add readme infer.py train.py

fluid/text_classification/README.md

-The minimum PaddlePaddle version needed for the code sample in this directory is the lastest develop branch. If you are on a version of PaddlePaddle earlier than this, [please update your installation](http://www.paddlepaddle.org/docs/develop/documentation/en/build_and_install/pip_install_en.html).
+# 文本分类
 
----
+以下是本例的简要目录结构及说明：
 
-# Text Classification
-
-## Data Preparation
-```
-wget http://ai.stanford.edu/%7Eamaas/data/sentiment/aclImdb_v1.tar.gz
-tar zxf aclImdb_v1.tar.gz
+```text
+.
+├── nets.py              # 模型定义
+├── README.md            # 文档
+├── train.py             # 训练脚本
+├── infer.py             # 预测脚本
+└── utils.py             # 定义通用函数，从外部获取
 ```
 
-## Training
+
+## 简介，模型详解
+
+在PaddlePaddle v2版本[文本分类](https://github.com/PaddlePaddle/models/blob/develop/text/README.md)中对于文本分类任务有较详细的介绍，在本例中不再重复介绍。
+在模型上，我们采用了bow, cnn, lstm, gru四种常见的文本分类模型。
+
+## 训练
+
+1. 运行命令 `python train.py bow` 开始训练模型。
+    ```python
+    python train.py bow    # bow指定网络结构，可替换成cnn, lstm, gru
+    ```
+
+2. (可选）想自定义网络结构，需在[nets.py](./nets.py)中自行添加，并设置[train.py](./train.py)中的相应参数。
+    ```python
+    def train(train_reader,     # 训练数据
+        word_dict,              # 数据字典
+        network,                # 模型配置
+        use_cuda,               # 是否用GPU
+        parallel,               # 是否并行
+        save_dirname,           # 保存模型路径
+        lr=0.2,                 # 学习率大小
+        batch_size=128,         # 每个batch的样本数
+        pass_num=30):           # 训练的轮数
+    ```
+
+## 训练结果示例
+```text
+    pass_id: 0, avg_acc: 0.848040, avg_cost: 0.354073
+    pass_id: 1, avg_acc: 0.914200, avg_cost: 0.217945
+    pass_id: 2, avg_acc: 0.929800, avg_cost: 0.184302
+    pass_id: 3, avg_acc: 0.938680, avg_cost: 0.164240
+    pass_id: 4, avg_acc: 0.945120, avg_cost: 0.149150
+    pass_id: 5, avg_acc: 0.951280, avg_cost: 0.137117
+    pass_id: 6, avg_acc: 0.955360, avg_cost: 0.126434
+    pass_id: 7, avg_acc: 0.961400, avg_cost: 0.117405
+    pass_id: 8, avg_acc: 0.963560, avg_cost: 0.110070
+    pass_id: 9, avg_acc: 0.965840, avg_cost: 0.103273
+    pass_id: 10, avg_acc: 0.969800, avg_cost: 0.096314
+    pass_id: 11, avg_acc: 0.971720, avg_cost: 0.090206
+    pass_id: 12, avg_acc: 0.974800, avg_cost: 0.084970
+    pass_id: 13, avg_acc: 0.977400, avg_cost: 0.078981
+    pass_id: 14, avg_acc: 0.980000, avg_cost: 0.073685
+    pass_id: 15, avg_acc: 0.981080, avg_cost: 0.069898
+    pass_id: 16, avg_acc: 0.982080, avg_cost: 0.064923
+    pass_id: 17, avg_acc: 0.984680, avg_cost: 0.060861
+    pass_id: 18, avg_acc: 0.985840, avg_cost: 0.057095
+    pass_id: 19, avg_acc: 0.988080, avg_cost: 0.052424
+    pass_id: 20, avg_acc: 0.989160, avg_cost: 0.049059
+    pass_id: 21, avg_acc: 0.990120, avg_cost: 0.045882
+    pass_id: 22, avg_acc: 0.992080, avg_cost: 0.042140
+    pass_id: 23, avg_acc: 0.992280, avg_cost: 0.039722
+    pass_id: 24, avg_acc: 0.992840, avg_cost: 0.036607
+    pass_id: 25, avg_acc: 0.994440, avg_cost: 0.034040
+    pass_id: 26, avg_acc: 0.995000, avg_cost: 0.031501
+    pass_id: 27, avg_acc: 0.995440, avg_cost: 0.028988
+    pass_id: 28, avg_acc: 0.996240, avg_cost: 0.026639
+    pass_id: 29, avg_acc: 0.996960, avg_cost: 0.024186
 ```
-python train.py --dict_path 'aclImdb/imdb.vocab'
+
+## 预测
+1. 运行命令 `python infer.py bow_model`, 开始预测。
+    ```python
+    python infer.py bow_model     # bow_model指定需要导入的模型
+
+## 预测结果示例
+```text
+    model_path: bow_model/epoch0, avg_acc: 0.882800
+    model_path: bow_model/epoch1, avg_acc: 0.882360
+    model_path: bow_model/epoch2, avg_acc: 0.881400
+    model_path: bow_model/epoch3, avg_acc: 0.877800
+    model_path: bow_model/epoch4, avg_acc: 0.872920
+    model_path: bow_model/epoch5, avg_acc: 0.872640
+    model_path: bow_model/epoch6, avg_acc: 0.869960
+    model_path: bow_model/epoch7, avg_acc: 0.865160
+    model_path: bow_model/epoch8, avg_acc: 0.863680
+    model_path: bow_model/epoch9, avg_acc: 0.861200
+    model_path: bow_model/epoch10, avg_acc: 0.853520
+    model_path: bow_model/epoch11, avg_acc: 0.850400
+    model_path: bow_model/epoch12, avg_acc: 0.855960
+    model_path: bow_model/epoch13, avg_acc: 0.853480
+    model_path: bow_model/epoch14, avg_acc: 0.855960
+    model_path: bow_model/epoch15, avg_acc: 0.854120
+    model_path: bow_model/epoch16, avg_acc: 0.854160
+    model_path: bow_model/epoch17, avg_acc: 0.852240
+    model_path: bow_model/epoch18, avg_acc: 0.852320
+    model_path: bow_model/epoch19, avg_acc: 0.850280
+    model_path: bow_model/epoch20, avg_acc: 0.849760
+    model_path: bow_model/epoch21, avg_acc: 0.850160
+    model_path: bow_model/epoch22, avg_acc: 0.846800
+    model_path: bow_model/epoch23, avg_acc: 0.845440
+    model_path: bow_model/epoch24, avg_acc: 0.845640
+    model_path: bow_model/epoch25, avg_acc: 0.846200
+    model_path: bow_model/epoch26, avg_acc: 0.845880
+    model_path: bow_model/epoch27, avg_acc: 0.844880
+    model_path: bow_model/epoch28, avg_acc: 0.844680
+    model_path: bow_model/epoch29, avg_acc: 0.844960
 ```
+注：过拟合导致acc持续下降，请忽略

