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https://gitee.com/fastnlp/fastNLP.git
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881ce01762
* 1. CRF增加支持bmeso类型的tag 2. vocabulary中增加注释 * BucketSampler增加一条错误检测 * 1.修改ClipGradientCallback的bug；删除LRSchedulerCallback中的print，之后应该传入pbar进行打印;2.增加MLP注释 * update MLP module * 增加metric注释；修改trainer save过程中的bug * Update README.md fix tutorial link * Add ENAS (Efficient Neural Architecture Search) * add ignore_type in DataSet.add_field * * AutoPadder will not pad when dtype is None * add ignore_type in DataSet.apply * 修复fieldarray中padder潜在bug * 修复crf中typo; 以及可能导致数值不稳定的地方 * 修复CRF中可能存在的bug * change two default init arguments of Trainer into None * Changes to Callbacks: * 给callback添加给定几个只读属性 * 通过manager设置这些属性 * 代码优化，减轻@transfer的负担 * * 将enas相关代码放到automl目录下 * 修复fast_param_mapping的一个bug * Trainer添加自动创建save目录 * Vocabulary的打印，显示内容 * * 给vocabulary添加遍历方法 * 修复CRF为负数的bug * add SQuAD metric * add sigmoid activate function in MLP * - add star transformer model - add ConllLoader, for all kinds of conll-format files - add JsonLoader, for json-format files - add SSTLoader, for SST-2 & SST-5 - change Callback interface - fix batch multi-process when killed - add README to list models and their performance * - fix test * - fix callback & tests * - update README * 修改部分bug；调整callback * 准备发布0.4.0版本“ * update readme * support parallel loss * 防止多卡的情况导致无法正确计算loss“ * update advance_tutorial jupyter notebook * 1. 在embedding_loader中增加新的读取函数load_with_vocab(), load_without_vocab, 比之前的函数改变主要在(1)不再需要传入embed_dim(2)自动判断当前是word2vec还是glove. 2. vocabulary增加from_dataset(), index_dataset()函数。避免需要多行写index dataset的问题。 3. 在utils中新增一个cache_result()修饰器，用于cache函数的返回值。 4. callback中新增update_every属性 * 1.DataSet.apply()报错时提供错误的index 2.Vocabulary.from_dataset(), index_dataset()提供报错时的vocab顺序 3.embedloader在embed读取时遇到不规则的数据跳过这一行. * update attention * doc tools * fix some doc errors * 修改为中文注释，增加viterbi解码方法 * 样例版本 * - add pad sequence for lstm - add csv, conll, json filereader - update dataloader - remove useless dataloader - fix trainer loss print - fix tests * - fix test_tutorial * 注释增加 * 测试文档 * 本地暂存 * 本地暂存 * 修改文档的顺序 * - add document * 本地暂存 * update pooling * update bert * update documents in MLP * update documents in snli * combine self attention module to attention.py * update documents on losses.py * 对DataSet的文档进行更新 * update documents on metrics * 1. 删除了LSTM中print的内容; 2. 将Trainer和Tester的use_cuda修改为了device; 3.补充Trainer的文档 * 增加对Trainer的注释 * 完善了trainer，callback等的文档; 修改了部分代码的命名以使得代码从文档中隐藏 * update char level encoder * update documents on embedding.py * - update doc * 补充注释，并修改部分代码 * - update doc - add get_embeddings * 修改了文档配置项 * 修改embedding为init_embed初始化 * 1.增加对Trainer和Tester的多卡支持; * - add test - fix jsonloader * 删除了注释教程 * 给 dataset 增加了get_field_names * 修复bug * - add Const - fix bugs * 修改部分注释 * - add model runner for easier test models - add model tests * 修改了 docs 的配置和架构 * 修改了核心部分的一大部分文档，TODO： 1. 完善 trainer 和 tester 部分的文档 2. 研究注释样例与测试 * core部分的注释基本检查完成 * 修改了 io 部分的注释 * 全部改为相对路径引用 * 全部改为相对路径引用 * small change * 1. 从安装文件中删除api/automl的安装 2. metric中存在seq_len的bug 3. sampler中存在命名错误，已修改 * 修复 bug ：兼容 cpu 版本的 PyTorch TODO：其它地方可能也存在类似的 bug * 修改文档中的引用部分 * 把 tqdm.autonotebook 换成tqdm.auto * - fix batch & vocab * 上传了文档文件 *.rst * 上传了文档文件和若干 TODO * 讨论并整合了若干模块 * core部分的测试和一些小修改 * 删除了一些冗余文档 * update init files * update const files * update const files * 增加cnn的测试 * fix a little bug * - update attention - fix tests * 完善测试 * 完成快速入门教程 * 修改了sequence_modeling 命名为 sequence_labeling 的文档 * 重新 apidoc 解决改名的遗留问题 * 修改文档格式 * 统一不同位置的seq_len_to_mask, 现统一到core.utils.seq_len_to_mask * 增加了一行提示 * 在文档中展示 dataset_loader * 提示 Dataset.read_csv 会被 CSVLoader 替换 * 完成 Callback 和 Trainer 之间的文档 * index更新了部分 * 删除冗余的print * 删除用于分词的metric，因为有可能引起错误 * 修改文档中的中文名称 * 完成了详细介绍文档 * tutorial 的 ipynb 文件 * 修改了一些介绍文档 * 修改了 models 和 modules 的主页介绍 * 加上了 titlesonly 这个设置 * 修改了模块文档展示的标题 * 修改了 core 和 io 的开篇介绍 * 修改了 modules 和 models 开篇介绍 * 使用 .. todo:: 隐藏了可能被抽到文档中的 TODO 注释 * 修改了一些注释 * delete an old metric in test * 修改 tutorials 的测试文件 * 把暂不发布的功能移到 legacy 文件夹 * 删除了不能运行的测试 * 修改 callback 的测试文件 * 删除了过时的教程和测试文件 * cache_results 参数的修改 * 修改 io 的测试文件; 删除了一些过时的测试 * 修复bug * 修复无法通过test_utils.py的测试 * 修复与pytorch1.1中的padsequence的兼容问题; 修改Trainer的pbar * 1. 