def get_args():
parser = argparse.ArgumentParser()
# BASICS
parser.add_argument('--seed', type=int, default=None, help='sets random seed')
parser.add_argument('--log-interval', type=int, default=100, help='print updates after n steps')
parser.add_argument('--base-log-dir', type=str, default='logs/gen/', help='path where logs for all runs are stored')
parser.add_argument('--log-name', type=str, default=None, help='subdirectory for this run')
parser.add_argument('--log-dir', type=str, default=None, help='override save path. constructed if None')
parser.add_argument('--data', type=str, default='digits', help='options are digits, fashion and 2d')
parser.add_argument('--create-dataset', action='store_true', default=True, help='if true, make 60k synthetic code_balanced')
# OPTIMIZATION
parser.add_argument('--batch-size', '-bs', type=int, default=500)
parser.add_argument('--test-batch-size', '-tbs', type=int, default=1000)
parser.add_argument('--gen-batch-size', '-gbs', type=int, default=1000)
parser.add_argument('--epochs', '-ep', type=int, default=5)
parser.add_argument('--lr', '-lr', type=float, default=0.01, help='learning rate')
parser.add_argument('--lr-decay', type=float, default=0.9, help='per epoch learning rate decay factor')
# MODEL DEFINITION
# parser.add_argument('--batch-norm', action='store_true', default=True, help='use batch norm in model')
parser.add_argument('--conv-gen', action='store_true', default=True, help='use convolutional generator')
parser.add_argument('--d-code', '-dcode', type=int, default=5, help='random code dimensionality')
parser.add_argument('--gen-spec', type=str, default='200', help='specifies hidden layers of generator')
parser.add_argument('--kernel-sizes', '-ks', type=str, default='5,5', help='specifies conv gen kernel sizes')
parser.add_argument('--n-channels', '-nc', type=str, default='16,8', help='specifies conv gen kernel sizes')
# DP SPEC
parser.add_argument('--d-rff', type=int, default=1000, help='number of random filters for apprixmate mmd')
parser.add_argument('--rff-sigma', '-rffsig', type=str, default=None, help='standard dev. for filter sampling')
parser.add_argument('--noise-factor', '-noise', type=float, default=5.0, help='privacy noise parameter')
# ALTERNATE MODES
parser.add_argument('--single-release', action='store_true', default=True, help='get 1 data mean embedding only')
parser.add_argument('--loss-type', type=str, default='rff', help='how to approx mmd',
choices=['rff', 'kmeans', 'real_mmd'])
# parser.add_argument('--real-mmd', action='store_true', default=False, help='for debug: dont approximate mmd')
# parser.add_argument('--kmeans-mmd', action='store_true', default=False, help='for debug: dont approximate mmd')
parser.add_argument('--n-means', type=int, default=10, help='number of means to find per class')
parser.add_argument('--dp-kmeans-encoding-dim', type=int, default=10, help='dimension the data is projected to')
parser.add_argument('--tgt-epsilon', type=float, default=1.0, help='privacy epsilon for dp k-means')
parser.add_argument('--kmeans-delta', type=float, default=0.01, help='soft failure probability in dp k-means')
parser.add_argument('--mmd-type', type=str, default='sphere', help='how to approx mmd', choices=['sphere', 'r+r'])
parser.add_argument('--center-data', action='store_true', default=False, help='k-means requires centering')
# synth_d2 data
parser.add_argument('--synth-spec-string', type=str, default='disc_k5_n10000_row5_col5_noise0.2', help='')
parser.add_argument('--test-split', type=float, default=0.1, help='only relevant for synth_2d so far')
ar = parser.parse_args()
preprocess_args(ar)
log_args(ar.log_dir, ar)
return ar
def get_args():
parser = argparse.ArgumentParser()
# BASICS
parser.add_argument('--seed', type=int, default=None, help='sets random seed')
parser.add_argument('--n-labels', type=int, default=10, help='number of labels/classes in data')
parser.add_argument('--log-interval', type=int, default=10000, help='print updates after n steps')
