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run_experiments.py
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run_experiments.py
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# runs all experiments and saves all results for given datasets
#
# USAGE:
# python run_experiments.py dataset1 dataset2 ...
import sys
import experiments as ex
def run_experiments_power():
n_hiddens = 100
n_layers = 5
n_comps = 10
act_fun = 'relu'
mode = 'sequential'
ex.load_data('power')
ex.train_made([n_hiddens]*1, act_fun, mode)
ex.train_made([n_hiddens]*2, act_fun, mode)
ex.train_mog_made([n_hiddens]*1, act_fun, n_comps, mode)
ex.train_mog_made([n_hiddens]*2, act_fun, n_comps, mode)
ex.train_realnvp([n_hiddens]*1, 'tanh', 'relu', n_layers*1)
ex.train_realnvp([n_hiddens]*2, 'tanh', 'relu', n_layers*1)
ex.train_realnvp([n_hiddens]*1, 'tanh', 'relu', n_layers*2)
ex.train_realnvp([n_hiddens]*2, 'tanh', 'relu', n_layers*2)
ex.train_maf([n_hiddens]*1, act_fun, n_layers*1, mode)
ex.train_maf([n_hiddens]*2, act_fun, n_layers*1, mode)
ex.train_maf([n_hiddens]*1, act_fun, n_layers*2, mode)
ex.train_maf([n_hiddens]*2, act_fun, n_layers*2, mode)
ex.train_maf_on_made([n_hiddens]*1, act_fun, n_layers, n_comps, mode)
ex.train_maf_on_made([n_hiddens]*2, act_fun, n_layers, n_comps, mode)
def run_experiments_gas():
n_hiddens = 100
n_layers = 5
n_comps = 10
act_fun = 'tanh'
mode = 'sequential'
ex.load_data('gas')
ex.train_made([n_hiddens]*1, act_fun, mode)
ex.train_made([n_hiddens]*2, act_fun, mode)
ex.train_mog_made([n_hiddens]*1, act_fun, n_comps, mode)
ex.train_mog_made([n_hiddens]*2, act_fun, n_comps, mode)
ex.train_realnvp([n_hiddens]*1, 'tanh', 'relu', n_layers*1)
ex.train_realnvp([n_hiddens]*2, 'tanh', 'relu', n_layers*1)
ex.train_realnvp([n_hiddens]*1, 'tanh', 'relu', n_layers*2)
ex.train_realnvp([n_hiddens]*2, 'tanh', 'relu', n_layers*2)
ex.train_maf([n_hiddens]*1, act_fun, n_layers*1, mode)
ex.train_maf([n_hiddens]*2, act_fun, n_layers*1, mode)
ex.train_maf([n_hiddens]*1, act_fun, n_layers*2, mode)
ex.train_maf([n_hiddens]*2, act_fun, n_layers*2, mode)
ex.train_maf_on_made([n_hiddens]*1, act_fun, n_layers, n_comps, mode)
ex.train_maf_on_made([n_hiddens]*2, act_fun, n_layers, n_comps, mode)
def run_experiments_hepmass():
n_hiddens = 512
n_layers = 5
n_comps = 10
act_fun = 'relu'
mode = 'sequential'
ex.load_data('hepmass')
ex.train_made([n_hiddens]*1, act_fun, mode)
ex.train_made([n_hiddens]*2, act_fun, mode)
ex.train_mog_made([n_hiddens]*1, act_fun, n_comps, mode)
ex.train_mog_made([n_hiddens]*2, act_fun, n_comps, mode)
ex.train_realnvp([n_hiddens]*1, 'tanh', 'relu', n_layers*1)
ex.train_realnvp([n_hiddens]*2, 'tanh', 'relu', n_layers*1)
ex.train_realnvp([n_hiddens]*1, 'tanh', 'relu', n_layers*2)
ex.train_realnvp([n_hiddens]*2, 'tanh', 'relu', n_layers*2)
ex.train_maf([n_hiddens]*1, act_fun, n_layers*1, mode)
ex.train_maf([n_hiddens]*2, act_fun, n_layers*1, mode)
ex.train_maf([n_hiddens]*1, act_fun, n_layers*2, mode)
ex.train_maf([n_hiddens]*2, act_fun, n_layers*2, mode)
ex.train_maf_on_made([n_hiddens]*1, act_fun, n_layers, n_comps, mode)
ex.train_maf_on_made([n_hiddens]*2, act_fun, n_layers, n_comps, mode)
def run_experiments_miniboone():
n_hiddens = 512
n_layers = 5
n_comps = 10
act_fun = 'relu'
mode = 'sequential'
ex.load_data('miniboone')
ex.train_made([n_hiddens]*1, act_fun, mode)
ex.train_made([n_hiddens]*2, act_fun, mode)
ex.train_mog_made([n_hiddens]*1, act_fun, n_comps, mode)
ex.train_mog_made([n_hiddens]*2, act_fun, n_comps, mode)
ex.train_realnvp([n_hiddens]*1, 'tanh', 'relu', n_layers*1)
ex.train_realnvp([n_hiddens]*2, 'tanh', 'relu', n_layers*1)
ex.train_realnvp([n_hiddens]*1, 'tanh', 'relu', n_layers*2)
ex.train_realnvp([n_hiddens]*2, 'tanh', 'relu', n_layers*2)
ex.train_maf([n_hiddens]*1, act_fun, n_layers*1, mode)
