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深度学习（八）RBM受限波尔兹曼机学习-未完待续
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RBM受限波爾茲曼機學(xué)習(xí)
原文地址：
作者：hjimce
[python]?view plain?copy
?? import?timeit?? ?? try:?? ????import?PIL.Image?as?Image?? except?ImportError:?? ????import?Image?? ?? import?numpy?? ?? import?theano?? import?theano.tensor?as?T?? import?os?? ?? from?theano.tensor.shared_randomstreams?import?RandomStreams?? ?? from?utils?import?tile_raster_images?? from?logistic_sgd?import?load_data?? ?? ?? ?? class?RBM(object):?? ????def?__init__(self,input=None,n_visible=784,n_hidden=500,W=None,hbias=None,vbias=None,numpy_rng=None,theano_rng=None):?? ?? ????????self.n_visible?=?n_visible?? ????????self.n_hidden?=?n_hidden?? ?? ????????if?numpy_rng?is?None:?? ????????????numpy_rng?=?numpy.random.RandomState(1234)?? ????????if?theano_rng?is?None:?? ????????????theano_rng?=?RandomStreams(numpy_rng.randint(2?**?30))?? ?????????? ????????if?W?is?None:?? ????????????initial_W?=?numpy.asarray(?? ????????????????numpy_rng.uniform(?? ????????????????????low=-4?*?numpy.sqrt(6.?/?(n_hidden?+?n_visible)),?? ????????????????????high=4?*?numpy.sqrt(6.?/?(n_hidden?+?n_visible)),?? ????????????????????size=(n_visible,?n_hidden)?? ????????????????),?? ????????????????dtype=theano.config.floatX?? ????????????)?? ?????????????? ????????????W?=?theano.shared(value=initial_W,?name='W',?borrow=True)?? ?????????? ????????if?hbias?is?None:?? ????????????hbias?=?theano.shared(value=numpy.zeros(n_hidden,dtype=theano.config.floatX),name='hbias',borrow=True)?? ?????????? ????????if?vbias?is?None:?? ????????????vbias?=?theano.shared(value=numpy.zeros(n_visible,dtype=theano.config.floatX),name='vbias',borrow=True)?? ?? ?????????? ????????self.input?=?input?? ????????if?not?input:?? ????????????self.input?=?T.matrix('input')?? ?? ????????self.W?=?W?? ????????self.hbias?=?hbias?? ????????self.vbias?=?vbias?? ????????self.theano_rng?=?theano_rng?? ?????????? ????????self.params?=?[self.W,?self.hbias,?self.vbias]?? ?? ?????? ????def?free_energy(self,?v_sample):?? ????????''?? ????????wx_b?=?T.dot(v_sample,?self.W)?+?self.hbias?? ????????vbias_term?=?T.dot(v_sample,?self.vbias)?? ????????hidden_term?=?T.sum(T.log(1?+?T.exp(wx_b)),?axis=1)?? ????????return?-hidden_term?-?vbias_term?? ?????? ????def?propup(self,?vis):?? ????????pre_sigmoid_activation?=?T.dot(vis,?self.W)?+?self.hbias?? ????????return?[pre_sigmoid_activation,?T.nnet.sigmoid(pre_sigmoid_activation)]?? ?????? ????def?sample_h_given_v(self,?v0_sample):?? ?????????? ????????pre_sigmoid_h1,?h1_mean?=?self.propup(v0_sample)?? ?????????? ?????????? ????????h1_sample?=?self.theano_rng.binomial(size=h1_mean.shape,n=1,p=h1_mean,dtype=theano.config.floatX)?? ????????return?[pre_sigmoid_h1,?h1_mean,?h1_sample]?? ?????? ????def?propdown(self,?hid):?? ????????pre_sigmoid_activation?=?T.dot(hid,?self.W.T)?+?self.vbias?? ????????return?[pre_sigmoid_activation,?T.nnet.sigmoid(pre_sigmoid_activation)]?? ?????? ????def?sample_v_given_h(self,?h0_sample):?? ????????pre_sigmoid_v1,?v1_mean?=?self.propdown(h0_sample)?? ????????v1_sample?=?self.theano_rng.binomial(size=v1_mean.shape,?? ?????????????????????????????????????????????n=1,?p=v1_mean,?? ?????????????????????????????????????????????dtype=theano.config.floatX)?? ????????return?[pre_sigmoid_v1,?v1_mean,?v1_sample]?? ?????? ????def?gibbs_hvh(self,?h0_sample):?? ????????pre_sigmoid_v1,?v1_mean,?v1_sample?=?self.sample_v_given_h(h0_sample)?? ????????pre_sigmoid_h1,?h1_mean,?h1_sample?=?self.sample_h_given_v(v1_sample)?? ????????return?