修复metric中的bug; 2.增加metric测试 * add model summary * 增加别名 * 删除encoder中的嵌套层 * 修改了 core 部分 import 的顺序，__all__ 暴露的内容 * 修改了 models 部分 import 的顺序，__all__ 暴露的内容 * 修改了文件名 * 修改了 modules 模块的__all__ 和 import * fix var runn * 增加vocab的clear方法 * 一些符合 PEP8 的微调 * 更新了cache_results的例子 * 1. 对callback中indices潜在None作出提示;2.DataSet支持通过List进行index * 修改了一个typo * 修改了 README.md * update documents on bert * update documents on encoder/bert * 增加一个fitlog callback，实现与fitlog实验记录 * typo * - update dataset_loader * 增加了到 fitlog 文档的链接。 * 增加了 DataSet Loader 的文档 * - add star-transformer reproduction
832 lines
24 KiB
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832 lines
24 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": true
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},
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"source": [
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"# 详细指南"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 数据读入"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'raw_sentence': A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . type=str,\n",
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"'label': 1 type=str}"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from fastNLP.io import CSVLoader\n",
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"\n",
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"loader = CSVLoader(headers=('raw_sentence', 'label'), sep='\\t')\n",
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"dataset = loader.load(\"./sample_data/tutorial_sample_dataset.csv\")\n",
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"dataset[0]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Instance表示一个样本，由一个或多个field（域，属性，特征）组成，每个field有名字和值。\n",
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"\n",
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"在初始化Instance时即可定义它包含的域，使用 \"field_name=field_value\"的写法。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'raw_sentence': fake data type=str,\n",
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"'label': 0 type=str}"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from fastNLP import Instance\n",
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"\n",
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"dataset.append(Instance(raw_sentence='fake data', label='0'))\n",
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"dataset[-1]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 数据处理"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'raw_sentence': A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . type=str,\n",
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"'label': 1 type=str,\n",
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"'sentence': a series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . type=str,\n",
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"'words': [4, 1, 6, 1, 1, 2, 1, 11, 153, 10, 28, 17, 2, 1, 10, 1, 28, 17, 2, 1, 5, 154, 6, 149, 1, 1, 23, 1, 6, 149, 1, 8, 30, 6, 4, 35, 3] type=list,\n",
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"'target': 1 type=int}"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from fastNLP import Vocabulary\n",
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"\n",
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"# 将所有字母转为小写, 并所有句子变成单词序列\n",
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"dataset.apply(lambda x: x['raw_sentence'].lower(), new_field_name='sentence')\n",
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"dataset.apply_field(lambda x: x.split(), field_name='sentence', new_field_name='words')\n",
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"\n",
