ift6266: deep/rbm/rbm.py comparison

comparison deep/rbm/rbm.py @ 372:1e99dc965b5b

correcting some bugs

author	goldfinger
date	Sun, 25 Apr 2010 13:28:45 -0400
parents	d81284e13d77
children	e36ccffb3870

comparison

equal deleted inserted replaced

-:d81284e13d77
+:1e99dc965b5b
 import numpy
 import pylab
 import time
 import theano.tensor.nnet
 import pylearn
-import ift6266
+#import ift6266
-import theano,pylearn.version,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
 from utils import tile_raster_images
 from logistic_sgd import load_data
 return cross_entropy
-def test_rbm(b_size = 25, nhidden = 1000, kk = 1, persistance = 0,
+def test_rbm(b_size = 20, nhidden = 1000, kk = 1, persistance = 0):
-dataset= 0):
 """
 Demonstrate ***
 This is demonstrated on MNIST.
 """
 learning_rate=0.1
-if data_set==0:
+#    if data_set==0:
-	datasets=datasets.nist_all()
+#   	datasets=datasets.nist_all()
-elif data_set==1:
+#    elif data_set==1:
-datasets=datasets.nist_P07()
+#        datasets=datasets.nist_P07()
-elif data_set==2:
+#    elif data_set==2:
-datasets=datasets.PNIST07()
+#        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)
 theano_rng = RandomStreams( rng.randint(2**30))
 cost, updates = rbm.cd(lr=learning_rate, persistent=None, k= kk)
 #################################
 #     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
 # go through training epochs
 costs = []
 for epoch in xrange(training_epochs):
 # go through the training set
 mean_cost = []
-for mini_x, mini_y in datasets.train(b_size):
+for batch_index in xrange(n_train_batches):
-mean_cost += [train_rbm(mini_x)]
+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))
 #learning_rate = learning_rate/10
 end_time = time.clock()
 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])
 # define one step of Gibbs sampling (mf = mean-field)
 image.save('sample_%i_step_%i.png'%(idx,idx*jdx))
 #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):
 (mean_cost, time_execution) = test_rbm(b_size = state.b_size,\
 nhidden = state.ndidden,\
 kk = state.kk,\
 persistance = state.persistance,\
-dataset = state.dataset)
+)
 state.mean_costs = mean_costs
 state.time_execution = time_execution
 pylearn.version.record_versions(state,[theano,ift6266,pylearn])
 return channel.COMPLETE
 if __name__ == '__main__':
-test_rbm()
+TABLE_NAME='RBM_tapha'
+# 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')

Mercurial > ift6266

comparison deep/rbm/rbm.py @ 372:1e99dc965b5b