[pre_sigmoid_v1,?v1_mean,?v1_sample,?? ????????????????pre_sigmoid_h1,?h1_mean,?h1_sample]?? ?????? ????def?gibbs_vhv(self,?v0_sample):?? ????????pre_sigmoid_h1,?h1_mean,?h1_sample?=?self.sample_h_given_v(v0_sample)?? ????????pre_sigmoid_v1,?v1_mean,?v1_sample?=?self.sample_v_given_h(h1_sample)?? ????????return?[pre_sigmoid_h1,?h1_mean,?h1_sample,?? ????????????????pre_sigmoid_v1,?v1_mean,?v1_sample]?? ?? ?????? ????def?get_cost_updates(self,?lr=0.1,?persistent=None,?k=1):?? ?? ?????????? ????????pre_sigmoid_ph,?ph_mean,?ph_sample?=?self.sample_h_given_v(self.input)?? ?? ?????????? ?????????? ?????????? ????????if?persistent?is?None:?? ????????????chain_start?=?ph_sample?? ????????else:?? ????????????chain_start?=?persistent?? ?????????? ????????(?? ????????????[?? ????????????????pre_sigmoid_nvs,?? ????????????????nv_means,?? ????????????????nv_samples,?? ????????????????pre_sigmoid_nhs,?? ????????????????nh_means,?? ????????????????nh_samples?? ????????????],?? ????????????updates?? ????????)?=?theano.scan(self.gibbs_hvh,outputs_info=[None,?None,?None,?None,?None,?chain_start],n_steps=k)?? ?????????? ????????chain_end?=?nv_samples[-1]?? ?????????? ????????cost?=?T.mean(self.free_energy(self.input))?-?T.mean(?? ????????????self.free_energy(chain_end))?? ?????????? ????????gparams?=?T.grad(cost,?self.params,?consider_constant=[chain_end])?? ????????for?gparam,?param?in?zip(gparams,?self.params):?? ????????????updates[param]?=?param?-?gparam?*?T.cast(lr,dtype=theano.config.floatX)?? ?? ????????if?persistent:?? ?????????????? ????????????updates[persistent]?=?nh_samples[-1]?? ?????????????? ????????????monitoring_cost?=?self.get_pseudo_likelihood_cost(updates)?? ????????else:?? ?????????????? ????????????monitoring_cost?=?self.get_reconstruction_cost(updates,pre_sigmoid_nvs[-1])?? ?? ????????return?monitoring_cost,?updates?? ?????????? ?? ????def?get_pseudo_likelihood_cost(self,?updates):?? ?????????? ?? ?????????? ????????bit_i_idx?=?theano.shared(value=0,?name='bit_i_idx')?? ?? ?????????? ????????xi?=?T.round(self.input)?? ?? ?????????? ????????fe_xi?=?self.free_energy(xi)?? ?? ?????????? ?????????? ?????????? ????????xi_flip?=?T.set_subtensor(xi[:,?bit_i_idx],?1?-?xi[:,?bit_i_idx])?? ?? ?????????? ????????fe_xi_flip?=?self.free_energy(xi_flip)?? ?? ?????????? ????????cost?=?T.mean(self.n_visible?*?T.log(T.nnet.sigmoid(fe_xi_flip?-?? ????????????????????????????????????????????????????????????fe_xi)))?? ?? ?????????? ????????updates[bit_i_idx]?=?(bit_i_idx?+?1)?%?self.n_visible?? ?? ????????return?cost?? ?? ????def?get_reconstruction_cost(self,?updates,?pre_sigmoid_nv):?? ????????? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?? ?? ????????cross_entropy?=?T.mean(?? ????????????T.sum(?? ????????????????self.input?*?T.log(T.nnet.sigmoid(pre_sigmoid_nv))?+?? ????????????????(1?-?self.input)?*?T.log(1?-?T.nnet.sigmoid(pre_sigmoid_nv)),?? ????????????????axis=1?? ????????????)?? ????????)?? ?? ????????return?cross_entropy?? ?? ?? def?test_rbm(learning_rate=0.1,?training_epochs=15,?? ?????????????dataset='mnist.pkl.gz',?batch_size=20,?? ?????????????n_chains=20,?n_samples=10,?output_folder='rbm_plots',?? ?????????????n_hidden=500):?? ?? ????datasets?=?load_data(dataset)?? ?? ????train_set_x,?train_set_y?=?datasets[0]?? ????test_set_x,?test_set_y?=?datasets[2]?? ?? ?????? ????n_train_batches?=?train_set_x.get_value(borrow=True).shape[0]?/?batch_size?? ?? ?? ????index?=?T.lscalar()?????? ????x?=?T.matrix('x')???? ?? ????rng?=?numpy.random.RandomState(123)?? ????theano_rng?=?RandomStreams(rng.randint(2?