def write_model_to_raw(self, file_path):
# output the model to tmp_path; this format is readable by PDNN
_nnet2file(self.layers, filename=file_path, input_factor = self.input_dropout_factor, factor = self.dropout_factor)
train_fn, valid_fn = dnn.build_finetune_functions(
(cfg.train_x, cfg.train_y), (cfg.valid_x, cfg.valid_y),
batch_size=cfg.batch_size)
log('> ... finetuning the model')
while (cfg.lrate.get_rate() != 0):
# one epoch of sgd training
train_error = train_sgd(train_fn, cfg)
log('> epoch %d, training error %f ' % (cfg.lrate.epoch, 100*numpy.mean(train_error)) + '(%)')
# validation
valid_error = validate_by_minibatch(valid_fn, cfg)
log('> epoch %d, lrate %f, validation error %f ' % (cfg.lrate.epoch, cfg.lrate.get_rate(), 100*numpy.mean(valid_error)) + '(%)')
cfg.lrate.get_next_rate(current_error = 100*numpy.mean(valid_error))
# output nnet parameters and lrate, for training resume
if cfg.lrate.epoch % cfg.model_save_step == 0:
_nnet2file(dnn.layers, filename=wdir + '/nnet.tmp')
_lrate2file(cfg.lrate, wdir + '/training_state.tmp')
# save the model and network configuration
if cfg.param_output_file != '':
_nnet2file(dnn.layers, filename=cfg.param_output_file, input_factor = cfg.input_dropout_factor, factor = cfg.dropout_factor)
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)
def write_model_to_raw(self, file_path):
# output the model to tmp_path; this format is readable by PDNN
_nnet2file(self.layers, filename=file_path)
