ChenXin
7 years ago
10 changed files with 383 additions and 513 deletions
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docs/source/index.rst
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| @@ -24,7 +24,7 @@ fastNLP 是一款轻量级的 NLP 处理套件。你既可以使用它快速地 | |||
| | module type | functionality | example | | |||
| +=======================+=======================+=======================+ | |||
| | encoder | 将输入编码为具有具 | embedding, RNN, CNN, | | |||
| | | 有表示能力的向量 | transformer | | |||
| | | 有表示能力的向量 | transformer | | |||
| +-----------------------+-----------------------+-----------------------+ | |||
| | aggregator | 从多个向量中聚合信息 | self-attention, | | |||
| | | | max-pooling | | |||
| @@ -39,16 +39,15 @@ For example: | |||
| .. image:: figures/text_classification.png | |||
| .. todo:: | |||
| 各个任务上的结果 | |||
| 各个任务上的结果 | |||
| ----------------------- | |||
| （TODO） | |||
| 内置的模型 | |||
| ---------------- | |||
| 用户手册 | |||
| --------------- | |||
| ---------------- | |||
| .. toctree:: | |||
| :maxdepth: 1 | |||
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docs/source/tutorials/fastnlp_10tmin_tutorial.rst
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| fastNLP 10分钟上手教程 | |||
| =============== | |||
| 教程原文见 https://github.com/fastnlp/fastNLP/blob/master/tutorials/fastnlp_10min_tutorial.ipynb | |||
| fastNLP提供方便的数据预处理，训练和测试模型的功能 | |||
| DataSet & Instance | |||
| ------------------ | |||
| fastNLP用DataSet和Instance保存和处理数据。每个DataSet表示一个数据集，每个Instance表示一个数据样本。一个DataSet存有多个Instance，每个Instance可以自定义存哪些内容。 | |||
| 有一些read\_\*方法，可以轻松从文件读取数据，存成DataSet。 | |||
| .. code:: ipython3 | |||
| from fastNLP import DataSet | |||
| from fastNLP import Instance | |||
| # 从csv读取数据到DataSet | |||
| win_path = "C:\\Users\zyfeng\Desktop\FudanNLP\\fastNLP\\test\\data_for_tests\\tutorial_sample_dataset.csv" | |||
| dataset = DataSet.read_csv(win_path, headers=('raw_sentence', 'label'), sep='\t') | |||
| print(dataset[0]) | |||
| .. parsed-literal:: | |||
| {'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 ., | |||
| 'label': 1} | |||
| .. code:: ipython3 | |||
| # DataSet.append(Instance)加入新数据 | |||
| dataset.append(Instance(raw_sentence='fake data', label='0')) | |||
| dataset[-1] | |||
| .. parsed-literal:: | |||
| {'raw_sentence': fake data, | |||
| 'label': 0} | |||
| .. code:: ipython3 | |||
| # DataSet.apply(func, new_field_name)对数据预处理 | |||
| # 将所有数字转为小写 | |||
| dataset.apply(lambda x: x['raw_sentence'].lower(), new_field_name='raw_sentence') | |||
| # label转int | |||
| dataset.apply(lambda x: int(x['label']), new_field_name='label_seq', is_target=True) | |||
| # 使用空格分割句子 | |||
| dataset.drop(lambda x: len(x['raw_sentence'].split()) == 0) | |||
| def split_sent(ins): | |||
| return ins['raw_sentence'].split() | |||
| dataset.apply(split_sent, new_field_name='words', is_input=True) | |||
| .. code:: ipython3 | |||
| # DataSet.drop(func)筛除数据 | |||
| # 删除低于某个长度的词语 | |||
| dataset.drop(lambda x: len(x['words']) <= 3) | |||
| .. code:: ipython3 | |||
| # 分出测试集、训练集 | |||
| test_data, train_data = dataset.split(0.3) | |||
| print("Train size: ", len(test_data)) | |||
| print("Test size: ", len(train_data)) | |||
| .. parsed-literal:: | |||
| Train size: 54 | |||
| Test size: | |||
| Vocabulary | |||
| ---------- | |||
| fastNLP中的Vocabulary轻松构建词表，将词转成数字 | |||
| .. code:: ipython3 | |||
| from fastNLP import Vocabulary | |||
| # 构建词表, Vocabulary.add(word) | |||
| vocab = Vocabulary(min_freq=2) | |||
| train_data.apply(lambda x: [vocab.add(word) for word in x['words']]) | |||
| vocab.build_vocab() | |||
| # index句子, Vocabulary.to_index(word) | |||