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"# 使用Vocabulary类统计单词，并将单词序列转化为数字序列\n",
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"vocab = Vocabulary(min_freq=2).from_dataset(dataset, field_name='words')\n",
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"vocab.index_dataset(dataset, field_name='words',new_field_name='words')\n",
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"\n",
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"# 将label转为整数\n",
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"dataset.apply(lambda x: int(x['label']), new_field_name='target')\n",
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"dataset[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'raw_sentence': A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . type=str,\n",
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"'label': 1 type=str,\n",
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"'sentence': a series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . type=str,\n",
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"'words': [4, 1, 6, 1, 1, 2, 1, 11, 153, 10, 28, 17, 2, 1, 10, 1, 28, 17, 2, 1, 5, 154, 6, 149, 1, 1, 23, 1, 6, 149, 1, 8, 30, 6, 4, 35, 3] type=list,\n",
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"'target': 1 type=int,\n",
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"'seq_len': 37 type=int}\n"
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]
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}
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],
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"source": [
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"# 增加长度信息\n",
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"dataset.apply_field(lambda x: len(x), field_name='words', new_field_name='seq_len')\n",
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"print(dataset[0])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 使用内置模块CNNText\n",
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"设置为符合内置模块的名称"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"CNNText(\n",
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" (embed): Embedding(\n",
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" 177, 50\n",
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" (dropout): Dropout(p=0.0)\n",
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" )\n",
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" (conv_pool): ConvMaxpool(\n",
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" (convs): ModuleList(\n",
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" (0): Conv1d(50, 3, kernel_size=(3,), stride=(1,), padding=(2,))\n",
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" (1): Conv1d(50, 4, kernel_size=(4,), stride=(1,), padding=(2,))\n",
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" (2): Conv1d(50, 5, kernel_size=(5,), stride=(1,), padding=(2,))\n",
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" )\n",
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" )\n",
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" (dropout): Dropout(p=0.1)\n",
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" (fc): Linear(in_features=12, out_features=5, bias=True)\n",
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")"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from fastNLP.models import CNNText\n",
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"\n",
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"model_cnn = CNNText((len(vocab),50), num_classes=5, padding=2, dropout=0.1)\n",
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"model_cnn"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"我们在使用内置模块的时候，还应该使用应该注意把 field 设定成符合内置模型输入输出的名字。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"words\n",
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"seq_len\n",
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"target\n"
