print time.time() - ep_t
print 'done with epoch ', epoch, ' epoch error = ', epoch_error, ' min error = ', min_error
lstring = 'done with epoch ' + str(epoch) + ' epoch error = ' + str(epoch_error) \
+ ' min error = ' + str(min_error) + '\n\n'
log.write(lstring)
log.flush()
# save parameters if the current model is better than previous best model
if epoch_error < min_error:
min_error = epoch_error
print 'saving model...'
params = unroll_params(r, d, len_voc, deep = 3)
cPickle.dump( params, open(param_file, 'wb'))
# reset adagrad weights
if epoch % args['adagrad_reset'] == 0 and epoch != 0:
ag.reset_weights()
# check accuracy on validation set
if epoch % args['do_val'] == 0 and epoch != 0:
print 'validating...'
params = unroll_params(r, d, len_voc, deep = 3)
evaluate(train_qs, val_qs, params, d)
print '\n\n'
log.close()
print 'step 2 of 2: training classifier over all answers (takes 10-15 hours depending on number of answers)'
params = cPickle.load(open(param_file, 'rb'))
evaluate(train_qs, val_qs, params, d)
## and compare performance to bag of words and dependency relation baselines
## - be sure to train a model first by running qanta.py
if __name__ == '__main__':
# command line arguments
parser = argparse.ArgumentParser(description='QANTA evaluation')
parser.add_argument('-data',
help='location of dataset',
default='data/hist_split')
parser.add_argument('-model',
help='location of trained model',
default='models/hist_params')
parser.add_argument('-d',
help='word embedding dimension',
type=int,
default=100)
args = vars(parser.parse_args())
print 'qanta performance: '
evaluate(args['data'], args['model'], args['d'], rnn_feats=True, \
bow_feats=False, rel_feats=False)
print '\n\n\n bow performance: '
evaluate(args['data'], args['model'], args['d'], rnn_feats=False, \
bow_feats=True, rel_feats=False)
print '\n\n\n bow-dt performance: '
evaluate(args['data'], args['model'], args['d'], rnn_feats=False, \
bow_feats=True, rel_feats=True)
from classify.learn_classifiers import evaluate
import argparse
## - evaluate QANTA's learned representations on history questions
## and compare performance to bag of words and dependency relation baselines
## - be sure to train a model first by running qanta.py
if __name__ == '__main__':
# command line arguments
parser = argparse.ArgumentParser(description='QANTA evaluation')
parser.add_argument('-data', help='location of dataset', default='data/hist_split')
parser.add_argument('-model', help='location of trained model', default='models/hist_params')
parser.add_argument('-d', help='word embedding dimension', type=int, default=100)
args = vars(parser.parse_args())
print 'qanta performance: '
evaluate(args['data'], args['model'], args['d'], rnn_feats=True, \
bow_feats=False, rel_feats=False)
print '\n\n\n bow performance: '
evaluate(args['data'], args['model'], args['d'], rnn_feats=False, \
bow_feats=True, rel_feats=False)
print '\n\n\n bow-dt performance: '
evaluate(args['data'], args['model'], args['d'], rnn_feats=False, \
bow_feats=True, rel_feats=True)
print time.time() - ep_t
print 'done with epoch ', epoch, ' epoch error = ', epoch_error, ' min error = ', min_error
lstring = 'done with epoch ' + str(epoch) + ' epoch error = ' + str(epoch_error) \
+ ' min error = ' + str(min_error) + '\n\n'
log.write(lstring)
log.flush()
# save parameters if the current model is better than previous best model
if epoch_error < min_error:
min_error = epoch_error
print 'saving model...'
params = unroll_params(r, d, len_voc, deep = 3)
cPickle.dump( params, open(param_file, 'wb'))
# reset adagrad weights
if epoch % args['adagrad_reset'] == 0 and epoch != 0:
ag.reset_weights()
# check accuracy on validation set
if epoch % args['do_val'] == 0 and epoch != 0:
print 'validating...'
params = unroll_params(r, d, len_voc, deep = 3)
evaluate(train_qs, val_qs, params, d)
print '\n\n'
log.close()
print 'step 2 of 2: training classifier over all answers (takes 10-15 hours depending on number of answers)'
params = cPickle.load(open(param_file, 'rb'))
evaluate(train_qs, val_qs, params, d)