def pento(n_trials):
ri = numpy.random.random_integers
state = DD()
with open('mnist_powerup_temp.yaml') as ymtmp:
state.yaml_string = ymtmp.read()
state.powerup_nunits = 240
state.powerup_npieces = 5
state.W_lr_scale = 0.04
state.p_lr_scale = 0.01
state.lr_rate = 0.1
state.l2_pen = 1e-5
state.l2_pen2 = 0.0000
state.init_mom = 0.5
state.final_mom = 0.5
state.decay_factor = 0.5
state.max_col_norm = 1.9365
state.max_col_norm2 = 1.8365
state.batch_size = 128
state.save_path = './'
n_pieces = [2, 3, 4, 5, 6, 8, 10, 12, 14, 16]
n_units = [200, 240, 280, 320, 360, 420, 480]
batch_sizes = [128, 256, 512]
learning_rates = numpy.logspace(numpy.log10(0.001), numpy.log10(1.0), 30)
learning_rate_scalers = numpy.logspace(numpy.log10(0.01), numpy.log10(1), 30)
l2_pen = numpy.logspace(numpy.log10(1e-6), numpy.log10(8*1e-3), 100)
max_col_norms = [1.7365, 1.8365, 1.9365, 2.1365, 2.2365, 2.4365]
ind = 0
TABLE_NAME = "powerup_mnist_1layer_fixed"
db = api0.open_db('postgresql://[email protected]/gulcehrc_db?table=' + TABLE_NAME)
for i in xrange(n_trials):
state.lr_rate = learning_rates[ri(learning_rates.shape[0]) - 1]
state.powerup_nunits = n_units[ri(len(n_units)) - 1]
state.powerup_npieces = n_pieces[ri(len(n_pieces)) - 1]
state.W_lr_scale = learning_rate_scalers[ri(len(learning_rate_scalers)) - 1]
state.p_lr_scale = learning_rate_scalers[ri(len(learning_rate_scalers)) - 1]
state.batch_size = batch_sizes[ri(len(batch_sizes)) - 1]
state.l2_pen = l2_pen[ri(l2_pen.shape[0]) - 1]
state.init_mom = numpy.random.uniform(low=0.3, high=0.6)
state.final_mom = numpy.random.uniform(low=state.init_mom + 0.1, high=0.9)
state.decay_factor = numpy.random.uniform(low=0.01, high=0.05)
state.max_col_norm = max_col_norms[ri(len(max_col_norms)) - 1]
alphabet = list('abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUWXYZ0123456789')
numpy.random.shuffle(alphabet)
state.save_path = './'
state.save_path += ''.join(alphabet[:7]) + '_'
sql.insert_job(experiment, flatten(state), db)
ind += 1
db.createView(TABLE_NAME + '_view')
print "{} jobs submitted".format(ind)
def tfd(n_trials):
ri = numpy.random.random_integers
state = DD()
with open('mnist_powerup_temp_l2.yaml') as ymtmp:
state.yaml_string = ymtmp.read()
state.powerup_nunits = 240
state.powerup_npieces = 5
state.powerup_nunits2 = 240
state.powerup_npieces2 = 5
state.W_lr_scale = 0.04
state.p_lr_scale = 0.01
state.lr_rate = 0.1
state.init_mom = 0.5
state.final_mom = 0.5
state.decay_factor = 0.5
state.max_col_norm = 1.9365
state.save_path = './'
n_pieces = [2, 3, 4, 5]
n_units = [200, 240, 320, 360, 420, 480]
learning_rates = numpy.logspace(numpy.log10(0.09), numpy.log10(1.2), 60)
learning_rate_scalers = numpy.logspace(numpy.log10(0.1), numpy.log10(1), 50)
decay_factors = numpy.logspace(numpy.log10(0.001), numpy.log10(0.06), 40)
max_col_norms = [1.8365, 1.9365, 2.1365, 2.2365, 2.3486]
ind = 0
TABLE_NAME = "powerup_mnist_finest_large_2l"
db = api0.open_db('postgresql://[email protected]/gulcehrc_db?table=' + TABLE_NAME)
