def main(args):
# Create a new database
db = sql.db('%s?table=%s' % (args.database, args.table_name))
# Create a jobman state
state = DD()
# Loop over the search space and schedule jobs
config_generator = gen_configurations(args.configurations)
for i, params in enumerate(config_generator):
# Complete parameters dictionary and add to the state
state.parameters = params
state.parameters['model'] = args.model
state.parameters['dataset'] = args.dataset
state.parameters['nb_epoch'] = args.n_epochs
state.parameters['batch_size'] = args.batch_size
# Insert the job into the database
if args.model == 'ADIOS':
state.parameters['labels_order'] = args.labels_order
state.parameters['n_label_splits'] = args.n_label_splits
for i in xrange(args.n_label_splits):
state.parameters['label_split'] = i + 1
sql.insert_job(train_adios, flatten(state), db)
else: # args.model == 'MLP'
sql.insert_job(train_mlp, flatten(state), db)
# Create a view for the new database table
db.createView(args.table_name + '_view')
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 create_jobman_jobs():
#Database operations
TABLE_NAME = "arcade_post_mlp_cv_binary_8x8_40k"
db = api0.open_db('postgresql://[email protected]/gulcehrc_db?table=' + TABLE_NAME)
ri = numpy.random.random_integers
# Default values
state = DD()
state.dataset = \
"/home/gulcehre/dataset/pentomino/experiment_data/pento64x64_40k_seed_23112222.npy"
state.no_of_folds = 5
state.exid = 0
state.n_hiddens = [100, 200, 300]
state.n_hidden_layers = 3
state.learning_rate = 0.001
state.l1_reg = 1e-5
state.l2_reg = 1e-3
state.n_epochs = 2
state.batch_size = 120
state.save_exp_data = True
self.no_of_patches = 64
state.cost_type = "crossentropy"
state.n_in = 8*8
state.n_out = 1
state.best_valid_error = 0.0
state.best_test_error = 0.0
state.valid_obj_path_error = 0.0
state.test_obj_path_error = 0.0
l1_reg_values = [0., 1e-6, 1e-5, 1e-4]
l2_reg_values = [0., 1e-5, 1e-4]
learning_rates = numpy.logspace(numpy.log10(0.0001), numpy.log10(1), 36)
num_hiddens = numpy.logspace(numpy.log10(256), numpy.log10(2048), 24)
for i in xrange(NO_OF_TRIALS):
state.exid = i
state.n_hidden_layers = ri(4)
n_hiddens = []
for i in xrange(state.n_hidden_layers):
n_hiddens.append(int(num_hiddens[ri(num_hiddens.shape[0]) - 1]))
state.n_hiddens = n_hiddens
state.learning_rate = learning_rates[ri(learning_rates.shape[0]) - 1]
state.l1_reg = l1_reg_values[ri(len(l1_reg_values)) - 1]
state.l2_reg = l2_reg_values[ri(len(l2_reg_values)) - 1]
sql.insert_job(experiment, flatten(state), db)
db.createView(TABLE_NAME + "_view")
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("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)
def insert_jobexp(exp_args, jobman_args):
"""
Insert jobman experiment jobs in a sql database.
Remarks: We have this in a separate module since we can't refer to the jobexp.run function
from within the jobexp module by it's absolute name (else jobman won't find the experiment when run)
:param exp_args:
:param jobman_args:
:return:
"""
table_name = jobman_args.get("table_name", "experiment")
db = api0.open_db('postgres://*****:*****@127.0.0.1/jobbase?table='+table_name)
for arg in jobman_args:
sql.insert_job(predictive_rl.rlglueexp.jobexp.run, flatten(arg), db)
def load_experiments_jobman(experiment_module, jobargs):
"""
Load jobs from experiment onto postgresql database table.
