Mercurial > ift6266
changeset 421:0ffef3667865
merge
| author | Xavier Glorot <glorotxa@iro.umontreal.ca> |
|---|---|
| date | Fri, 30 Apr 2010 16:23:45 -0400 |
| parents | a3a4a9c6476d (current diff) c91d7b67fa41 (diff) |
| children | e4eb3ee7a0cf c06a3d9b5664 |
| files | |
| diffstat | 2 files changed, 241 insertions(+), 2 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/deep/stacked_dae/v_sylvain/nist_apriori_error.py Fri Apr 30 16:23:45 2010 -0400 @@ -0,0 +1,239 @@ +__docformat__ = 'restructedtext en' + +import pdb +import numpy +from numpy import array +import time +import datetime +import pylearn +import copy +import sys +import os +import os.path +from pylearn.io import filetensor as ft +from jobman import DD +from ift6266 import datasets +import cPickle +from copy import copy +import math + +from config import * + +data_path = '/data/lisa/data/nist/by_class/' +test_data = 'all/all_train_data.ft' +test_labels = 'all/all_train_labels.ft' +state = DD(DEFAULT_HP_NIST) + +#sda_model -> path for the parameters file +#dataset -> the dataset we use for the test +#part -> 0=train, 1=valid, 2=test +#type -> non-linearity type 0=sigmoid, 1=tanh +def test_data(sda_model,dataset,part=2,type=0): + + + f = open(sda_model) + parameters_pre=cPickle.load(f) + f.close() + + W1 = array(copy(parameters_pre[0])) + #print 'W1: ' + str(W1.shape) + b1 = array(copy(parameters_pre[1])) + #print 'b1: ' + str(b1.shape) + W2 = array(copy(parameters_pre[2])) + #print 'W2: ' + str(W2.shape) + b2 = array(copy(parameters_pre[3])) + #print 'b2: ' + str(b2.shape) + W3 = array(copy(parameters_pre[4])) + #print 'W3: ' + str(W3.shape) + b3 = array(copy(parameters_pre[5])) + #print 'b3: ' + str(b3.shape) + if state['num_hidden_layers'] == 4: + W4 = array(copy(parameters_pre[6])) + b4 = array(copy(parameters_pre[7])) + Wo = array(copy(parameters_pre[8])) + bo = array(copy(parameters_pre[9])) + elif state['num_hidden_layers'] == 3: + Wo = array(copy(parameters_pre[6])) + #print 'Wo: ' + str(Wo.shape) + bo = array(copy(parameters_pre[7])) + #print 'bo: ' + str(bo.shape) + W4=None + b4=None + else: + print('Number of layers not implemented yet, please do it') + + + total_error_count=0 + total_exemple_count=0 + if part == 0: + iter = dataset.train(1) + if part == 1: + iter = dataset.valid(1) + if part == 2: + iter = dataset.test(1) + for x,y in iter: + total_exemple_count = total_exemple_count +1 + if type == 1: + #get output for layer 1 + out1=(numpy.tanh(numpy.dot(x,W1) + b1)+1.0)/2.0 + #get output for layer 2 + out2=(numpy.tanh(numpy.dot(out1,W2) + b2)+1.0)/2.0 + #get output for layer 3 + out3=(numpy.tanh(numpy.dot(out2,W3) + b3)+1.0)/2.0 + #if there is a fourth layer + if state['num_hidden_layers'] == 4: + outf = (numpy.tanh(numpy.dot(out3,W4) + b4)+1.0)/2.0 + else: + outf = array(out3) + else: + #get output for layer 1 + out1=1.0/(1.0+numpy.exp(-(numpy.dot(x,W1)+b1))) + #get output for layer 2 + out2 = 1.0/(1.0+numpy.exp(-(numpy.dot(out1,W2)+b2))) + #get output for layer 3 + out3 = 1.0/(1.0+numpy.exp(-(numpy.dot(out2,W3)+b3))) + #if there is a fourth layer + if state['num_hidden_layers'] == 4: + outf = 1.0/(1.0+numpy.exp(-(numpy.dot(out3,W4)+b4))) + else: + outf = out3 + + out_act = numpy.dot(outf,Wo)+bo + + #add non linear function for output activation (softmax) + #We can also use sigmoid and results will be the same + out = numpy.zeros(len(out_act[0]),float) + a1_exp = numpy.exp(out_act) + sum_a1=numpy.sum(a1_exp) + out=a1_exp/sum_a1 +## for i in xrange(len(out_act[0])): +## out[i]=sigmoid(array(out_act[0,i])) + + #get grouped based error + #with a priori + if(y>9 and y<35): + predicted_class=numpy.argmax(out[0,10:35])+10 + if(predicted_class!=y): + total_error_count+=1 + + if(y<10): + predicted_class=numpy.argmax(out[0,0:10]) + if(predicted_class!=y): + total_error_count+=1 + if(y>34): + predicted_class=numpy.argmax(out[0,35:])+35 + if(predicted_class!=y): + total_error_count+=1 + + print '\t total exemples count: '+str(total_exemple_count) + print '\t total error count: '+str(total_error_count) + print '\t percentage of error: '+str(total_error_count*100.0/total_exemple_count*1.0)+' %' + + +def sigmoid(value): +## if len(value) > 1: +## retour = numpy.zeros(len(value),float) +## for i in xrange(len(value)): +## retour[i] = (1.0/(1.0+math.exp(-float(value[i])))) +## return retour +## else: +## print len(value) + return (1.0/(1.0+math.exp(-value))) + +if __name__ == '__main__': + + args = sys.argv[1:] + + if len(args) > 0 and args[0] == 'sigmoid': + type = 0 + elif len(args) > 0 and args[0] == 'tanh': + type = 1 + + part = 2 #0=train, 1=valid, 2=test + + PATH = '' #Can