From d3c755df3fe6009ed2cde1b5dca41196e4024aa7 Mon Sep 17 00:00:00 2001
From: hedaoyuan <hedaoyuan@github.com>
Date: Thu, 2 Mar 2017 13:41:51 +0800
Subject: [PATCH] Refine code

---
 demo/sentiment/train_v2.py | 95 ++++++++++++++++----------------------
 1 file changed, 39 insertions(+), 56 deletions(-)

diff --git a/demo/sentiment/train_v2.py b/demo/sentiment/train_v2.py
index 779bfee5b..3d595fad3 100644
--- a/demo/sentiment/train_v2.py
+++ b/demo/sentiment/train_v2.py
@@ -1,3 +1,4 @@
+import sys
 from os.path import join as join_path
 import paddle.trainer_config_helpers.attrs as attrs
 from paddle.trainer_config_helpers.poolings import MaxPooling
@@ -188,88 +189,69 @@ def stacked_lstm_net(input_dim,
     return cost
 
 
-def data_reader():
-    data_dir = "./data/pre-imdb"
-    train_file = "train_part_000"
-    test_file = "test_part_000"
-    dict_file = "dict.txt"
-    train_file = join_path(data_dir, train_file)
-    test_file = join_path(data_dir, test_file)
-    dict_file = join_path(data_dir, dict_file)
-
-    with open(dict_file, 'r') as fdict, open(train_file, 'r') as fdata:
-        dictionary = dict()
-        for i, line in enumerate(fdict):
-            dictionary[line.split('\t')[0]] = i
-
-        for line_count, line in enumerate(fdata):
-            label, comment = line.strip().split('\t\t')
-            label = int(label)
-            words = comment.split()
-            word_slot = [dictionary[w] for w in words if w in dictionary]
-            yield (word_slot, label)
-
-
-def test_reader():
-    data_dir = "./data/pre-imdb"
-    train_file = "train_part_000"
-    test_file = "test_part_000"
-    dict_file = "dict.txt"
-    train_file = join_path(data_dir, train_file)
-    test_file = join_path(data_dir, test_file)
-    dict_file = join_path(data_dir, dict_file)
-
-    with open(dict_file, 'r') as fdict, open(test_file, 'r') as ftest:
-        dictionary = dict()
-        for i, line in enumerate(fdict):
-            dictionary[line.split('\t')[0]] = i
-
-        for line_count, line in enumerate(ftest):
-            label, comment = line.strip().split('\t\t')
-            label = int(label)
-            words = comment.split()
-            word_slot = [dictionary[w] for w in words if w in dictionary]
-            yield (word_slot, label)
+def data_reader(data_file, dict_file):
+    def reader():
+        with open(dict_file, 'r') as fdict, open(data_file, 'r') as fdata:
+            dictionary = dict()
+            for i, line in enumerate(fdict):
+                dictionary[line.split('\t')[0]] = i
+
+            for line_count, line in enumerate(fdata):
+                label, comment = line.strip().split('\t\t')
+                label = int(label)
+                words = comment.split()
+                word_slot = [dictionary[w] for w in words if w in dictionary]
+                yield (word_slot, label)
+
+    return reader
 
 
 if __name__ == '__main__':
-    data_dir = "./data/pre-imdb"
-    train_list = "train.list"
-    test_list = "test.list"
-    dict_file = "dict.txt"
-    dict_dim = len(open(join_path(data_dir, "dict.txt")).readlines())
-    class_dim = len(open(join_path(data_dir, 'labels.list')).readlines())
-    is_predict = False
+    # data file
+    train_file = "./data/pre-imdb/train_part_000"
+    test_file = "./data/pre-imdb/test_part_000"
+    dict_file = "./data/pre-imdb/dict.txt"
+    labels = "./data/pre-imdb/labels.list"
 
     # init
     paddle.init(use_gpu=True, trainer_count=4)
 
     # network config
-    # cost = convolution_net(dict_dim, class_dim=class_dim, is_predict=is_predict)
-    cost = stacked_lstm_net(
-        dict_dim, class_dim=class_dim, stacked_num=3, is_predict=is_predict)
+    dict_dim = len(open(dict_file).readlines())
+    class_dim = len(open(labels).readlines())
+
+    # Please choose the way to build the network
+    # by uncommenting the corresponding line.
+    cost = convolution_net(dict_dim, class_dim=class_dim)
+    # cost = stacked_lstm_net(dict_dim, class_dim=class_dim, stacked_num=3)
 
     # create parameters
     parameters = paddle.parameters.create(cost)
 
+    # create optimizer
     adam_optimizer = paddle.optimizer.Adam(
         learning_rate=2e-3,
         regularization=paddle.optimizer.L2Regularization(rate=8e-4),
         model_average=paddle.optimizer.ModelAverage(average_window=0.5))
 
+    # End batch and end pass event handler
     def event_handler(event):
         if isinstance(event, paddle.event.EndIteration):
             if event.batch_id % 100 == 0:
-                print "Pass %d, Batch %d, Cost %f, %s" % (
+                print "\nPass %d, Batch %d, Cost %f, %s" % (
                     event.pass_id, event.batch_id, event.cost, event.metrics)
+            else:
+                sys.stdout.write('.')
+                sys.stdout.flush()
         if isinstance(event, paddle.event.EndPass):
             result = trainer.test(
                 reader=paddle.reader.batched(
-                    test_reader, batch_size=128),
+                    data_reader(test_file, dict_file), batch_size=128),
                 reader_dict={'word': 0,
                              'label': 1})
-            print "Test with Pass %d, %s" % (event.pass_id, result.metrics)
+            print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
 
+    # create trainer
     trainer = paddle.trainer.SGD(cost=cost,
                                  parameters=parameters,
                                  update_equation=adam_optimizer)
@@ -277,7 +259,8 @@ if __name__ == '__main__':
     trainer.train(
         reader=paddle.reader.batched(
             paddle.reader.shuffle(
-                data_reader, buf_size=4096), batch_size=128),
+                data_reader(train_file, dict_file), buf_size=4096),
+            batch_size=128),
         event_handler=event_handler,
         reader_dict={'word': 0,
                      'label': 1},
-- 
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