parser.add_argument('--base-log-dir', type=str, default='logs/gen/', help='path where logs for all runs are stored')
parser.add_argument('--log-name', type=str, default=None, help='subdirectory for this run')
parser.add_argument('--log-dir', type=str, default=None, help='override save path. constructed if None')
parser.add_argument('--data', type=str, default='digits', help='options are digits and fashion')
parser.add_argument('--synth-code_balanced', action='store_true', default=True, help='if true, make 60k synthetic code_balanced')
# OPTIMIZATION
parser.add_argument('--batch-size', '-bs', type=int, default=100)
parser.add_argument('--test-batch-size', '-tbs', type=int, default=1000)
parser.add_argument('--epochs', '-ep', type=int, default=5)
parser.add_argument('--lr', '-lr', type=float, default=0.01, help='learning rate')
parser.add_argument('--lr-decay', type=float, default=0.9, help='per epoch learning rate decay factor')
# MODEL DEFINITION
# parser.add_argument('--batch-norm', action='store_true', default=True, help='use batch norm in model')
parser.add_argument('--d-code', '-dcode', type=int, default=5, help='random code dimensionality')
parser.add_argument('--gen-spec', type=str, default='200', help='specifies hidden layers of generator')
parser.add_argument('--kernel-sizes', '-ks', type=str, default='5,5', help='specifies conv gen kernel sizes')
parser.add_argument('--n-channels', '-nc', type=str, default='16,8', help='specifies conv gen kernel sizes')
parser.add_argument('--mmd-type', type=str, default='sphere', help='how to approx mmd', choices=['sphere', 'r+r'])
# DP SPEC
parser.add_argument('--d-rff', type=int, default=1000, help='number of random filters for apprixmate mmd')
parser.add_argument('--rff-sigma', '-rffsig', type=str, default=None, help='standard dev. for filter sampling')
parser.add_argument('--noise-factor', '-noise', type=float, default=5.0, help='privacy noise parameter')
parser.add_argument('--flip-mnist', action='store_true', default=False, help='')
ar = parser.parse_args()
preprocess_args(ar)
log_args(ar.log_dir, ar)
return ar
def get_args():
parser = argparse.ArgumentParser()
# BASICS
parser.add_argument('--no-cuda',
action='store_true',
default=False,
help='turns off gpu')
parser.add_argument('--seed',
type=int,
default=None,
help='sets random seed')
parser.add_argument('--n-labels',
type=int,
default=10,
help='number of labels/classes in data')
parser.add_argument('--log-interval',
type=int,
default=100,
help='print updates after n steps')
parser.add_argument('--base-log-dir',
type=str,
default='logs/gen/',
help='path where logs for all runs are stored')
parser.add_argument('--log-name',
type=str,
default=None,
help='subdirectory for this run')
parser.add_argument('--log-dir',
type=str,
default=None,
help='override save path. constructed if None')
parser.add_argument('--data',
type=str,
default='digits',
help='options are digits and fashion')
parser.add_argument('--synth-mnist',
action='store_true',
default=True,
help='if true, make 60k synthetic mnist')
# OPTIMIZATION
parser.add_argument('--batch-size', '-bs', type=int, default=500)
parser.add_argument('--test-batch-size', '-tbs', type=int, default=1000)
parser.add_argument('--epochs', '-ep', type=int, default=5)
parser.add_argument('--lr',
'-lr',
type=float,
default=0.01,
help='learning rate')
parser.add_argument('--lr-decay',
type=float,
default=0.9,
help='per epoch learning rate decay factor')
# MODEL DEFINITION
parser.add_argument('--batch-norm',
action='store_true',
default=True,
help='use batch norm in model')
parser.add_argument('--gen-labels',
action='store_true',
default=True,
help='generate labels as well as samples')
parser.add_argument('--uniform-labels',
action='store_true',
default=True,
help='assume uniform label distribution')
parser.add_argument('--d-code',
'-dcode',
type=int,
default=5,
help='random code dimensionality')
parser.add_argument('--gen-spec',
type=str,
default='200,100',
help='specifies hidden layers of generator')
