├── .gitignore ├── .idea ├── EasyUse_FastApi.iml ├── deployment.xml ├── misc.xml ├── modules.xml └── vcs.xml ├── LICENSE ├── README.md ├── Text-CNN-client.py ├── Text-CNN-server.py ├── checkpoints └── textcnn │ ├── 99 │ ├── best_validation.data-00000-of-00001 │ ├── best_validation.index │ ├── best_validation.meta │ └── checkpoint │ ├── best_validation.data-00000-of-00001 │ ├── best_validation.index │ ├── best_validation.meta │ └── checkpoint ├── data ├── 1-1.txt ├── 5-1.txt ├── data │ ├── bk │ │ ├── test.txt │ │ ├── train.txt │ │ └── train_all.txt │ ├── neg_all.txt │ ├── pos_all.txt │ ├── process.py │ ├── test.txt │ ├── train.txt │ ├── val.txt │ └── vocab.txt └── processing.py ├── model ├── cnn_model.py ├── data_loader.py └── data_processing.py ├── pic ├── api.png ├── backend.png └── inference.png └── requirement.txt /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; 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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [![996.icu](https://img.shields.io/badge/link-996.icu-red.svg)](https://996.icu) 2 | 3 | 4 | # EasyUse_FastApi 5 | #### 快速使用FastAPI部署机器学习模型,使用全局定义和全局加载模型,提升inference速度。在初始化的时候(第一次推理的时候速度较慢,在第二次使用则恢复正常,在10ms左右) 6 | #### 利用FastApi搭配uvicorn自带的异步的方式进行数据的推理,比Flask+gunicorn更加方便和快一些 7 | 8 | ##### 启动服务 uvicorn Text-CNN-server:app --reload ,开启热启动模式 9 | ##### 启动服务 uvicorn Text-CNN-server:app --port=5000 --workers=4,开启生产模式, 注意: Sanic(FastAPI) 的性能的确很棒,当时技术验证时,测试的时候,不同业务逻辑下,基本都能保证其性能在 Flask 的 1.5 倍以上。但是就目前的使用经验来说 Sanic((FastAPI))距离真正生产可用,还有相当长一段路要走。无论是内部的架构,还是周边的生态,亦或者是其他。大家可以没事拿来玩玩,但是如果要上生产线,请做好被坑的准备 10 | 11 | > backend 12 | 13 |
14 | 15 | > frontend 16 | 17 |
18 | 19 | 20 | > inference 21 | 22 |
23 | -------------------------------------------------------------------------------- /Text-CNN-client.py: -------------------------------------------------------------------------------- 1 | import requests 2 | import time 3 | import json 4 | # 使用注意,在初始化的时候(第一次使用的时候速度较慢,在第二次使用则恢复正常,在10ms左右) 5 | if __name__ == '__main__': 6 | st = time.clock() 7 | content = '这家酒店真垃圾' 8 | api_url = "http://127.0.0.1:5000/sentiment_analysis_api/{}".format(content) 9 | model_result = requests.get(api_url).json() 10 | print(model_result) 11 | print('time used:{}'.format(time.clock() - st)) -------------------------------------------------------------------------------- /Text-CNN-server.py: -------------------------------------------------------------------------------- 1 | import os 2 | import tensorflow as tf 3 | import numpy as np 4 | import tensorflow.contrib.keras as kr 5 | from model.cnn_model import TCNNConfig, TextCNN 6 | from model.data_processing import read_category, read_vocab 7 | from fastapi import FastAPI 8 | app = FastAPI() 9 | def global_(): 10 | # 全局定义和全局加载模型,提升inference速度 11 | global base_dir, vocab_dir, save_dir, save_path, graph, model 12 | base_dir = 'data/data' 13 | vocab_dir = os.path.join(base_dir, 'vocab.txt') 14 | save_dir = 'checkpoints/textcnn' 15 | save_path = os.path.join(save_dir, 'best_validation') 16 | graph = tf.get_default_graph() 17 | model = CnnModel() 18 | class CnnModel: 19 | def __init__(self): 20 | self.config = TCNNConfig() 21 | self.categories, self.cat_to_id = read_category() 22 | self.words, self.word_to_id = read_vocab(vocab_dir) 23 | self.config.vocab_size = len(self.words) 24 | self.model = TextCNN(self.config) 25 | self.session = tf.Session() 26 | self.session.run(tf.global_variables_initializer()) 27 | saver = tf.train.Saver() 28 | saver.restore(sess=self.session, save_path=save_path) 29 | def emotion_score(self, message): 30 | data = [self.word_to_id[x] for x in message if x in self.word_to_id] 31 | feed_dict = { 32 | self.model.input_x: kr.preprocessing.sequence.pad_sequences([data], self.config.seq_length), 33 | self.model.keep_prob: 1.0} 34 | # 类别概率的输出 35 | predictions = self.session.run(self.model.softmax_tensor1, feed_dict=feed_dict) 36 | return np.squeeze(predictions)[1] 37 | global_() 38 | @app.get("/sentiment_analysis_api/{content}") 39 | async def predict(content: str): 40 | with graph.as_default(): 41 | sa = model.emotion_score(content) 42 | return {"comment": content, "sa": ("%.5f" % sa)} -------------------------------------------------------------------------------- /checkpoints/textcnn/99/best_validation.data-00000-of-00001: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CarryChang/EasyUse_FastApi/HEAD/checkpoints/textcnn/99/best_validation.data-00000-of-00001 -------------------------------------------------------------------------------- /checkpoints/textcnn/99/best_validation.index: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CarryChang/EasyUse_FastApi/HEAD/checkpoints/textcnn/99/best_validation.index -------------------------------------------------------------------------------- /checkpoints/textcnn/99/best_validation.meta: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CarryChang/EasyUse_FastApi/HEAD/checkpoints/textcnn/99/best_validation.meta -------------------------------------------------------------------------------- /checkpoints/textcnn/99/checkpoint: -------------------------------------------------------------------------------- 1 | model_checkpoint_path: "best_validation" 2 | all_model_checkpoint_paths: "best_validation" 3 | -------------------------------------------------------------------------------- /checkpoints/textcnn/best_validation.data-00000-of-00001: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CarryChang/EasyUse_FastApi/HEAD/checkpoints/textcnn/best_validation.data-00000-of-00001 -------------------------------------------------------------------------------- /checkpoints/textcnn/best_validation.index: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CarryChang/EasyUse_FastApi/HEAD/checkpoints/textcnn/best_validation.index -------------------------------------------------------------------------------- /checkpoints/textcnn/best_validation.meta: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CarryChang/EasyUse_FastApi/HEAD/checkpoints/textcnn/best_validation.meta -------------------------------------------------------------------------------- /checkpoints/textcnn/checkpoint: -------------------------------------------------------------------------------- 1 | model_checkpoint_path: "best_validation" 2 | all_model_checkpoint_paths: "best_validation" 3 | -------------------------------------------------------------------------------- /data/data/process.py: -------------------------------------------------------------------------------- 1 | #!