for i in xrange(n_trials):
state.lr_rate = learning_rates[ri(learning_rates.shape[0]) - 1]
state.powerup_nunits = n_units[ri(len(n_units)) - 1]
state.powerup_npieces = n_pieces[ri(len(n_pieces) - 1)]
state.powerup_nunits2 = state.powerup_nunits
state.powerup_npieces2 = state.powerup_npieces
state.W_lr_scale = numpy.random.uniform(low=0.09, high=1.0)
state.p_lr_scale = numpy.random.uniform(low=0.09, high=1.0)
state.init_mom = numpy.random.uniform(low=0.3, high=0.6)
state.final_mom = numpy.random.uniform(low=state.init_mom + 0.1, high=0.9)
state.decay_factor = decay_factors[ri(len(decay_factors)) - 1]
state.max_col_norm = max_col_norms[ri(len(max_col_norms)) - 1]
alphabet = list('abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUWXYZ0123456789')
state.save_path = './'
state.save_path += ''.join(alphabet[:7]) + '_'
sql.insert_job(experiment, flatten(state), db)
ind += 1
db.createView(TABLE_NAME + '_view')
print "{} jobs submitted".format(ind)
def tfd(n_trials):
ri = numpy.random.random_integers
state = DD()
with open("tfd_powerup_temp.yaml") as ymtmp:
state.yaml_string = ymtmp.read()
state.powerup_nunits = 240
state.powerup_npieces = 5
state.W_lr_scale = 0.04
state.p_lr_scale = 0.01
state.lr_rate = 0.1
state.l2_pen = 1e-5
state.l2_pen2 = 0.0000
state.init_mom = 0.5
state.final_mom = 0.5
state.decay_factor = 0.5
state.max_col_norm = 1.9365
state.max_col_norm2 = 1.8365
state.save_path = "./"
n_pieces = [2, 3, 4, 5, 6, 8, 10, 12, 14, 16]
n_units = [200, 240, 280, 320, 420]
learning_rates = numpy.logspace(numpy.log10(0.001), numpy.log10(1.0), 32)
learning_rate_scalers = numpy.logspace(numpy.log10(0.01), numpy.log10(1), 30)
l2_pen = numpy.logspace(numpy.log10(1e-6), numpy.log10(3 * 1e-3), 100)
max_col_norms = [1.8365, 1.9365, 2.1365, 2.2365, 2.3486, 2.4365]
ind = 0
TABLE_NAME = "powerup_tfd_1layer_finer_large2"
db = api0.open_db("postgresql://[email protected]/gulcehrc_db?table=" + TABLE_NAME)
for i in xrange(n_trials):
state.lr_rate = learning_rates[ri(learning_rates.shape[0]) - 1]
state.powerup_nunits = n_units[ri(len(n_units)) - 1]
if state.powerup_nunits >= 320:
state.powerup_npieces = n_pieces[ri(low=0, high=5)]
else:
state.powerup_npieces = n_pieces[ri(low=3, high=(len(n_pieces) - 1))]
state.W_lr_scale = learning_rate_scalers[ri(len(learning_rate_scalers)) - 1]
state.p_lr_scale = learning_rate_scalers[ri(len(learning_rate_scalers)) - 1]
state.l2_pen = l2_pen[ri(l2_pen.shape[0]) - 1]
state.init_mom = numpy.random.uniform(low=0.3, high=0.6)
state.final_mom = numpy.random.uniform(low=state.init_mom + 1.0, high=0.9)
state.decay_factor = numpy.random.uniform(low=0.01, high=0.05)
state.max_col_norm = max_col_norms[ri(len(max_col_norms)) - 1]
alphabet = list("abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUWXYZ0123456789")
state.save_path = "./"
state.save_path += "".join(alphabet[:7]) + "_"
sql.insert_job(experiment, flatten(state), db)
ind += 1
db.createView(TABLE_NAME + "_view")
print "{} jobs submitted".format(ind)
