가해주면 끝난다.
body { counter-reset: theorem; counter-reset: definition; } .theorem { display: block; margin: 12px 0; font-style: italic; counter-increment: theorem } .theorem:before { content: "Theorem " counter(theorem) "."; font-weight: bold; font-style: normal; } .proposition { display: block; margin: 12px 0; font-style: normal; counter-increment: proposition } .proposition:before { content: "Proposition " counter(proposition) "."; font-weight: bold; font-style: normal; } .lemma { display: block; margin: 12px 0; font-style: italic; counter-increment: theorem; } .lemma:before { content: "Lemma " counter(theorem) "."; font-weight: bold; font-style: normal; } .proof { display: block; margin: 12px 0; font-style: normal; } .proof:before { content: "Proof."; font-style: italic; } .proof:after { content: "\25FC"; float:right; } .definition { display: block; margin: 12px 0; font-style: normal; counter-increment: definition } .definition:before { content: "Definition " counter(definition) "."; font-weight: bold; font-style: normal; }
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