fix bug in saving model (invoke the wrong function)

ctr/train.py

@@ -95,7 +95,7 @@ def train():
                     args.model_output_prefix, event.pass_id, event.batch_id,
                     result.cost)
                 with gzip.open(path, 'w') as f:
-                    params.to_tar(f)
+                    trainer.save_parameter_to_tar(f)
 
     trainer.train(
         reader=paddle.batch(

deep_speech_2/model_utils/model.py

@@ -111,7 +111,7 @@ class DeepSpeech2Model(object):
                     output_model_path = os.path.join(output_model_dir,
                                                      "params.latest.tar.gz")
                     with gzip.open(output_model_path, 'w') as f:
-                        self._parameters.to_tar(f)
+                        trainer.save_parameter_to_tar(f)
                     print("\nPass: %d, Batch: %d, TrainCost: %f" %
                           (event.pass_id, event.batch_id + 1,
                            cost_sum / cost_counter))
@@ -136,7 +136,7 @@ class DeepSpeech2Model(object):
                 output_model_path = os.path.join(
                     output_model_dir, "params.pass-%d.tar.gz" % event.pass_id)
                 with gzip.open(output_model_path, 'w') as f:
-                    self._parameters.to_tar(f)
+                    trainer.save_parameter_to_tar(f)
 
         # run train
         trainer.train(

dssm/train.py

@@ -237,7 +237,7 @@ def train(train_data_path=None,
                 with open("%sdssm_%s_pass_%05d.tar" %
                           (args.model_output_prefix, model_desc,
                            event.pass_id), "w") as f:
-                    parameters.to_tar(f)
+                    trainer.save_parameter_to_tar(f)
 
     trainer.train(
         reader=train_reader,

generate_chinese_poetry/train.py

@@ -12,9 +12,9 @@ logger = logging.getLogger("paddle")
 logger.setLevel(logging.INFO)
 
 
-def save_model(save_path, parameters):
+def save_model(trainer, save_path, parameters):
     with gzip.open(save_path, "w") as f:
-        parameters.to_tar(f)
+        trainer.save_parameter_to_tar(f)
 
 
 def load_initial_model(model_path, parameters):
@@ -111,7 +111,7 @@ def train(num_passes,
                 save_path = os.path.join(save_dir_path,
                                          "pass_%05d_batch_%05d.tar.gz" %
                                          (event.pass_id, event.batch_id))
-                save_model(save_path, parameters)
+                save_model(trainer, save_path, parameters)
 
             if not event.batch_id % 5:
                 logger.info("Pass %d, Batch %d, Cost %f, %s" % (
@@ -120,7 +120,7 @@ def train(num_passes,
         if isinstance(event, paddle.event.EndPass):
             save_path = os.path.join(save_dir_path,
                                      "pass_%05d.tar.gz" % event.pass_id)
-            save_model(save_path, parameters)
+            save_model(trainer, save_path, parameters)
 
     # start training
     trainer.train(

generate_sequence_by_rnn_lm/train.py

@@ -60,7 +60,7 @@ def train(topology,
                                          "rnn_lm_pass_%05d_batch_%03d.tar.gz" %
                                          (event.pass_id, event.batch_id))
                 with gzip.open(save_name, "w") as f:
-                    parameters.to_tar(f)
+                    trainer.save_parameter_to_tar(f)
 
         if isinstance(event, paddle.event.EndPass):
             if test_reader is not None:
@@ -70,7 +70,7 @@ def train(topology,
             save_name = os.path.join(model_save_dir, "rnn_lm_pass_%05d.tar.gz" %
                                      (event.pass_id))
             with gzip.open(save_name, "w") as f:
-                parameters.to_tar(f)
+                trainer.save_parameter_to_tar(f)
 
     logger.info("start training...")
     trainer.train(

globally_normalized_reader/train.py

@@ -43,7 +43,7 @@ def load_pretrained_parameters(path):
     return np.load(path)
 
 
-def save_model(save_path, parameters):
+def save_model(trainer, save_path, parameters):
     """ Save the trained parameters.
 