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]
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}
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],
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"source": [
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"from fastNLP import Const\n",
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"\n",
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"dataset.rename_field('words', Const.INPUT)\n",
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"dataset.rename_field('seq_len', Const.INPUT_LEN)\n",
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"dataset.rename_field('target', Const.TARGET)\n",
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"\n",
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"dataset.set_input(Const.INPUT, Const.INPUT_LEN)\n",
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"dataset.set_target(Const.TARGET)\n",
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"\n",
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"print(Const.INPUT)\n",
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"print(Const.INPUT_LEN)\n",
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"print(Const.TARGET)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 分割训练集/验证集/测试集"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(64, 7, 7)"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"train_dev_data, test_data = dataset.split(0.1)\n",
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"train_data, dev_data = train_dev_data.split(0.1)\n",
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"len(train_data), len(dev_data), len(test_data)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 训练(model_cnn)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### loss\n",
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"训练模型需要提供一个损失函数\n",
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"\n",
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"下面提供了一个在分类问题中常用的交叉熵损失。注意它的**初始化参数**。\n",
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"\n",
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"pred参数对应的是模型的forward返回的dict的一个key的名字，这里是\"output\"。\n",
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"\n",
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"target参数对应的是dataset作为标签的field的名字，这里是\"label_seq\"。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"from fastNLP import CrossEntropyLoss\n",
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"\n",
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"# loss = CrossEntropyLoss()\n",
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"# 等价于\n",
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"loss = CrossEntropyLoss(pred=Const.OUTPUT, target=Const.TARGET)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Metric\n",
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"定义评价指标\n",
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"\n",
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"这里使用准确率。参数的“命名规则”跟上面类似。\n",
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"\n",
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"pred参数对应的是模型的predict方法返回的dict的一个key的名字，这里是\"predict\"。\n",
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"\n",
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"target参数对应的是dataset作为标签的field的名字，这里是\"label_seq\"。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"from fastNLP import AccuracyMetric\n",
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"\n",
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"# metrics=AccuracyMetric()\n",
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"# 等价于\n",
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"metrics=AccuracyMetric(pred=Const.OUTPUT, target=Const.TARGET)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"input fields after batch(if batch size is 2):\n",
