add weight decay

fluid/machine_reading_comprehesion/args.py

@@ -58,6 +58,11 @@ def parse_args():
         type=float,
         default=0.001,
         help="Learning rate used to train the model. (default: %(default)f)")
+    parser.add_argument(
+        "--weight_decay",
+        type=float,
+        default=0.001,
+        help="Weight decay. (default: %(default)f)")
     parser.add_argument(
         "--use_gpu",
         type=distutils.util.strtobool,

fluid/machine_reading_comprehesion/run.py

@@ -21,6 +21,7 @@ import time
 import os
 import random
 import json
+import six 
 
 import paddle
 import paddle.fluid as fluid
@@ -209,6 +210,8 @@ def validation(inference_program, avg_cost, s_probs, e_probs, feed_order, place,
                     'yesno_answers': []
                 })
     if args.result_dir is not None and args.result_name is not None:
+        if not os.path.exists(args.result_dir):
+            os.makedirs(args.result_dir)
         result_file = os.path.join(args.result_dir, args.result_name + '.json')
         with open(result_file, 'w') as fout:
             for pred_answer in pred_answers:
@@ -235,7 +238,10 @@ def validation(inference_program, avg_cost, s_probs, e_probs, feed_order, place,
 def train(logger, args):
     logger.info('Load data_set and vocab...')
     with open(os.path.join(args.vocab_dir, 'vocab.data'), 'rb') as fin:
-        vocab = pickle.load(fin)
+        if six.PY2:
+            vocab = pickle.load(fin)
+        else:
+            vocab = pickle.load(fin, encoding='bytes')
         logger.info('vocab size is {} and embed dim is {}'.format(vocab.size(
         ), vocab.embed_dim))
     brc_data = BRCDataset(args.max_p_num, args.max_p_len, args.max_q_len,
@@ -259,13 +265,20 @@ def train(logger, args):
             # build optimizer
             if args.optim == 'sgd':
                 optimizer = fluid.optimizer.SGD(
-                    learning_rate=args.learning_rate)
+                    learning_rate=args.learning_rate,
+                    regularization=fluid.regularizer.L2DecayRegularizer(
+                        regularization_coeff=args.weight_decay))
             elif args.optim == 'adam':
                 optimizer = fluid.optimizer.Adam(
-                    learning_rate=args.learning_rate)
+                    learning_rate=args.learning_rate,
+                    regularization=fluid.regularizer.L2DecayRegularizer(
+                        regularization_coeff=args.weight_decay))
+
             elif args.optim == 'rprop':
                 optimizer = fluid.optimizer.RMSPropOptimizer(
-                    learning_rate=args.learning_rate)
+                    learning_rate=args.learning_rate,
+                    regularization=fluid.regularizer.L2DecayRegularizer(
+                        regularization_coeff=args.weight_decay))
             else:
                 logger.error('Unsupported optimizer: {}'.format(args.optim))
                 exit(-1)