if this_validation_loss < best_validation_loss:
with open(output_file_name + '.pickle', 'wb') as f:
cPickle.dump(nnet, f, -1)
# improve patience if loss improvement is good enough
if (this_validation_loss < best_validation_loss *
improvement_threshold):
patience = max(patience, iteration * patience_increase)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
# test it on the test set
test_losses = test_scoref()
test_score = np.mean(test_losses) # TODO this is a mean of means (with different lengths)
print((' epoch %i, test error of best model %f') %
(epoch, test_score))
pred_counts = np.bincount(np.concatenate([nnet.predict(x) for x, _ in test_set_iterator]), minlength=n_outs)
print pred_counts
pl.figure(figsize=(24,18))
pl.bar(np.arange(n_outs), pred_counts, width=1.)
pl.xticks(np.arange(n_outs), le.inverse_transform(np.arange(n_outs)), rotation=90, fontsize=9)
pl.savefig("hist_preds_" + str(epoch) + ".png")
if patience <= iteration: # TODO correct that
done_looping = True
break
end_time = time.clock()
print(('Optimization complete with best validation score of %f, '
'with test performance %f') %
(best_validation_loss, test_score))
print >> sys.stderr, ('The fine tuning code for file ' +
