for batch_index in xrange(cfg.train_sets.cur_frame_num / cfg.batch_size): # loop over mini-batches
[reconstruction_cost, free_energy_cost] = pretraining_fns[i](index=batch_index, lr=pretrain_lr,
momentum=momentum)
r_c.append(reconstruction_cost)
fe_c.append(free_energy_cost)
cfg.train_sets.initialize_read()
log('> pre-training layer %i, epoch %d, r_cost %f, fe_cost %f' % (i, epoch, numpy.mean(r_c), numpy.mean(fe_c)))
# output nnet parameters and training state, for training resume
_nnet2file(dnn.layers, filename=wdir + '/nnet.tmp')
save_two_integers((i, epoch+1), wdir + '/training_state.tmp')
start_epoch_index = 0
save_two_integers((i+1, 0), wdir + '/training_state.tmp')
# save the pretrained nnet to file
# save the model and network configuration
if cfg.param_output_file != '':
_nnet2file(dnn.layers, filename=cfg.param_output_file)
log('> ... the final PDNN model parameter is ' + cfg.param_output_file)
if cfg.cfg_output_file != '':
_cfg2file(dnn.cfg, filename=cfg.cfg_output_file)
log('> ... the final PDNN model config is ' + cfg.cfg_output_file)
# output the model into Kaldi-compatible format
if cfg.kaldi_output_file != '':
dnn.write_model_to_kaldi(cfg.kaldi_output_file)
log('> ... the final Kaldi model is ' + cfg.kaldi_output_file)
# finally remove the training-resuming files
os.remove(wdir + '/nnet.tmp')
os.remove(wdir + '/training_state.tmp')
lr=pretrain_lr,
momentum=momentum)
r_c.append(reconstruction_cost)
fe_c.append(free_energy_cost)
cfg.train_sets.initialize_read()
log('> pre-training layer %i, epoch %d, r_cost %f, fe_cost %f' %
(i, epoch, numpy.mean(r_c), numpy.mean(fe_c)))
# output nnet parameters and training state, for training resume
_nnet2file(dnn.layers, filename=wdir + '/nnet.tmp')
save_two_integers((i, epoch + 1), wdir + '/training_state.tmp')
start_epoch_index = 0
save_two_integers((i + 1, 0), wdir + '/training_state.tmp')
# save the pretrained nnet to file
# save the model and network configuration
if cfg.param_output_file != '':
_nnet2file(dnn.layers, filename=cfg.param_output_file)
log('> ... the final PDNN model parameter is ' + cfg.param_output_file)
if cfg.cfg_output_file != '':
_cfg2file(dnn.cfg, filename=cfg.cfg_output_file)
log('> ... the final PDNN model config is ' + cfg.cfg_output_file)
# output the model into Kaldi-compatible format
if cfg.kaldi_output_file != '':
dnn.write_model_to_kaldi(cfg.kaldi_output_file)
log('> ... the final Kaldi model is ' + cfg.kaldi_output_file)
# finally remove the training-resuming files
os.remove(wdir + '/nnet.tmp')
os.remove(wdir + '/training_state.tmp')