| train_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True) | |||
| test_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True) | |||
| print(test_data[0]) | |||
| .. parsed-literal:: | |||
| {'raw_sentence': the plot is romantic comedy boilerplate from start to finish ., | |||
| 'label': 2, | |||
| 'label_seq': 2, | |||
| 'words': ['the', 'plot', 'is', 'romantic', 'comedy', 'boilerplate', 'from', 'start', 'to', 'finish', '.'], | |||
| 'word_seq': [2, 13, 9, 24, 25, 26, 15, 27, 11, 28, 3]} | |||
| .. code:: ipython3 | |||
| # 假设你们需要做强化学习或者gan之类的项目，也许你们可以使用这里的dataset | |||
| from fastNLP.core.batch import Batch | |||
| from fastNLP.core.sampler import RandomSampler | |||
| batch_iterator = Batch(dataset=train_data, batch_size=2, sampler=RandomSampler()) | |||
| for batch_x, batch_y in batch_iterator: | |||
| print("batch_x has: ", batch_x) | |||
| print("batch_y has: ", batch_y) | |||
| break | |||
| .. parsed-literal:: | |||
| batch_x has: {'words': array([list(['this', 'kind', 'of', 'hands-on', 'storytelling', 'is', 'ultimately', 'what', 'makes', 'shanghai', 'ghetto', 'move', 'beyond', 'a', 'good', ',', 'dry', ',', 'reliable', 'textbook', 'and', 'what', 'allows', 'it', 'to', 'rank', 'with', 'its', 'worthy', 'predecessors', '.']), | |||
| list(['the', 'entire', 'movie', 'is', 'filled', 'with', 'deja', 'vu', 'moments', '.'])], | |||
| dtype=object), 'word_seq': tensor([[ 19, 184, 6, 1, 481, 9, 206, 50, 91, 1210, 1609, 1330, | |||
| 495, 5, 63, 4, 1269, 4, 1, 1184, 7, 50, 1050, 10, | |||
| 8, 1611, 16, 21, 1039, 1, 2], | |||
| [ 3, 711, 22, 9, 1282, 16, 2482, 2483, 200, 2, 0, 0, | |||
| 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, | |||
| 0, 0, 0, 0, 0, 0, 0]])} | |||
| batch_y has: {'label_seq': tensor([3, 2])} | |||
| Model | |||
| ----- | |||
| .. code:: ipython3 | |||
| # 定义一个简单的Pytorch模型 | |||
| from fastNLP.models import CNNText | |||
| model = CNNText(embed_num=len(vocab), embed_dim=50, num_classes=5, padding=2, dropout=0.1) | |||
| model | |||
| .. parsed-literal:: | |||
| CNNText( | |||
| (embed): Embedding( | |||
| (embed): Embedding(77, 50, padding_idx=0) | |||
| (dropout): Dropout(p=0.0) | |||
| ) | |||
| (conv_pool): ConvMaxpool( | |||
| (convs): ModuleList( | |||
| (0): Conv1d(50, 3, kernel_size=(3,), stride=(1,), padding=(2,)) | |||
| (1): Conv1d(50, 4, kernel_size=(4,), stride=(1,), padding=(2,)) | |||
| (2): Conv1d(50, 5, kernel_size=(5,), stride=(1,), padding=(2,)) | |||
| ) | |||
| ) | |||
| (dropout): Dropout(p=0.1) | |||
| (fc): Linear( | |||
| (linear): Linear(in_features=12, out_features=5, bias=True) | |||
| ) | |||
| ) | |||
| Trainer & Tester | |||
| ---------------- | |||
| 使用fastNLP的Trainer训练模型 | |||
| .. code:: ipython3 | |||
| from fastNLP import Trainer | |||
| from copy import deepcopy | |||
| from fastNLP import CrossEntropyLoss | |||
| from fastNLP import AccuracyMetric | |||
| .. code:: ipython3 | |||
| # 进行overfitting测试 | |||
| copy_model = deepcopy(model) | |||
| overfit_trainer = Trainer(model=copy_model, | |||
| train_data=test_data, | |||
| dev_data=test_data, | |||
| loss=CrossEntropyLoss(pred="output", target="label_seq"), | |||
| metrics=AccuracyMetric(), | |||
| n_epochs=10, | |||
| save_path=None) | |||
| overfit_trainer.train() | |||
| .. parsed-literal:: | |||
| training epochs started 2018-12-07 14:07:20 | |||
| .. parsed-literal:: | |||
| HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=20), HTML(value='')), layout=Layout(display='… | |||
| .. parsed-literal:: | |||
| Epoch 1/10. Step:2/20. AccuracyMetric: acc=0.037037 | |||
| Epoch 2/10. Step:4/20. AccuracyMetric: acc=0.296296 | |||
| Epoch 3/10. Step:6/20. AccuracyMetric: acc=0.333333 | |||