"""
dbdescr = get_desc(jobargs)
db = api0.open_db(dbdescr)
experiment = imp.load_source("module.name", experiment_module)
for i, items in enumerate(experiment.generator):
hyperparams = experiment.default_hyperparams
state = DD()
set_hyper_parameters(hyperparams, **dict((k, v) for k, v in items))
state.hyperparams = translate(hyperparams, "knex")
state["out&path"] = path.abspath(jobargs["out_path"])
state["experiment&module"] = path.abspath(experiment_module)
state["dbdescr"] = dbdescr
sql.insert_job(run_experiment_jobman, flatten(state), db)
db.createView("%s" % jobargs["table"])
def load_experiments_jobman(experiment_module, jobargs):
"""
Load jobs from experiment onto postgresql database table.
"""
dbdescr = get_desc(jobargs)
db = api0.open_db(dbdescr)
experiment = imp.load_source("module.name", experiment_module)
for i, items in enumerate(experiment.generator):
hyperparams = experiment.default_hyperparams
state = DD()
set_hyper_parameters(hyperparams, **dict((k, v) for k, v in items))
state.hyperparams = translate(hyperparams, "knex")
state["out&path"] = path.abspath(jobargs["out_path"])
state["experiment&module"] = path.abspath(experiment_module)
state["dbdescr"] = dbdescr
sql.insert_job(run_experiment_jobman,
flatten(state),
db)
db.createView("%s" % jobargs["table"])
def load_experiments(args):
dataset_name = args.dataset_name
db = sql.db(
"postgres://%(user)s@%(host)s:%(port)d/%(database)s?table=%(table)s" %
{
"user": args.user,
"host": args.host,
"port": args.port,
"database": args.database,
"table": args.table,
})
logger.info("Getting dataset info for %s%s" %
(dataset_name, ", transposed" if args.transposed else ""))
data_path = serial.preprocess("${PYLEARN2_NI_PATH}/" + args.dataset_name)
if args.transposed:
logger.info("Data in transpose...")
mri = MRI.MRI_Transposed(dataset_name=args.dataset_name,
unit_normalize=True,
even_input=True,
apply_mask=True)
input_dim = mri.X.shape[1]
variance_map_file = path.join(data_path, "transposed_variance_map.npy")
else:
mask_file = path.join(data_path, "mask.npy")
mask = np.load(mask_file)
input_dim = (mask == 1).sum()
if input_dim % 2 == 1:
input_dim -= 1
mri = MRI.MRI_Standard(which_set="full",
dataset_name=args.dataset_name,
unit_normalize=True,
even_input=True,
apply_mask=True)
variance_map_file = path.join(data_path, "variance_map.npy")
mri_nifti.save_variance_map(mri, variance_map_file)
for items in nested_generator(
layer_depth_generator("encoder.layer_depths", xrange(4, 6), 5),
hidden_generator("encoder.nhid", 4),
float_generator("weight_decay.coeffs.z",
3,
0.1,
0.001,
log_scale=True)):
# logger.info("Adding NICE experiment with hyperparameters %s" % (items, ))
state = DD()
experiment_hyperparams = nice_experiment.default_hyperparams(input_dim)
if args.transposed:
experiment_hyperparams["data_class"] = "MRI_Transposed"
if args.logistic:
experiment_hyperparams["prior"]["__builder__"] =\
"nice.pylearn2.models.nice.StandardLogistic"
for key, value in items:
split_keys = key.split(".")
entry = experiment_hyperparams
for k in split_keys[:-1]:
entry = entry[k]
entry[split_keys[-1]] = value
experiment_hyperparams["dataset_name"] = dataset_name
h = abs(hash(frozenset(flatten(experiment_hyperparams).keys() +\
[tuple(v) if isinstance(v, list) else v for v in flatten(experiment_hyperparams).values()])))
user = path.expandvars("$USER")
save_path = serial.preprocess(
"/export/mialab/users/%s/pylearn2_outs/%d" % (user, h))
file_params = {
"save_path": save_path,
"variance_map_file": variance_map_file,
}
state.file_parameters = file_params
state.hyper_parameters = experiment_hyperparams
sql.insert_job(nice_experiment.experiment, flatten(state), db)
db.createView("%s_view" % args.table)
# to decode the tf*idf.
state.inputtype = 'binary'
state.seed = 123
state.activation_regularization_coeff = [0]
#here is the for loops that does the grid:
for i in [0.01,0.001]:
state.lr = [i]
for j in [0.5,0.25,0.125,0.05]:
state.noise_lvl=[j]
for k in [1400,2500,5000]:
state.n_hid = [k]
sql.insert_job(NLPSDAE, flatten(state), db) #this submit the current state DD to the db, if it already exist in the db no additionnal job is added.
