diff --git a/fluid/text_classification/README.md b/fluid/text_classification/README.md index 500ee6ae6db28e9d844d206a1cc894c36f1db09f..43c15934fa62af3db2261be37803ce21ba6bf946 100644 --- a/fluid/text_classification/README.md +++ b/fluid/text_classification/README.md @@ -1,16 +1,112 @@ -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 diff --git a/fluid/text_classification/infer.py b/fluid/text_classification/infer.py index b377a053e415d630f46a45729d5df5cf3de89e83..3d4ee357beb783d1cecaced3dc86ecb63bc090f8 100644 --- a/fluid/text_classification/infer.py +++ b/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) diff --git a/fluid/text_classification/train.py b/fluid/text_classification/train.py index 0e295882134321f4609ae77a5eb230376c3dc2e7..56d59069ba46c1698e3e7ffe21d4d9e72d88b7a5 100644 --- a/fluid/text_classification/train.py +++ b/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":