#!/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)