update

demo/semantic_role_labeling/api_train_v2.py

-import numpy
+import numpy as np
 import paddle.v2 as paddle
 from model_v2 import db_lstm
 
@@ -31,10 +31,6 @@ word_dict_len = len(word_dict)
 label_dict_len = len(label_dict)
 pred_len = len(predicate_dict)
 
-print 'word_dict_len=%d' % word_dict_len
-print 'label_dict_len=%d' % label_dict_len
-print 'pred_len=%d' % pred_len
-
 
 def train_reader(file_name="data/feature"):
     def reader():
@@ -65,25 +61,34 @@ def train_reader(file_name="data/feature"):
     return reader
 
 
+def load_parameter(file_name, h, w):
+    with open(file_name, 'rb') as f:
+        f.read(16)  # skip header for float type.
+        return np.fromfile(f, dtype=np.float32).reshape(h, w)
+
+
 def main():
     paddle.init(use_gpu=False, trainer_count=1)
 
     # define network topology
     crf_cost, crf_dec = db_lstm(word_dict_len, label_dict_len, pred_len)
 
-    #parameters = paddle.parameters.create([crf_cost, crf_dec])
-    parameters = paddle.parameters.create(crf_cost)
+    parameters = paddle.parameters.create([crf_cost, crf_dec])
     optimizer = paddle.optimizer.Momentum(momentum=0.01, learning_rate=2e-2)
 
     def event_handler(event):
         if isinstance(event, paddle.event.EndIteration):
-            print "Pass %d, Batch %d, Cost %f" % (event.pass_id, event.batch_id,
-                                                  event.cost)
-
+            if event.batch_id % 100 == 0:
+                print "Pass %d, Batch %d, Cost %f, %s" % (
+                    event.pass_id, event.batch_id, event.cost, event.metrics)
         else:
             pass
 
-    trainer = paddle.trainer.SGD(update_equation=optimizer)
+    trainer = paddle.trainer.SGD(cost=crf_cost,
+                                 parameters=parameters,
+                                 update_equation=optimizer)
+
+    parameters.set('emb', load_parameter("data/emb", 44068, 32))
 
     reader_dict = {
         'word_data': 0,
@@ -96,18 +101,14 @@ def main():
         'mark_data': 7,
         'target': 8,
     }
-    #trn_reader = paddle.reader.batched(
-    #    paddle.reader.shuffle(
-    #        train_reader(), buf_size=8192), batch_size=2)
-    trn_reader = paddle.reader.batched(train_reader(), batch_size=1)
+    trn_reader = paddle.reader.batched(
+        paddle.reader.shuffle(
+            train_reader(), buf_size=8192), batch_size=10)
     trainer.train(
         reader=trn_reader,
-        cost=crf_cost,
-        parameters=parameters,
         event_handler=event_handler,
         num_passes=10000,
         reader_dict=reader_dict)
-    #cost=[crf_cost, crf_dec],
 
 
 if __name__ == '__main__':