From c4b2a8bac382aa5f165d4b8c7769734d41a9200b Mon Sep 17 00:00:00 2001 From: ChenXin Date: Thu, 27 Feb 2020 13:40:05 +0800 Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20tutorial=5F5=20=E5=92=8C?= =?UTF-8?q?=20tutorial=5F6=20=E7=9A=84=E5=AE=9E=E9=AA=8C=E7=BB=93=E6=9E=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../tutorials/tutorial_5_loss_optimizer.rst | 6 +- .../tutorials/tutorial_6_datasetiter.rst | 213 +++++++++--------- 2 files changed, 109 insertions(+), 110 deletions(-) diff --git a/docs/source/tutorials/tutorial_5_loss_optimizer.rst b/docs/source/tutorials/tutorial_5_loss_optimizer.rst index 9e02175a..5874f66d 100644 --- a/docs/source/tutorials/tutorial_5_loss_optimizer.rst +++ b/docs/source/tutorials/tutorial_5_loss_optimizer.rst @@ -232,6 +232,6 @@ 训练过程输出如下:: - Evaluate data in 0.19 seconds! - [tester] - AccuracyMetric: acc=0.889109 + Evaluate data in 0.43 seconds! + [tester] + AccuracyMetric: acc=0.773333 diff --git a/docs/source/tutorials/tutorial_6_datasetiter.rst b/docs/source/tutorials/tutorial_6_datasetiter.rst index 40d1ade6..e1e060b4 100644 --- a/docs/source/tutorials/tutorial_6_datasetiter.rst +++ b/docs/source/tutorials/tutorial_6_datasetiter.rst @@ -1,9 +1,8 @@ ============================================================================== -动手实现一个文本分类器II-使用DataSetIter实现自定义训练过程 +使用DataSetIter实现自定义训练过程 ============================================================================== -我们使用和 :doc:`/user/quickstart` 中一样的任务来进行详细的介绍。给出一段评价性文字，预测其情感倾向是积极的（label=0）、 -还是消极的（label=1），使用 :class:`~fastNLP.DataSetIter` 类来编写自己的训练过程。 +我们使用前面介绍过的 :doc:`/tutorials/文本分类` 任务来进行详细的介绍。这里我们把数据集换成了SST2，使用 :class:`~fastNLP.DataSetIter` 类来编写自己的训练过程。 DataSetIter初探之前的内容与 :doc:`/tutorials/tutorial_5_loss_optimizer` 中的完全一样，如已经阅读过可以跳过。 @@ -55,18 +54,19 @@ DataSetIter初探之前的内容与 :doc:`/tutorials/tutorial_5_loss_optimizer` 除了可以对数据进行读入的Pipe类，fastNLP还提供了读入和下载数据的Loader类，不同数据集的Pipe和Loader及其用法详见 :doc:`/tutorials/tutorial_4_load_dataset` 。 数据集分割 - 由于SST2数据集的测试集并不带有标签数值，故我们分割出一部分训练集作为测试集。下面这段代码展示了 :meth:`~fastNLP.DataSet.split` 的使用方法 + 由于SST2数据集的测试集并不带有标签数值，故我们分割出一部分训练集作为测试集。下面这段代码展示了 :meth:`~fastNLP.DataSet.split` 的使用方法， + 为了能让读者快速运行完整个教程，我们只取了训练集的前5000个数据。 .. code-block:: python - train_data = databundle.get_dataset('train') + train_data = databundle.get_dataset('train')[:5000] train_data, test_data = train_data.split(0.015) dev_data = databundle.get_dataset('dev') print(len(train_data),len(dev_data),len(test_data)) 输出结果为:: - - 66339 872 1010 + + 4925 872 75 数据集 :meth:`~fastNLP.DataSet.set_input` 和 :meth:`~fastNLP.DataSet.set_target` 函数 :class:`~fastNLP.io.SST2Pipe` 类的 :meth:`~fastNLP.io.SST2Pipe.process_from_file` 方法在预处理过程中还将训练、测试、验证集 @@ -162,33 +162,33 @@ DataSetIter自动padding 输出结果如下:: - batch_x: {'words': tensor([[ 4, 278, 686, 18, 7], - [15619, 3205, 5, 1676, 0]]), 'seq_len': tensor([5, 4])} - batch_y: {'target': tensor([1, 1])} - batch_x: {'words': tensor([[ 44, 753, 328, 181, 10, 15622, 16, 71, 8905, 9, - 1218, 7, 0, 0, 0, 0, 0, 0, 0, 0], - [ 880, 