from keras.models import Model, load_model
from subtlenet import config
import subtlenet.generators.gen as gen
from paths import basedir
from subtlenet.backend.layers import *
gen.truncate = int(argv[1])
config.limit = int(argv[2])
name = 'dense'
print 'inferring', name
shallow = load_model('dense_models/classifier_v4_trunc%i_limit%i_best.h5' %
(gen.truncate, config.limit),
custom_objects={'DenseBroadcast': DenseBroadcast})
coll = gen.make_coll(basedir + '/PARTITION/*_CATEGORY.npy')
msd_norm_factor = 1. / config.max_mass
pt_norm_factor = 1. / (config.max_pt - config.min_pt)
msd_index = config.gen_singletons['msd']
pt_index = config.gen_singletons['pt']
def predict_t(data):
msd = data['singletons'][:, msd_index] * msd_norm_factor
pt = (data['singletons'][:, pt_index] - config.min_pt) * pt_norm_factor
if msd.shape[0] > 0:
if config.limit:
particles = data['particles'][:, :config.limit, :gen.truncate]
else:
particles = data['particles'][:, :, :gen.truncate]
from paths import basedir, figsdir
### some global definitions ###
NEPOCH = 20
TRAIN_BASELINE = False
generator.truncate = int(argv[1])
config.limit = int(argv[2])
config.bin_decorr = False
APOSTLE = 'v4_trunc%i_limit%i'%(generator.truncate, config.limit)
modeldir = 'mse_adversary/'
system('mkdir -p %s'%modeldir)
system('cp %s %s/train_%s.py'%(argv[0], modeldir, APOSTLE))
### instantiate data loaders ###
top = make_coll(basedir + '/PARTITION/Top_*_CATEGORY.npy')
qcd = make_coll(basedir + '/PARTITION/QCD_*_CATEGORY.npy')
data = [top, qcd]
dims = get_dims(top)
### first build the classifier! ###
# set up data
opts = {
'learn_mass' : True,
'learn_pt' : True,
'decorr_mass':False
}
classifier_train_gen = generate(data, partition='train', batch=1000, **opts)
classifier_validation_gen = generate(data, partition='validate', batch=10000, **opts)
OUTPUT = figsdir + '/'
system('mkdir -p %s' % OUTPUT)
components = [
'singletons',
'shallow',
'baseline2_7_100',
'kltest_7_100',
'categorical_crossentropy2_7_100',
'categorical_crossentropytest2_7_100',
'categorical_crossentropytesttest2_7_100',
]
colls = {
't':
make_coll(basedir + '/PARTITION/Top_*_CATEGORY.npy',
categories=components),
'q':
make_coll(basedir + '/PARTITION/QCD_*_CATEGORY.npy',
categories=components),
}
# run DNN
def predict(data, model):
return data[model]
def access(data, v):
return data['singletons'][:, config.gen_singletons[v]]
# 'trunc4_limit50_best',
'trunc7_limit100_best',
]
components_gen = [
'singletons',
'shallow_best',
# 'baseline_trunc4_limit50_best',
'baseline_Adam_7_100',
]
basedir = '/local/snarayan/genarrays/v_deepgen_4_finegrid_small'
basedir_gen = '/fastscratch/snarayan/genarrays/v_deepgen_4_small/'
colls = {
't' : make_coll(basedir + '/PARTITION/Top_*_CATEGORY.npy',categories=components),
'q' : make_coll(basedir + '/PARTITION/QCD_*_CATEGORY.npy',categories=components),
}
colls_gen = {
't' : make_coll(basedir_gen + '/PARTITION/Top_*_CATEGORY.npy',categories=components_gen),
'q' : make_coll(basedir_gen + '/PARTITION/QCD_*_CATEGORY.npy',categories=components_gen),
}
# run DNN
def predict(data,model):
return data[model]
def access(data, v):
return data['singletons'][:,config.gen_singletons[v]]
'shallow_nopt',
'dense',
# 'trunc4_limit10_best',
# 'trunc7_limit10_best',
'trunc4_limit50_best',
'smeared_trunc7_limit50_best',
# 'baseline_trunc4_limit50_best',
# 'trunc7_limit50_best',
# 'dense_trunc7_limit50_best',
# 'dense_trunc4_limit100_best',
# 'trunc7_limit100_best',
# 'trunc4_limit100_best',
]
colls = {
't' : make_coll(paths.basedir + '/PARTITION/Top_*_CATEGORY.npy',categories=components),
'q' : make_coll(paths.basedir + '/PARTITION/QCD_*_CATEGORY.npy',categories=components),
}
# run DNN
def predict(data,model):
return data[model]
def access(data, v):
return data['singletons'][:,config.gen_singletons[v]]
def div(data, num, den):
return access(data, num) / np.clip(access(data, den), 0.0001, 999)
f_vars = {
]
components_base = map(lambda x: x.replace('baseline', 'baseline_Adam'),
components)
basedir = {
# 'base' : '/data/t3serv014/snarayan/deep/v_deepgen_4_eta5_small/',
'0p02': '/local/snarayan/genarrays/v_deepgen_4_noetaphi_small/',
# '0p001' : '/local/snarayan/genarrays/v_deepgen_4_finegrid_small/',
}
basedir_base = '/fastscratch/snarayan/genarrays/v_deepgen_4_small/'
colls = {}
for d, b in basedir.iteritems():
colls[d] = {
't': make_coll(b + '/PARTITION/Top_*_CATEGORY.npy',
categories=components),
'q': make_coll(b + '/PARTITION/QCD_*_CATEGORY.npy',
categories=components),
}
colls_base = {
't':
make_coll(basedir_base + '/PARTITION/Top_*_CATEGORY.npy',
categories=components_base),
'q':
make_coll(basedir_base + '/PARTITION/QCD_*_CATEGORY.npy',
categories=components_base),
}
# run DNN
