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cfg.py
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cfg.py
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import argparse
def parse_args():
parser = argparse.ArgumentParser(description="Experiment", add_help=False)
parser.add_argument('--batch_size', type=int, default=64,
help="Batch size")
parser.add_argument('--concur_size', type=int, default=100,
help="Concurrent size")
parser.add_argument('--patience', type=int, default=50,
help="Patience")
parser.add_argument('--epoch_1', type=int, default=100000,
help="Training Epoch")
parser.add_argument('--epoch_2', type=int, default=300,
help="Optimizing Epoch")
parser.add_argument('--loss_func_1', type=str, default="l1",
choices=["l1", "l2"],
help="Loss Function for Training")
parser.add_argument('--loss_func_2', type=str, default="l1",
choices=["l1", "l2"],
help="Loss Function for Optimization")
parser.add_argument('--dataset', type=str, default="abilene",
choices=["abilene", "geant"],
help="Dataset")
parser.add_argument('--dim_mults', type=int, default=[1, 2, 4],
nargs='+', help="Dimensional Multiple")
parser.add_argument('--lr_1', type=float, default=1e-4,
help="Training Learning Rate")
parser.add_argument('--lr_2', type=float, default=4e-2,
help="Optimizing Learning Rate (Start)")
parser.add_argument('--hd', type=int, default=32,
help="Basic Hidden Dimension")
parser.add_argument('--tt', type=int, default=1000,
help="Training Timesteps")
parser.add_argument('--st', type=int, default=200,
help="Sampling Timesteps")
parser.add_argument('--schedule', type=str, default="cosine",
choices=["linear", "cosine"],
help="Noise Schedule")
parser.add_argument('--regularize', type=bool, default=False,
help="Adding a Regularization Term or not")
parser.add_argument('--plot', type=bool, default=True,
help="Plotting Similarity Comparision")
parser.add_argument('--visualize', type=str, default="tsne",
choices=["tsne", "pca"],
help="Plotting t-SNE or PCA")
parser.add_argument('--lamb', type=float, default=1e-4,
help="Lambda")
parser.add_argument('--init_num', type=int, default=1000,
help="Initial Point Searching Epochs")
parser.add_argument('--pre_ep', type=int, default=10000,
help="Pre-training Epochs of Embedding-Recovery Network")
parser.add_argument('--nodes_num', type=int, default=12,
help="Number of nodes in dataset")
parser.add_argument('--emb_size', type=int, default=8,
help="Size of embedding space")
args = parser.parse_args()
return args