| Epoch 4/10. Step:8/20. AccuracyMetric: acc=0.555556 | |||
| Epoch 5/10. Step:10/20. AccuracyMetric: acc=0.611111 | |||
| Epoch 6/10. Step:12/20. AccuracyMetric: acc=0.481481 | |||
| Epoch 7/10. Step:14/20. AccuracyMetric: acc=0.62963 | |||
| Epoch 8/10. Step:16/20. AccuracyMetric: acc=0.685185 | |||
| Epoch 9/10. Step:18/20. AccuracyMetric: acc=0.722222 | |||
| Epoch 10/10. Step:20/20. AccuracyMetric: acc=0.777778 | |||
| .. code:: ipython3 | |||
| # 实例化Trainer，传入模型和数据，进行训练 | |||
| trainer = Trainer(model=model, | |||
| train_data=train_data, | |||
| dev_data=test_data, | |||
| loss=CrossEntropyLoss(pred="output", target="label_seq"), | |||
| metrics=AccuracyMetric(), | |||
| n_epochs=5) | |||
| trainer.train() | |||
| print('Train finished!') | |||
| .. parsed-literal:: | |||
| training epochs started 2018-12-07 14:08:10 | |||
| .. parsed-literal:: | |||
| HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=5), HTML(value='')), layout=Layout(display='i… | |||
| .. parsed-literal:: | |||
| Epoch 1/5. Step:1/5. AccuracyMetric: acc=0.037037 | |||
| Epoch 2/5. Step:2/5. AccuracyMetric: acc=0.037037 | |||
| Epoch 3/5. Step:3/5. AccuracyMetric: acc=0.037037 | |||
| Epoch 4/5. Step:4/5. AccuracyMetric: acc=0.185185 | |||
| Epoch 5/5. Step:5/5. AccuracyMetric: acc=0.240741 | |||
| Train finished! | |||
| .. code:: ipython3 | |||
| from fastNLP import Tester | |||
| tester = Tester(data=test_data, model=model, metrics=AccuracyMetric()) | |||
| acc = tester.test() | |||
| .. parsed-literal:: | |||
| [tester] | |||
| AccuracyMetric: acc=0.240741 | |||
| In summary | |||
| ---------- | |||
| fastNLP Trainer的伪代码逻辑 | |||
| --------------------------- | |||
| 1. 准备DataSet，假设DataSet中共有如下的fields | |||
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | |||
| :: | |||
| ['raw_sentence', 'word_seq1', 'word_seq2', 'raw_label','label'] | |||
| 通过 | |||
| DataSet.set_input('word_seq1', word_seq2', flag=True)将'word_seq1', 'word_seq2'设置为input | |||
| 通过 | |||
| DataSet.set_target('label', flag=True)将'label'设置为target | |||
| 2. 初始化模型 | |||
| ~~~~~~~~~~~~~ | |||
| :: | |||
| class Model(nn.Module): | |||
| def __init__(self): | |||
| xxx | |||
| def forward(self, word_seq1, word_seq2): | |||
| # (1) 这里使用的形参名必须和DataSet中的input field的名称对应。因为我们是通过形参名, 进行赋值的 | |||
| # (2) input field的数量可以多于这里的形参数量。但是不能少于。 | |||
| xxxx | |||
| # 输出必须是一个dict | |||
| 3. Trainer的训练过程 | |||
| ~~~~~~~~~~~~~~~~~~~~ | |||
| :: | |||
| (1) 从DataSet中按照batch_size取出一个batch，调用Model.forward | |||
| (2) 将 Model.forward的结果 与 标记为target的field 传入Losser当中。 | |||
| 由于每个人写的Model.forward的output的dict可能key并不一样，比如有人是{'pred':xxx}, {'output': xxx}; | |||
| 另外每个人将target可能也会设置为不同的名称, 比如有人是label, 有人设置为target； | |||
| 为了解决以上的问题，我们的loss提供映射机制 | |||
| 比如CrossEntropyLosser的需要的输入是(prediction, target)。但是forward的output是{'output': xxx}; 'label'是target | |||
| 那么初始化losser的时候写为CrossEntropyLosser(prediction='output', target='label')即可 | |||
| (3) 对于Metric是同理的 | |||
| Metric计算也是从 forward的结果中取值 与 设置target的field中取值。 也是可以通过映射找到对应的值 | |||
| 一些问题. | |||
| --------- | |||
| 1. DataSet中为什么需要设置input和target | |||
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | |||
| :: | |||
| 只有被设置为input或者target的数据才会在train的过程中被取出来 | |||
| (1.1) 我们只会在设置为input的field中寻找传递给Model.forward的参数。 | |||
| (1.2) 我们在传递值给losser或者metric的时候会使用来自: | |||
| (a)Model.forward的output | |||
| (b)被设置为target的field | |||
| 2. 我们是通过forwad中的形参名将DataSet中的field赋值给对应的参数 | |||
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | |||
| :: | |||
| (1.1) 构建模型过程中， | |||
| 例如: | |||
| DataSet中x，seq_lens是input，那么forward就应该是 | |||
| def forward(self, x, seq_lens): | |||
| pass | |||