db.createView('opentablegpuview')
# First run this script
# PYTHONPATH=$PYTHONPATH:.. python DARPAjobs.py
# Test the jobs are in the database:
# psql -d ift6266h10_sandbox_db -h gershwin.iro.umontreal.ca -U ift6266h10
# select id,lr,noiselvl,nhid as reg,jobman_status from opentablegpuview;
# password: f0572cd63b
# Set some values
# update opentablegpukeyval set ival=0 where name='jobman.status';
# update opentablegpukeyval set ival=0 where name='jobman.status' where dict_id=20;
def load_experiments(args):
dataset_name = args.dataset_name
# Load the database and table.
db = sql.db("postgres://%(user)s@%(host)s:%(port)d/%(database)s?table=%(table)s"
% {"user": args.user,
"host": args.host,
"port": args.port,
"database": args.database,
"table": args.table,
})
# Don't worry about this yet.
input_handler = InputHandler()
# For generating models, we use a special set of jobman generators, made
# for convenience.
for items in jg.nested_generator(
jg.float_generator("learning_rate", 3, 0.01, 0.0001, log_scale=True),
jg.list_generator("nhid", [50, 100, 200, 300]),
):
logger.info("Adding RBM experiment across hyperparameters %s" % (items, ))
state = DD()
# Load experiment hyperparams from experiment
experiment_hyperparams = experiment.default_hyperparams()
# Set them with values in our loop.
for key, value in items:
split_keys = key.split(".")
entry = experiment_hyperparams
for k in split_keys[:-1]:
entry = entry[k]
assert split_keys[-1] in entry, ("Key not found in hyperparams: %s"
% split_keys[-1])
entry[split_keys[-1]] = value
# Set the dataset name
experiment_hyperparams["dataset_name"] = dataset_name
# Get the input dim and variance map. Don't worry about variance maps right now,
# they aren't used here.
input_dim, variance_map_file = input_handler.get_input_params(args,
experiment_hyperparams)
logger.info("%s\n%s\n" % (input_dim, variance_map_file))
# Set the input dimensionality by the data
experiment_hyperparams["nvis"] = input_dim
# Set the minimum learning rate relative to the initial learning rate.
experiment_hyperparams["min_lr"] = experiment_hyperparams["learning_rate"] / 10
# Make a unique hash for experiments. Remember that lists, dicts, and other data
# types may not be hashable, so you may need to do some special processing. In
# this case we convert the lists to tuples.
h = abs(hash(frozenset(flatten(experiment_hyperparams).keys() +\
[tuple(v) if isinstance(v, list)
else v
for v in flatten(experiment_hyperparams).values()])))
# Save path for the experiments. In this case we are sharing a directory in my
# export directory so IT can blame me.
save_path = serial.preprocess("/export/mialab/users/dhjelm/pylearn2_outs/rbm_demo/%d"
% h)
# We save file params separately as they aren't model specific.
file_params = {
"save_path": save_path,
"variance_map_file": variance_map_file,
}
state.file_parameters = file_params
state.hyper_parameters = experiment_hyperparams
user = path.expandvars("$USER")
state.created_by = user
# Finally we add the experiment to the table.
sql.insert_job(
experiment.experiment,
flatten(state),
db
)
# A view can be used when querying the database using psql. May not be needed in future.
db.createView("%s_view" % args.table)
def load_experiments(args):
dataset_name = args.dataset_name
# Load the database and table.
db = sql.db(
"postgres://%(user)s@%(host)s:%(port)d/%(database)s?table=%(table)s" %
{
"user": args.user,
"host": args.host,
"port": args.port,
"database": args.database,
"table": args.table,
})
# Don't worry about this yet.
input_handler = InputHandler()
# For generating models, we use a special set of jobman generators, made
# for convenience.
for items in jg.nested_generator(
jg.float_generator("learning_rate",
3,
0.01,
0.0001,
log_scale=True),
jg.list_generator("nhid", [50, 100, 200, 300]),
):
logger.info("Adding RBM experiment across hyperparameters %s" %
(items, ))
state = DD()
# Load experiment hyperparams from experiment
experiment_hyperparams = experiment.default_hyperparams()
# Set them with values in our loop.
for key, value in items:
split_keys = key.split(".")
