is assigned an index (dense). The index-to-object mapping is stored in files
"entity_ids.del" and "relation_ids.del", resp. The triples (as indexes) are stored in
files "train.del", "valid.del", and "test.del". Additionally, the splits
"train_sample.del" (a random subset of train) and "valid_without_unseen.del" and
"test_without_unseen.del" are stored. The "test/valid_without_unseen.del" files are
subsets of "valid.del" and "test.del" resp. where all triples containing entities
or relations not existing in "train.del" have been filtered out.
Metadata information is stored in a file "dataset.yaml".
"""
import util
if __name__ == "__main__":
args = util.default_parser().parse_args()
field_map = {
"S": args.subject_field,
"P": args.predicate_field,
"O": args.object_field,
}
print(f"Preprocessing {args.folder}...")
# register raw splits
train_raw = util.RawSplit(
file="train.txt",
field_map=field_map,
collect_entities=True,
collect_relations=True,
)
colors = [vz.colors.all_one_lg, vz.colors.linear_lg]
models = [
MLP.MLP(10, cg.uniform, n_units=100),
MLP.MLP(10, cg.linear, n_units=100),
]
comp_ratios = np.linspace(0.1, 1.0, 20)
acc_dict = {}
ratios_dict = {}
for name, model in zip(names, models):
util.load_or_train_model(model, train, test, args)
acc_dict[name] = util.sweep_idp(model, test, comp_ratios, args)
ratios_dict[name] = [100. * cr for cr in comp_ratios]
filename = "MLP_{}".format(args.dataset)
vz.plot(ratios_dict,
acc_dict,
names,
filename,
colors=colors,
folder=args.figure_path,
ext=args.ext,
title='MLP (MNIST)',
xlabel='IDP (%)',
ylabel='Classification Accuracy (%)',
ylim=(85, 100))
if __name__ == '__main__':
args = util.default_parser('MLP Example').parse_args()
run(args)
colors = [vz.colors.all_one_lg, vz.colors.linear_lg]
models = [
BinaryNet.BinaryConvNet(10, cg.uniform, 'all'),
BinaryNet.BinaryConvNet(10, cg.linear, 'slow_exp'),
]
comp_ratios = np.linspace(0.1, 1.0, 20)
acc_dict = {}
ratios_dict = {}
for name, model in zip(names, models):
util.load_or_train_model(model, train, test, args)
acc_dict[name] = util.sweep_idp(model, test, comp_ratios, args)
ratios_dict[name] = [100. * cr for cr in comp_ratios]
filename = "BinaryNet_{}".format(args.dataset)
vz.plot(ratios_dict,
acc_dict,
names,
filename,
colors=colors,
folder=args.figure_path,
ext=args.ext,
title='BinaryNet (MNIST)',
xlabel='IDP (%)',
ylabel='Classification Accuracy (%)',
ylim=(90, 100))
if __name__ == '__main__':
args = util.default_parser('BinaryNet Example').parse_args()
run(args)
colors = [vz.colors.all_one_lg, vz.colors.linear_lg]
models = [
VGG.VGG(10, cg.uniform, 'all'),
VGG.VGG(10, cg.linear, 'slow_exp')
]
comp_ratios = np.linspace(0.1, 1.0, 20)
acc_dict = {}
ratios_dict = {}
for name, model in zip(names, models):
util.load_or_train_model(model, train, test, args)
acc_dict[name] = util.sweep_idp(model, test, comp_ratios, args)
ratios_dict[name] = [100. * cr for cr in comp_ratios]
filename = "VGG_{}".format(args.dataset)
vz.plot(ratios_dict,
acc_dict,
names,
filename,
colors=colors,
folder=args.figure_path,
ext=args.ext,
title='VGG (CIFAR-10)',
xlabel='IDP (%)',
ylabel='Classification Accuracy (%)',
ylim=(90, 100))
if __name__ == '__main__':
args = util.default_parser('VGG Example').parse_args()
run(args)
import os
import sys
sys.path.insert(0,
os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
import util
from ebnn.utils import binary_util
if __name__ == '__main__':
parser = util.default_parser('MLP Example')
args = parser.parse_args()
# get the dataset (default is MNIST)
train, test = util.get_dataset(args.dataset)
x_str = binary_util.np_to_floatC(train._datasets[0][0:20], 'train_data',
'row_major')
y_str = binary_util.np_to_floatC(train._datasets[1][0:20], 'train_labels',
'row_major')
with open(args.dataset, 'w+') as fp:
fp.write(x_str)
fp.write(y_str)
import visualize
import cupy
import chainer