| 我们是通过形参名称进行匹配的field的 | |||
| 1. 加载数据到DataSet | |||
| ~~~~~~~~~~~~~~~~~~~~ | |||
| 2. 使用apply操作对DataSet进行预处理 | |||
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | |||
| :: | |||
| (2.1) 处理过程中将某些field设置为input，某些field设置为target | |||
| 3. 构建模型 | |||
| ~~~~~~~~~~~ | |||
| :: | |||
| (3.1) 构建模型过程中，需要注意forward函数的形参名需要和DataSet中设置为input的field名称是一致的。 | |||
| 例如: | |||
| DataSet中x，seq_lens是input，那么forward就应该是 | |||
| def forward(self, x, seq_lens): | |||
| pass | |||
| 我们是通过形参名称进行匹配的field的 | |||
| (3.2) 模型的forward的output需要是dict类型的。 | |||
| 建议将输出设置为{"pred": xx}. | |||
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| @@ -1,113 +0,0 @@ | |||
| FastNLP 1分钟上手教程 | |||
| ===================== | |||
| 教程原文见 https://github.com/fastnlp/fastNLP/blob/master/tutorials/fastnlp_1min_tutorial.ipynb | |||
| step 1 | |||
| ------ | |||
| 读取数据集 | |||
| .. code:: ipython3 | |||
| from fastNLP import DataSet | |||
| # linux_path = "../test/data_for_tests/tutorial_sample_dataset.csv" | |||
| win_path = "C:\\Users\zyfeng\Desktop\FudanNLP\\fastNLP\\test\\data_for_tests\\tutorial_sample_dataset.csv" | |||
| ds = DataSet.read_csv(win_path, headers=('raw_sentence', 'label'), sep='\t') | |||
| step 2 | |||
| ------ | |||
| 数据预处理 1. 类型转换 2. 切分验证集 3. 构建词典 | |||
| .. code:: ipython3 | |||
| # 将所有数字转为小写 | |||
| ds.apply(lambda x: x['raw_sentence'].lower(), new_field_name='raw_sentence') | |||
| # label转int | |||
| ds.apply(lambda x: int(x['label']), new_field_name='label_seq', is_target=True) | |||
| def split_sent(ins): | |||
| return ins['raw_sentence'].split() | |||
| ds.apply(split_sent, new_field_name='words', is_input=True) | |||
| .. code:: ipython3 | |||
| # 分割训练集/验证集 | |||
| train_data, dev_data = ds.split(0.3) | |||
| print("Train size: ", len(train_data)) | |||
| print("Test size: ", len(dev_data)) | |||
| .. parsed-literal:: | |||
| Train size: 54 | |||
| Test size: 23 | |||
| .. code:: ipython3 | |||
| from fastNLP import Vocabulary | |||
| vocab = Vocabulary(min_freq=2) | |||
| train_data.apply(lambda x: [vocab.add(word) for word in x['words']]) | |||
| # index句子, Vocabulary.to_index(word) | |||
| train_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True) | |||
| dev_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True) | |||
| step 3 | |||
| ------ | |||
| 定义模型 | |||
| .. code:: ipython3 | |||
| from fastNLP.models import CNNText | |||
| model = CNNText(embed_num=len(vocab), embed_dim=50, num_classes=5, padding=2, dropout=0.1) | |||
| step 4 | |||
| ------ | |||
| 开始训练 | |||
| .. code:: ipython3 | |||
| from fastNLP import Trainer, CrossEntropyLoss, AccuracyMetric | |||
| trainer = Trainer(model=model, | |||
| train_data=train_data, | |||
| dev_data=dev_data, | |||
| loss=CrossEntropyLoss(), | |||
| metrics=AccuracyMetric() | |||
| ) | |||
| trainer.train() | |||
| print('Train finished!') | |||
| .. parsed-literal:: | |||
| training epochs started 2018-12-07 14:03:41 | |||
| .. parsed-literal:: | |||
| HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=6), HTML(value='')), layout=Layout(display='i… | |||
| .. parsed-literal:: | |||
| Epoch 1/3. Step:2/6. AccuracyMetric: acc=0.26087 | |||
| Epoch 2/3. Step:4/6. AccuracyMetric: acc=0.347826 | |||
| Epoch 3/3. Step:6/6. AccuracyMetric: acc=0.608696 | |||
| Train finished! | |||
| 本教程结束。更多操作请参考进阶教程。 | |||
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | |||
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| fastNLP 进阶教程 | |||
| =============== | |||
| 教程原文见 https://github.com/fastnlp/fastNLP/blob/master/tutorials/fastnlp_advanced_tutorial/advance_tutorial.ipynb | |||
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| fastNLP 开发者指南 | |||