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"\twords: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 16]) \n",
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"\tseq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n",
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"target fields after batch(if batch size is 2):\n",
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"\ttarget: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n",
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"\n",
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||
"training epochs started 2019-05-12-21-38-34\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=20), HTML(value='')), layout=Layout(display='…"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Evaluation at Epoch 1/10. Step:2/20. AccuracyMetric: acc=0.285714\n",
|
||
"\n",
|
||
"Evaluation at Epoch 2/10. Step:4/20. AccuracyMetric: acc=0.428571\n",
|
||
"\n",
|
||
"Evaluation at Epoch 3/10. Step:6/20. AccuracyMetric: acc=0.428571\n",
|
||
"\n",
|
||
"Evaluation at Epoch 4/10. Step:8/20. AccuracyMetric: acc=0.428571\n",
|
||
"\n",
|
||
"Evaluation at Epoch 5/10. Step:10/20. AccuracyMetric: acc=0.428571\n",
|
||
"\n",
|
||
"Evaluation at Epoch 6/10. Step:12/20. AccuracyMetric: acc=0.428571\n",
|
||
"\n",
|
||
"Evaluation at Epoch 7/10. Step:14/20. AccuracyMetric: acc=0.428571\n",
|
||
"\n",
|
||
"Evaluation at Epoch 8/10. Step:16/20. AccuracyMetric: acc=0.857143\n",
|
||
"\n",
|
||
"Evaluation at Epoch 9/10. Step:18/20. AccuracyMetric: acc=0.857143\n",
|
||
"\n",
|
||
"Evaluation at Epoch 10/10. Step:20/20. AccuracyMetric: acc=0.857143\n",
|
||
"\n",
|
||
"\n",
|
||
"In Epoch:8/Step:16, got best dev performance:AccuracyMetric: acc=0.857143\n",
|
||
"Reloaded the best model.\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'best_eval': {'AccuracyMetric': {'acc': 0.857143}},\n",
|
||
" 'best_epoch': 8,\n",
|
||
" 'best_step': 16,\n",
|
||
" 'seconds': 0.21}"
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from fastNLP import Trainer\n",
|
||
"\n",
|
||
"trainer = Trainer(model=model_cnn, train_data=train_data, dev_data=dev_data, loss=loss, metrics=metrics)\n",
|
||
"trainer.train()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 测试(model_cnn)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[tester] \n",
|
||
"AccuracyMetric: acc=0.857143\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'AccuracyMetric': {'acc': 0.857143}}"
|
||
]
|
||
},
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from fastNLP import Tester\n",
|
||
"\n",
|
||
"tester = Tester(test_data, model_cnn, metrics=AccuracyMetric())\n",
|
||
"tester.test()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 编写自己的模型\n",
|
||
"\n",
|
||
"完全支持 pytorch 的模型，与 pytorch 唯一不同的是返回结果是一个字典，字典中至少需要包含 \"pred\" 这个字段"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import torch\n",
|
||
"import torch.nn as nn\n",
|
||
"\n",
|
||
"class LSTMText(nn.Module):\n",
|
||
" def __init__(self, vocab_size, embedding_dim, output_dim, hidden_dim=64, num_layers=2, dropout=0.5):\n",
|
||
" super().__init__()\n",
|
||
"\n",
|
||
" self.embedding = nn.Embedding(vocab_size, embedding_dim)\n",
|
||
" self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers=num_layers, bidirectional=True, dropout=dropout)\n",
|
||
" self.fc = nn.Linear(hidden_dim * 2, output_dim)\n",
|
||
" self.dropout = nn.Dropout(dropout)\n",
|
||
"\n",
|
||
" def forward(self, words):\n",
|
||
" # (input) words : (batch_size, seq_len)\n",
|
||
" words = words.permute(1,0)\n",
|
||
" # words : (seq_len, batch_size)\n",
|
||
"\n",
|
||
" embedded = self.dropout(self.embedding(words))\n",
|
||
" # embedded : (seq_len, batch_size, embedding_dim)\n",
|
||
" output, (hidden, cell) = self.lstm(embedded)\n",
|
||
" # output: (seq_len, batch_size, hidden_dim * 2)\n",
|
||
" # hidden: (num_layers * 2, batch_size, hidden_dim)\n",
|
||
" # cell: (num_layers * 2, batch_size, hidden_dim)\n",
|
||
"\n",
|