ex.train_maf([n_hiddens]*2, act_fun, n_layers*1, mode)
ex.train_maf([n_hiddens]*1, act_fun, n_layers*2, mode)
ex.train_maf([n_hiddens]*2, act_fun, n_layers*2, mode)
ex.train_maf_on_made([n_hiddens]*1, act_fun, n_layers, n_comps, mode)
ex.train_maf_on_made([n_hiddens]*2, act_fun, n_layers, n_comps, mode)
def run_experiments_bsds300():
n_layers = 5
n_comps = 10
act_fun = 'relu'
mode = 'sequential'
ex.load_data('bsds300')
for n_hiddens in [512, 1024]:
ex.train_made([n_hiddens]*1, act_fun, mode)
ex.train_made([n_hiddens]*2, act_fun, mode)
ex.train_mog_made([n_hiddens]*1, act_fun, n_comps, mode)
ex.train_mog_made([n_hiddens]*2, act_fun, n_comps, mode)
ex.train_realnvp([n_hiddens]*1, 'tanh', 'relu', n_layers*1)
ex.train_realnvp([n_hiddens]*2, 'tanh', 'relu', n_layers*1)
ex.train_realnvp([n_hiddens]*1, 'tanh', 'relu', n_layers*2)
ex.train_realnvp([n_hiddens]*2, 'tanh', 'relu', n_layers*2)
ex.train_maf([n_hiddens]*1, act_fun, n_layers*1, mode)
ex.train_maf([n_hiddens]*2, act_fun, n_layers*1, mode)
ex.train_maf([n_hiddens]*1, act_fun, n_layers*2, mode)
ex.train_maf([n_hiddens]*2, act_fun, n_layers*2, mode)
ex.train_maf_on_made([n_hiddens]*1, act_fun, n_layers, n_comps, mode)
ex.train_maf_on_made([n_hiddens]*2, act_fun, n_layers, n_comps, mode)
def run_experiments_mnist():
n_hiddens = 1024
n_layers = 5
n_comps = 10
act_fun = 'relu'
mode = 'sequential'
ex.load_data('mnist')
ex.train_made([n_hiddens]*2, act_fun, mode)
ex.train_made_cond([n_hiddens]*2, act_fun, mode)
ex.train_mog_made([n_hiddens]*2, act_fun, n_comps, mode)
ex.train_mog_made_cond([n_hiddens]*2, act_fun, n_comps, mode)
for i in [1, 2]:
ex.train_realnvp([n_hiddens]*2, 'tanh', 'relu', n_layers*i)
ex.train_realnvp_cond([n_hiddens]*2, 'tanh', 'relu', n_layers*i)
ex.train_maf([n_hiddens]*2, act_fun, n_layers*i, mode)
ex.train_maf_cond([n_hiddens]*2, act_fun, n_layers*i, mode)
ex.train_maf_on_made([n_hiddens]*2, act_fun, n_layers, n_comps, mode)
ex.train_maf_on_made_cond([n_hiddens]*2, act_fun, n_layers, n_comps, mode)
def run_experiments_cifar10():
n_layers = 5
n_comps = 10
act_fun = 'relu'
mode = 'random'
ex.load_data('cifar10')
for n_hiddens in [1024, 2048]:
ex.train_made([n_hiddens]*1, act_fun, mode)
ex.train_made([n_hiddens]*2, act_fun, mode)
ex.train_made_cond([n_hiddens]*1, act_fun, mode)
ex.train_made_cond([n_hiddens]*2, act_fun, mode)
ex.train_mog_made([n_hiddens]*1, act_fun, n_comps, mode)
ex.train_mog_made([n_hiddens]*2, act_fun, n_comps, mode)
ex.train_mog_made_cond([n_hiddens]*1, act_fun, n_comps, mode)
ex.train_mog_made_cond([n_hiddens]*2, act_fun, n_comps, mode)
for i in [1, 2]:
ex.train_realnvp([n_hiddens]*1, 'tanh', 'relu', n_layers*i)
ex.train_realnvp([n_hiddens]*2, 'tanh', 'relu', n_layers*i)
ex.train_realnvp_cond([n_hiddens]*1, 'tanh', 'relu', n_layers*i)
ex.train_realnvp_cond([n_hiddens]*2, 'tanh', 'relu', n_layers*i)
ex.train_maf([n_hiddens]*1, act_fun, n_layers*i, mode)
ex.train_maf([n_hiddens]*2, act_fun, n_layers*i, mode)
ex.train_maf_cond([n_hiddens]*1, act_fun, n_layers*i, mode)
ex.train_maf_cond([n_hiddens]*2, act_fun, n_layers*i, mode)
ex.train_maf_on_made([n_hiddens]*1, act_fun, n_layers, n_comps, mode)
ex.train_maf_on_made([n_hiddens]*2, act_fun, n_layers, n_comps, mode)
ex.train_maf_on_made_cond([n_hiddens]*1, act_fun, n_layers, n_comps, mode)
ex.train_maf_on_made_cond([n_hiddens]*2, act_fun, n_layers, n_comps, mode)
def main():
methods = dict()
methods['power'] = run_experiments_power
methods['gas'] = run_experiments_gas
methods['hepmass'] = run_experiments_hepmass
methods['miniboone'] = run_experiments_miniboone
methods['bsds300'] = run_experiments_bsds300
methods['mnist'] = run_experiments_mnist
methods['cifar10'] = run_experiments_cifar10
for name in sys.argv[1:]:
methods[name]()
if __name__ == '__main__':
main()