entry = experiment_hyperparams
for k in split_keys[:-1]:
entry = entry[k]
assert split_keys[-1] in entry, (
"Key not found in hyperparams: %s" % split_keys[-1])
entry[split_keys[-1]] = value
# Set the dataset name
experiment_hyperparams["dataset_name"] = dataset_name
# Get the input dim and variance map. Don't worry about variance maps right now,
# they aren't used here.
input_dim, variance_map_file = input_handler.get_input_params(
args, experiment_hyperparams)
logger.info("%s\n%s\n" % (input_dim, variance_map_file))
# Set the input dimensionality by the data
experiment_hyperparams["nvis"] = input_dim
# Set the minimum learning rate relative to the initial learning rate.
experiment_hyperparams[
"min_lr"] = experiment_hyperparams["learning_rate"] / 10
# Make a unique hash for experiments. Remember that lists, dicts, and other data
# types may not be hashable, so you may need to do some special processing. In
# this case we convert the lists to tuples.
h = abs(hash(frozenset(flatten(experiment_hyperparams).keys() +\
[tuple(v) if isinstance(v, list)
else v
for v in flatten(experiment_hyperparams).values()])))
# Save path for the experiments. In this case we are sharing a directory in my
# export directory so IT can blame me.
save_path = serial.preprocess(
"/export/mialab/users/dhjelm/pylearn2_outs/rbm_demo/%d" % h)
# We save file params separately as they aren't model specific.
file_params = {
"save_path": save_path,
"variance_map_file": variance_map_file,
}
state.file_parameters = file_params
state.hyper_parameters = experiment_hyperparams
user = path.expandvars("$USER")
state.created_by = user
# Finally we add the experiment to the table.
sql.insert_job(experiment.experiment, flatten(state), db)
# A view can be used when querying the database using psql. May not be needed in future.
db.createView("%s_view" % args.table)
state.batch_size = 20
state.n_hidden = 10
# Hyperparameter exploration
for n_hidden in 20, 30:
print "h_hidden =",h_hidden
state.n_hidden = n_hidden
# Explore L1 regularization w/o L2
state.L2_reg = 0.
for L1_reg in 0., 1e-6, 1e-5, 1e-4:
print "L1_reg =",L1_reg
state.L1_reg = L1_reg
# Insert job
sql.insert_job(experiment, flatten(state), db)
# Explore L2 regularization w/o L1
state.L1_reg = 0.
for L2_reg in 1e-5, 1e-4:
print "L2_reg =",L2_reg
state.L1_reg = L1_reg
# Insert job
sql.insert_job(experiment, flatten(state), db)
# Create the view
db.createView(TABLE_NAME+'_view')
from jobman import sql
from jobman.parse import filemerge
from Experimentsbatchpretrain import *
import numpy
db = sql.db('postgres://[email protected]/glorotxa_db/pretrainexpe')
state = DD()
state.curridata = DD(filemerge('Curridata.conf'))
state.depth = 3
state.tie = True
state.n_hid = 1000 #nb of unit per layer
state.act = 'tanh'
state.sup_lr = 0.01
state.unsup_lr = 0.001
state.noise = 0.25
state.seed = 1
state.nbepochs_unsup = 30 #maximal number of supervised updates
state.nbepochs_sup = 1000 #maximal number of unsupervised updates per layer
state.batchsize = 10
for i in ['MNIST','CIFAR10','ImageNet','shapesetbatch']:
state.dat =i
sql.insert_job(pretrain, flatten(state), db)
db.createView('pretrainexpeview')
state = DD()
state.learning_rate = 0.01
state.L1_reg = 0.00
state.L2_reg = 0.0001
state.n_iter = 50
state.batch_size = 20
state.n_hidden = 10
# Hyperparameter exploration
for n_hidden in 20, 30:
state.n_hidden = n_hidden
# Explore L1 regularization w/o L2
state.L2_reg = 0.
