Kuiz berkaitan panduan · Sederhana Tahap
Kuiz Kadar Pembelajaran Kitaran
Menyemak kitaran kadar pembelajaran segi tiga, ujian julat, tingkah laku momentum dan perbezaan antara kitaran berulang dan latihan satu kitaran.
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Apakah andaian teras yang dicabar oleh kadar pembelajaran kitaran?
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Jawapan dan penjelasan muncul semasa anda mengambil kuiz.
- Apakah andaian teras yang dicabar oleh kadar pembelajaran kitaran?
- Bahawa kadar pembelajaran hanya akan berkurangan semasa latihan
- Saiz kumpulan itu mesti kekal malar
- Model itu memerlukan GPU
- Data itu mesti dikocok
- What did Smith report for cyclical learning rates on tested classification tasks?
- They always lowered training loss to zero
- They improved accuracy versus fixed rates, often in fewer iterations
- They removed the need for validation data
- They guaranteed escape from every sharp minimum
- Apakah yang dilakukan oleh 'ujian julat LR'?
- Mengukur penggunaan memori GPU
- Mengira bilangan parameter model
- Menyapu kadar pembelajaran ke atas sambil melihat kerugian untuk memilih sempadan yang baik
- Menguji model pada data luar pengedaran
- Dalam dasar satu kitaran, bagaimanakah momentum biasanya berkelakuan berbanding dengan kadar pembelajaran?
- Momentum kekal tetap sepanjang masa
- Momentum ditetapkan kepada sifar sepanjang masa
- Momentum meningkat tepat mengikut kadar
- Momentum bergerak secara songsang — tinggi apabila kadar rendah dan rendah apabila kadarnya tinggi
- During a triangular cyclical learning-rate cycle, how does the rate move between its bounds?
- It rises from the base rate to the maximum, then falls back toward the base
- It only decreases from the first step
- It stays fixed at the maximum
- It jumps randomly at every minibatch
- Siapa yang memperkenalkan kadar pembelajaran kitaran dan ujian julat LR?
- Geoffrey Hinton
- Leslie Smith
- Yann LeCun
- Andrej Karpathy
- What sets the duration of a cyclical learning-rate cycle?
- The number of model layers
- Saiz perbendaharaan kata
- A chosen number of training iterations for the up-and-down pattern
- The number of optimizer parameters
- What distinguishes the one-cycle policy from repeating several triangular cycles?
- It keeps the learning rate constant throughout
- It removes the optimizer’s momentum
- It measures the learning rate only once
- It uses one long learning-rate cycle for the training run