Compute weighted average of individual dev set losses

Author: reuben

DeepSpeech.py

@@ -506,7 +506,7 @@ def __call__(self, progress, data, **kwargs):
 
             pbar.finish()
             mean_loss = total_loss / step_count if step_count > 0 else 0.0
-            return mean_loss
+            return mean_loss, step_count
 
         log_info('STARTING Optimization')
         train_start_time = datetime.utcnow()
@@ -516,19 +516,21 @@ def __call__(self, progress, data, **kwargs):
             for epoch in range(FLAGS.epochs):
                 # Training
                 log_progress('Training epoch %d...' % epoch)
-                train_loss = run_set('train', epoch, train_init_op)
+                train_loss, _ = run_set('train', epoch, train_init_op)
                 log_progress('Finished training epoch %d - loss: %f' % (epoch, train_loss))
                 checkpoint_saver.save(session, checkpoint_path, global_step=global_step)
 
                 if FLAGS.dev_files:
                     # Validation
                     dev_loss = 0.0
+                    total_steps = 0
                     for csv, init_op in zip(dev_csvs, dev_init_ops):
                         log_progress('Validating epoch %d on %s...' % (epoch, csv))
-                        set_loss = run_set('dev', epoch, init_op, dataset=csv)
-                        dev_loss += set_loss
+                        set_loss, steps = run_set('dev', epoch, init_op, dataset=csv)
+                        dev_loss += set_loss * steps
+                        total_steps += steps
                         log_progress('Finished validating epoch %d on %s - loss: %f' % (epoch, csv, set_loss))
-                    dev_loss = dev_loss / len(dev_csvs)
+                    dev_loss = dev_loss / total_steps
 
                     dev_losses.append(dev_loss)