def jobman_insert_random(n_jobs,
table_name="emotiw_mlp_audio_fixed_pool2_mixed_grbmx2"
):
JOBDB = 'postgresql://[email protected]/gulcehrc_db?table=' + table_name
EXPERIMENT_PATH = "experiment_cg_2layer_sigm_hyper2_fixed2_pool2_save_mixed_grbmx2.jobman_entrypoint"
nlr = 45
learning_rates = numpy.logspace(numpy.log10(0.0008), numpy.log10(0.09),
nlr)
max_col_norms = [1.9835, 1.8256, 1.2124, 0.98791]
jobs = []
for _ in range(n_jobs):
job = DD()
id_lr = numpy.random.random_integers(0, nlr - 1)
rnd_maxcn = numpy.random.random_integers(0, len(max_col_norms) - 1)
job.n_hiddens = numpy.random.random_integers(
2, 5) * 100 + 2 * numpy.random.random_integers(0, 15)
job.n_layers = 2
job.learning_rate = learning_rates[id_lr]
job.momentum = 10.**numpy.random.uniform(-1, -0)
job.hidden_dropout = numpy.random.uniform(low=0.1, high=0.2)
job.layer_dropout = 0
job.topN_pooling = 1
job.no_final_dropout = 1
job.l2 = numpy.random.random_integers(1, 20) * 1e-3
job.rmsprop = 1
job.normalize_acts = 0
job.enable_standardization = 0
job.response_normalize = 0
job.rbm_epochs = 15
job.rho = 0.94
job.validerror = 0.0
job.loss = 0.0
job.epoch = 0
job.epoch_time = 0
job.use_nesterov = 1
job.trainerror = 0.0
job.features = "full.pca"
job.max_col_norm = max_col_norms[rnd_maxcn]
job.example_dropout = numpy.random.randint(60, 200)
job.tag = "relu_nlayers_dbn"
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 --mem=3G --gpu --env=THEANO_FLAGS='floatX=float32, device=gpu' --repeat_jobs=%d jobman sql -n 1 '%s' ." % (
len(jobs), JOBDB)
def jobman_insert_random(n_jobs):
JOBDB = 'postgres://[email protected]/dauphiya_db/emotiw_mlp_audio'
EXPERIMENT_PATH = "experiment.jobman_entrypoint"
jobs = []
for _ in range(n_jobs):
job = DD()
job.n_hiddens = numpy.random.randint(8, 512)
job.n_layers = numpy.random.randint(1, 4)
job.learning_rate = 10.**numpy.random.uniform(-3, -0)
job.momentum = 10.**numpy.random.uniform(-1, -0)
job.features = ["minimal.pca", "full.pca"][numpy.random.binomial(1, 0.5)]
job.example_dropout = numpy.random.randint(16, 200)
job.rbm_learning_rate = 10.**numpy.random.uniform(-3, -0)
job.rbm_epochs = numpy.random.randint(8, 100)
job.tag = "pretrain"
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 --repeat_jobs=%d jobman sql -n 1 'postgres://[email protected]/dauphiya_db/emotiw_mlp_audio' ." % len(jobs)
def jobman_insert_random(n_jobs):
JOBDB = 'postgres://[email protected]/dauphiya_db/emotiw_mlp_audio'
EXPERIMENT_PATH = "experiment.jobman_entrypoint"
jobs = []
for _ in range(n_jobs):
job = DD()
job.n_hiddens = numpy.random.randint(8, 512)
job.n_layers = numpy.random.randint(1, 4)
job.learning_rate = 10.**numpy.random.uniform(-3, -0)
job.momentum = 10.**numpy.random.uniform(-1, -0)
job.features = ["minimal.pca",
"full.pca"][numpy.random.binomial(1, 0.5)]
job.example_dropout = numpy.random.randint(16, 200)
job.rbm_learning_rate = 10.**numpy.random.uniform(-3, -0)
job.rbm_epochs = numpy.random.randint(8, 100)
job.tag = "pretrain"
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 --repeat_jobs=%d jobman sql -n 1 'postgres://[email protected]/dauphiya_db/emotiw_mlp_audio' ." % len(
jobs)
def jobman_insert_random(n_jobs, table_name="emotiw_mlp_audio_fixed_pool2_mixed_grbmx2"):
JOBDB = 'postgresql://[email protected]/gulcehrc_db?table=' + table_name
EXPERIMENT_PATH = "experiment_cg_2layer_sigm_hyper2_fixed2_pool2_save_mixed_grbmx2.jobman_entrypoint"
nlr = 45
learning_rates = numpy.logspace(numpy.log10(0.0008), numpy.log10(0.09), nlr)
max_col_norms = [1.9835, 1.8256, 1.2124, 0.98791]
jobs = []
for _ in range(n_jobs):
job = DD()
id_lr = numpy.random.random_integers(0, nlr-1)
rnd_maxcn = numpy.random.random_integers(0, len(max_col_norms)-1)
job.n_hiddens = numpy.random.random_integers(2,5) * 100 + 2 * numpy.random.random_integers(0,15)
job.n_layers = 2
job.learning_rate = learning_rates[id_lr]
job.momentum = 10.**numpy.random.uniform(-1, -0)
job.hidden_dropout = numpy.random.uniform(low=0.1, high=0.2)
job.layer_dropout = 0
job.topN_pooling = 1
job.no_final_dropout = 1
job.l2 = numpy.random.random_integers(1, 20) * 1e-3
job.rmsprop = 1
job.normalize_acts = 0