| =============== | |||
| 原文见 https://github.com/fastnlp/fastNLP/blob/master/tutorials/tutorial_for_developer.md | |||
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| @@ -121,4 +121,4 @@ | |||
| In Epoch:6/Step:12, got best dev performance:AccuracyMetric: acc=0.8 | |||
| Reloaded the best model. | |||
| 这份教程只是简单地介绍了使用 fastNLP 工作的流程，具体的细节分析见 :doc:`/user/tutorials` | |||
| 这份教程只是简单地介绍了使用 fastNLP 工作的流程，具体的细节分析见 :doc:`/user/tutorial_one` | |||
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| ===================== | |||
| 用 fastNLP 分类 | |||
| ===================== | |||
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| ===================== | |||
| 用 fastNLP 分词 | |||
| ===================== | |||
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| =============== | |||
| 详细指南 | |||
| =============== | |||
| 我们使用和 :doc:`/user/quickstart` 中一样的任务来进行详细的介绍。给出一段文字，预测它的标签是0~4中的哪一个 | |||
| (数据来源 `kaggle <https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews>`_ )。 | |||
| -------------- | |||
| 数据处理 | |||
| -------------- | |||
| 数据读入 | |||
| 我们可以使用 fastNLP :mod:`fastNLP.io` 模块中的 :class:`~fastNLP.io.CSVLoader` 类，轻松地从 csv 文件读取我们的数据。 | |||
| 这里的 dataset 是 fastNLP 中 :class:`~fastNLP.DataSet` 类的对象 | |||
| .. code-block:: python | |||
| from fastNLP.io import CSVLoader | |||
| loader = CSVLoader(headers=('raw_sentence', 'label'), sep='\t') | |||
| dataset = loader.load("./sample_data/tutorial_sample_dataset.csv") | |||
| 除了读取数据外，fastNLP 还提供了读取其它文件类型的 Loader 类、读取 Embedding的 Loader 等。详见 :doc:`/fastNLP.io` 。 | |||
| Instance 和 DataSet | |||
| fastNLP 中的 :class:`~fastNLP.DataSet` 类对象类似于二维表格，它的每一列是一个 :mod:`~fastNLP.core.field` | |||
| 每一行是一个 :mod:`~fastNLP.core.instance` 。我们可以手动向数据集中添加 :class:`~fastNLP.Instance` 类的对象 | |||
| .. code-block:: python | |||
| from fastNLP import Instance | |||
| dataset.append(Instance(raw_sentence='fake data', label='0')) | |||
| 此时的 ``dataset[-1]`` 的值如下,可以看到，数据集中的每个数据包含 ``raw_sentence`` 和 ``label`` 两个 | |||
| :mod:`~fastNLP.core.field` ，他们的类型都是 ``str`` :: | |||
| {'raw_sentence': fake data type=str, 'label': 0 type=str} | |||
| field 的修改 | |||
| 我们使用 :class:`~fastNLP.DataSet` 类的 :meth:`~fastNLP.DataSet.apply` 方法将 ``raw_sentence`` 中字母变成小写，并将句子分词。 | |||
| 同时也将 ``label`` :mod:`~fastNLP.core.field` 转化为整数并改名为 ``target`` | |||
| .. code-block:: python | |||
| dataset.apply(lambda x: x['raw_sentence'].lower(), new_field_name='sentence') | |||
| dataset.apply_field(lambda x: x.split(), field_name='sentence', new_field_name='words') | |||
| dataset.apply(lambda x: int(x['label']), new_field_name='target') | |||
| ``words`` 和 ``target`` 已经足够用于 :class:`~fastNLP.models.CNNText` 的训练了，但我们从其文档 | |||
| :class:`~fastNLP.models.CNNText` 中看到，在 :meth:`~fastNLP.models.CNNText.forward` 的时候，还可以传入可选参数 ``seq_len`` 。 | |||
| 所以，我们再使用 :meth:`~fastNLP.DataSet.apply_field` 方法增加一个名为 ``seq_len`` 的 :mod:`~fastNLP.core.field` 。 | |||
| .. code-block:: python | |||
| dataset.apply_field(lambda x: len(x), field_name='words', new_field_name='seq_len') | |||
| 观察可知： :meth:`~fastNLP.DataSet.apply_field` 与 :meth:`~fastNLP.DataSet.apply` 类似， | |||
| 但所传入的 `lambda` 函数是针对一个 :class:`~fastNLP.Instance` 中的一个 :mod:`~fastNLP.core.field` 的； | |||
| 而 :meth:`~fastNLP.DataSet.apply` 所传入的 `lambda` 函数是针对整个 :class:`~fastNLP.Instance` 的。 | |||
| .. note:: | |||
| `lambda` 函数即匿名函数，是 Python 的重要特性。 ``lambda x: len(x)`` 和下面的这个函数的作用相同:: | |||
| def func_lambda(x): | |||
| return len(x) | |||
| 你也可以编写复杂的函数做为 :meth:`~fastNLP.DataSet.apply_field` 与 :meth:`~fastNLP.DataSet.apply` 的参数 | |||
| Vocabulary 的使用 | |||
| 我们再用 :class:`~fastNLP.Vocabulary` 类来统计数据中出现的单词，并使用 :meth:`~fastNLP.Vocabularyindex_dataset` | |||
