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- #!/usr/bin/env python
- # -*- coding: utf-8 -*-
- # Python version: 3.6
- import argparse
- def args_parser():
- #parser = argparse.ArgumentParser()
- parser = argparse.ArgumentParser(description="Arguments for Neural Net")
-
- # federated arguments (Notation for the arguments followed from paper)
- parser.add_argument('--epochs', type=int, default=5,
- help="number of rounds of training")
- parser.add_argument('--num_users', type=int, default=100,
- help="number of users: K")
- parser.add_argument('--frac', type=float, default=0.1,
- help='the fraction of clients: C')
- parser.add_argument('--local_ep', type=int, default=1,
- help="the number of local epochs: E")
- parser.add_argument('--local_bs', type=int, default=10,
- help="local batch size: B")
- parser.add_argument('--lr', type=float, default=0.01,
- help="learning rate")
- parser.add_argument('--momentum', type=float, default=0.5,
- help="SGD momentum (default: 0.5)")
- # model arguments
- parser.add_argument('--model', type=str, default='mlp', help="model name")
- parser.add_argument('--kernel_num', type=int, default=9,
- help="number of each kind of kernel")
- parser.add_argument('--kernel_sizes', type=str, default='3,4,5',
- help="comma-separated kernel size to \
- use for convolution")
- parser.add_argument('--num_channels', type=int, default=1, help="number \
- of channels of imgs")
- parser.add_argument('--norm', type=str, default='batch_norm',
- help="batch_norm, layer_norm, or None")
- parser.add_argument('--num_filters', type=int, default=32,
- help="number of filters for conv nets -- 32 for \
- mini-imagenet, 64 for omiglot.")
- parser.add_argument('--max_pool', type=str, default='True',
- help="Whether use max pooling rather than \
- strided convolutions")
- # other arguments
- parser.add_argument('--dataset', type=str, default='mnist', help="name \
- of datasetS")
- parser.add_argument('--num_classes', type=int, default=10, help="number \
- of classes")
- parser.add_argument('--gpu', type=int, default=0, help="To use cuda, set \
- to 1. Default set to use CPU.")
- parser.add_argument('--optimizer', type=str, default='sgd', help="type \
- of optimizer")
- parser.add_argument('--iid', type=int, default=1,
- help="Default set to IID. Set to 0 for non-IID.")
- parser.add_argument('--unequal', type=int, default=0,
- help="whether to use unequal data splits for \
- non-i.i.d setting (use 0 for equal splits)")
- parser.add_argument('--stopping_rounds', type=int, default=10,
- help="rounds of early stopping")
- parser.add_argument('--verbose', type=int, default=1, help="verbose")
- parser.add_argument('--seed', type=int, default=1, help="random seed")
- # Add arguments
- parser.add_argument('--num_clusters', type=int, default=2, help="the number of clusters")
- parser.add_argument('--test_acc', type=int, default=95, help="target test accuracy")
- parser.add_argument('--Cepochs', type=int, default=5,help="number of rounds of training in each cluster")
- parser.add_argument('--mlpdim', type=int, default=200,help="MLP model hidden dimension")
- parser.add_argument('--gpu_id', default='cuda:0', help="To set GPU device \
- ID if cuda is availlable")
- parser.add_argument('--model_dtype', default='torch.float32', help="Dtype \
- for model")
- parser.add_argument('--loss_dtype', default='torch.float32', help="Dtype \
- for loss or criterion")
-
-
- args = parser.parse_args()
- return args
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