else:
x_mat = np.loadtxt('in_data/gauss_train.txt')
n = ''
NP_SEED = 8
OPT_SEED = 3
LOG_NAME = 'gauss_l1_' + n + str(NP_SEED) + '_' + str(OPT_SEED)
#Train with l1 penalty until convergence
path_pen(x_mat=x_mat,
y_mat=x_mat,
NP_SEED=NP_SEED,
TF_SEED=OPT_SEED,
BATCH_DIV=5,
LOAD_APDX=None,
DIM_H=dim_h,
DIM_Z=dim_z,
EPOCHS=epochs1,
LBDA_L1=LBDA_L1,
START_BEST=100,
LOG_NAME=LOG_NAME,
PRINT=True)
os.system('bash ../copy_params.sh phase0_' + LOG_NAME + '_best ' + LOG_NAME +
'_best')
#Find thershold with 4 remaining connections
zero_thresh = 0.01
while count_conns('phase0_' + LOG_NAME + '_best', zero_thresh) > 4:
zero_thresh += 0.01
#Threshold and train without penalty with fixed zeros until convergennce
exec(sys.argv[arg])
if NOISE:
x_mat = np.loadtxt('in_data/gauss_train_n.txt')
n = 'n_'
NP_SEED = 4
OPT_SEED = 2
else:
x_mat = np.loadtxt('in_data/gauss_train.txt')
n = ''
NP_SEED = 2
OPT_SEED = 1
LOG_NAME = 'gauss_spae_' + n + str(NP_SEED) + '_' + str(OPT_SEED)
#Train with KL penalty until convergence
path_pen(x_mat=x_mat,
y_mat=x_mat,
NP_SEED=OPT_SEED,
TF_SEED=OPT_SEED,
BATCH_DIV=5,
LOAD_APDX=None,
DIM_H=dim_h,
DIM_Z=dim_z,
EPOCHS=epochs1,
LBDA_KL=LBDA_KL,
START_BEST=1000,
LOG_NAME=LOG_NAME)
os.system('bash ../copy_params.sh phase0_' + LOG_NAME + '_best ' + LOG_NAME +
'_best')
import sys,os
sys.path.insert(1,'..')
from path_pen import path_pen
x_mat=np.loadtxt('in_data/news_train.txt')
OPT_SEED=1
LBDA_L1=0.004
ZERO_THRESH=.0081
DIM_Z=4
epochs1=100000
epochs2=100000
epochs3=100000
epochs4=100000
dim_h=50
LOG_NAME = 'news_l14'
##Train with l1 penalty until convergence
path_pen(x_mat=x_mat, y_mat=x_mat, NP_SEED=OPT_SEED, TF_SEED=OPT_SEED, BATCH_DIV=5, LOAD_APDX=None, DIM_H=dim_h, DIM_Z=DIM_Z, EPOCHS=epochs1, LBDA_L1=LBDA_L1, LOG_NAME=LOG_NAME, START_BEST=2000)
os.system('bash ../copy_params.sh phase0_'+LOG_NAME+'_best '+LOG_NAME+'_best')
#Threshold and train without penalty with fixed zeros until convergennce
path_pen(x_mat=x_mat, y_mat=x_mat, NP_SEED=OPT_SEED, TF_SEED=OPT_SEED, BATCH_DIV=5, LOAD_APDX='phase0_'+LOG_NAME+'_best', DIM_H=dim_h, DIM_Z=DIM_Z, EPOCHS=epochs2, ZERO_THRESH=ZERO_THRESH, LOG_NAME=LOG_NAME, START_BEST=1000)
os.system('bash ../copy_params.sh phase4_'+LOG_NAME+'_best '+LOG_NAME+'_best')
epochs1 = 100000
epochs2 = 100000
epochs3 = 100000
epochs4 = 100000
dim_h = 50
LOG_NAME = 'news_pl4'
SAVE_NAME = LOG_NAME
#Train without penalty until convergence
path_pen(x_mat=x_mat,
y_mat=x_mat,
NP_SEED=OPT_SEED,
TF_SEED=OPT_SEED,
BATCH_DIV=5,
LOAD_APDX=None,
DIM_H=dim_h,
DIM_Z=DIM_Z,
EPOCHS=epochs1,
LOG_NAME=LOG_NAME,
START_BEST=2000)
os.system('bash ../copy_params.sh phase0_' + LOG_NAME + '_best ' + LOG_NAME +
'_best')
#Train with exclusive lasso until convergence
path_pen(x_mat=x_mat,
y_mat=x_mat,
NP_SEED=OPT_SEED,
TF_SEED=OPT_SEED,
BATCH_DIV=5,
LOAD_APDX='phase0_' + SAVE_NAME + '_best',
exec(sys.argv[arg])
if NOISE:
x_mat = np.loadtxt('in_data/gauss_train_n.txt')
n = 'n_'
NP_SEED = 5
OPT_SEED = 2
else:
x_mat = np.loadtxt('in_data/gauss_train.txt')
n = ''
NP_SEED = 4
OPT_SEED = 1
LOG_NAME = 'gauss_ae_' + n + str(NP_SEED) + '_' + str(OPT_SEED)
#Train without penalty until convergence
path_pen(x_mat=x_mat,
y_mat=x_mat,
NP_SEED=NP_SEED,
TF_SEED=OPT_SEED,
BATCH_DIV=5,
LOAD_APDX=None,
DIM_H=dim_h,
DIM_Z=dim_z,
EPOCHS=epochs1,
START_BEST=100,
LOG_NAME=LOG_NAME,
PRINT=True)
os.system('bash ../copy_params.sh phase0_' + LOG_NAME + '_best ' + LOG_NAME +
'_best')