for L1_reg in 0., 1e-6, 1e-5, 1e-4:
state.L1_reg = L1_reg
# Insert job
sql.insert_job(experiment, flatten(state), db)
# Explore L2 regularization w/o L1
state.L1_reg = 0.
for L2_reg in 1e-5, 1e-4:
state.L1_reg = L1_reg
# Insert job
sql.insert_job(experiment, flatten(state), db)
# Create the view
db.createView(TABLE_NAME + '_view')
from jobman.tools import DD, flatten
from jobman import api0, sql
from jobman.examples.def_addition import addition_example
TABLE_NAME = 'test_add_'
# DB path...
db = api0.open_db('postgres://<user>:<pass>@<server>/<database>?table=' +
TABLE_NAME)
state = DD()
for first in 0, 2, 4, 6, 8, 10:
state.first = first
for second in 1, 3, 5, 7, 9:
state.second = second
sql.insert_job(addition_example, flatten(state), db)
# decoding activation function is for the first layer
# e.g. inputtype 'tfidf' ('tf*idf'?) uses activation function softplus
# to decode the tf*idf.
state.inputtype = 'binary'
state.seed = 123
#here is the for loops that does the grid:
for i in [0.01, 0.001, 0.0001]:
state.lr = [i]
for j in [(0.7, 0.0041), (0.5, 0.003), (0.8, 0.005)]:
state.noise_lvl = [j]
for k in [0.001, 0.00001, 0.0]:
state.activation_regularization_coeff = [k]
sql.insert_job(
NLPSDAE, flatten(state), db
) #this submit the current state DD to the db, if it already exist in the db no additionnal job is added.
db.createView('opentablegpuview')
#in order to access the db from a compute node you need to create an tunnel ssh connection on ang23:
#(to do one time, I think you should keep the shell open or you can create the tunnel on a screen and detached it)
#ssh -v -f -o ServerAliveInterval=60 -o ServerAliveCountMax=60 -N -L *:5432:localhost:5432 gershwin.iro.umontreal.ca
#you will need to give your LISA password.
#here is the command you use to launch 1 jobs of the db.
#THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 sqsub -q gpu -r 2d -n 1 --gpp=1 --memperproc=2.5G -o the_output_you_want jobman sql 'postgres://glorotxa@ang23/glorotxa_db/opentablegpu' /scratch/glorotxa/
def load_experiments(args):
dataset_name = args.dataset_name
db = sql.db(
"postgres://%(user)s@%(host)s:"
"%(port)d/%(database)s?table=%(table)s" % {
"user": args.user,
"host": args.host,
"port": args.port,
"database": args.database,
"table": args.table,
})
input_handler = InputHandler()
for items in jg.nested_generator(
jg.list_generator(
"encoder.layer_depths",
[[3, 5, 5, 5, 3], [5, 5, 5, 5, 5], [2, 4, 4, 2]]),
jg.list_generator("variance_normalize", [False, 2]),
jg.float_generator("weight_decay.coeff",
4,
0.1,
0.0001,
log_scale=True),
jg.list_generator("prior.__builder__", [
"nice.pylearn2.models.nice.StandardNormal",
"nice.pylearn2.models.nice.StandardLogistic"
])):
logger.info("Adding NICE experiment across hyperparameters %s" %
(items, ))
state = DD()
experiment_hyperparams = nice_experiment.default_hyperparams()
for key, value in items:
split_keys = key.split(".")