/user/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | # @Time : 2020/2/13 0013 20:08 4 | # @Author : CarryChang 5 | # @Software: PyCharm 6 | # @email: coolcahng@gmail.com 7 | # @web :CarryChang.top 8 | path_list = ['neg_all.txt', 'pos_all.txt'] 9 | train_all = open('train_all.txt', 'w', encoding='utf-8') 10 | for path in path_list: 11 | if 'pos' in path: 12 | with open(path, 'r', encoding='utf-8') as file: 13 | for content in file: 14 | train_all.write('5'+'\t'+content.strip() + '\n') 15 | else: 16 | with open(path, 'r', encoding='utf-8') as file: 17 | for content in file: 18 | train_all.write('1' + '\t' + content.strip() + '\n') 19 | train_all.close() 20 | -------------------------------------------------------------------------------- /data/data/vocab.txt: -------------------------------------------------------------------------------- 1 | 2 | , 3 | 的 4 | 不 5 | 。 6 | 是 7 | 了 8 | 有 9 | 很 10 | 我 11 | 好 12 | 一 13 | 房 14 | 间 15 | 这 16 | 还 17 | ! 18 | , 19 | 个 20 | 店 21 | 人 22 | 没 23 | 就 24 | 也 25 | 在 26 | 服 27 | 酒 28 | 到 29 | 上 30 | 务 31 | 来 32 | 大 33 | 34 | . 35 | 都 36 | 住 37 | 说 38 | 要 39 | 以 40 | 点 41 | 0 42 | 差 43 | 小 44 | 时 45 | 价 46 | 看 47 | 可 48 | 比 49 | 们 50 | 本 51 | 错 52 | 生 53 | 后 54 | 下 55 | 1 56 | 多 57 | 能 58 | 去 59 | 过 60 | 2 61 | 书 62 | 么 63 | 得 64 | 位 65 | 感 66 | 前 67 | 用 68 | 会 69 | 次 70 | 觉 71 | 太 72 | 和 73 | 天 74 | 家 75 | 置 76 | 为 77 | 子 78 | 地 79 | 但 80 | 里 81 | 方 82 | 对 83 | 电 84 | 出 85 | 、 86 | 中 87 | 面 88 | 给 89 | 然 90 | 常 91 | 他 92 | 实 93 | 便 94 | 那 95 | 台 96 | 高 97 | 真 98 | 样 99 | 非 100 | 卫 101 | 开 102 | 较 103 | 度 104 | 自 105 | 心 106 | 入 107 | 道 108 | 理 109 | 只 110 | 格 111 | 想 112 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4763 | 汞 4764 | 俎 4765 | 濡 4766 | 镛 4767 | 岖 4768 | 侷 4769 | 綠 4770 | 構 4771 | 黠 4772 | 勐 4773 | 垅 4774 | 槐 4775 | 刖 4776 | 飓 4777 | 饋 4778 | 萦 4779 | 吒 4780 | 迸 4781 | 凛 4782 | 倜 4783 | 傥 4784 | 珏 4785 | 婢 4786 | 瓢 4787 | 芡 4788 | 焯 4789 | 呒 4790 | 酵 4791 | 忖 4792 | 賬 4793 | 矜 4794 | 峻 4795 | 掇 4796 | 谏 4797 | 偉 4798 | 詞 4799 | 頓 4800 | 諾 4801 | 獎 4802 | 執 4803 | 閃 4804 | 撲 4805 | 襲 4806 | 彪 4807 | 抺 4808 | 拴 4809 | 捎 4810 | 哔 4811 | 欸 4812 |  4813 | 捋 4814 | 烬 4815 | 🌟 4816 | 🍄 4817 | 掐 4818 | 霹 4819 | 雳 4820 | 蜷 4821 | 🤙 4822 | 😅 4823 | 🤷 4824 | ‍ 4825 | ♀ 4826 | ⃣ 4827 | ﹏ 4828 | 💡 4829 | 🌝 4830 | 讥 4831 | 💆 4832 | 灬 4833 | 😪 4834 | 梗 4835 | 睫 4836 | 匾 4837 | ☝ 4838 | 扈 4839 | 胩 4840 | ❌ 4841 | ☄ 4842 | 龅 4843 | 叵 4844 | 橇 4845 | 呪 4846 | 📺 4847 | 丶 4848 | 𣎴 4849 | 䃼 4850 | 誤 4851 | 慶 4852 | 師 4853 | 終 4854 | 譜 4855 | 脅 4856 | 順 4857 | 驗 4858 | 颧 4859 | 蚕 4860 | 👊 4861 | 🚧 4862 | 獗 4863 | 犟 4864 | 🔑 4865 | 😥 4866 | 漕 4867 | 懊 4868 | 噗 4869 | 欻 4870 | 簇 4871 | 😩 4872 | 😫 4873 | 💰 4874 | 蔗 4875 | 呤 4876 | 旮 4877 | 旯 4878 | 蹾 4879 | 碱 4880 | 槟 4881 | Ω 4882 | 氺 4883 | 椎 4884 | 😳 4885 | 㐅 4886 | 🏪 4887 | 囗 4888 | 㟍 4889 | 🎉 4890 | 😛 4891 | 蛟 4892 | 腌 4893 | 啾 4894 | 🙊 4895 | ㅂ 4896 | に 4897 | か 4898 | っ 4899 | た 4900 | で 4901 | よ 4902 | ね 4903 | あ 4904 | り 4905 | が 4906 | と 4907 | う 4908 | ご 4909 | ざ 4910 | い 4911 | ま 4912 | 磅 4913 | 💛 4914 | 😌 4915 | 璾 4916 | 🍺 4917 | 岔 4918 | 拚 4919 | 焗 4920 | 🉐 4921 | 吖 4922 | づ 4923 | ど 4924 | 柚 4925 | 🚗 4926 | ☕ 4927 | 皿 4928 | ₃ 4929 | ۶ 4930 | з 4931 | 攸 4932 | 犷 4933 | 👉 4934 | 👈 4935 | 冮 4936 | ❀ 4937 | 燜 4938 | 賣 4939 | 婊 4940 | 💁 4941 | 睦 4942 | 💊 4943 | 👄 4944 | 😶 4945 | ௰ 4946 | 🔻 4947 | 🌚 4948 | 👋 4949 | 菠 4950 | 👻 4951 | 💕 4952 | 珊 4953 | 💜 4954 | 💞 4955 | 撘 4956 | 淇 4957 | 🌸 4958 | 殺 4959 | 遊 4960 | 🙈 4961 | 👧 4962 | 懿 4963 | 〜 4964 | 碚 4965 | 砚 4966 | -------------------------------------------------------------------------------- /data/processing.py: -------------------------------------------------------------------------------- 1 | #!