D = {'func': 'networks.D_paper'}
D_loss = {'func': 'loss.D_wgangp_acgan'}
D_opt = {'beta1': 0.0, 'beta2': 0.99, 'epsilon': 1e-08}
EasyDict = <class 'config.EasyDict'>
G = {'func': 'networks.G_paper'}
G_loss = {'func': 'loss.G_wgan_acgan'}
G_opt = {'beta1': 0.0, 'beta2': 0.99, 'epsilon': 1e-08}
data_dir = /scratch/users/suihong/training_data/TrainingData(MultiChannels_Version4)/
dataset = {'tfrecord_dir': 'Imgs_Labels_Prob_Wells_tfrecords'}
desc = pgan-unconditional-2gpu
env = {'TF_CPP_MIN_LOG_LEVEL': '0'}
grid = {'size': '6by8', 'layout': 'random'}
num_gpus = 2
random_seed = 1000
result_dir = /scratch/users/suihong/ProGAN_MultiChannel_Reusults_ConditionedtoMultiConditions_TF/002-Pro-GAN-Unconditional-related/
sched = {'minibatch_base': 32, 'minibatch_dict': {4: 32, 8: 32, 16: 32, 32: 32, 64: 32}, 'G_lrate_dict': {4: 0.0025, 8: 0.005, 16: 0.005, 32: 0.0035, 64: 0.0025}, 'D_lrate_dict': {4: 0.0025, 8: 0.005, 16: 0.005, 32: 0.0035, 64: 0.0025}, 'max_minibatch_per_gpu': {32: 32, 64: 32}}
tf_config = {'graph_options.place_pruned_graph': True}
train = {'func': 'train.train_progressive_gan', 'total_kimg': 60000}
