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configuration.py
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configuration.py
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import argparse
def str2bool(v):
if isinstance(v, bool):
return v
if str(v).lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif str(v).lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
parser = argparse.ArgumentParser(prog = 'Printed Neural Networks',
description = 'Evolutionary Based Variation Aware Training for Printed Neural Networks')
# printing-related hyperparameters for pNNs
parser.add_argument('--gmin', type=float, default=0.01, help='minimal printable conductance value')
parser.add_argument('--gmax', type=float, default=10., help='maximal printable conductance value')
parser.add_argument('--T', type=float, default=0.1, help='measuring threshold')
parser.add_argument('--m', type=float, default=0.3, help='measuring margin')
# learnable tanh circuits
parser.add_argument('--ACT_R1n', type=float, default=12.7865, help='resistance in nonlinear circuit')
parser.add_argument('--ACT_R2n', type=float, default=-3.1871, help='resistance in nonlinear circuit')
parser.add_argument('--ACT_W1n', type=float, default=-10.4537, help='width of the transistor 1')
parser.add_argument('--ACT_L1n', type=float, default=10.5460, help='length of the transistor 1')
parser.add_argument('--ACT_W2n', type=float, default=-5.8496, help='width of the transistor 2')
parser.add_argument('--ACT_L2n', type=float, default=4.2337, help='length of the transistor 2')
# learnable sigmoid circuits
parser.add_argument('--S_R1n', type=float, default=-11.7481, help='resistance in nonlinear circuit')
parser.add_argument('--S_R2n', type=float, default=-0.3905, help='resistance in nonlinear circuit')
parser.add_argument('--S_W1n', type=float, default=-9.1690, help='width of the transistor')
parser.add_argument('--S_L1n', type=float, default=0.3913, help='length of the transistor')
parser.add_argument('--S_W2n', type=float, default=7.2004, help='width of the transistor')
parser.add_argument('--S_L2n', type=float, default=15.5558, help='length of the transistor')
# learnable pReLU circuits
parser.add_argument('--ReLU_RHn', type=float, default=-1.9557, help='resistance in nonlinear circuit')
parser.add_argument('--ReLU_RLn', type=float, default=-20.1581, help='resistance in nonlinear circuit')
parser.add_argument('--ReLU_RDn', type=float, default=16.6486, help='resistance in nonlinear circuit')
parser.add_argument('--ReLU_RBn', type=float, default=-14.3472, help='resistance in nonlinear circuit')
parser.add_argument('--ReLU_Wn', type=float, default=16.6550, help='width of the transistor')
parser.add_argument('--ReLU_Ln', type=float, default=-21.7591, help='length of the transistor')
# learnable hard sigmoid circuits
parser.add_argument('--HS_Rn', type=float, default=-0.3766, help='resistance in nonlinear circuit')
parser.add_argument('--HS_Wn', type=float, default=0.5192, help='width of the transistor')
parser.add_argument('--HS_Ln', type=float, default=4.6293, help='length of the transistor')
# learnable negative weight circuits
parser.add_argument('--NEG_R1n', type=float, default=-7.0846, help='resistance in nonlinear circuit')
parser.add_argument('--NEG_R2n', type=float, default=-2.5695, help='resistance in nonlinear circuit')
parser.add_argument('--NEG_R3n', type=float, default=16.1542, help='resistance in nonlinear circuit')
parser.add_argument('--NEG_W1n', type=float, default=13.8696, help='width of the transistor')
parser.add_argument('--NEG_L1n', type=float, default=2.7635, help='length of the transistor')
parser.add_argument('--NEG_W2n', type=float, default=1.2674, help='width of the transistor')
parser.add_argument('--NEG_L2n', type=float, default=-10.4803, help='length of the transistor')
parser.add_argument('--NEG_W3n', type=float, default=-6.4386, help='width of the transistor')
parser.add_argument('--NEG_L3n', type=float, default=5.1005, help='length of the transistor')
# machine-learning-related hyperparameters
# dataset-related
parser.add_argument('--task', type=str, default='normal', help='train normal pNN or split manufacturing, or temporal information')
parser.add_argument('--DATASET', type=int, default=0, help='index of training dataset')
parser.add_argument('--DataPath', type=str, default='./dataset', help='path to dataset')
# data augmentation
parser.add_argument('--InputNoise', type=float, default=0., help='noise of input signal')
parser.add_argument('--IN_test', type=float, default=0., help='noise of input signal for test')
parser.add_argument('--R_train', type=int, default=1, help='number of sampling for input noise in training')
parser.add_argument('--R_test', type=int, default=1, help='number of sampling for input noise in testing')
# temporal information processing
parser.add_argument('--N_time', type=int, default=32, help='number of sampling for temporal information processing')