/user/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | # @Time : 2020/2/13 0013 13:20 4 | # @Author : CarryChang 5 | # @Software: PyCharm 6 | # @email: coolcahng@gmail.com 7 | # @web :CarryChang.top 8 | def file_read(number,path): 9 | pos_val = [] 10 | with open(path, 'r', encoding='utf-8') as content: 11 | for con1 in content.readlines()[:number]: 12 | pos_val.append(con1.strip()) 13 | return pos_val 14 | # if __name__ == '__main__': 15 | # number = 100 16 | # path = '1-1.txt' 17 | # content = file_read(number,path) 18 | # print(len(content)) 19 | # print(content) -------------------------------------------------------------------------------- /model/cnn_model.py: -------------------------------------------------------------------------------- 1 | # coding: utf-8 2 | import tensorflow as tf 3 | class TCNNConfig(object): 4 | """CNN配置参数""" 5 | embedding_dim = 300 # 词向量维度 6 | seq_length = 200 # 序列长度 7 | num_classes = 2 # 类别数 8 | num_filters = 512 # 卷积核数目 9 | kernel_size = 4 # 卷积核尺寸 10 | vocab_size = 5000 # 词汇表大小 11 | hidden_dim = 512 # 全连接层神经元 12 | dropout_keep_prob = 0.1 # dropout保留比例 13 | learning_rate = 1e-4 # 学习率 14 | batch_size = 512 # 每批训练大小 15 | num_epochs = 30 # 总迭代轮次 16 | print_per_batch = 64 # 每多少轮输出一次结果 17 | save_per_batch = 128 # 每多少轮存入tensorboard 18 | class TextCNN(object): 19 | """文本分类,CNN模型""" 20 | def __init__(self, config): 21 | self.config = config 22 | # 三个待输入的数据 23 | self.softmax_tensor1 = None 24 | self.input_x = tf.placeholder(tf.int32, [None, self.config.seq_length], name='input_x') 25 | self.input_y = tf.placeholder(tf.float32, [None, self.config.num_classes], name='input_y') 26 | self.keep_prob = tf.placeholder(tf.float32, name='keep_prob') 27 | self.cnn() 28 | def cnn(self): 29 | """CNN模型""" 30 | # 词向量映射,使用GPU 31 | with tf.device('/gpu:0'): 32 | embedding = tf.get_variable('embedding', [self.config.vocab_size, self.config.embedding_dim]) 33 | embedding_inputs = tf.nn.embedding_lookup(embedding, self.input_x) 34 | # 单层cnn,一层convolutin layer,一层max_pooling 35 | with tf.name_scope("cnn"): 36 | # CNN layer 37 | conv = tf.layers.conv1d(embedding_inputs, self.config.num_filters, self.config.kernel_size, name='conv') 38 | # global max pooling layer 39 | gmp = tf.reduce_max(conv, reduction_indices=[1], name='gmp') 40 | with tf.name_scope("score"): 41 | # 全连接层,后面接dropout以及relu激活 42 | fc = tf.layers.dense(gmp, self.config.hidden_dim, name='fc1') 43 | fc = tf.contrib.layers.dropout(fc, self.keep_prob) 44 | fc = tf.nn.relu(fc) 45 | # 分类器 46 | self.logits = tf.layers.dense(fc, self.config.num_classes, name='fc2') 47 | self.softmax_tensor1 = tf.nn.softmax(self.logits) 48 | self.y_pred_cls = tf.argmax(tf.nn.softmax(self.logits), 1) 49 | with tf.name_scope("optimize"): 50 | # 损失函数,交叉熵 51 | cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=self.logits, labels=self.input_y) 52 | self.loss = tf.reduce_mean(cross_entropy) 53 | # 优化器 54 | self.optim = tf.train.AdamOptimizer(learning_rate=self.config.learning_rate).minimize(self.loss) 55 | with tf.name_scope("accuracy"): 56 | # 准确率 57 | correct_pred = tf.equal(tf.argmax(self.input_y, 1), self.y_pred_cls) 58 | self.acc = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) 59 | 60 | # Confusion Matrix... 