entry = experiment_hyperparams
for k in split_keys[:-1]:
entry = entry[k]
assert split_keys[-1] in entry,\
("Key not found in hyperparams: %s, "
"found: %s" % (split_keys[-1], entry.keys()))
entry[split_keys[-1]] = value
experiment_hyperparams["dataset_name"] = dataset_name
input_dim, variance_map_file = input_handler.get_input_params(
args, experiment_hyperparams)
logger.info("%s\n%s\n" % (input_dim, variance_map_file))
experiment_hyperparams["nvis"] = input_dim
experiment_hyperparams["encoder"]["nvis"] = input_dim
h = abs(hash(frozenset(
flatten(experiment_hyperparams).keys() +\
[tuple(v) if isinstance(v, list)
else v for v in flatten(experiment_hyperparams).values()])))
user = path.expandvars("$USER")
save_path = serial.preprocess(
"/export/mialab/users/%s/pylearn2_outs/%d" % (user, h))
file_params = {
"save_path": save_path,
"variance_map_file": variance_map_file,
}
state.file_parameters = file_params
state.hyper_parameters = experiment_hyperparams
sql.insert_job(nice_experiment.experiment, flatten(state), db)
db.createView("%s_view" % args.table)
def load_experiments(args):
dataset_name = args.dataset_name
db = sql.db("postgres://%(user)s@%(host)s:%(port)d/%(database)s?table=%(table)s"
% {"user": args.user,
"host": args.host,
"port": args.port,
"database": args.database,
"table": args.table,
})
logger.info("Getting dataset info for %s" % dataset_name)
data_path = serial.preprocess("${PYLEARN2_NI_PATH}/" + dataset_name)
mask_file = path.join(data_path, "mask.npy")
mask = np.load(mask_file)
input_dim = (mask == 1).sum()
if input_dim % 2 == 1:
input_dim -= 1
mri = MRI.MRI_Standard(which_set="full",
dataset_name=dataset_name,
unit_normalize=True,
even_input=True,
apply_mask=True)
variance_map_file = path.join(data_path, "variance_map.npy")
mri_nifti.save_variance_map(mri, variance_map_file)
for items in jg.nested_generator(jg.hidden_generator("nhid1", 1),
jg.hidden_generator("nhid2", 1),
):
state = DD()
experiment_hyperparams = mlp_experiment.default_hyperparams(input_dim)
for key, value in items:
split_keys = key.split(".")
entry = experiment_hyperparams
for k in split_keys[:-1]:
entry = entry[k]
entry[split_keys[-1]] = value
experiment_hyperparams["dataset_name"] = dataset_name
h = abs(hash(frozenset(flatten(experiment_hyperparams).keys() +\
flatten(experiment_hyperparams).values())))
user = path.expandvars("$USER")
save_path = serial.preprocess("/export/mialab/users/%s/pylearn2_outs/%d"
% (user, h))
file_params = {
"save_path": save_path,
"variance_map_file": variance_map_file,
}
state.file_parameters = file_params
state.hyper_parameters = experiment_hyperparams
state.pid = 0
sql.insert_job(
mlp_experiment.experiment,
flatten(state),
db
)
db.createView("%s_view" % args.table)
def load_experiments(args):
dataset_name = args.dataset_name
db = sql.db("postgres://%(user)s@%(host)s:%(port)d/%(database)s?table=%(table)s"
% {"user": args.user,
"host": args.host,
"port": args.port,
"database": args.database,
"table": args.table,
})
logger.info("Getting dataset info for %s%s"
% (dataset_name, ", transposed" if args.transposed else ""))
data_path = serial.preprocess("${PYLEARN2_NI_PATH}/" + args.dataset_name)
if args.transposed:
logger.info("Data in transpose...")