||
" hidden = torch.cat((hidden[-2, :, :], hidden[-1, :, :]), dim=1)\n",
|
||
" hidden = self.dropout(hidden)\n",
|
||
" # hidden: (batch_size, hidden_dim * 2)\n",
|
||
"\n",
|
||
" pred = self.fc(hidden.squeeze(0))\n",
|
||
" # result: (batch_size, output_dim)\n",
|
||
" return {\"pred\":pred}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"input fields after batch(if batch size is 2):\n",
|
||
"\twords: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 16]) \n",
|
||
"\tseq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n",
|
||
"target fields after batch(if batch size is 2):\n",
|
||
"\ttarget: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n",
|
||
"\n",
|
||
"training epochs started 2019-05-12-21-38-36\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=20), HTML(value='')), layout=Layout(display='…"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Evaluation at Epoch 1/10. Step:2/20. AccuracyMetric: acc=0.571429\n",
|
||
"\n",
|
||
"Evaluation at Epoch 2/10. Step:4/20. AccuracyMetric: acc=0.571429\n",
|
||
"\n",
|
||
"Evaluation at Epoch 3/10. Step:6/20. AccuracyMetric: acc=0.571429\n",
|
||
"\n",
|
||
"Evaluation at Epoch 4/10. Step:8/20. AccuracyMetric: acc=0.571429\n",
|
||
"\n",
|
||
"Evaluation at Epoch 5/10. Step:10/20. AccuracyMetric: acc=0.714286\n",
|
||
"\n",
|
||
"Evaluation at Epoch 6/10. Step:12/20. AccuracyMetric: acc=0.857143\n",
|
||
"\n",
|
||
"Evaluation at Epoch 7/10. Step:14/20. AccuracyMetric: acc=0.857143\n",
|
||
"\n",
|
||
"Evaluation at Epoch 8/10. Step:16/20. AccuracyMetric: acc=0.857143\n",
|
||
"\n",
|
||
"Evaluation at Epoch 9/10. Step:18/20. AccuracyMetric: acc=0.857143\n",
|
||
"\n",
|
||
"Evaluation at Epoch 10/10. Step:20/20. AccuracyMetric: acc=0.857143\n",
|
||
"\n",
|
||
"\n",
|
||
"In Epoch:6/Step:12, got best dev performance:AccuracyMetric: acc=0.857143\n",
|
||
"Reloaded the best model.\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'best_eval': {'AccuracyMetric': {'acc': 0.857143}},\n",
|
||
" 'best_epoch': 6,\n",
|
||
" 'best_step': 12,\n",
|
||
" 'seconds': 2.15}"
|
||
]
|
||
},
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"model_lstm = LSTMText(len(vocab),50,5)\n",
|
||
"trainer = Trainer(model=model_lstm, train_data=train_data, dev_data=dev_data, loss=loss, metrics=metrics)\n",
|
||
"trainer.train()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[tester] \n",
|
||
"AccuracyMetric: acc=0.857143\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'AccuracyMetric': {'acc': 0.857143}}"
|
||
]
|
||
},
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"tester = Tester(test_data, model_lstm, metrics=AccuracyMetric())\n",
|
||
"tester.test()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 使用 Batch编写自己的训练过程"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Epoch 0 Avg Loss: 3.11 18ms\n",
|
||
"Epoch 1 Avg Loss: 2.88 30ms\n",
|
||
"Epoch 2 Avg Loss: 2.69 42ms\n",
|
||
"Epoch 3 Avg Loss: 2.47 54ms\n",
|
||
"Epoch 4 Avg Loss: 2.38 67ms\n",
|
||
"Epoch 5 Avg Loss: 2.10 78ms\n",
|
||
"Epoch 6 Avg Loss: 2.06 91ms\n",
|
||
"Epoch 7 Avg Loss: 1.92 103ms\n",
|
||
"Epoch 8 Avg Loss: 1.91 114ms\n",
|
||
"Epoch 9 Avg Loss: 1.76 126ms\n",
|
||
"[tester] \n",
|
||
"AccuracyMetric: acc=0.571429\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'AccuracyMetric': {'acc': 0.571429}}"
|
||
]
|
||
},
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from fastNLP import BucketSampler\n",
|
||
"from fastNLP import Batch\n",
|
||
"import torch\n",
|
||
"import time\n",
|
||
"\n",
|
||
"model = CNNText((len(vocab),50), num_classes=5, padding=2, dropout=0.1)\n",
|
||
"\n",
|
||
"def train(epoch, data):\n",
|
||
" optim = torch.optim.Adam(model.parameters(), lr=0.001)\n",
|
||
" lossfunc = torch.nn.CrossEntropyLoss()\n",
|
||
" batch_size = 32\n",
|
||
"\n",
|
||
" # 定义一个Batch，传入DataSet，规定batch_size和去batch的规则。\n",
|
||
" # 顺序（Sequential），随机（Random），相似长度组成一个batch（Bucket）\n",
|
||