     Arguments:
@@ -51,7 +51,7 @@ def save_model(save_path, parameters):
         - parameters:   The trained model parameters.
     """
     with gzip.open(save_path, "w") as f:
-        parameters.to_tar(f)
+        trainer.save_parameter_to_tar(f)
 
 
 def show_parameter_init_info(parameters):
@@ -161,7 +161,7 @@ def build_event_handler(config, parameters, trainer):
             if event.batch_id and not event.batch_id % config.checkpoint_period:
                 save_path = os.path.join(config.save_dir,
                                          "checkpoint_param.latest.tar.gz")
-                save_model(save_path, parameters)
+                save_model(trainer, save_path, parameters)
 
             if not event.batch_id % config.log_period:
                 logger.info("Pass %d, Batch %d, Cost %f" %
@@ -170,7 +170,7 @@ def build_event_handler(config, parameters, trainer):
         if isinstance(event, paddle.event.EndPass):
             save_path = os.path.join(config.save_dir,
                                      "pass_%05d.tar.gz" % event.pass_id)
-            save_model(save_path, parameters)
+            save_model(trainer, save_path, parameters)
 
     return event_handler
 

hsigmoid/train.py

@@ -18,13 +18,19 @@ def main(save_dir="models"):
     dict_size = len(word_dict)
     cost = ngram_lm(hidden_size=256, embed_size=32, dict_size=dict_size)
 
+    parameters = paddle.parameters.create(cost)
+    adam_optimizer = paddle.optimizer.Adam(
+        learning_rate=3e-3,
+        regularization=paddle.optimizer.L2Regularization(8e-4))
+    trainer = paddle.trainer.SGD(cost, parameters, adam_optimizer)
+
     def event_handler(event):
         if isinstance(event, paddle.event.EndPass):
             model_name = os.path.join(save_dir, "hsigmoid_pass_%05d.tar.gz" %
                                       event.pass_id)
             logger.info("Save model into %s ..." % model_name)
             with gzip.open(model_name, "w") as f:
-                parameters.to_tar(f)
+                trainer.save_parameter_to_tar(f)
 
         if isinstance(event, paddle.event.EndIteration):
             if event.batch_id and event.batch_id % 10 == 0:
@@ -35,12 +41,6 @@ def main(save_dir="models"):
                     "Pass %d, Batch %d, Cost %f, Test Cost %f" %
                     (event.pass_id, event.batch_id, event.cost, result.cost))
 
-    parameters = paddle.parameters.create(cost)
-    adam_optimizer = paddle.optimizer.Adam(
-        learning_rate=3e-3,
-        regularization=paddle.optimizer.L2Regularization(8e-4))
-    trainer = paddle.trainer.SGD(cost, parameters, adam_optimizer)
-
     trainer.train(
         paddle.batch(
             paddle.reader.shuffle(

image_classification/train.py

@@ -81,6 +81,13 @@ def main():
         # reader.test_reader('val.list'),
         batch_size=BATCH_SIZE)
 
+    # Create trainer
+    trainer = paddle.trainer.SGD(
+        cost=cost,
+        parameters=parameters,
+        update_equation=optimizer,
+        extra_layers=extra_layers)
+
     # End batch and end pass event handler
     def event_handler(event):
         if isinstance(event, paddle.event.EndIteration):
@@ -89,18 +96,11 @@ def main():
                     event.pass_id, event.batch_id, event.cost, event.metrics)
         if isinstance(event, paddle.event.EndPass):
             with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f:
-                parameters.to_tar(f)
+                trainer.save_parameter_to_tar(f)
 
             result = trainer.test(reader=test_reader)
             print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
 
-    # Create trainer
-    trainer = paddle.trainer.SGD(
-        cost=cost,
-        parameters=parameters,
-        update_equation=optimizer,
-        extra_layers=extra_layers)
-
     trainer.train(
         reader=train_reader, num_passes=200, event_handler=event_handler)
 

ltr/lambda_rank.py

@@ -82,7 +82,7 @@ def train_lambda_rank(num_passes):
             print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
             with gzip.open("lambda_rank_params_%d.tar.gz" % (event.pass_id),
                            "w") as f:
-                parameters.to_tar(f)
+                trainer.save_parameter_to_tar(f)
 
