if __name__ == '__main__':
MODE = 10
LATENT_DIM = 100
IMAGE_SIZE = 64
DATASET_LEN = 20000
PRELOAD_LEN = 20000
EPOCHS = 50000
device = 'cuda'
gloss_cfg = LossConfig('BCE')
dloss_cfg = LossConfig('BCE')
sampler_cfg = SamplerConfig(
name='Onehot',
out_shape=PRELOAD_LEN,
latent_dim=LATENT_DIM,
random_sampling=False
)
goptim_cfg = OptimConfig('Adam', lr=0.0002)
doptim_cfg = OptimConfig('Adam', lr=0.0002)
dataset_cfg = DatasetConfig('CelebA', dataset_len=DATASET_LEN, image_size=IMAGE_SIZE, preload_len=PRELOAD_LEN)
loader_cfg = LoaderConfig('naive', batch_size=128, shuffle=True)
gen_cfg = ModelConfig(
'EDCG', input_size=LATENT_DIM,
hidden_size=128, output_size=3,
data_num=PRELOAD_LEN, out_64=IMAGE_SIZE == 64
)
dis_cfg = ModelConfig('DCD', input_size=3, hidden_size=64, output_size=1, out_64=IMAGE_SIZE == 64)
save_image(img, os.path.join(logger.img_dir, 'gen_{0:05d}.jpg'.format(postfix)))
if __name__ == '__main__':
MODE = 10
LATENT_DIM = 100
IMAGE_SIZE = 32
DATASET_LEN = 20000
EPOCHS = 30000
device = 'cuda'
gloss_cfg = LossConfig('BCE')
dloss_cfg = LossConfig('BCE')
sampler_cfg = SamplerConfig(
name='Onehot',
out_shape=DATASET_LEN,
latent_dim=LATENT_DIM
)
goptim_cfg = OptimConfig('Adam', lr=1e-3)
doptim_cfg = OptimConfig('Adam', lr=1e-3)
dataset_cfg = DatasetConfig('MNIST', train=True, stack=False, along_width=False, size=32)
loader_cfg = LoaderConfig('naive', batch_size=128, shuffle=True)
gen_cfg = ModelConfig('EDCG', input_size=LATENT_DIM, hidden_size=128, output_size=1, data_num=DATASET_LEN)
dis_cfg = ModelConfig('DCD', input_size=1, hidden_size=128, output_size=1)
train_data_cfg = DatasetConfig('MNIST', stack=False, train=True, along_width=False)
test_data_cfg = DatasetConfig('MNIST', stack=False, train=False, along_width=False)
train_loader = LoaderConfig('naive', batch_size=128, shuffle=True)
os.path.join(logger.img_dir,
'sampler_modes_{0:05d}.jpg'.format(postfix)))
plt.close()
if __name__ == '__main__':
dataset_len = 2000
mode = 20
latent_dim = 10
device = 'cuda'
gloss_cfg = LossConfig('BCE')
dloss_cfg = LossConfig('BCE')
sampler_cfg = SamplerConfig(
name='Onehot',
out_shape=dataset_len,
)
goptim_cfg = OptimConfig('Adam', lr=1e-3)
doptim_cfg = OptimConfig('Adam', lr=1e-3)
dataset_cfg = DatasetConfig('Spiral',
mode=mode,
sig=1,
num_per_mode=dataset_len // mode,
return_idx=True)
# dataset_cfg = DatasetConfig('GMM', mode=mode, sig=1, num_per_mode=dataset_len // mode, return_idx=True)
loader_cfg = LoaderConfig('naive', batch_size=128, shuffle=True)
gen_cfg = ModelConfig('MLG',
input_size=latent_dim,
os.path.join(logger.img_dir, 'embed_{0:05d}.jpg'.format(postfix)))
plt.close()
if __name__ == '__main__':
DATASET_LEN = 2000
MODE = 20
LATENT_DIM = 10
GAN_EPOCHS = 40000
device = 'cuda'
gloss_cfg = LossConfig('BCE')
dloss_cfg = LossConfig('BCE')
sampler_cfg = SamplerConfig(
name='Onehot',
out_shape=DATASET_LEN,
)
goptim_cfg = OptimConfig('Adam', lr=1e-3)
doptim_cfg = OptimConfig('Adam', lr=1e-3)
dataset_cfg = DatasetConfig('Spiral',
mode=MODE,
sig=1,
num_per_mode=DATASET_LEN // MODE,
return_idx=True)
# dataset_cfg = DatasetConfig('GMM', mode=mode, sig=1, num_per_mode=dataset_len // mode, return_idx=True)
loader_cfg = LoaderConfig('naive', batch_size=128, shuffle=True)
gen_cfg = ModelConfig('EMLG',
input_size=LATENT_DIM,
plt.scatter(self.dataset.data[:, 0], self.dataset.data[:, 1], s=1, alpha=0.3)
plt.savefig(os.path.join(logger.img_dir, 'scatter_gen_{}.jpg'.format(iter_num)))
plt.close()
if __name__ == '__main__':
dataset_len = 2000
mode = 20
latent_dim = 10
device = 'cuda'
gloss_cfg = LossConfig('BCE')
dloss_cfg = LossConfig('BCE')
sampler_cfg = SamplerConfig(
name='Gaussian',
out_shape=latent_dim,
)
goptim_cfg = OptimConfig('Adam', lr=1e-3)
doptim_cfg = OptimConfig('Adam', lr=1e-3)
dataset_cfg = DatasetConfig('Spiral', mode=mode, sig=1, num_per_mode=dataset_len // mode)
# dataset_cfg = DatasetConfig('GMM', mode=mode, sig=1, num_per_mode=dataset_len // mode)
loader_cfg = LoaderConfig('naive', batch_size=128, shuffle=True)
gen_cfg = ModelConfig('MLG', input_size=latent_dim, hidden_size=32, output_size=2)
dis_cfg = ModelConfig('MLD', input_size=2, hidden_size=32, output_size=1)
gan_cfg = GanConfig(
name='Vanilla', gen_cfg=gen_cfg, dis_cfg=dis_cfg,
gen_step=1, dis_step=1, gan_epoch=15000, loader_cfg=loader_cfg,
if __name__ == '__main__':
dataset_len = 2000
mode = 10
latent_dim = 10
device = 'cuda'
gloss_cfg = LossConfig('BCE')
dloss_cfg = LossConfig('BCE')
sampler_loss_cfg = LossConfig('MSE')
sampler_optim_cfg = OptimConfig('Adam', lr=0.001)
sampler_cfg = SamplerConfig(
name='Learning',
out_shape=latent_dim,
data_num=dataset_len,
batch_size=2000,
epoch=50,
alpha=0,
loss_cfg=sampler_loss_cfg,
optim_cfg=sampler_optim_cfg,
)
goptim_cfg = OptimConfig('Adam', lr=1e-3)
doptim_cfg = OptimConfig('Adam', lr=1e-3)
# dataset_cfg = DatasetConfig('Spiral', mode=mode, sig=1, num_per_mode=dataset_len // mode)
dataset_cfg = DatasetConfig('GMM',
mode=mode,
sig=1,
num_per_mode=dataset_len // mode)
loader_cfg = LoaderConfig('naive', batch_size=128, shuffle=True)