语言：Python
测试环境：Win10
测试硬件：RTX3090
依赖：numpy，jittor
参数：
parser.add_argument(‘–n_epochs’, type=int, default=100, help=’number of epochs of training’)
parser.add_argument(‘–batch_size’, type=int, default=64, help=’size of the batches’)
parser.add_argument(‘–lr’, type=float, default=0.0002, help=’adam: learning rate’)
parser.add_argument(‘–b1’, type=float, default=0.5, help=’adam: decay of first order momentum of gradient’)
parser.add_argument(‘–b2’, type=float, default=0.999, help=’adam: decay of first order momentum of gradient’)
parser.add_argument(‘–n_cpu’, type=int, default=8, help=’number of cpu threads to use during batch generation’)
parser.add_argument(‘–latent_dim’, type=int, default=100, help=’dimensionality of the latent space’)
parser.add_argument(‘–n_classes’, type=int, default=10, help=’number of classes for dataset’)
parser.add_argument(‘–img_size’, type=int, default=32, help=’size of each image dimension’)
parser.add_argument(‘–channels’, type=int, default=1, help=’number of image channels’)
parser.add_argument(‘–sample_interval’, type=int, default=1000, help=’interval between image sampling’)
关于
A Jittor implementation of Conditional GAN (CGAN).
CGAN_jittor
语言：Python 测试环境：Win10 测试硬件：RTX3090 依赖：numpy，jittor 参数： parser.add_argument(‘–n_epochs’, type=int, default=100, help=’number of epochs of training’) parser.add_argument(‘–batch_size’, type=int, default=64, help=’size of the batches’) parser.add_argument(‘–lr’, type=float, default=0.0002, help=’adam: learning rate’) parser.add_argument(‘–b1’, type=float, default=0.5, help=’adam: decay of first order momentum of gradient’) parser.add_argument(‘–b2’, type=float, default=0.999, help=’adam: decay of first order momentum of gradient’) parser.add_argument(‘–n_cpu’, type=int, default=8, help=’number of cpu threads to use during batch generation’) parser.add_argument(‘–latent_dim’, type=int, default=100, help=’dimensionality of the latent space’) parser.add_argument(‘–n_classes’, type=int, default=10, help=’number of classes for dataset’) parser.add_argument(‘–img_size’, type=int, default=32, help=’size of each image dimension’) parser.add_argument(‘–channels’, type=int, default=1, help=’number of image channels’) parser.add_argument(‘–sample_interval’, type=int, default=1000, help=’interval between image sampling’)