**?30))?? ?? ?? ????persistent_chain?=?theano.shared(numpy.zeros((batch_size,?n_hidden),dtype=theano.config.floatX),borrow=True)?? ?? ?????? ????rbm?=?RBM(input=x,?n_visible=28?*?28,n_hidden=n_hidden,?numpy_rng=rng,?theano_rng=theano_rng)?? ?? ?????? ????cost,?updates?=?rbm.get_cost_updates(lr=learning_rate,persistent=persistent_chain,?k=15)?? ?? ?????? ?????? ?????? ????if?not?os.path.isdir(output_folder):?? ????????os.makedirs(output_folder)?? ????os.chdir(output_folder)?? ?? ?????? ?????? ?????? ????train_rbm?=?theano.function([index],cost,updates=updates,givens={x:?train_set_x[index?*?batch_size:?(index?+?1)?*?batch_size]},name='train_rbm')?? ?? ????plotting_time?=?0.?? ????start_time?=?timeit.default_timer()?? ?? ?????? ????for?epoch?in?xrange(training_epochs):?? ?? ?????????? ????????mean_cost?=?[]?? ????????for?batch_index?in?xrange(n_train_batches):?? ????????????mean_cost?+=?[train_rbm(batch_index)]?? ?? ????????print?'Training?epoch?%d,?cost?is?'?%?epoch,?numpy.mean(mean_cost)?? ?? ?????????? ????????plotting_start?=?timeit.default_timer()?? ?????????? ????????image?=?Image.fromarray(?? ????????????tile_raster_images(?? ????????????????X=rbm.W.get_value(borrow=True).T,?? ????????????????img_shape=(28,?28),?? ????????????????tile_shape=(10,?10),?? ????????????????tile_spacing=(1,?1)?? ????????????)?? ????????)?? ????????image.save('filters_at_epoch_%i.png'?%?epoch)?? ????????plotting_stop?=?timeit.default_timer()?? ????????plotting_time?+=?(plotting_stop?-?plotting_start)?? ?? ????end_time?=?timeit.default_timer()?? ?? ????pretraining_time?=?(end_time?-?start_time)?-?plotting_time?? ?? ????print?('Training?took?%f?minutes'?%?(pretraining_time?/?60.))?? ?????? ?????? ?????? ?????? ?????? ????number_of_test_samples?=?test_set_x.get_value(borrow=True).shape[0]?? ?? ?????? ????test_idx?=?rng.randint(number_of_test_samples?-?n_chains)?? ????persistent_vis_chain?=?theano.shared(?? ????????numpy.asarray(?? ????????????test_set_x.get_value(borrow=True)[test_idx:test_idx?+?n_chains],?? ????????????dtype=theano.config.floatX?? ????????)?? ????)?? ?????? ????plot_every?=?1000?? ?????? ?????? ?????? ????(?? ????????[?? ????????????presig_hids,?? ????????????hid_mfs,?? ????????????hid_samples,?? ????????????presig_vis,?? ????????????vis_mfs,?? ????????????vis_samples?? ????????],?? ????????updates?? ????)?=?theano.scan(?? ????????rbm.gibbs_vhv,?? ????????outputs_info=[None,?None,?None,?None,?None,?persistent_vis_chain],?? ????????n_steps=plot_every?? ????)?? ?? ?????? ?????? ????updates.update({persistent_vis_chain:?vis_samples[-1]})?? ?????? ?????? ?????? ????sample_fn?=?theano.function(?? ????????[],?? ????????[?? ????????????vis_mfs[-1],?? ????????????vis_samples[-1]?? ????????],?? ????????updates=updates,?? ????????name='sample_fn'?? ????)?? ?? ?????? ?????? ????image_data?=?numpy.zeros(?? ????????(29?*?n_samples?+?1,?29?*?n_chains?-?1),?? ????????dtype='uint8'?? ????)?? ????for?idx?in?xrange(n_samples):?? ?????????? ?????????? ????????vis_mf,?vis_sample?=?sample_fn()?? ????????print?'?...?plotting?sample?',?idx?? ????????image_data[29?*?idx:29?*?idx?+?28,?:]?=?tile_raster_images(?? ????????????X=vis_mf,?? ????????????img_shape=(28,?28),?? ????????????tile_shape=(1,?n_chains),?? ????????????tile_spacing=(1,?1)?? ????????)?? ?? ?????? ????image?=?Image.fromarray(image_data)?? ????image.save('samples.png')?? ?????? ????os.chdir('../')?? ?? if?__name__?==?'__main__':?? ????test_rbm()??
from:?http://blog.csdn.net/hjimce/article/details/48949197
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