     feeding = {"label": 0, "data": 1}
     trainer.train(

ltr/ranknet.py

@@ -86,7 +86,7 @@ def train_ranknet(num_passes):
             print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
             with gzip.open("ranknet_params_%d.tar.gz" % (event.pass_id),
                            "w") as f:
-                parameters.to_tar(f)
+                trainer.save_parameter_to_tar(f)
 
     trainer.train(
         reader=train_reader,

mt_with_external_memory/train.py

@@ -129,7 +129,7 @@ def train():
                 print "Pass: %d, Batch: %d, TrainCost: %f, %s" % (
                     event.pass_id, event.batch_id, event.cost, event.metrics)
                 with gzip.open("checkpoints/params.latest.tar.gz", 'w') as f:
-                    parameters.to_tar(f)
+                    trainer.save_parameter_to_tar(f)
             else:
                 sys.stdout.write('.')
                 sys.stdout.flush()
@@ -139,7 +139,7 @@ def train():
                                                   result.metrics)
             with gzip.open("checkpoints/params.pass-%d.tar.gz" % event.pass_id,
                            'w') as f:
-                parameters.to_tar(f)
+                trainer.save_parameter_to_tar(f)
 
     # run train
     if not os.path.exists('checkpoints'):

nce_cost/train.py

@@ -38,7 +38,7 @@ def train(model_save_dir):
                                      "model_pass_%05d.tar.gz" % event.pass_id)
             logger.info("Save model into %s ..." % save_path)
             with gzip.open(save_path, "w") as f:
-                parameters.to_tar(f)
+                trainer.save_parameter_to_tar(f)
 
     trainer.train(
         paddle.batch(

nested_sequence/text_classification/train.py

@@ -179,7 +179,7 @@ def train(train_data_dir, test_data_dir, word_dict_path, label_dict_path,
             with gzip.open(
                     os.path.join(model_save_dir, "params_pass_%05d.tar.gz" %
                                  event.pass_id), "w") as f:
-                parameters.to_tar(f)
+                trainer.save_parameter_to_tar(f)
 
     # begin training network
     trainer.train(

nmt_without_attention/train.py

@@ -50,7 +50,7 @@ def train(save_dir_path, source_dict_dim, target_dict_dim):
                         os.path.join(save_path,
                                      "nmt_without_att_%05d_batch_%05d.tar.gz" %
                                      event.pass_id, event.batch_id), "w") as f:
-                    parameters.to_tar(f)
+                    trainer.save_parameter_to_tar(f)
 
             if event.batch_id and not event.batch_id % 10:
                 logger.info("Pass %d, Batch %d, Cost %f, %s" % (

scheduled_sampling/scheduled_sampling.py

@@ -264,7 +264,7 @@ def main():
                 # save parameters
                 with gzip.open('params_pass_%d.tar.gz' % event.pass_id,
                                'w') as f:
-                    parameters.to_tar(f)
+                    trainer.save_parameter_to_tar(f)
 
         # start to train
         trainer.train(

sequence_tagging_for_ner/train.py

@@ -87,7 +87,7 @@ def main(train_data_file,
             with gzip.open(
                     os.path.join(model_save_dir, "params_pass_%d.tar.gz" %
                                  event.pass_id), "w") as f:
-                parameters.to_tar(f)
+                trainer.save_parameter_to_tar(f)
 
             result = trainer.test(reader=test_reader, feeding=feeding)
             logger.info("\nTest with Pass %d, %s" % (event.pass_id,

ssd/train.py

@@ -55,7 +55,7 @@ def train(train_file_list, dev_file_list, data_args, init_model_path):
         if isinstance(event, paddle.event.EndPass):
             with gzip.open('checkpoints/params_pass_%05d.tar.gz' % \
                     event.pass_id, 'w') as f:
-                parameters.to_tar(f)
+                trainer.save_parameter_to_tar(f)
             result = trainer.test(reader=dev_reader, feeding=feeding)
             print "\nTest with Pass %d, TestCost: %f, Detection mAP=%g" % \
                     (event.pass_id,

text_classification/train.py

@@ -141,7 +141,7 @@ def train(topology,
             with gzip.open(
                     os.path.join(model_save_dir, "dnn_params_pass_%05d.tar.gz" %
                                  event.pass_id), "w") as f:
-                parameters.to_tar(f)
+                trainer.save_parameter_to_tar(f)
 
     trainer.train(
         reader=train_reader,