| 将单词序列转化为训练可用的数字序列。 | |||
| .. code-block:: python | |||
| from fastNLP import Vocabulary | |||
| vocab = Vocabulary(min_freq=2).from_dataset(dataset, field_name='words') | |||
| vocab.index_dataset(dataset, field_name='words',new_field_name='words') | |||
| 数据集分割 | |||
| 除了修改 :mod:`~fastNLP.core.field` 之外，我们还可以对 :class:`~fastNLP.DataSet` 进行分割，以供训练、开发和测试使用。 | |||
| 下面这段代码展示了 :meth:`~fastNLP.DataSet.split` 的使用方法（但实际应该放在后面两段改名和设置输入的代码之后） | |||
| .. code-block:: python | |||
| train_dev_data, test_data = dataset.split(0.1) | |||
| train_data, dev_data = train_dev_data.split(0.1) | |||
| len(train_data), len(dev_data), len(test_data) | |||
| --------------------- | |||
| 使用内置模型训练 | |||
| --------------------- | |||
| 内置模型的输入输出命名 | |||
| fastNLP内置了一些完整的神经网络模型，详见 :doc:`/fastNLP.models` , 我们使用其中的 :class:`~fastNLP.models.CNNText` 模型进行训练。 | |||
| 为了使用内置的 :class:`~fastNLP.models.CNNText`，我们必须修改 :class:`~fastNLP.DataSet` 中 :mod:`~fastNLP.core.field` 的名称。 | |||
| 在这个例子中模型输入 (forward方法的参数) 为 ``words`` 和 ``seq_len`` ; 预测输出为 ``pred`` ;标准答案为 ``target`` 。 | |||
| 具体的命名规范可以参考 :doc:`/fastNLP.core.const` 。 | |||
| 如果不想查看文档，您也可以使用 :class:`~fastNLP.Const` 类进行命名。下面的代码展示了给 :class:`~fastNLP.DataSet` 中 | |||
| :mod:`~fastNLP.core.field` 改名的 :meth:`~fastNLP.DataSet.rename_field` 方法，以及 :class:`~fastNLP.Const` 类的使用方法。 | |||
| .. code-block:: python | |||
| from fastNLP import Const | |||
| dataset.rename_field('words', Const.INPUT) | |||
| dataset.rename_field('seq_len', Const.INPUT_LEN) | |||
| dataset.rename_field('target', Const.TARGET) | |||
| 在给 :class:`~fastNLP.DataSet` 中 :mod:`~fastNLP.core.field` 改名后，我们还需要设置训练所需的输入和目标，这里使用的是 | |||
| :meth:`~fastNLP.DataSet.set_input` 和 :meth:`~fastNLP.DataSet.set_target` 两个函数。 | |||
| .. code-block:: python | |||
| dataset.set_input(Const.INPUT, Const.INPUT_LEN) | |||
| dataset.set_target(Const.TARGET) | |||
| 快速训练 | |||
| 现在我们可以导入 fastNLP 内置的文本分类模型 :class:`~fastNLP.models.CNNText` ，并使用 :class:`~fastNLP.Trainer` 进行训练了 | |||
| （其中 ``loss`` 和 ``metrics`` 的定义，我们将在后续两段代码中给出）。 | |||
| .. code-block:: python | |||
| from fastNLP.models import CNNText | |||
| from fastNLP import Trainer | |||
| model = CNNText((len(vocab),50), num_classes=5, padding=2, dropout=0.1) | |||
| trainer = Trainer(model=model_cnn, train_data=train_data, dev_data=dev_data, | |||
| loss=loss, metrics=metrics) | |||
| trainer.train() | |||
| 训练过程的输出如下:: | |||
| input fields after batch(if batch size is 2): | |||
| words: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 26]) | |||
| target fields after batch(if batch size is 2): | |||
| target: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) | |||
| training epochs started 2019-05-09-10-59-39 | |||
| Evaluation at Epoch 1/10. Step:2/20. AccuracyMetric: acc=0.333333 | |||
| Evaluation at Epoch 2/10. Step:4/20. AccuracyMetric: acc=0.533333 | |||
| Evaluation at Epoch 3/10. Step:6/20. AccuracyMetric: acc=0.533333 | |||
| Evaluation at Epoch 4/10. Step:8/20. AccuracyMetric: acc=0.533333 | |||
| Evaluation at Epoch 5/10. Step:10/20. AccuracyMetric: acc=0.6 | |||
| Evaluation at Epoch 6/10. Step:12/20. AccuracyMetric: acc=0.8 | |||
| Evaluation at Epoch 7/10. Step:14/20. AccuracyMetric: acc=0.8 | |||
| Evaluation at Epoch 8/10. Step:16/20. AccuracyMetric: acc=0.733333 | |||
| Evaluation at Epoch 9/10. Step:18/20. AccuracyMetric: acc=0.733333 | |||
| Evaluation at Epoch 10/10. Step:20/20. AccuracyMetric: acc=0.733333 | |||
| In Epoch:6/Step:12, got best dev performance:AccuracyMetric: acc=0.8 | |||
| Reloaded the best model. | |||
| 损失函数 | |||
| 训练模型需要提供一个损失函数, 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** 。 | |||