parser.add_argument('--big-gen',
action='store_true',
default=False,
help='False: gen as 2 hidden layers. True: 4')
parser.add_argument('--real-mmd',
action='store_true',
default=False,
help='for debug: dont approximate mmd')
# DP SPEC
parser.add_argument('--d-rff',
type=int,
default=1000,
help='number of random filters for apprixmate mmd')
parser.add_argument('--rff-sigma',
'-rffsig',
type=float,
default=127.0,
help='standard dev. for filter sampling')
parser.add_argument('--noise-factor',
'-noise',
type=float,
default=0.595,
help='privacy noise parameter')
ar = parser.parse_args()
if ar.log_dir is None:
ar.log_dir = get_log_dir(ar)
if not os.path.exists(ar.log_dir):
os.makedirs(ar.log_dir)
preprocess_args(ar)
log_args(ar.log_dir, ar)
return ar
def get_args():
parser = argparse.ArgumentParser()
# BASICS
parser.add_argument('--no-cuda',
action='store_true',
default=False,
help='turns off gpu')
parser.add_argument('--seed',
type=int,
default=None,
help='sets random seed')
parser.add_argument('--n-labels',
type=int,
default=10,
help='number of labels/classes in data')
parser.add_argument('--log-interval',
type=int,
default=100,
help='print updates after n steps')
parser.add_argument('--base-log-dir',
type=str,
default='logs/gen/',
help='path where logs for all runs are stored')
parser.add_argument('--log-name',
type=str,
default=None,
help='subdirectory for this run')
parser.add_argument('--log-dir',
type=str,
default=None,
help='override save path. constructed if None')
parser.add_argument('--data',
type=str,
default='digits',
help='options are digits and fashion')
parser.add_argument('--synth-mnist',
action='store_true',
default=True,
help='if true, make 60k synthetic mnist')
# OPTIMIZATION
parser.add_argument('--batch-size', '-bs', type=int, default=500)
parser.add_argument('--test-batch-size', '-tbs', type=int, default=1000)
parser.add_argument('--epochs', '-ep', type=int, default=7)
parser.add_argument('--lr',
'-lr',
type=float,
default=1e-3,
help='learning rate')
parser.add_argument('--lr-decay',
type=float,
default=0.9,
help='per epoch learning rate decay factor')
# MODEL DEFINITION
parser.add_argument('--batch-norm',
action='store_true',
default=True,
help='use batch norm in model')
parser.add_argument('--gen-labels',
action='store_true',
default=True,
help='generate labels as well as samples')
parser.add_argument('--uniform-labels',
action='store_true',
default=True,
help='assume uniform label distribution')
parser.add_argument('--d-enc',
'-denc',
type=int,
default=10,
help='embedding space dimensionality')
parser.add_argument('--d-code',
'-dcode',
type=int,
default=10,
help='random code dimensionality')
parser.add_argument('--gen-spec',
type=str,
default='100,100',
help='specifies hidden layers of generator')
# DP SPEC
parser.add_argument('--d-rff',
type=int,
default=10000,
help='number of random filters for apprixmate mmd')
parser.add_argument('--rff-sigma',
'-rffsig',
type=float,
default=80.0,
help='standard dev. for filter sampling')
parser.add_argument('--noise-factor',
'-noise',
type=float,
default=0.7,
help='privacy noise parameter')
# Autoencoder info
parser.add_argument('--ae-no-bias',
action='store_true',
default=True,
help='if true, AE has no bias units')
parser.add_argument('--ae-bn',
action='store_true',
default=True,
help='if true, AE uses batch norm')
parser.add_argument('--ae-enc-spec',
type=str,
default='300,100',
help='AE encoder hidden layers')
parser.add_argument('--ae-dec-spec',
type=str,
default='100',
help='AE decoder hidden layers')
parser.add_argument('--ae-norm-data',
action='store_true',
default=False,
help='if true, data was pre-processed')
parser.add_argument('--ae-load-dir',
type=str,
default=None,
help='path to where AE is stored')
# outdated variants and args only relevant for automated loading - don't worry about those
parser.add_argument('--ae-conv', action='store_true', default=False)
parser.add_argument('--ae-label', action='store_true', default=False)
