if (iter + 1) % validation_frequency == 0:
validation_losses = validate_model()
this_validation_loss = mean(validation_losses)
print_flush("... " + time.strftime("%c") + ": epoch %i, minibatch %i/%i, validation error %.20f" % (epoch, minibatch_index + 1,
n_train_batches,this_validation_loss))
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
# improve patience if loss improvement is good enough
if this_validation_loss < best_validation_loss * improvement_threshold:
patience = max(patience, iter * patience_increase)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
stacked_autoencoder.save(result_path)
if patience <= iter:
done_looping = True
break
end_time = time.clock()
print_flush("... optimization complete with best validation score of %.20f on iteration %i" % (best_validation_loss,
best_iter + 1))
print_flush("... the training code for file " + os.path.split(__file__)[1] + " ran for %.2fm" % \
((end_time - start_time) / 60.))