mri = MRI.MRI_Transposed(dataset_name=args.dataset_name,
unit_normalize=True,
even_input=True,
apply_mask=True)
input_dim = mri.X.shape[1]
variance_map_file = path.join(data_path, "transposed_variance_map.npy")
else:
mask_file = path.join(data_path, "mask.npy")
mask = np.load(mask_file)
input_dim = (mask == 1).sum()
if input_dim % 2 == 1:
input_dim -= 1
mri = MRI.MRI_Standard(which_set="full",
dataset_name=args.dataset_name,
unit_normalize=True,
even_input=True,
apply_mask=True)
variance_map_file = path.join(data_path, "variance_map.npy")
mri_nifti.save_variance_map(mri, variance_map_file)
for items in nested_generator(layer_depth_generator("encoder.layer_depths",
xrange(4, 6), 5),
hidden_generator("encoder.nhid", 4),
float_generator("weight_decay.coeffs.z", 3, 0.1, 0.001, log_scale=True)):
# logger.info("Adding NICE experiment with hyperparameters %s" % (items, ))
state = DD()
experiment_hyperparams = nice_experiment.default_hyperparams(input_dim)
if args.transposed:
experiment_hyperparams["data_class"] = "MRI_Transposed"
if args.logistic:
experiment_hyperparams["prior"]["__builder__"] =\
"nice.pylearn2.models.nice.StandardLogistic"
for key, value in items:
split_keys = key.split(".")
entry = experiment_hyperparams
for k in split_keys[:-1]:
entry = entry[k]
entry[split_keys[-1]] = value
experiment_hyperparams["dataset_name"] = dataset_name
h = abs(hash(frozenset(flatten(experiment_hyperparams).keys() +\
[tuple(v) if isinstance(v, list) else v for v in flatten(experiment_hyperparams).values()])))
user = path.expandvars("$USER")
save_path = serial.preprocess("/export/mialab/users/%s/pylearn2_outs/%d"
% (user, h))
file_params = {
"save_path": save_path,
"variance_map_file": variance_map_file,
}
state.file_parameters = file_params
state.hyper_parameters = experiment_hyperparams
sql.insert_job(
nice_experiment.experiment,
flatten(state),
db
)
db.createView("%s_view" % args.table)
state.mem_nel = 200
state.mem_size = 28
np.random.seed(3)
ri = np.random.random_integers
learning_rates = np.logspace(np.log10(lr_min), np.log10(lr_max), 100)
stds = np.random.uniform(std_min, std_max, 100)
#Change the table name everytime you try
TABLE_NAME = "adam_grusoft_model_search_v0"
# You should have an account for jobman
db = api0.open_db('postgresql://[email protected]/gulcehrc_db?table=' + TABLE_NAME)
ind = 0
for i in xrange(n_trials):
state.lr = learning_rates[ri(learning_rates.shape[0]) - 1]
state.std = stds[ri(len(stds)) - 1]
state.batch_size = batches[ri(len(batches)) - 1]
state.renormalization_scale = renormalization_scale[ri(len(renormalization_scale)) - 1]
state.mem_nel = mem_nels[ri(len(mem_nels)) - 1]
state.mem_size = mem_sizes[ri(len(mem_sizes)) - 1]
state.std = stds[ri(stds.shape[0]) - 1]
sql.insert_job(memnet.train_model_adam_gru_soft.search_model_adam_gru_soft, flatten(state), db)
ind += 1
db.createView(TABLE_NAME + "_view")
print "{} jobs submitted".format(ind)
def load_experiments(args):
dataset_name = args.dataset_name
db = sql.db(
"postgres://%(user)s@%(host)s:%(port)d/%(database)s?table=%(table)s" %
{
"user": args.user,
"host": args.host,
"port": args.port,
"database": args.database,
"table": args.table,
})
logger.info("Getting dataset info for %s" % dataset_name)
data_path = serial.preprocess("${PYLEARN2_NI_PATH}/" + dataset_name)
mask_file = path.join(data_path, "mask.npy")
mask = np.load(mask_file)
input_dim = (mask == 1).sum()
if input_dim % 2 == 1:
input_dim -= 1
mri = MRI.MRI_Standard(which_set="full",
dataset_name=dataset_name,
unit_normalize=True,
even_input=True,
apply_mask=True)
variance_map_file = path.join(data_path, "variance_map.npy")
mri_nifti.save_variance_map(mri, variance_map_file)
for items in jg.nested_generator(
jg.hidden_generator("nhid1", 1),
jg.hidden_generator("nhid2", 1),
):
state = DD()
experiment_hyperparams = mlp_experiment.default_hyperparams(input_dim)
for key, value in items:
split_keys = key.split(".")
