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_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_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_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)