from chainer.datasets import get_mnist, get_cifar10
import chainer.functions as F
import numpy as np
from binary.bst import bst
import net
import util
from binary.ww_bconv_v3 import uniform_seq, harmonic_seq, linear_seq, exp_seq, uniform_exp_seq
parser = util.default_parser('Activation Function Experiment')
args = parser.parse_args()
train, test = util.get_dataset(args.dataset)
nclass = np.bincount(test._datasets[1]).shape[0]
large_settings = util.get_net_settings(args.dataset, size='large')
comp_ratios = [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
names = [
'binary', r'ternary $(\epsilon=0.5)$', r'ternary $(\epsilon=1)$',
r'ternary $(\epsilon=2)$'
]
colors = ['#762a83', '#5aae61', '#1b7837', '#00441b']
models = [
net.ApproxNet(nclass,
*large_settings,
m=0,
comp_f='id',
act='binary',
import visualize
import cupy
import chainer
from chainer.datasets import get_mnist, get_cifar10
import chainer.functions as F
import numpy as np
from binary.bst import bst
import net
import util
from binary.ww_bconv_v3 import uniform_seq, harmonic_seq, linear_seq, exp_seq, uniform_exp_seq
parser = util.default_parser('Weight Coefficents Experiment')
args = parser.parse_args()
train, test = util.get_dataset(args.dataset)
nclass = np.bincount(test._datasets[1]).shape[0]
large_settings = util.get_net_settings(args.dataset, size='large')
comp_ratios = [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
acc_dict = {}
ratios_dict = {}
names = ['all-one (standard)', 'harmonic', 'linear', 'exp', 'half-one']
colors = ['#e41a1c', '#377eb8', '#4daf4a', '#984ea3', '#ff7f00']
models = [
net.ApproxNet(nclass,
*large_settings,
m=1,
comp_f='id',
act='ternary',
coeffs_generator=uniform_seq),
import os
import sys
sys.path.insert(0,
os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
import numpy as np
import visualize as vz
import util
import idp.coeffs_generator as cg
args = util.default_parser('Generate Weight Functions').parse_args()
funcs = [cg.uniform, cg.harmonic, cg.linear, cg.uniform_exp]
names = ['all-one', 'harmonic', 'linear', 'half_one']
colors = [
vz.colors.all_one_lg, vz.colors.harmonic_lg, vz.colors.linear_lg,
vz.colors.half_one_lg
]
xs = {}
ys = {}
n = 16
for name, func in zip(names, funcs):
xs[name] = np.arange(n) + 1
ys[name] = func(n)
vz.plot(
xs,
ys,
names,
'coef_functions',
colors,
colors = [vz.colors.all_one_lg, vz.colors.linear_lg]
models = [
MobileNet.MobileNet(10, cg.uniform, 'all'),
MobileNet.MobileNet(10, cg.linear, 'slow_exp'),
]
comp_ratios = np.linspace(.1, 1, 20)
acc_dict = {}
ratios_dict = {}
for name, model in zip(names, models):
util.load_or_train_model(model, train, test, args)
acc_dict[name] = util.sweep_idp(model, test, comp_ratios, args)
ratios_dict[name] = [100. * cr for cr in comp_ratios]
filename = "MobileNet_{}".format(args.dataset)
vz.plot(ratios_dict,
acc_dict,
names,
filename,
colors=colors,
folder=args.figure_path,
ext=args.ext,
title='MobileNet (CIFAR-10)',
xlabel='IDP (%)',
ylabel='Classification Accuracy (%)')
if __name__ == '__main__':
args = util.default_parser('MobileNet Example').parse_args()
run(args)
MLP.MLP(10, cg.harmonic),
MLP.MLP(10, cg.linear),
MLP.MLP(10, cg.uniform_exp),
]
comp_ratios = np.linspace(0.1, 1.0, 20)
acc_dict = {}
ratios_dict = {}
for name, model in zip(names, models):
util.load_or_train_model(model, train, test, args)
acc_dict[name] = util.sweep_idp(model, test, comp_ratios, args)
ratios_dict[name] = [100. * cr for cr in comp_ratios]
filename = "MLP_coef_comparison_{}".format(args.dataset)
vz.plot(ratios_dict,
acc_dict,
names,
filename,
colors=colors,
folder=args.figure_path,
ext=args.ext,
xlabel='IDP (%)',
ylabel='Classification Accuracy (%)',
title='MLP (MNIST)',
legend_loc='lower right',
ylim=(85, 100))
if __name__ == '__main__':
args = util.default_parser('MLP Coef Functions Comparison').parse_args()
run(args)
