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 jobman_insert_random(n_jobs):
JOBDB = 'postgres://*****:*****@opter.iro.umontreal.ca/devries_db/lvq_mnist'
EXPERIMENT_PATH = "lvq_mnist.jobman_entrypoint"
jobs = []
for _ in range(n_jobs):
job = DD()
job.n_hiddens = numpy.random.randint(1000, high=3000)
job.n_out = numpy.random.randint(100, high=500)
job.noise_std = numpy.random.uniform(low=0.0, high=0.8)
job.learning_rate = 10.**numpy.random.uniform(-2, 0)
job.momentum = 10.**numpy.random.uniform(-2, 0)
job.gamma = numpy.random.uniform(low=1.0, high=3.0)
job.tag = "lvq_mnist"
jobs.append(job)
print job
answer = raw_input("Submit %d jobs?[y/N] " % len(jobs))
if answer == "y":
numpy.random.shuffle(jobs)
db = jobman.sql.db(JOBDB)
for job in jobs:
job.update({jobman.sql.EXPERIMENT: EXPERIMENT_PATH})
jobman.sql.insert_dict(job, db)
print "inserted %d jobs" % len(jobs)
print "To run: jobdispatch --gpu --env=THEANO_FLAGS='floatX=float32, device=gpu' --repeat_jobs=%d jobman sql -n 1 'postgres://*****:*****@opter.iro.umontreal.ca/devries_db/lvq_mnist' ." % len(
jobs)
def jobman_insert_random(n_jobs):
JOBDB = 'postgres://*****:*****@opter.iro.umontreal.ca/dauphiya_db/saddle_mnist_ae'
EXPERIMENT_PATH = "ilya_experiment.jobman_entrypoint"
jobs = []
for _ in range(n_jobs):
job = DD()
job.learning_rate = 10.**numpy.random.uniform(-2, 0)
job.momentum = 10.**numpy.random.uniform(-2, 0)
job.batch_size = 200
job.tag = "ilya_fixed"
jobs.append(job)
print job
answer = raw_input("Submit %d jobs?[y/N] " % len(jobs))
if answer == "y":
numpy.random.shuffle(jobs)
db = jobman.sql.db(JOBDB)
for job in jobs:
job.update({jobman.sql.EXPERIMENT: EXPERIMENT_PATH})
jobman.sql.insert_dict(job, db)
print "inserted %d jobs" % len(jobs)
print "To run: jobdispatch --condor --gpu --env=THEANO_FLAGS='floatX=float32, device=gpu' --repeat_jobs=%d jobman sql -n 1 'postgres://*****:*****@opter.iro.umontreal.ca/dauphiya_db/saddle_mnist_ae' ." % len(jobs)
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")
print "jobman loaded"
# Experiment function
from test_exp import experiment
print "open database"
# Database
TABLE_NAME = 'mlp_dumi'
db = api0.open_db('postgres://ift6266h13@gershwin/ift6266h13_sandbox_db?table='+TABLE_NAME)
print "database loaded"
# Default values
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:
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:
from jobman import api0, sql
from jobman.tools import DD, flatten
# Experiment function
from mlp_jobman import experiment
# Database
TABLE_NAME = 'mlp_dumi'
db = api0.open_db(
'postgres://ift6266h10@gershwin/ift6266h10_sandbox_db?table=' + TABLE_NAME)
# Default values
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)
