Enable min_batch_num in train.py and update train info print.

deep_speech_2/train.py

@@ -143,11 +143,13 @@ def train():
     train_batch_reader = train_generator.batch_reader_creator(
         manifest_path=args.train_manifest_path,
         batch_size=args.batch_size,
+        min_batch_size=args.trainer_count,
         sortagrad=args.use_sortagrad if args.init_model_path is None else False,
         batch_shuffle=True)
     test_batch_reader = test_generator.batch_reader_creator(
         manifest_path=args.dev_manifest_path,
         batch_size=args.batch_size,
+        min_batch_size=1,  # must be 1, but will have errors.
         sortagrad=False,
         batch_shuffle=False)
 
@@ -157,11 +159,11 @@ def train():
         if isinstance(event, paddle.event.EndIteration):
             cost_sum += event.cost
             cost_counter += 1
-            if event.batch_id % 50 == 0:
-                print("\nPass: %d, Batch: %d, TrainCost: %f" %
-                      (event.pass_id, event.batch_id, cost_sum / cost_counter))
+            if (event.batch_id + 1) % 100 == 0:
+                print("\nPass: %d, Batch: %d, TrainCost: %f" % (
+                    event.pass_id, event.batch_id + 1, cost_sum / cost_counter))
                 cost_sum, cost_counter = 0.0, 0
-                with gzip.open("params_tmp.tar.gz", 'w') as f:
+                with gzip.open("params.tar.gz", 'w') as f:
                     parameters.to_tar(f)
             else:
                 sys.stdout.write('.')