#!/usr/bin/python
from jobman.tools import DD, flatten
from jobman import sql
#from DARPAscript import NLPSDAE
from DARPAscript_simplified import NLPSDAE
#db = sql.db('postgres://[email protected]/glorotxa_db/opentablegpu') # you should change this line to match the database you need
#db = sql.db('postgres://[email protected]/ift6266h10_sandbox_db/opentablegpu')
db = sql.db('postgres://*****:*****@gershwin.iro.umontreal.ca/ift6266h10_sandbox_db/opentablegpu')
state = DD()
state.act = ['tanh']
state.depth = 1
state.noise = ['gaussian']
state.weight_regularization_type = 'l2'
state.weight_regularization_coeff = [0.0,0.0]
state.activation_regularization_type = 'l1'
# Number of pretraining epochs, one-per-layer
state.nepochs = [128]
# Different validation runs
# - 100 training examples (x20 different samples of 100 training examples)
# - 1000 training examples (x10 different samples of 1000 training examples)
# - 10000 training examples (x1 different sample of 10000 training examples)
# (because of jobman, the keys have to be strings, not ints)
from jobman.tools import DD, flatten
from jobman import sql
from DARPAscript import NLPSDAE
db = sql.db(
'postgres://[email protected]/glorotxa_db/opentablegpu'
) # you should change this line to match the database you need
state = DD()
state.act = ['tanh']
state.depth = 1
state.n_hid = [5000]
state.noise = ['binomial_NLP']
state.weight_regularization_type = 'l2'
state.weight_regularization_coeff = [0.0, 0.0]
state.activation_regularization_type = 'l1'
# Number of pretraining epochs, one-per-layer
state.nepochs = [30]
# Different validation runs
# - 100 training examples (x20 different samples of 100 training examples)
# - 1000 training examples (x10 different samples of 1000 training examples)
# - 10000 training examples (x1 different sample of 10000 training examples)
# (because of jobman, the keys have to be strings, not ints)
# NOTE: Probably you don't want to make trainsize larger than 10K,
# because it will be too large for CPU memory.
state.validation_runs_for_each_trainingsize = {
from jobman.tools import DD, flatten
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)