fluid/text_classification/infer.py

@@ -11,13 +11,14 @@ import contextlib
 import utils
 
 
-def infer(test_reader, use_cuda, 
-        save_dirname=None):
+def infer(test_reader,
+          use_cuda, 
+          model_path=None):
     """
     inference function
     """
-    if save_dirname is None:
-        print(str(save_dirname) + " cannot be found")
+    if model_path is None:
+        print(str(model_path) + " cannot be found")
         return
 
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
@@ -26,7 +27,7 @@ def infer(test_reader, use_cuda,
     inference_scope = fluid.core.Scope()
     with fluid.scope_guard(inference_scope):
         [inference_program, feed_target_names,
-        fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
+        fetch_targets] = fluid.io.load_inference_model(model_path, exe)
 
         total_acc = 0.0
         total_count = 0
@@ -37,7 +38,9 @@ def infer(test_reader, use_cuda,
                     return_numpy=True)
             total_acc += acc[0] * len(data)
             total_count += len(data)
-        print("test_acc: %f" % (total_acc / total_count))
+        
+        avg_acc = total_acc / total_count
+        print("model_path: %s, avg_acc: %f" % (model_path, avg_acc))
 
 
 if __name__ == "__main__":
@@ -46,6 +49,8 @@ if __name__ == "__main__":
             batch_size = 128, buf_size = 50000)
 
     model_path = sys.argv[1]
-    infer(test_reader, use_cuda=False,
-            save_dirname=model_path)
+    for i in range(30):
+        epoch_path = model_path + "/" + "epoch" + str(i)
+        infer(test_reader, use_cuda=False,
+                model_path=epoch_path)
 

fluid/text_classification/train.py

@@ -65,19 +65,26 @@ def train(train_reader,
 
     exe.run(fluid.default_startup_program())
     for pass_id in xrange(pass_num):
-        avg_cost_list, avg_acc_list = [], []
+        data_size, data_count, total_acc, total_cost = 0, 0, 0.0, 0.0
         for data in train_reader():
             avg_cost_np, avg_acc_np = exe.run(fluid.default_main_program(),
                                         feed=feeder.feed(data),
                                         fetch_list=[cost, acc])
-            avg_cost_list.append(avg_cost_np)
-            avg_acc_list.append(avg_acc_np)
-        print("pass_id: %d, avg_acc: %f" % (pass_id, np.mean(avg_acc_list)))
-    # save_model
-    fluid.io.save_inference_model(
-            save_dirname, 
-            ["words", "label"],
-            acc, exe)
+            data_size = len(data)
+            total_acc += data_size * avg_acc_np
+            total_cost += data_size * avg_cost_np
+            data_count += data_size
+        
+        avg_cost = total_cost / data_count
+        avg_acc = total_acc / data_count
+        print("pass_id: %d, avg_acc: %f, avg_cost: %f" % (pass_id, avg_acc, avg_cost))
+        
+        epoch_model = save_dirname + "/" + "epoch" + str(pass_id)
+        fluid.io.save_inference_model(
+                epoch_model, 
+                ["words", "label"],
+                acc, exe)
+
 
 def train_net():
     word_dict, train_reader, test_reader = utils.prepare_data(
@@ -85,7 +92,7 @@ def train_net():
             batch_size = 128, buf_size = 50000)
     
     if sys.argv[1] == "bow":
-        train(train_reader, word_dict, bow_net, use_cuda=True,
+        train(train_reader, word_dict, bow_net, use_cuda=False,
                 parallel=False, save_dirname="bow_model", lr=0.002,
                 pass_num=30, batch_size=128)
     elif sys.argv[1] == "cnn":