| ``pred`` 参数对应的是模型的 forward 方法返回的 dict 中的一个 key 的名字。 | |||
| ``target`` 参数对应的是 :class:`~fastNLP.DataSet` 中作为标签的 :mod:`~fastNLP.core.field` 的名字。 | |||
| 这里我们用 :class:`~fastNLP.Const` 来辅助命名，如果你自己编写模型中 forward 方法的返回值或 | |||
| 数据集中 :mod:`~fastNLP.core.field` 的名字与本例不同， 你可以把 ``pred`` 参数和 ``target`` 参数设定符合自己代码的值。 | |||
| .. code-block:: python | |||
| from fastNLP import CrossEntropyLoss | |||
| # loss = CrossEntropyLoss() 在本例中与下面这行代码等价 | |||
| loss = CrossEntropyLoss(pred=Const.OUTPUT, target=Const.TARGET) | |||
| 评价指标 | |||
| 训练模型需要提供一个评价指标。这里使用准确率做为评价指标。参数的 `命名规则` 跟上面类似。 | |||
| ``pred`` 参数对应的是模型的 forward 方法返回的 dict 中的一个 key 的名字。 | |||
| ``target`` 参数对应的是 :class:`~fastNLP.DataSet` 中作为标签的 :mod:`~fastNLP.core.field` 的名字。 | |||
| .. code-block:: python | |||
| from fastNLP import AccuracyMetric | |||
| # metrics=AccuracyMetric() 在本例中与下面这行代码等价 | |||
| metrics=AccuracyMetric(pred=Const.OUTPUT, target=Const.TARGET) | |||
| 快速测试 | |||
| 与 :class:`~fastNLP.Trainer` 对应，fastNLP 也提供了 :class:`~fastNLP.Tester` 用于快速测试，用法如下 | |||
| .. code-block:: python | |||
| from fastNLP import Tester | |||
| tester = Tester(test_data, model_cnn, metrics=AccuracyMetric()) | |||
| tester.test() | |||
| --------------------- | |||
| 编写自己的模型 | |||
| --------------------- | |||
| 因为 fastNLP 是基于 `PyTorch <https://pytorch.org/>`_ 开发的框架，所以我们可以基于 PyTorch 模型编写自己的神经网络模型。 | |||
| 与标准的 PyTorch 模型不同，fastNLP 模型中 forward 方法返回的是一个字典，字典中至少需要包含 "pred" 这个字段。 | |||
| 而 forward 方法的参数名称必须与 :class:`~fastNLP.DataSet` 中用 :meth:`~fastNLP.DataSet.set_input` 设定的名称一致。 | |||
| 模型定义的代码如下: | |||
| .. code-block:: python | |||
| import torch | |||
| import torch.nn as nn | |||
| class LSTMText(nn.Module): | |||
| def __init__(self, vocab_size, embedding_dim, output_dim, hidden_dim=64, num_layers=2, dropout=0.5): | |||
| super().__init__() | |||
| self.embedding = nn.Embedding(vocab_size, embedding_dim) | |||
| self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers=num_layers, bidirectional=True, dropout=dropout) | |||
| self.fc = nn.Linear(hidden_dim * 2, output_dim) | |||
| self.dropout = nn.Dropout(dropout) | |||
| def forward(self, words): | |||
| # (input) words : (batch_size, seq_len) | |||
| words = words.permute(1,0) | |||
| # words : (seq_len, batch_size) | |||
| embedded = self.dropout(self.embedding(words)) | |||
| # embedded : (seq_len, batch_size, embedding_dim) | |||
| output, (hidden, cell) = self.lstm(embedded) | |||
| # output: (seq_len, batch_size, hidden_dim * 2) | |||
| # hidden: (num_layers * 2, batch_size, hidden_dim) | |||
| # cell: (num_layers * 2, batch_size, hidden_dim) | |||
| hidden = torch.cat((hidden[-2, :, :], hidden[-1, :, :]), dim=1) | |||
| hidden = self.dropout(hidden) | |||
| # hidden: (batch_size, hidden_dim * 2) | |||
| pred = self.fc(hidden.squeeze(0)) | |||
| # result: (batch_size, output_dim) | |||
| return {"pred":pred} | |||
| 模型的使用方法与内置模型 :class:`~fastNLP.models.CNNText` 一致 | |||
| .. code-block:: python | |||
| model_lstm = LSTMText(len(vocab),50,5) | |||
| trainer = Trainer(model=model_lstm, train_data=train_data, dev_data=dev_data, | |||
| loss=loss, metrics=metrics) | |||
| trainer.train() | |||
| tester = Tester(test_data, model_lstm, metrics=AccuracyMetric()) | |||
| tester.test() | |||
| .. todo:: | |||
| 使用 :doc:`/fastNLP.modules` 编写模型 | |||
| -------------------------- | |||
| 自己编写训练过程 | |||
| -------------------------- | |||
| 如果你想用类似 PyTorch 的使用方法，自己编写训练过程，你可以参考下面这段代码。其中使用了 fastNLP 提供的 :class:`~fastNLP.Batch` | |||
| 来获得小批量训练的小批量数据，使用 :class:`~fastNLP.BucketSampler` 做为 :class:`~fastNLP.Batch` 的参数来选择采样的方式。 | |||