entry = experiment_hyperparams
for k in split_keys[:-1]:
entry = entry[k]
entry[split_keys[-1]] = value
experiment_hyperparams["dataset_name"] = dataset_name
h = abs(hash(frozenset(flatten(experiment_hyperparams).keys() +\
flatten(experiment_hyperparams).values())))
user = path.expandvars("$USER")
save_path = serial.preprocess(
"/export/mialab/users/%s/pylearn2_outs/%d" % (user, h))
file_params = {
"save_path": save_path,
"variance_map_file": variance_map_file,
}
state.file_parameters = file_params
state.hyper_parameters = experiment_hyperparams
state.pid = 0
sql.insert_job(mlp_experiment.experiment, flatten(state), db)
db.createView("%s_view" % args.table)
from jobman.tools import DD, flatten
from jobman import api0, sql
from jobman.examples.def_addition import addition_example
TABLE_NAME = 'test_add_'
# DB path...
db = api0.open_db(
'postgres://<user>:<pass>@<server>/<database>?table=' + TABLE_NAME)
state = DD()
for first in 0, 2, 4, 6, 8, 10:
state.first = first
for second in 1, 3, 5, 7, 9:
state.second = second
sql.insert_job(addition_example, flatten(state), db)
method_name: contraction_penalty
},
coefficient: %(coefficient)f
}
],
"termination_criterion" : %(term_crit)s,
}
}
'''
state.hyper_parameters = {
"file": "${PYLEARN2_DATA_PATH}/UTLC/pca/sylvester_train_x_pca32.npy",
"nvis": 32,
"nhid": 6,
"learning_rate": 0.1,
"batch_size": 10,
"coefficient": 0.5,
"term_crit": {
"__builder__": "pylearn2.training_algorithms.sgd.EpochCounter",
"max_epochs": 2
}
}
state.extract_results = "pylearn2.scripts.jobman.tester.result_extractor"
sql.insert_job(
experiment.train_experiment,
flatten(state),
db,
force_dup=True)
def create_jobman_jobs():
#Database operations
TABLE_NAME = "arcade_multi_prmlp_cv_binary_8x8_40k"
db = api0.open_db(
'postgresql://[email protected]/gulcehrc_db?table=' +
TABLE_NAME)
ri = numpy.random.random_integers
# Default values
state = DD()
state.dataset = \
"/home/gulcehre/dataset/pentomino/experiment_data/pento64x64_40k_seed_23112222.npy"
state.no_of_folds = 5
state.exid = 0
state.n_hiddens = [100, 200, 300]
state.n_hidden_layers = 3
state.learning_rate = 0.001
state.l1_reg = 1e-5
state.l2_reg = 1e-3
state.n_epochs = 2
state.batch_size = 120
state.save_exp_data = True
self.no_of_patches = 64
state.cost_type = "crossentropy"
state.n_in = 8 * 8
state.n_out = 1
state.best_valid_error = 0.0
state.best_test_error = 0.0
state.valid_obj_path_error = 0.0
state.test_obj_path_error = 0.0
l1_reg_values = [0., 1e-6, 1e-5, 1e-4]
l2_reg_values = [0., 1e-5, 1e-4]
learning_rates = numpy.logspace(numpy.log10(0.0001), numpy.log10(1), 36)
num_hiddens = numpy.logspace(numpy.log10(256), numpy.log10(2048), 24)
for i in xrange(NO_OF_TRIALS):
state.exid = i
state.n_hidden_layers = ri(4)
n_hiddens = []
for i in xrange(state.n_hidden_layers):
n_hiddens.append(int(num_hiddens[ri(num_hiddens.shape[0]) - 1]))
state.n_hiddens = n_hiddens
state.learning_rate = learning_rates[ri(learning_rates.shape[0]) - 1]
state.l1_reg = l1_reg_values[ri(len(l1_reg_values)) - 1]
state.l2_reg = l2_reg_values[ri(len(l2_reg_values)) - 1]
sql.insert_job(experiment, flatten(state), db)
db.createView(TABLE_NAME + "_view")
