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commands.sh
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commands.sh
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# Fashion-MNIST
CUDA_VISIBLE_DEVICES=0 python train.py --dataset=fashion_mnist --epoch=25 --adversarial_loss_mode=gan
CUDA_VISIBLE_DEVICES=0 python train.py --dataset=fashion_mnist --epoch=25 --adversarial_loss_mode=gan --gradient_penalty_mode=1-gp --gradient_penalty_sample_mode=dragan
CUDA_VISIBLE_DEVICES=0 python train.py --dataset=fashion_mnist --epoch=25 --adversarial_loss_mode=lsgan
CUDA_VISIBLE_DEVICES=0 python train.py --dataset=fashion_mnist --epoch=50 --adversarial_loss_mode=wgan --gradient_penalty_mode=1-gp --gradient_penalty_sample_mode=line --n_d=5
# CelebA
CUDA_VISIBLE_DEVICES=0 python train.py --dataset=celeba --epoch=25 --adversarial_loss_mode=gan
CUDA_VISIBLE_DEVICES=0 python train.py --dataset=celeba --epoch=25 --adversarial_loss_mode=gan --gradient_penalty_mode=1-gp --gradient_penalty_sample_mode=dragan
CUDA_VISIBLE_DEVICES=0 python train.py --dataset=celeba --epoch=25 --adversarial_loss_mode=gan --gradient_penalty_mode=lp --gradient_penalty_sample_mode=dragan
CUDA_VISIBLE_DEVICES=0 python train.py --dataset=celeba --epoch=25 --adversarial_loss_mode=lsgan
CUDA_VISIBLE_DEVICES=0 python train.py --dataset=celeba --epoch=50 --adversarial_loss_mode=wgan --gradient_penalty_mode=1-gp --gradient_penalty_sample_mode=line --n_d=5
CUDA_VISIBLE_DEVICES=0 python train.py --dataset=celeba --epoch=50 --adversarial_loss_mode=wgan --gradient_penalty_mode=lp --gradient_penalty_sample_mode=line --n_d=5
# Anime
CUDA_VISIBLE_DEVICES=0 python train.py --dataset=anime --epoch=100 --adversarial_loss_mode=gan --gradient_penalty_mode=1-gp --gradient_penalty_sample_mode=dragan
CUDA_VISIBLE_DEVICES=0 python train.py --dataset=anime --epoch=100 --adversarial_loss_mode=gan --gradient_penalty_mode=lp --gradient_penalty_sample_mode=dragan
CUDA_VISIBLE_DEVICES=0 python train.py --dataset=anime --epoch=200 --adversarial_loss_mode=wgan --gradient_penalty_mode=1-gp --gradient_penalty_sample_mode=line --n_d=5
CUDA_VISIBLE_DEVICES=0 python train.py --dataset=anime --epoch=200 --adversarial_loss_mode=wgan --gradient_penalty_mode=lp --gradient_penalty_sample_mode=line --n_d=5