# HG changeset patch # User goldfinger # Date 1272216525 14400 # Node ID 1e99dc965b5bad2b64027726c97ec4bbe304fbe6 # Parent d81284e13d77bc3404856c02b01f5c8ce5c43a18 correcting some bugs diff -r d81284e13d77 -r 1e99dc965b5b deep/rbm/rbm.py --- a/deep/rbm/rbm.py Sat Apr 24 11:32:26 2010 -0400 +++ b/deep/rbm/rbm.py Sun Apr 25 13:28:45 2010 -0400 @@ -16,10 +16,13 @@ import time import theano.tensor.nnet import pylearn -import ift6266 -import theano,pylearn.version,ift6266 +#import ift6266 +import theano,pylearn.version #,ift6266 from pylearn.io import filetensor as ft -from ift6266 import datasets +#from ift6266 import datasets + +from jobman.tools import DD, flatten +from jobman import sql from theano.tensor.shared_randomstreams import RandomStreams @@ -240,8 +243,7 @@ -def test_rbm(b_size = 25, nhidden = 1000, kk = 1, persistance = 0, - dataset= 0): +def test_rbm(b_size = 20, nhidden = 1000, kk = 1, persistance = 0): """ Demonstrate *** @@ -257,28 +259,47 @@ learning_rate=0.1 - if data_set==0: - datasets=datasets.nist_all() - elif data_set==1: - datasets=datasets.nist_P07() - elif data_set==2: - datasets=datasets.PNIST07() +# if data_set==0: +# datasets=datasets.nist_all() +# elif data_set==1: +# datasets=datasets.nist_P07() +# elif data_set==2: +# datasets=datasets.PNIST07() + data_path = '/data/lisa/data/nist/by_class/' + f = open(data_path+'all/all_train_data.ft') + g = open(data_path+'all/all_train_labels.ft') + h = open(data_path+'all/all_test_data.ft') + i = open(data_path+'all/all_test_labels.ft') + + train_set_x = theano.shared(ft.read(f)) + train_set_y = ft.read(g) + test_set_x = ft.read(h) + test_set_y = ft.read(i) + + f.close() + g.close() + i.close() + h.close() + + #t = len(train_set_x) + print train_set_x.value.shape + # revoir la recuperation des donnees ## dataset = load_data(dataset) ## ## train_set_x, train_set_y = datasets[0] ## test_set_x , test_set_y = datasets[2] -## training_epochs = 10 # a determiner + training_epochs = 1 # a determiner batch_size = b_size # size of the minibatch # compute number of minibatches for training, validation and testing - #n_train_batches = train_set_x.value.shape[0] / batch_size + n_train_batches = train_set_x.value.shape[0] / batch_size # allocate symbolic variables for the data - index = T.lscalar() # index to a [mini]batch + index = T.scalar() # index to a [mini]batch x = T.matrix('x') # the data is presented as rasterized images rng = numpy.random.RandomState(123) @@ -304,16 +325,18 @@ ################################# # Training the RBM # ################################# - dirname = 'data=%i'%dataset + ' persistance=%i'%persistance + ' n_hidden=%i'%n_hidden + 'batch_size=i%'%b_size + #os.chdir('~') + dirname = str(persistance) + '_' + str(nhidden) + '_' + str(b_size) + '_'+ str(kk) os.makedirs(dirname) os.chdir(dirname) - + print 'yes' # it is ok for a theano function to have no output # the purpose of train_rbm is solely to update the RBM parameters - train_rbm = theano.function([x], cost, + train_rbm = theano.function([index], cost, updates = updates, - ) + givens = { x: train_set_x[index*batch_size:(index+1)*batch_size]}) + print 'yep' plotting_time = 0.0 start_time = time.clock() bufsize = 1000 @@ -324,8 +347,10 @@ # go through the training set mean_cost = [] - for mini_x, mini_y in datasets.train(b_size): - mean_cost += [train_rbm(mini_x)] + for batch_index in xrange(n_train_batches): + mean_cost += [train_rbm(batch_index)] +# for mini_x, mini_y in datasets.train(b_size): +# mean_cost += [train_rbm(mini_x)] ## learning_rate = learning_rate - 0.0001 ## learning_rate = learning_rate/(tau+( epoch*batch_index*batch_size)) @@ -348,18 +373,16 @@ pretraining_time = (end_time - start_time) - plotting_time - - - ################################# # Sampling from the RBM # ################################# # find out the number of test samples - number_of_test_samples = 1000 + #number_of_test_samples = 100 + number_of_test_samples = test_set_x.value.shape[0] - test_set_x, test_y = datasets.test(100*b_size) + #test_set_x, test_y = datasets.test(100*b_size) # pick random test examples, with which to initialize the persistent chain test_idx = rng.randint(number_of_test_samples - b_size) persistent_vis_chain = theano.shared(test_set_x.value[test_idx:test_idx+b_size]) @@ -403,10 +426,10 @@ #save the model model = [rbm.W, rbm.vbias, rbm.hbias] f = fopen('params.txt', 'w') - pickle.dump(model, f) + cPickle.dump(model, f, protocol = -1) f.close() #os.chdir('./..') - return numpy.mean(costs), pretraining_time/360 + return numpy.mean(costs), pretraining_time*36 def experiment(state, channel): @@ -415,7 +438,7 @@ nhidden = state.ndidden,\ kk = state.kk,\ persistance = state.persistance,\ - dataset = state.dataset) + ) state.mean_costs = mean_costs state.time_execution = time_execution @@ -423,5 +446,23 @@ return channel.COMPLETE if __name__ == '__main__': + + TABLE_NAME='RBM_tapha' - test_rbm() + # DB path... + test_rbm() + #db = sql.db('postgres://ift6266h10:f0572cd63b@gershwin/ift6266h10_db/'+ TABLE_NAME) + + #state = DD() + #for b_size in 50, 75, 100: + # state.b_size = b_size + # for nhidden in 1000,1250,1500: + # state.nhidden = nhidden + # for kk in 1,2,3,4: + # state.kk = kk + # for persistance in 0,1: + # state.persistance = persistance + # sql.insert_job(rbm.experiment, flatten(state), db) + + + #db.createView(TABLE_NAME + 'view')