How to fixed the eval output sentence?

[shahuzi]

Hi, I found that the eval_model's output sentence is not always be the same sentence, I think we'd better use the same output during the different eval steps to compare. So how can I to make the eval_model's output sentence always be the same? I paste part of code for the eval_mode to output sentence here.

            if step % args.eval_interval == 0:
                    #Run eval and save eval stats
                    log('\nRunning evaluation at step {}'.format(step))

                    eval_losses = []
                    before_losses = []
                    after_losses = []
                    stop_token_losses = []
                    linear_losses = []
                    linear_loss = None

                    if hparams.predict_linear:
                        for i in tqdm(range(feeder.test_steps)):
                            eloss, before_loss, after_loss, stop_token_loss, linear_loss, mel_p, mel_t, t_len, align, lin_p, lin_t = sess.run([
                                eval_model.tower_loss[0], eval_model.tower_before_loss[0], eval_model.tower_after_loss[0],
                                eval_model.tower_stop_token_loss[0], eval_model.tower_linear_loss[0], eval_model.tower_mel_outputs[0][0],
                                eval_model.tower_mel_targets[0][0], eval_model.tower_targets_lengths[0][0],
                                eval_model.tower_alignments[0][0], eval_model.tower_linear_outputs[0][0],
                                eval_model.tower_linear_targets[0][0],
                                ])
                            eval_losses.append(eloss)
                            before_losses.append(before_loss)
                            after_losses.append(after_loss)
                            stop_token_losses.append(stop_token_loss)
                            linear_losses.append(linear_loss)
                        linear_loss = sum(linear_losses) / len(linear_losses)

                        if hparams.GL_on_GPU:
                            wav = sess.run(GLGPU_lin_outputs, feed_dict={GLGPU_lin_inputs: lin_p})
                            wav = audio.inv_preemphasis(wav, hparams.preemphasis, hparams.preemphasize)
                        else:
                            wav = audio.inv_linear_spectrogram(lin_p.T, hparams)
                        audio.save_wav(wav, os.path.join(eval_wav_dir, 'step-{}-eval-wave-from-linear.wav'.format(step)), sr=hparams.sample_rate)

                    else:
                        for i in tqdm(range(feeder.test_steps)):
                            eloss, before_loss, after_loss, stop_token_loss, mel_p, mel_t, t_len, align = sess.run([
                                eval_model.tower_loss[0], eval_model.tower_before_loss[0], eval_model.tower_after_loss[0],
                                eval_model.tower_stop_token_loss[0], eval_model.tower_mel_outputs[0][0], eval_model.tower_mel_targets[0][0],
                                eval_model.tower_targets_lengths[0][0], eval_model.tower_alignments[0][0]
                                ])
                            eval_losses.append(eloss)
                            before_losses.append(before_loss)
                            after_losses.append(after_loss)
                            stop_token_losses.append(stop_token_loss)

                    eval_loss = sum(eval_losses) / len(eval_losses)
                    before_loss = sum(before_losses) / len(before_losses)
                    after_loss = sum(after_losses) / len(after_losses)
                    stop_token_loss = sum(stop_token_losses) / len(stop_token_losses)

                    log('Saving eval log to {}..'.format(eval_dir))
                    #Save some log to monitor model improvement on same unseen sequence
                    if hparams.GL_on_GPU:
                        wav = sess.run(GLGPU_mel_outputs, feed_dict={GLGPU_mel_inputs: mel_p})
                        wav = audio.inv_preemphasis(wav, hparams.preemphasis, hparams.preemphasize)
                    else:
                        wav = audio.inv_mel_spectrogram(mel_p.T, hparams)
                    audio.save_wav(wav, os.path.join(eval_wav_dir, 'step-{}-eval-wave-from-mel.wav'.format(step)), sr=hparams.sample_rate)

                    plot.plot_alignment(align, os.path.join(eval_plot_dir, 'step-{}-eval-align.png'.format(step)),
                        title='{}, {}, step={}, loss={:.5f}'.format(args.model, time_string(), step, eval_loss),
                        max_len=t_len // hparams.outputs_per_step)
                    plot.plot_spectrogram(mel_p, os.path.join(eval_plot_dir, 'step-{}-eval-mel-spectrogram.png'.format(step)),
                        title='{}, {}, step={}, loss={:.5f}'.format(args.model, time_string(), step, eval_loss), target_spectrogram=mel_t,
                        max_len=t_len)

                    if hparams.predict_linear:
                        plot.plot_spectrogram(lin_p, os.path.join(eval_plot_dir, 'step-{}-eval-linear-spectrogram.png'.format(step)),
                            title='{}, {}, step={}, loss={:.5f}'.format(args.model, time_string(), step, eval_loss), target_spectrogram=lin_t,
                            max_len=t_len, auto_aspect=True)

                    log('Eval loss for global step {}: {:.3f}'.format(step, eval_loss))
                    log('Writing eval summary!')
                    add_eval_stats(summary_writer, step, linear_loss, before_loss, after_loss, stop_token_loss, eval_loss)