61 | # [[1985 15] 62 | # [ 24 1976]] 63 | -------------------------------------------------------------------------------- /model/data_loader.py: -------------------------------------------------------------------------------- 1 | # coding: utf-8 2 | import sys 3 | from collections import Counter 4 | import numpy as np 5 | import tensorflow.contrib.keras as kr 6 | def open_file(filename, mode='r'): 7 | return open(filename, mode, encoding='utf-8', errors='ignore') 8 | def read_file(filename): 9 | """读取文件数据""" 10 | contents, labels = [], [] 11 | with open_file(filename) as f: 12 | for line in f: 13 | try: 14 | label, content = line.strip().split('\t') 15 | if content: 16 | contents.append(list(content)) 17 | labels.append(label) 18 | except: 19 | pass 20 | return contents, labels 21 | def build_vocab(train_dir, vocab_dir, vocab_size=5000): 22 | """根据训练集构建词汇表,存储""" 23 | data_train, _ = read_file(train_dir) 24 | all_data = [] 25 | for content in data_train: 26 | all_data.extend(content) 27 | counter = Counter(all_data) 28 | count_pairs = counter.most_common(vocab_size - 1) 29 | words, _ = list(zip(*count_pairs)) 30 | # 添加一个 来将所有文本pad为同一长度 31 | words = [''] + list(words) 32 | open_file(vocab_dir, mode='w').write('\n'.join(words) + '\n') 33 | def read_vocab(vocab_dir): 34 | """读取词汇表""" 35 | # words = open_file(vocab_dir).read().strip().split('\n') 36 | with open_file(vocab_dir) as fp: 37 | # 如果是py2 则每个值都转化为unicode 38 | words = [_.strip() for _ in fp.readlines()] 39 | word_to_id = dict(zip(words, range(len(words)))) 40 | return words, word_to_id 41 | def read_category(): 42 | """读取分类目录,固定""" 43 | categories = ['1', '5'] 44 | categories = [x for x in categories] 45 | cat_to_id = dict(zip(categories, range(len(categories)))) 46 | return categories, cat_to_id 47 | def to_words(content, words): 48 | """将id表示的内容转换为文字""" 49 | return ''.join(words[x] for x in content) 50 | def process_file(filename, word_to_id, cat_to_id, max_length=200): 51 | """将文件转换为id表示""" 52 | contents, labels = read_file(filename) 53 | data_id, label_id = [], [] 54 | for i in range(len(contents)): 55 | data_id.append([word_to_id[x] for x in contents[i] if x in word_to_id]) 56 | label_id.append(cat_to_id[labels[i]]) 57 | # 使用keras提供的pad_sequences来将文本pad为固定长度 58 | x_pad = kr.preprocessing.sequence.pad_sequences(data_id, max_length) 59 | y_pad = kr.utils.to_categorical(label_id, num_classes=len(cat_to_id)) # 将标签转换为one-hot表示 60 | return x_pad, y_pad 61 | def batch_iter(x, y, batch_size=64): 62 | """生成批次数据""" 63 | data_len = len(x) 64 | num_batch = int((data_len - 1) / batch_size) + 1 65 | indices = np.random.permutation(np.arange(data_len)) 66 | x_shuffle = x[indices] 67 | y_shuffle = y[indices] 68 | for i in range(num_batch): 69 | start_id = i * batch_size 70 | end_id = min((i + 1) * batch_size, data_len) 71 | yield x_shuffle[start_id:end_id], y_shuffle[start_id:end_id] 72 | -------------------------------------------------------------------------------- /model/data_processing.py: -------------------------------------------------------------------------------- 1 | # coding: utf-8 2 | import sys 3 | from collections import