be changed too if model is not in the current drectory + + if os.path.exists(PATH+'params_finetune_NIST.txt'): + start_time = time.clock() + print ('\n finetune = NIST ') + print "NIST DIGITS" + test_data(PATH+'params_finetune_NIST.txt',datasets.nist_digits(),part=part,type=type) + print "NIST LOWER CASE" + test_data(PATH+'params_finetune_NIST.txt',datasets.nist_lower(),part=part,type=type) + print "NIST UPPER CASE" + test_data(PATH+'params_finetune_NIST.txt',datasets.nist_upper(),part=part,type=type) + end_time = time.clock() + print ('It took %f minutes' %((end_time-start_time)/60.)) + + + if os.path.exists(PATH+'params_finetune_P07.txt'): + start_time = time.clock() + print ('\n finetune = P07 ') + print "NIST DIGITS" + test_data(PATH+'params_finetune_P07.txt',datasets.nist_digits(),part=part,type=type) + print "NIST LOWER CASE" + test_data(PATH+'params_finetune_P07.txt',datasets.nist_lower(),part=part,type=type) + print "NIST UPPER CASE" + test_data(PATH+'params_finetune_P07.txt',datasets.nist_upper(),part=part,type=type) + end_time = time.clock() + print ('It took %f minutes' %((end_time-start_time)/60.)) + + + if os.path.exists(PATH+'params_finetune_NIST_then_P07.txt'): + start_time = time.clock() + print ('\n finetune = NIST then P07') + print "NIST DIGITS" + test_data(PATH+'params_finetune_NIST_then_P07.txt',datasets.nist_digits(),part=part,type=type) + print "NIST LOWER CASE" + test_data(PATH+'params_finetune_NIST_then_P07.txt',datasets.nist_lower(),part=part,type=type) + print "NIST UPPER CASE" + test_data(PATH+'params_finetune_NIST_then_P07.txt',datasets.nist_upper(),part=part,type=type) + end_time = time.clock() + print ('It took %f minutes' %((end_time-start_time)/60.)) + + if os.path.exists(PATH+'params_finetune_P07_then_NIST.txt'): + start_time = time.clock() + print ('\n finetune = P07 then NIST') + print "NIST DIGITS" + test_data(PATH+'params_finetune_P07_then_NIST.txt',datasets.nist_digits(),part=part,type=type) + print "NIST LOWER CASE" + test_data(PATH+'params_finetune_P07_then_NIST.txt',datasets.nist_lower(),part=part,type=type) + print "NIST UPPER CASE" + test_data(PATH+'params_finetune_P07_then_NIST.txt',datasets.nist_upper(),part=part,type=type) + end_time = time.clock() + print ('It took %f minutes' %((end_time-start_time)/60.)) + + if os.path.exists(PATH+'params_finetune_PNIST07.txt'): + start_time = time.clock() + print ('\n finetune = PNIST07') + print "NIST DIGITS" + test_data(PATH+'params_finetune_PNIST07.txt',datasets.nist_digits(),part=part,type=type) + print "NIST LOWER CASE" + test_data(PATH+'params_finetune_PNIST07.txt',datasets.nist_lower(),part=part,type=type) + print "NIST UPPER CASE" + test_data(PATH+'params_finetune_PNIST07.txt',datasets.nist_upper(),part=part,type=type) + end_time = time.clock() + print ('It took %f minutes' %((end_time-start_time)/60.)) + + if os.path.exists(PATH+'params_finetune_PNIST07_then_NIST.txt'): + start_time = time.clock() + print ('\n finetune = PNIST07 then NIST') + print "NIST DIGITS" + test_data(PATH+'params_finetune_PNIST07_then_NIST.txt',datasets.nist_digits(),part=part,type=type) + print "NIST LOWER CASE" + test_data(PATH+'params_finetune_PNIST07_then_NIST.txt',datasets.nist_lower(),part=part,type=type) + print "NIST UPPER CASE" + test_data(PATH+'params_finetune_PNIST07_then_NIST.txt',datasets.nist_upper(),part=part,type=type) + end_time = time.clock() + print ('It took %f minutes' %((end_time-start_time)/60.)) + + + + + + + + + + + \ No newline at end of file
--- a/deep/stacked_dae/v_sylvain/nist_sda.py Fri Apr 30 16:23:15 2010 -0400 +++ b/deep/stacked_dae/v_sylvain/nist_sda.py Fri Apr 30 16:23:45 2010 -0400 @@ -158,7 +158,7 @@ optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1,special=1,decrease=decrease_lr) if finetune_choice == 4: print ('\n\n\tFinetune with PNIST07 then NIST\n\n') - optimizer.reload_parameters('params)pretrain.txt') + optimizer.reload_parameters('params_pretrain.txt') optimizer.finetune(datasets.PNIST07(),datasets.nist_all(),max_finetune_epoch_NIST,ind_test=30,decrease=decrease_lr,dataset_test2=datasets.nist_P07()) optimizer.finetune(datasets.nist_all(),datasets.PNIST07(),max_finetune_epoch_NIST,ind_test=31,decrease=decrease_lr,dataset_test2=datasets.nist_P07()) @@ -187,7 +187,7 @@ channel.save() print ('\n\n\tFinetune with PNIST07\n\n') sys.stdout.flush() - optimizer.reload_parameters('params)pretrain.txt') + optimizer.reload_parameters('params_pretrain.txt') optimizer.finetune(datasets.PNIST07(),datasets.nist_all(),max_finetune_epoch_NIST,ind_test=2,decrease=decrease_lr,dataset_test2=datasets.nist_P07()) channel.save() sys.stdout.flush()