job.enable_standardization = 0
job.response_normalize = 0
job.rbm_epochs = 15
job.rho = 0.94
job.validerror = 0.0
job.loss = 0.0
job.epoch = 0
job.epoch_time = 0
job.use_nesterov = 1
job.trainerror = 0.0
job.features = "full.pca"
job.max_col_norm = max_col_norms[rnd_maxcn]
job.example_dropout = numpy.random.randint(60, 200)
job.tag = "relu_nlayers_dbn"
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 --mem=3G --gpu --env=THEANO_FLAGS='floatX=float32, device=gpu' --repeat_jobs=%d jobman sql -n 1 '%s' ." % (len(jobs), JOBDB)
def jobman_insert_random(n_jobs, table_name="emotiw_mlp_audio_sigm_fixed_pool2_mixed_norbm3"):
JOBDB = 'postgresql://[email protected]/gulcehrc_db?table=' + table_name
EXPERIMENT_PATH = "experiment_cg_2layer_sigm_hyper2_fixed2_pool2_save_mixed_norbm3.jobman_entrypoint"
nlr = 45
learning_rates = numpy.logspace(numpy.log10(0.0008), numpy.log10(0.1), nlr)
max_col_norms = [1.8256, 1.5679, 1.2124, 0.98791]
rhos = [0.96, 0.92, 0.88]
jobs = []
for _ in range(n_jobs):
job = DD()
id_lr = numpy.random.random_integers(0, nlr-1)
rnd_maxcn = numpy.random.random_integers(0, len(max_col_norms)-1)
rnd_rho = numpy.random.random_integers(0, len(rhos)-1)
job.n_hiddens = numpy.random.randint(80, 500)
job.n_layers = numpy.random.random_integers(1, 2)
job.learning_rate = learning_rates[id_lr]
job.momentum = 10.**numpy.random.uniform(-1, -0)
job.rmsprop = 1
job.rho = rhos[rnd_rho]
job.validerror = 0.0
job.loss = 0.0
job.seed = 1938471
job.rbm_epochs = 0
job.epoch = 0
job.epoch_time = 0
job.use_nesterov = 1
job.trainerror = 0.0
job.features = "full.pca"
job.max_col_norm = max_col_norms[rnd_maxcn]
job.example_dropout = numpy.random.randint(60, 200)
job.tag = "sigm_norm_const_fixed_pool2_norbm3"
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 --mem=3G --gpu --env=THEANO_FLAGS='floatX=float32, device=gpu' --repeat_jobs=%d jobman sql -n 1 '%s' ." % (len(jobs), JOBDB)
def jobman_insert_random(n_jobs, table_name="emotiw_mlp_audio_tanh"):
JOBDB = 'postgresql://[email protected]/gulcehrc_db?table=' + table_name
EXPERIMENT_PATH = "experiment_cg.jobman_entrypoint"
nlr = 50
learning_rates = numpy.logspace(numpy.log10(0.001), numpy.log10(0.3), nlr)
jobs = []
for _ in range(n_jobs):
job = DD()
id_lr = numpy.random.random_integers(0, nlr - 1)
job.n_hiddens = numpy.random.randint(100, 800)
job.n_layers = numpy.random.randint(1, 4)
job.learning_rate = learning_rates[id_lr]
job.momentum = 10.**numpy.random.uniform(-1, -0)
job.rmsprop = numpy.random.binomial(1, 0.5)
job.validerror = 0.0
job.loss = 0.0
job.epoch = 0
job.epoch_time = 0
job.trainerror = 0.0
job.features = "full.pca"
job.max_col_norm = 1.8456
job.example_dropout = numpy.random.randint(16, 200)
job.rbm_learning_rate = 10.**numpy.random.uniform(-3, -0)
job.rbm_epochs = 0 #numpy.random.randint(8, 100)
job.tag = "tanh_norm_const"
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 --mem=3G --gpu --env=THEANO_FLAGS='floatX=float32, device=gpu' --repeat_jobs=%d jobman sql -n 1 '%s' ." % (
len(jobs), JOBDB)
def jobman_insert_random(n_jobs, table_name="emotiw_mlp_audio_tanh"):
JOBDB = 'postgresql://[email protected]/gulcehrc_db?table=' + table_name
EXPERIMENT_PATH = "experiment_cg.jobman_entrypoint"
nlr = 50
learning_rates = numpy.logspace(numpy.log10(0.001), numpy.log10(0.3), nlr)
jobs = []
for _ in range(n_jobs):
job = DD()
id_lr = numpy.random.random_integers(0, nlr-1)
job.n_hiddens = numpy.random.randint(100, 800)
job.n_layers = numpy.random.randint(1, 4)
job.learning_rate = learning_rates[id_lr]
job.momentum = 10.**numpy.random.uniform(-1, -0)
job.rmsprop = numpy.random.binomial(1, 0.5)
job.validerror = 0.0
job.loss = 0.0
job.epoch = 0
job.epoch_time = 0
job.trainerror = 0.0
job.features = "full.pca"
job.max_col_norm = 1.8456
job.example_dropout = numpy.random.randint(16, 200)
job.rbm_learning_rate = 10.**numpy.random.uniform(-3, -0)
job.rbm_epochs = 0 #numpy.random.randint(8, 100)
job.tag = "tanh_norm_const"
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 --mem=3G --gpu --env=THEANO_FLAGS='floatX=float32, device=gpu' --repeat_jobs=%d jobman sql -n 1 '%s' ." % (len(jobs), JOBDB)