" train_sampler = BucketSampler(batch_size=batch_size, seq_len_field_name='seq_len')\n",
|
||
" train_batch = Batch(batch_size=batch_size, dataset=data, sampler=train_sampler)\n",
|
||
" \n",
|
||
" start_time = time.time()\n",
|
||
" for i in range(epoch):\n",
|
||
" loss_list = []\n",
|
||
" for batch_x, batch_y in train_batch:\n",
|
||
" optim.zero_grad()\n",
|
||
" output = model(batch_x['words'])\n",
|
||
" loss = lossfunc(output['pred'], batch_y['target'])\n",
|
||
" loss.backward()\n",
|
||
" optim.step()\n",
|
||
" loss_list.append(loss.item())\n",
|
||
" print('Epoch {:d} Avg Loss: {:.2f}'.format(i, sum(loss_list) / len(loss_list)),end=\" \")\n",
|
||
" print('{:d}ms'.format(round((time.time()-start_time)*1000)))\n",
|
||
" loss_list.clear()\n",
|
||
" \n",
|
||
"train(10, train_data)\n",
|
||
"tester = Tester(test_data, model, metrics=AccuracyMetric())\n",
|
||
"tester.test()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 使用 Callback 实现自己想要的效果"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"input fields after batch(if batch size is 2):\n",
|
||
"\twords: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 16]) \n",
|
||
"\tseq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n",
|
||
"target fields after batch(if batch size is 2):\n",
|
||
"\ttarget: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n",
|
||
"\n",
|
||
"training epochs started 2019-05-12-21-38-40\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=20), HTML(value='')), layout=Layout(display='…"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Evaluation at Epoch 1/10. Step:2/20. AccuracyMetric: acc=0.285714\n",
|
||
"\n",
|
||
"Sum Time: 51ms\n",
|
||
"\n",
|
||
"\n",
|
||
"Evaluation at Epoch 2/10. Step:4/20. AccuracyMetric: acc=0.285714\n",
|
||
"\n",
|
||
"Sum Time: 69ms\n",
|
||
"\n",
|
||
"\n",
|
||
"Evaluation at Epoch 3/10. Step:6/20. AccuracyMetric: acc=0.285714\n",
|
||
"\n",
|
||
"Sum Time: 91ms\n",
|
||
"\n",
|
||
"\n",
|
||
"Evaluation at Epoch 4/10. Step:8/20. AccuracyMetric: acc=0.571429\n",
|
||
"\n",
|
||
"Sum Time: 107ms\n",
|
||
"\n",
|
||
"\n",
|
||
"Evaluation at Epoch 5/10. Step:10/20. AccuracyMetric: acc=0.571429\n",
|
||
"\n",
|
||
"Sum Time: 125ms\n",
|
||
"\n",
|
||
"\n",
|
||
"Evaluation at Epoch 6/10. Step:12/20. AccuracyMetric: acc=0.571429\n",
|
||
"\n",
|
||
"Sum Time: 142ms\n",
|
||
"\n",
|
||
"\n",
|
||
"Evaluation at Epoch 7/10. Step:14/20. AccuracyMetric: acc=0.571429\n",
|
||
"\n",
|
||
"Sum Time: 158ms\n",
|
||
"\n",
|
||
"\n",
|
||
"Evaluation at Epoch 8/10. Step:16/20. AccuracyMetric: acc=0.571429\n",
|
||
"\n",
|
||
"Sum Time: 176ms\n",
|
||
"\n",
|
||
"\n",
|
||
"Evaluation at Epoch 9/10. Step:18/20. AccuracyMetric: acc=0.714286\n",
|
||
"\n",
|
||
"Sum Time: 193ms\n",
|
||
"\n",
|
||
"\n",
|
||
"Evaluation at Epoch 10/10. Step:20/20. AccuracyMetric: acc=0.857143\n",
|
||
"\n",
|
||
"Sum Time: 212ms\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"In Epoch:10/Step:20, got best dev performance:AccuracyMetric: acc=0.857143\n",
|
||
"Reloaded the best model.\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'best_eval': {'AccuracyMetric': {'acc': 0.857143}},\n",
|
||
" 'best_epoch': 10,\n",
|
||
" 'best_step': 20,\n",
|
||
" 'seconds': 0.2}"
|
||
]
|
||
},
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from fastNLP import Callback\n",
|
||
"\n",
|
||
"start_time = time.time()\n",
|
||
"\n",
|
||
"class MyCallback(Callback):\n",
|
||
" def on_epoch_end(self):\n",
|
||
" print('Sum Time: {:d}ms\\n\\n'.format(round((time.time()-start_time)*1000)))\n",
|
||
" \n",
|
||
"\n",
|
||
"model = CNNText((len(vocab),50), num_classes=5, padding=2, dropout=0.1)\n",
|
||
"trainer = Trainer(model=model, train_data=train_data, dev_data=dev_data,\n",
|
||
" loss=CrossEntropyLoss(), metrics=AccuracyMetric(), callbacks=[MyCallback()])\n",
|
||
"trainer.train()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.6.7"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 1
|
||
}
|