d_error.mean(),
g_fake_error.mean(),
fake_data.mean(),
fake_data.std())
if __name__ == '__main__':
basic_config = {
'g': {
'in_size': 1,
'hidden_size': 50,
'out_size': 1,
'learning_rate': 2e-3,
'betas': (0.9, 0.999),
},
'd': {
'in_size': 1,
'hidden_size': 50,
'out_size': 1,
'learning_rate': 2e-3,
'betas': (0.9, 0.999),
},
'd_steps': 1,
'g_steps': 1,
'batch_size': 100,
'num_epoch': 1000,
'print_interval': 100,
}
gan = VanillaGAN(structuralize('config', **basic_config))
gan.train()
if opt.cuda:
try:
device = 'cuda:%d' % (find_gpu(1)[0])
except:
logger.warning('CUDA device is unavailable, fall back to CPU')
device = 'cpu'
else:
device = 'cpu'
basic_config = {
'g': {
'in_channels': 100,
'learning_rate': 2e-4,
'betas': (0.5, 0.999),
},
'd': {
'learning_rate': 2e-4,
'betas': (0.5, 0.999),
},
'device': device,
'batch_size': 64,
'num_epoch': 10,
'print_interval': 5,
'verbose': opt.verbose,
'datadir': 'dataset/data/mnist',
}
gan = LSGAN(structuralize('config', **basic_config), logger)
gan.train()