Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

Warmup ati Cosine Annealing Schedules Quiz

Tests warmup’s early-training role, the transition to cosine decay, and how to read the schedule’s linear ramp and endpoint.

Jẹmọ itọsọna awọn ọnaWarmup Ati Cosine Annealing Schedule
Ibeere 1 ti 8

Kini idi akọkọ ti ipele igbona ni ibẹrẹ ikẹkọ?

Awotẹlẹ gbogbo ibeere8 ibeere

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

  1. Kini idi akọkọ ti ipele igbona ni ibẹrẹ ikẹkọ?
    • Lati ṣe idaduro ikẹkọ ni kutukutu nigbati awọn iwuwo jẹ laileto ati awọn gradients jẹ ariwo
    • Lati mu iwọn ipele pọ si diẹdiẹ
    • Lati di awọn fẹlẹfẹlẹ isalẹ ti awoṣe
    • Lati compress awọn awoṣe fun yiyara inference
  2. Annealing Cosine bajẹ oṣuwọn ẹkọ ni atẹle apẹrẹ wo?
    • Laini onigun taara
    • Igi cosine kan ti o bẹrẹ ni giga, irọrun, ati fifẹ nitosi odo
    • Iṣẹ igbesẹ ti o lọ silẹ ni awọn akoko ti o wa titi
    • Iwasoke alapin si oke
  3. Eyi ti o dara julọ ni anfani paapaa lati igbona nitori awọn iṣiro iyatọ rẹ ko ni igbẹkẹle ni kutukutu?
    • SGD itele ti ko si ipa
    • Wiwa akoj
    • Adam ati awọn oniwe-iyatọ
    • K-tumo si
  4. Ninu agbekalẹ cosine, kini T ṣe aṣoju?
    • Iwọn otutu awoṣe fun iṣapẹẹrẹ
    • Awọn nọmba ti GPUs lo
    • Iwọn fokabulari tokenizer
    • Nọmba apapọ ti awọn igbesẹ ikẹkọ ni iṣeto
  5. In the guide’s warmup-plus-cosine schedule, what begins after warmup reaches its target rate?
    • Cosine decay begins from the peak learning rate
    • All model weights are reset
    • The schedule switches to a constant zero rate
    • Momentum is removed from every optimizer
  6. A linear warmup rises from zero to its target rate over 200 steps. About what rate is used halfway through?
    • The full target rate
    • About half of the target rate
    • The learning rate is exactly zero
    • Twice the target rate
  7. At the cosine horizon t = T, what multiplier does the guide’s cosine formula produce?
    • Ọkan
    • One-half
    • Odo
    • It becomes negative one
  8. Why use a gradual cosine decay after warmup rather than abruptly setting the rate to zero?
    • It guarantees a global minimum
    • It increases the model’s parameter count
    • It removes the need to check training loss
    • It lets the rate ease down so optimization can settle gradually