update word2vec to be compatible with book ipynb

04.word2vec/train.py

@@ -12,17 +12,15 @@ def wordemb(inlayer):
         input=inlayer,
         size=embsize,
         param_attr=paddle.attr.Param(
-            name="_proj",
-            initial_std=0.001,
-            learning_rate=1,
-            l2_rate=0, ))
+            name="_proj", initial_std=0.001, learning_rate=1, l2_rate=0))
     return wordemb
 
 
 def main():
-    paddle.init(use_gpu=False, trainer_count=1)
+    paddle.init(use_gpu=False, trainer_count=3)
     word_dict = paddle.dataset.imikolov.build_dict()
     dict_size = len(word_dict)
+    # Every layer takes integer value of range [0, dict_size)
     firstword = paddle.layer.data(
         name="firstw", type=paddle.data_type.integer_value(dict_size))
     secondword = paddle.layer.data(
@@ -57,22 +55,26 @@ def main():
     def event_handler(event):
         if isinstance(event, paddle.event.EndIteration):
             if event.batch_id % 100 == 0:
-                result = trainer.test(
-                    paddle.batch(
-                        paddle.dataset.imikolov.test(word_dict, N), 32))
-                print "Pass %d, Batch %d, Cost %f, %s, Testing metrics %s" % (
-                    event.pass_id, event.batch_id, event.cost, event.metrics,
-                    result.metrics)
+                print "Pass %d, Batch %d, Cost %f, %s" % (
+                    event.pass_id, event.batch_id, event.cost, event.metrics)
+
+        if isinstance(event, paddle.event.EndPass):
+            result = trainer.test(
+                paddle.batch(paddle.dataset.imikolov.test(word_dict, N), 32))
+            print "Pass %d, Testing metrics %s" % (event.pass_id,
+                                                   result.metrics)
+            with open("model_%d.tar" % event.pass_id, 'w') as f:
+                parameters.to_tar(f)
 
     cost = paddle.layer.classification_cost(input=predictword, label=nextword)
     parameters = paddle.parameters.create(cost)
-    adam_optimizer = paddle.optimizer.Adam(
+    adagrad = paddle.optimizer.AdaGrad(
         learning_rate=3e-3,
         regularization=paddle.optimizer.L2Regularization(8e-4))
-    trainer = paddle.trainer.SGD(cost, parameters, adam_optimizer)
+    trainer = paddle.trainer.SGD(cost, parameters, adagrad)
     trainer.train(
         paddle.batch(paddle.dataset.imikolov.train(word_dict, N), 32),
-        num_passes=30,
+        num_passes=100,
         event_handler=event_handler)