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_custom(data_trn, data_val, labels_trn, labels_val):
n_hiddens = int(data_trn.shape[1] * 1.2)
n_layers = 5
n_comps = 10
act_fun = 'relu'
mode = 'sequential'
ex.load_data('custom', data_trn, data_val, labels_trn, labels_val)
model = ex.train_maf_cond_custom([n_hiddens] * 2, act_fun, n_layers, mode)
# ex.train_maf_on_made_cond([n_hiddens]*2, act_fun, n_layers, n_comps, mode)
return model
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 collect_results(data, n_hiddens, n_layers, n_comps, n_layers_comps, act_funs, modes, has_cond):
print('collecting for {0}...'.format(data))
ex.load_data(data)
# create file to write to
filename = ('{0}_{1}_bpp.txt' if bits_per_pixel else '{0}_{1}.txt').format(data, split)
util.make_folder(root_results)
f = open(root_results + filename, 'w')
f.write('Results for {0}\n'.format(data))
f.write('\n')
for act, mode in itertools.product(act_funs, modes):
f.write('actf: {0}\n'.format(act))
f.write('mode: {0}\n'.format(mode))
f.write('\n')
# gaussian
f.write('Gaussian\n')
res, err = ex.fit_and_evaluate_gaussian(split, cond=False, use_image_space=bits_per_pixel)
if bits_per_pixel:
res, err = calc_bits_per_pixel(res, err)
f.write(' {0:.2f} +/- {1:.2f}\n'.format(res, n_err * err))
if has_cond:
f.write('conditional\n')
res, err = ex.fit_and_evaluate_gaussian(split, cond=True, use_image_space=bits_per_pixel)
if bits_per_pixel:
res, err = calc_bits_per_pixel(res, err)
f.write(' {0:.2f} +/- {1:.2f}\n'.format(res, n_err * err))
f.write('\n')
# made
f.write('MADE 1 comp\n')
for nh in n_hiddens:
f.write(' [1 x {0}]: {1}\n'.format(nh, result('made', mode, [nh]*1, act)))
f.write(' [2 x {0}]: {1}\n'.format(nh, result('made', mode, [nh]*2, act)))
if has_cond:
f.write('conditional\n')
for nh in n_hiddens:
f.write(' [1 x {0}]: {1}\n'.format(nh, result('made_cond', mode, [nh]*1, act)))
f.write(' [2 x {0}]: {1}\n'.format(nh, result('made_cond', mode, [nh]*2, act)))
f.write('\n')
# mog made
for nc in n_comps:
f.write('MADE {0} comp\n'.format(nc))
for nh in n_hiddens:
f.write(' [1 x {0}]: {1}\n'.format(nh, result('made', mode, [nh]*1, act, nc)))
f.write(' [2 x {0}]: {1}\n'.format(nh, result('made', mode, [nh]*2, act, nc)))
if has_cond:
f.write('conditional\n')
for nh in n_hiddens:
f.write(' [1 x {0}]: {1}\n'.format(nh, result('made_cond', mode, [nh]*1, act, nc)))
f.write(' [2 x {0}]: {1}\n'.format(nh, result('made_cond', mode, [nh]*2, act, nc)))
f.write('\n')
# real nvp
for nl in n_layers:
f.write('RealNVP {0} layers\n'.format(nl))
for nh in n_hiddens:
f.write(' [1 x {0}]: {1}\n'.format(nh, result('realnvp', None, [nh]*1, 'tanhrelu', nl, True)))
f.write(' [2 x {0}]: {1}\n'.format(nh, result('realnvp', None, [nh]*2, 'tanhrelu', nl, True)))
if has_cond:
f.write('conditional\n')
for nh in n_hiddens:
f.write(' [1 x {0}]: {1}\n'.format(nh, result('realnvp_cond', None, [nh]*1, 'tanhrelu', nl, True)))
f.write(' [2 x {0}]: {1}\n'.format(nh, result('realnvp_cond', None, [nh]*2, 'tanhrelu', nl, True)))
f.write('\n')
# maf
for nl in n_layers:
f.write('MAF {0} layers\n'.format(nl))
for nh in n_hiddens:
f.write(' [1 x {0}]: {1}\n'.format(nh, result('maf', mode, [nh]*1, act, nl, True)))
f.write(' [2 x {0}]: {1}\n'.format(nh, result('maf', mode, [nh]*2, act, nl, True)))
if has_cond:
f.write('conditional\n')
for nh in n_hiddens:
f.write(' [1 x {0}]: {1}\n'.format(nh, result('maf_cond', mode, [nh]*1, act, nl, True)))
f.write(' [2 x {0}]: {1}\n'.format(nh, result('maf_cond', mode, [nh]*2, act, nl, True)))
f.write('\n')
# maf on made
for nl, nc in n_layers_comps:
f.write('MAF {0} layers on MADE {1} comp\n'.format(nl, nc))
for nh in n_hiddens:
f.write(' [1 x {0}]: {1}\n'.format(nh, result('maf_on_made', mode, [nh]*1, act, [nl, nc], True)))
f.write(' [2 x {0}]: {1}\n'.format(nh, result('maf_on_made', mode, [nh]*2, act, [nl, nc], True)))
if has_cond:
f.write('conditional\n')
for nh in n_hiddens:
f.write(' [1 x {0}]: {1}\n'.format(nh, result('maf_on_made_cond', mode, [nh]*1, act, [nl, nc], True)))
f.write(' [2 x {0}]: {1}\n'.format(nh, result('maf_on_made_cond', mode, [nh]*2, act, [nl, nc], True)))
f.write('\n')
# close file
f.close()
