Mercurial > ift6266
view deep/convolutional_dae/run_exp.py @ 532:2e33885730cf
changements aux charts.ods
author | Yoshua Bengio <bengioy@iro.umontreal.ca> |
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date | Tue, 01 Jun 2010 21:19:54 -0400 |
parents | 01445a75c702 |
children |
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from ift6266.deep.convolutional_dae.scdae import * class dumb(object): COMPLETE = None def save(self): pass def go(state, channel): from ift6266 import datasets from ift6266.deep.convolutional_dae.sgd_opt import sgd_opt import pylearn, theano, ift6266 import pylearn.version import sys # params: bsize, pretrain_lr, train_lr, nfilts1, nfilts2, nftils3, nfilts4 # pretrain_rounds, noise, mlp_sz pylearn.version.record_versions(state, [theano, ift6266, pylearn]) # TODO: maybe record pynnet version? channel.save() dset = datasets.nist_P07() nfilts = [] fsizes = [] if state.nfilts1 != 0: nfilts.append(state.nfilts1) fsizes.append((5,5)) if state.nfilts2 != 0: nfilts.append(state.nfilts2) fsizes.append((3,3)) if state.nfilts3 != 0: nfilts.append(state.nfilts3) fsizes.append((3,3)) if state.nfilts4 != 0: nfilts.append(state.nfilts4) fsizes.append((2,2)) subs = [(2,2)]*len(nfilts) noise = [state.noise]*len(nfilts) pretrain_funcs, trainf, evalf, net = build_funcs( img_size=(32, 32), batch_size=state.bsize, filter_sizes=fsizes, num_filters=nfilts, subs=subs, noise=noise, mlp_sizes=[state.mlp_sz], out_size=62, dtype=numpy.float32, pretrain_lr=state.pretrain_lr, train_lr=state.train_lr) t_it = repeat_itf(dset.train, state.bsize) pretrain_fs, train, valid, test = massage_funcs( t_it, t_it, dset, state.bsize, pretrain_funcs, trainf,evalf) series = create_series() print "pretraining ..." sys.stdout.flush() do_pretrain(pretrain_fs, state.pretrain_rounds, series['recons_error']) print "training ..." sys.stdout.flush() best_valid, test_score = sgd_opt(train, valid, test, training_epochs=800000, patience=2000, patience_increase=2., improvement_threshold=0.995, validation_frequency=500, series=series, net=net) state.best_valid = best_valid state.test_score = test_score channel.save() return channel.COMPLETE if __name__ == '__main__': st = dumb() st.bsize = 100 st.pretrain_lr = 0.01 st.train_lr = 0.1 st.nfilts1 = 4 st.nfilts2 = 4 st.nfilts3 = 0 st.pretrain_rounds = 500 st.noise=0.2 st.mlp_sz = 500 go(st, dumb())