val = numpy.random.rand() * (10**expo)
state['wiener_lambda'] = val
state['test_step'] = 10**numpy.random.randint(6)
n = numpy.random.randint(2)
if n == 0:
state['style'] = 'continous'
else:
state['style'] = 'random_pos'
n = numpy.random.randint(7)
if n == 0:
expo = -numpy.random.randint(3) - 1
val = numpy.random.rand() * (10**expo)
state['task_noise'] = val
else:
state['task_noise'] = 0.
n = numpy.random.randint(7)
if n == 0:
expo = -numpy.random.randint(3) - 1
val = numpy.random.rand() * (10**expo)
state['task_wout_noise'] = val
else:
state['task_wout_noise'] = 0.
state['name'] = 'rnn_%03d' % n_jobs
sql.add_experiments_to_db([state], db, verbose=1, force_dup=True)
print 'N_jobs ', n_jobs, TABLE_NAME
state['lr'] = .01
state['lsIters'] = 80
state['checkFreq'] = 5
state['krylovDim'] = 15
state['lbfgsIters'] = 30
state['data'] = '/home/pascanur/data/cifar10.npz'
state['mreg'] = 1.
state['adaptivedamp'] = 1
state['model'] = 'mlp'
state['metric'] = 'Gf'
state['algo'] = 'natNCG'
state['gbs'] = 40000
state['mbs'] = 5000
state['ebs'] = 5000
state['init'] = 'xavier'
for hids in [
'[2000,1000,1000]',
'[3000,1000,1000]',
'[2000,1000,1000,1000]',
'[3000,1500,1000,500]',
'[2000,1000,1000,1000,500,500]']:
state['hids'] = hids
n_jobs += 1
sql.add_experiments_to_db([state], db, verbose=1, force_dup = True)
print 'N_jobs ', n_jobs, TABLE_NAME