Counter 4 | import numpy as np 5 | import tensorflow.contrib.keras as kr 6 | def read_file(filename): 7 | """读取文件数据""" 8 | contents, labels = [], [] 9 | with open(filename, encoding='utf-8') as f: 10 | for line in f: 11 | try: 12 | label, content = line.strip().split('\t') 13 | if content: 14 | contents.append(list(content)) 15 | labels.append(label) 16 | except: 17 | pass 18 | return contents, labels 19 | def build_vocab(train_dir, vocab_dir, vocab_size=5000): 20 | """根据训练集构建词汇表,存储""" 21 | data_train, _ = read_file(train_dir) 22 | all_data = [] 23 | for content in data_train: 24 | all_data.extend(content) 25 | counter = Counter(all_data) 26 | count_pairs = counter.most_common(vocab_size - 1) 27 | words, _ = list(zip(*count_pairs)) 28 | # 添加一个 来将所有文本pad为同一长度 29 | words = [''] + list(words) 30 | open(vocab_dir, 'w', encoding='utf-8').write('\n'.join(words) + '\n') 31 | def read_vocab(vocab_dir): 32 | """读取词汇表""" 33 | # words = open_file(vocab_dir).read().strip().split('\n') 34 | with open(vocab_dir,encoding='utf-8') as fp: 35 | # 如果是py2 则每个值都转化为unicode 36 | words = [_.strip() for _ in fp.readlines()] 37 | word_to_id = dict(zip(words, range(len(words)))) 38 | return words, word_to_id 39 | def read_category(): 40 | """读取分类目录,固定""" 41 | categories = ['1', '5'] 42 | categories = [x for x in categories] 43 | cat_to_id = dict(zip(categories, range(len(categories)))) 44 | return categories, cat_to_id 45 | def to_words(content, words): 46 | """将id表示的内容转换为文字""" 47 | return ''.join(words[x] for x in content) 48 | def process_file(filename, word_to_id, cat_to_id, max_length=100): 49 | """将文件转换为id表示""" 50 | contents, labels = read_file(filename) 51 | data_id, label_id = [], [] 52 | for i in range(len(contents)): 53 | data_id.append([word_to_id[x] for x in contents[i] if x in word_to_id]) 54 | label_id.append(cat_to_id[labels[i]]) 55 | x_pad = kr.preprocessing.sequence.pad_sequences(data_id, max_length) 56 | # 将标签转换为one-hot表示 57 | y_pad = kr.utils.to_categorical(label_id, num_classes=len(cat_to_id)) 58 | return x_pad, y_pad 59 | def batch_iter(x, y, batch_size=64): 60 | """生成批次数据""" 61 | data_len = len(x) 62 | num_batch = int((data_len - 1) / batch_size) + 1 63 | indices = np.random.permutation(np.arange(data_len)) 64 | x_shuffle = x[indices] 65 | y_shuffle = y[indices] 66 | for i in range(num_batch): 67 | start_id = i * batch_size 68 | end_id = min((i + 1) * batch_size, data_len) 69 | yield x_shuffle[start_id:end_id], y_shuffle[start_id:end_id] 70 | -------------------------------------------------------------------------------- /pic/api.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CarryChang/EasyUse_FastApi/HEAD/pic/api.png -------------------------------------------------------------------------------- /pic/backend.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CarryChang/EasyUse_FastApi/HEAD/pic/backend.png -------------------------------------------------------------------------------- /pic/inference.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/CarryChang/EasyUse_FastApi/HEAD/pic/inference.png -------------------------------------------------------------------------------- /requirement.txt: -------------------------------------------------------------------------------- 1 | uvicorn 2 | fastapi 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