parser.add_argument('--ae-ce-loss', action='store_true', default=False)
parser.add_argument('--ae-clip', type=float, default=None)
parser.add_argument('--ae-noise', type=float, default=None)
parser.add_argument('--ae-siam-weight', '-wsiam', type=float, default=None)
parser.add_argument('--ae-siam-margin', '-msiam', type=float, default=None)
ar = parser.parse_args()
if ar.log_dir is None:
ar.log_dir = get_log_dir(ar)
if not os.path.exists(ar.log_dir):
os.makedirs(ar.log_dir)
preprocess_args(ar)
log_args(ar.log_dir, ar)
return ar
def get_args():
parser = argparse.ArgumentParser()
# BASICS
parser.add_argument('--no-cuda',
action='store_true',
default=False,
help='turns off gpu')
parser.add_argument('--seed',
type=int,
default=None,
help='sets random seed')
parser.add_argument('--n-labels',
type=int,
default=10,
help='number of labels/classes in data')
parser.add_argument('--log-interval',
type=int,
default=100,
help='print updates after n steps')
parser.add_argument('--base-log-dir',
type=str,
default='logs/ae/',
help='path where logs for all runs are stored')
parser.add_argument('--log-name',
type=str,
default=None,
help='subdirectory for this run')
parser.add_argument('--log-dir',
type=str,
default=None,
help='override save path. constructed if None')
parser.add_argument('--verbose',
action='store_true',
default=False,
help='turns on more debug/status printing')
parser.add_argument('--data',
type=str,
default='digits',
help='options are digits and fashion')
parser.add_argument('--norm-data',
action='store_true',
default=False,
help='if true, preprocess data')
# OPTIMIZATION
parser.add_argument('--batch-size', '-bs', type=int, default=500)
parser.add_argument('--test-batch-size', '-tbs', type=int, default=1000)
parser.add_argument('--epochs', '-ep', type=int, default=10)
parser.add_argument('--lr',
'-lr',
type=float,
default=1e-3,
help='learning rate')
parser.add_argument('--lr-decay',
type=float,
default=0.8,
help='per epoch learning rate decay factor')
parser.add_argument('--optimizer', '-opt', type=str, default='adam')
parser.add_argument('--siam-loss-weight',
'-wsiam',
type=float,
default=100.,
help='weight of siamese loss')
parser.add_argument('--siam-loss-margin',
'-msiam',
type=float,
default=5.,
help='margin used in siamese loss')
# MODEL DEFINITION
parser.add_argument('--d-enc',
'-denc',
type=int,
default=10,
help='embedding space dimensionality')
parser.add_argument('--no-bias',
action='store_true',
default=True,
help='remove bias units')
parser.add_argument('--batch-norm',
action='store_true',
default=True,
help='use batch norm in encoder')
parser.add_argument('--enc-spec',
'-s-enc',
type=str,
default='300,100',
help='encoder hidden layers')
parser.add_argument('--dec-spec',
'-s-dec',
type=str,
default='100',
help='decoder hidden layers')
parser.add_argument('--conv-ae',
action='store_true',
default=False,
help='if true, use convolutional AE (not used)')
parser.add_argument('--flat-dec',
action='store_true',
default=False,
help='if conv-ae, use smaller model (not used)')
parser.add_argument('--ce-loss',
action='store_true',
default=False,
help='pixelwise cross-entropy loss (not used)')
parser.add_argument('--label-ae',
action='store_true',
default=False,
help='autoencode labels as well (not used)')
# DP SPEC
parser.add_argument('--clip-norm',
'-clip',
type=float,
default=0.01,
help='samplewise gradient L2 clip norm')
parser.add_argument('--noise-factor',
'-noise',
type=float,
default=0.7,
help='DP-SGD noise parameter')
parser.add_argument('--clip-per-layer',
action='store_true',
default=False,
help='if true, clip per layer (not used)')
parser.add_argument('--layer-clip-norms',
'-layer-clip',
type=str,
default=None,
help='perlayer clip norms(not used)')
parser.add_argument('--noise-up-enc',
action='store_true',
default=False,
help='debug: apply DP-SGD to encoder too')
ar = parser.parse_args()
if ar.log_dir is None:
ar.log_dir = get_log_dir(ar)
if not os.path.exists(ar.log_dir):
os.makedirs(ar.log_dir)
preprocess_args(ar)
log_args(ar.log_dir, ar)
return ar