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grid_1_print.py
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#!/usr/bin/env python3
"""Grid search 1.
For MNIST <-> MNIST
"""
import re
shared_pattern = """\
--default_scratch "~/workspace/scratch/latent_transfer/" \
--config_A "mnist_0_nlatent100" --config_B "mnist_0_nlatent100" \
--config_classifier_A "mnist_classifier_0" --config_classifier_B "mnist_classifier_0" \
--n_latent 100 --n_latent_shared 8 \
--layers "512,512,512,512" \
--cls_layers "," \
--prior_loss_beta {plb} \
--unsup_align_loss_beta {ualb} \
--cls_loss_beta {clb} \
--n_sup {ns} \
--sig_extra "grid_1" \
--n_iters {ni} \
--use_interpolated {ui} \
--post_mortem=false \
"""
train_pattern = """\
run_ml_docker --no-it python3 ./train_joint2_mnist_family.py \
""" + shared_pattern
eval_pattern_list = [
"""\
run_ml_docker --no-it python3 ./evaluate_joint2_mnist_family.py \
""" + " " + shared_pattern + " " + """\
--load_ckpt_iter -1 \
--interpolate_labels "8,8,8,8,8,8,8" \
--nb_images_between_labels 4 \
--random_seed 1145141925 \
""", """\
run_ml_docker --no-it python3 ./evaluate_joint2_mnist_family.py \
""" + " " + shared_pattern + " " + """\
--load_ckpt_iter -1 \
--interpolate_labels "6,6,6,6,6,6,6" \
--nb_images_between_labels 4 \
--random_seed 1145141925 \
""", """\
run_ml_docker --no-it python3 ./evaluate_joint2_mnist_family.py \
""" + " " + shared_pattern + " " + """\
--load_ckpt_iter -1 \
--interpolate_labels "7,7,7,7,7,7,7" \
--nb_images_between_labels 4 \
--random_seed 1145141925 \
""", """\
run_ml_docker --no-it python3 ./evaluate_joint2_mnist_family.py \
""" + " " + shared_pattern + " " + """\
--load_ckpt_iter -1 \
--interpolate_labels "0,1,7,8,9,3,2" \
--nb_images_between_labels 4 \
--random_seed 1145141925 \
"""
]
n_latent_shared = 8
plb_base = 0.005
ualb_base = 1.0
clb_base = 0.05
train_cmds = []
eval_cmds = []
def add(plb, ualb, clb, ns, ni, ui, extra=''):
cmd = train_pattern.format(plb=plb, ualb=ualb, clb=clb, ns=ns, ni=ni, ui=ui)
cmd = re.sub(' +', ' ', cmd) + extra
# train_cmds.append(cmd)
for eval_pattern in eval_pattern_list:
cmd = eval_pattern.format(plb=plb, ualb=ualb, clb=clb, ns=ns, ni=ni, ui=ui)
cmd = re.sub(' +', ' ', cmd)
eval_cmds.append(cmd)
def main():
for plb in [plb_base]:
for ualb in [ualb_base]:
for clb in [clb_base]:
for ns in [-1]:
#for ns in [-1, 0, 10, 50, 100, 500, 1000, 5000, 10000]:
for ni in [20000]:
#for ui in [
# 'none', 'linear', 'linear-random', 'spherical',
# 'spherical-random'
#]:
for ui in ['none']:
if (clb == 0.0 and ns != -1) or (clb > 0.0 and ns == 0):
continue # no need to waste
add(plb=plb, ualb=ualb, clb=clb, ns=ns, ni=ni, ui=ui)
'''
add(plb=plb_base,
ualb=0.0,
clb=clb_base,
ns=-1,
ni=20000,
ui='none',
extra=' --use_domain=false')
'''
for _ in train_cmds + eval_cmds:
print(_)
if __name__ == '__main__':
main()