# regularization
parser.add_argument('--pathnorm', type=str2bool, default=False, help='path-norm as regularization for improving robustness against input noise')
# network-related
parser.add_argument('--hidden', type=list, default=[5], help='topology of the hidden layers')
parser.add_argument('--skipconnection', type=str2bool, default=False, help='whether there are skip connections in the network')
# training-related
parser.add_argument('--SEED', type=int, default=0, help='random seed')
parser.add_argument('--DEVICE', type=str, default='cpu', help='device for training')
parser.add_argument('--PATIENCE', type=int, default=500, help='patience for early-stopping')
parser.add_argument('--EPOCH', type=int, default=10**10, help='maximal epochs')
parser.add_argument('--LR', type=float, default=0.1, help='learning rate')
parser.add_argument('--PROGRESSIVE', type=str2bool, default=True, help='whether the learning rate will be adjusted')
parser.add_argument('--LR_PATIENCE', type=int, default=100, help='patience for updating learning rate')
parser.add_argument('--LR_DECAY', type=float, default=0.5, help='decay of learning rate for progressive lr')
parser.add_argument('--LR_MIN', type=float, default=1e-4, help='minimal learning rate for stop training')
# evaluation
parser.add_argument('--metric', type=str, default='acc', help='nominal accuracy or measuring-aware accuracy')
parser.add_argument('--SoftEva', type=str2bool, default=True, help='if True, evaluate only existing models, otherwise check all models')
# server-related
parser.add_argument('--TIMELIMITATION', type=float, default=71, help='maximal running time (in hour)')
# hardware-related hyperparameters
# aging-related hyperparameters
parser.add_argument('--MODE', type=str, default='nominal', help='training mode: aging, nominal')
parser.add_argument('--M_train', type=int, default=1, help='number of stochastic aging models during training')
parser.add_argument('--K_train', type=int, default=1, help='number of temporal sampling during training')
parser.add_argument('--M_test', type=int, default=1, help='number of stochastic aging models for testing')
parser.add_argument('--K_test', type=int, default=1, help='number of temporal sampling for testing')
parser.add_argument('--t_test_max', type=int, default=1, help='test time interval')
parser.add_argument('--integration', type=str, default='MC', help='method for integration: Monte-Carlo, Gaussian Quadrature')
# variation-related hyperparameters
parser.add_argument('--N_train', type=int, default=20, help='number of sampling for variation during training')
parser.add_argument('--e_train', type=float, default=0.1, help='variation during training')
parser.add_argument('--N_test', type=int, default=20, help='number of sampling for variation for testing')
parser.add_argument('--e_test', type=int, default=0.1, help='variation for testing')
# power
parser.add_argument('--powerestimator', type=str, default='none', help='the penalty term for encouraging lower energy')
parser.add_argument('--powerbalance', type=float, default=0., help='the scaling term for energy vs. accuracy')
parser.add_argument('--estimatorbalance', type=float, default=0., help='the scaling term for energy & weight decay')
parser.add_argument('--pgmin', type=float, default=1e-7, help='minimal printable conductance gmin')
# area
parser.add_argument('--areaestimator', type=str, default='none', help='the penalty term for encouraging lower area')
parser.add_argument('--areabalance', type=float, default=0., help='the scaling term for area vs. accuracy')
parser.add_argument('--area_theta', type=float, default=0.15, help='area of a single printed resistor mm^2')
parser.add_argument('--area_act', type=float, default=30., help='area of a single printed act circuit mm^2')
parser.add_argument('--area_neg', type=float, default=22.7, help='area of a single printed neg circuit mm^2')
# circuit learnability
parser.add_argument('--lnc', type=str2bool, default=True, help='learnable nonlinear components')
# log-file-related information
parser.add_argument('--projectname', type=str, default='project', help='name of the project')
parser.add_argument('--temppath', type=str, default='/temp', help='path to temp files')
parser.add_argument('--logfilepath', type=str, default='/log', help='path to log files')
parser.add_argument('--report_freq', type=int, default=10, help='write log in every N epochs')
parser.add_argument('--recording', type=str2bool, default=False, help='save information in each epoch')
parser.add_argument('--recordpath', type=str, default='/record', help='save information in each epoch')
parser.add_argument('--savepath', type=str, default='/experiment', help='save information in each epoch')
parser.add_argument('--loglevel', type=str, default='info', help='level of message logger')