refine test_understand_sentiment_lstm (#5781)

* fix

* Fix a bug

python/paddle/v2/fluid/tests/book/test_understand_sentiment_lstm.py

@@ -54,17 +54,17 @@ def to_lodtensor(data, place):
     return res
 
 
-def chop_data(data, chop_len=80, batch_len=50):
+def chop_data(data, chop_len=80, batch_size=50):
     data = [(x[0][:chop_len], x[1]) for x in data if len(x[0]) >= chop_len]
 
-    return data[:batch_len]
+    return data[:batch_size]
 
 
 def prepare_feed_data(data, place):
     tensor_words = to_lodtensor(map(lambda x: x[0], data), place)
 
     label = np.array(map(lambda x: x[1], data)).astype("int64")
-    label = label.reshape([50, 1])
+    label = label.reshape([len(label), 1])
     tensor_label = core.LoDTensor()
     tensor_label.set(label, place)
 
@@ -72,33 +72,41 @@ def prepare_feed_data(data, place):
 
 
 def main():
-    word_dict = paddle.dataset.imdb.word_dict()
-    cost, acc = lstm_net(dict_dim=len(word_dict), class_dim=2)
+    BATCH_SIZE = 100
+    PASS_NUM = 5
 
-    batch_size = 100
-    train_data = paddle.batch(
-        paddle.reader.buffered(
-            paddle.dataset.imdb.train(word_dict), size=batch_size * 10),
-        batch_size=batch_size)
+    word_dict = paddle.dataset.imdb.word_dict()
+    print "load word dict successfully"
+    dict_dim = len(word_dict)
+    class_dim = 2
 
-    data = chop_data(next(train_data()))
+    cost, acc = lstm_net(dict_dim=dict_dim, class_dim=class_dim)
 
+    train_data = paddle.batch(
+        paddle.reader.shuffle(
+            paddle.dataset.imdb.train(word_dict), buf_size=BATCH_SIZE * 10),
+        batch_size=BATCH_SIZE)
     place = core.CPUPlace()
-    tensor_words, tensor_label = prepare_feed_data(data, place)
     exe = Executor(place)
+
     exe.run(framework.default_startup_program())
 
-    while True:
-        outs = exe.run(framework.default_main_program(),
-                       feed={"words": tensor_words,
-                             "label": tensor_label},
-                       fetch_list=[cost, acc])
-        cost_val = np.array(outs[0])
-        acc_val = np.array(outs[1])
-
-        print("cost=" + str(cost_val) + " acc=" + str(acc_val))
-        if acc_val > 0.9:
-            break
+    for pass_id in xrange(PASS_NUM):
+        for data in train_data():
+            chopped_data = chop_data(data)
+            tensor_words, tensor_label = prepare_feed_data(chopped_data, place)
+
+            outs = exe.run(framework.default_main_program(),
+                           feed={"words": tensor_words,
+                                 "label": tensor_label},
+                           fetch_list=[cost, acc])
+            cost_val = np.array(outs[0])
+            acc_val = np.array(outs[1])
+
+            print("cost=" + str(cost_val) + " acc=" + str(acc_val))
+            if acc_val > 0.7:
+                exit(0)
+    exit(1)
 
 
 if __name__ == '__main__':