correct data_type

demo/seqToseq/api_train_v2.py

@@ -4,7 +4,8 @@ import paddle.v2 as paddle
 
 from seqToseq_net_v2 import seqToseq_net_v2
 
-### Data Definiation
+# Data Definiation.
+# TODO:This code should be merged to dataset package.
 data_dir = "./data/pre-wmt14"
 src_lang_dict = os.path.join(data_dir, 'src.dict')
 trg_lang_dict = os.path.join(data_dir, 'trg.dict')
@@ -68,15 +69,14 @@ def train_reader(file_name):
 def main():
     paddle.init(use_gpu=False, trainer_count=1)
 
-    # reader = train_reader("data/pre-wmt14/train/train")
     # define network topology
     cost = seqToseq_net_v2(source_dict_dim, target_dict_dim)
     parameters = paddle.parameters.create(cost)
-    optimizer = paddle.optimizer.Adam(batch_size=50, learning_rate=5e-4)
+    optimizer = paddle.optimizer.Adam(learning_rate=1e-4)
 
     def event_handler(event):
         if isinstance(event, paddle.event.EndIteration):
-            if event.batch_id % 100 == 0:
+            if event.batch_id % 10 == 0:
                 print "Pass %d, Batch %d, Cost %f, %s" % (
                     event.pass_id, event.batch_id, event.cost, event.metrics)
 
@@ -93,12 +93,12 @@ def main():
     trn_reader = paddle.reader.batched(
         paddle.reader.shuffle(
             train_reader("data/pre-wmt14/train/train"), buf_size=8192),
-        batch_size=10)
+        batch_size=10000)
 
     trainer.train(
         reader=trn_reader,
         event_handler=event_handler,
-        num_passes=10000,
+        num_passes=10,
         reader_dict=reader_dict)
 
 

demo/seqToseq/seqToseq_net_v2.py

@@ -14,7 +14,7 @@ def seqToseq_net_v2(source_dict_dim, target_dict_dim):
     #### Encoder
     src_word_id = layer.data(
         name='source_language_word',
-        type=data_type.dense_vector(source_dict_dim))
+        type=data_type.integer_value_sequence(source_dict_dim))
     src_embedding = layer.embedding(
         input=src_word_id,
         size=word_vector_dim,
@@ -67,7 +67,7 @@ def seqToseq_net_v2(source_dict_dim, target_dict_dim):
     trg_embedding = layer.embedding(
         input=layer.data(
             name='target_language_word',
-            type=data_type.dense_vector(target_dict_dim)),
+            type=data_type.integer_value_sequence(target_dict_dim)),
         size=word_vector_dim,
         param_attr=attr.ParamAttr(name='_target_language_embedding'))
     group_inputs.append(trg_embedding)
@@ -84,7 +84,7 @@ def seqToseq_net_v2(source_dict_dim, target_dict_dim):
 
     lbl = layer.data(
         name='target_language_next_word',
-        type=data_type.dense_vector(target_dict_dim))
+        type=data_type.integer_value_sequence(target_dict_dim))
     cost = layer.classification_cost(input=decoder, label=lbl)
 
     return cost