| 这段代码中使用了 PyTorch 的 `torch.optim.Adam` 优化器 和 `torch.nn.CrossEntropyLoss` 损失函数，并自己计算了正确率 | |||
| .. code-block:: python | |||
| from fastNLP import BucketSampler | |||
| from fastNLP import Batch | |||
| import torch | |||
| import time | |||
| model = CNNText((len(vocab),50), num_classes=5, padding=2, dropout=0.1) | |||
| def train(epoch, data): | |||
| optim = torch.optim.Adam(model.parameters(), lr=0.001) | |||
| lossfunc = torch.nn.CrossEntropyLoss() | |||
| batch_size = 32 | |||
| train_sampler = BucketSampler(batch_size=batch_size, seq_len_field_name='seq_len') | |||
| train_batch = Batch(batch_size=batch_size, dataset=data, sampler=train_sampler) | |||
| start_time = time.time() | |||
| for i in range(epoch): | |||
| loss_list = [] | |||
| for batch_x, batch_y in train_batch: | |||
| optim.zero_grad() | |||
| output = model(batch_x['words']) | |||
| loss = lossfunc(output['pred'], batch_y['target']) | |||
| loss.backward() | |||
| optim.step() | |||
| loss_list.append(loss.item()) | |||
| print('Epoch {:d} Avg Loss: {:.2f}'.format(i, sum(loss_list) / len(loss_list)),end=" ") | |||
| print('{:d}ms'.format(round((time.time()-start_time)*1000))) | |||
| loss_list.clear() | |||
| train(10, train_data) | |||
| tester = Tester(test_data, model, metrics=AccuracyMetric()) | |||
| tester.test() | |||
| 这段代码的输出如下:: | |||
| Epoch 0 Avg Loss: 2.76 17ms | |||
| Epoch 1 Avg Loss: 2.55 29ms | |||
| Epoch 2 Avg Loss: 2.37 41ms | |||
| Epoch 3 Avg Loss: 2.30 53ms | |||
| Epoch 4 Avg Loss: 2.12 65ms | |||
| Epoch 5 Avg Loss: 2.16 76ms | |||
| Epoch 6 Avg Loss: 1.88 88ms | |||
| Epoch 7 Avg Loss: 1.84 99ms | |||
| Epoch 8 Avg Loss: 1.71 111ms | |||
| Epoch 9 Avg Loss: 1.62 122ms | |||
| [tester] | |||
| AccuracyMetric: acc=0.142857 | |||
| ---------------------------------- | |||
| 使用 Callback 增强 Trainer | |||
| ---------------------------------- | |||
| 如果你不想自己实现繁琐的训练过程，只希望在训练过程中实现一些自己的功能（比如：输出从训练开始到当前 batch 结束的总时间）， | |||
| 你可以使用 fastNLP 提供的 :class:`~fastNLP.Callback` 类。下面的例子中，我们继承 :class:`~fastNLP.Callback` 类实现了这个功能。 | |||
| .. code-block:: python | |||
| from fastNLP import Callback | |||
| start_time = time.time() | |||
| class MyCallback(Callback): | |||
| def on_epoch_end(self): | |||
| print('Sum Time: {:d}ms\n\n'.format(round((time.time()-start_time)*1000))) | |||
| model = CNNText((len(vocab),50), num_classes=5, padding=2, dropout=0.1) | |||
| trainer = Trainer(model=model, train_data=train_data, dev_data=dev_data, | |||
| loss=CrossEntropyLoss(), metrics=AccuracyMetric(), callbacks=[MyCallback()]) | |||
| trainer.train() | |||
| 训练输出如下:: | |||
| input fields after batch(if batch size is 2): | |||
| words: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 16]) | |||
| seq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) | |||
| target fields after batch(if batch size is 2): | |||
| target: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) | |||
| training epochs started 2019-05-12-21-38-40 | |||
| Evaluation at Epoch 1/10. Step:2/20. AccuracyMetric: acc=0.285714 | |||
| Sum Time: 51ms | |||
| ………………………… | |||
| Evaluation at Epoch 10/10. Step:20/20. AccuracyMetric: acc=0.857143 | |||
| Sum Time: 212ms | |||
| In Epoch:10/Step:20, got best dev performance:AccuracyMetric: acc=0.857143 | |||
| Reloaded the best model. | |||
| 这个例子只是介绍了 :class:`~fastNLP.Callback` 类的使用方法。实际应用（比如：负采样、Learning Rate Decay、Early Stop 等）中 | |||
| 很多功能已经被 fastNLP 实现了。你可以直接 import 它们使用，详细请查看文档 :doc:`/fastNLP.core.callback` 。 | |||
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| @@ -0,0 +1,5 @@ | |||
| ================= | |||
| 科研向导 | |||
| ================= | |||
| 本文介绍使用 fastNLP 和 fitlog 进行科学研究的方法 | |||