97, 8, 1027, 12, 8068, 11, 13624, 8, 15620, - 4, 674, 663, 15, 4, 1155, 241, 640, 418, 7]]), 'seq_len': tensor([12, 20])} - batch_y: {'target': tensor([1, 0])} - batch_x: {'words': tensor([[ 1046, 11114, 16, 105, 5, 4, 177, 1825, 1705, 3, - 2, 18, 11, 4, 1019, 433, 144, 32, 246, 309, + batch_x: {'words': tensor([[ 13, 830, 7746, 174, 3, 47, 6, 83, 5752, 15, + 2177, 15, 63, 57, 406, 84, 1009, 4973, 27, 17, + 13785, 3, 533, 3687, 15623, 39, 375, 8, 15624, 8, + 1323, 4398, 7], + [ 1045, 11113, 16, 104, 5, 4, 176, 1824, 1704, 3, + 2, 18, 11, 4, 1018, 432, 143, 33, 245, 308, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0], - [ 13, 831, 7747, 175, 3, 46, 6, 84, 5753, 15, - 2178, 15, 62, 56, 407, 85, 1010, 4974, 26, 17, - 13786, 3, 534, 3688, 15624, 38, 376, 8, 15625, 8, - 1324, 4399, 7]]), 'seq_len': tensor([21, 33])} - batch_y: {'target': tensor([0, 1])} - batch_x: {'words': tensor([[ 14, 10, 438, 31, 78, 3, 78, 438, 7], - [ 14, 10, 4, 312, 5, 155, 1419, 610, 7]]), 'seq_len': tensor([9, 9])} + 0, 0, 0]]), 'seq_len': tensor([33, 21])} batch_y: {'target': tensor([1, 0])} - batch_x: {'words': tensor([[ 24, 96, 27, 45, 8, 337, 37, 240, 8, 2134, - 2, 18, 10, 15623, 1422, 6, 60, 5, 388, 7], - [ 2, 156, 3, 4427, 3, 240, 3, 740, 5, 1137, - 40, 42, 2428, 737, 2, 649, 10, 15621, 2286, 7]]), 'seq_len': tensor([20, 20])} + batch_x: {'words': tensor([[ 14, 10, 4, 311, 5, 154, 1418, 609, 7], + [ 14, 10, 437, 32, 78, 3, 78, 437, 7]]), 'seq_len': tensor([9, 9])} + batch_y: {'target': tensor([0, 1])} + batch_x: {'words': tensor([[ 4, 277, 685, 18, 7], + [15618, 3204, 5, 1675, 0]]), 'seq_len': tensor([5, 4])} + batch_y: {'target': tensor([1, 1])} + batch_x: {'words': tensor([[ 2, 155, 3, 4426, 3, 239, 3, 739, 5, 1136, + 41, 43, 2427, 736, 2, 648, 10, 15620, 2285, 7], + [ 24, 95, 28, 46, 8, 336, 38, 239, 8, 2133, + 2, 18, 10, 15622, 1421, 6, 61, 5, 387, 7]]), 'seq_len': tensor([20, 20])} batch_y: {'target': tensor([0, 0])} + batch_x: {'words': tensor([[ 879, 96, 8, 1026, 12, 8067, 11, 13623, 8, 15619, + 4, 673, 662, 15, 4, 1154, 240, 639, 417, 7], + [ 45, 752, 327, 180, 10, 15621, 16, 72, 8904, 9, + 1217, 7, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([20, 12])} + batch_y: {'target': tensor([0, 1])} - 可以看到那些设定为input的 :mod:`~fastNLP.core.field` 都出现在batch_x中，而设定为target的 :mod:`~fastNLP.core.field` 则出现在batch_y中。同时对于同一个batch_x中的两个数 据，长度偏短的那个会被自动padding到和长度偏长的句子长度一致，默认的padding值为0。 + 可以看到那些设定为input的 :mod:`~fastNLP.core.field` 都出现在batch_x中，而设定为target的 :mod:`~fastNLP.core.field` 则出现在batch_y中。同时对于同一个batch_x中的两个数据，长度偏短的那个会被自动padding到和长度偏长的句子长度一致，默认的padding值为0。 Dataset改变padding值 可以通过 :meth:`~fastNLP.core.Dataset.set_pad_val` 方法修改默认的pad值，代码如下： @@ -203,36 +203,36 @@ Dataset改变padding值 输出结果如下:: - batch_x: {'words': tensor([[15619, 3205, 5, 1676, -1], - [ 4, 278, 686, 18, 7]]), 'seq_len': tensor([4, 5])} - batch_y: {'target': tensor([1, 1])} - batch_x: {'words': tensor([[ 1046, 11114, 16, 105, 5, 4, 177, 1825, 1705, 3, - 2, 18, 11, 4, 1019, 433, 144, 32, 246, 309, + batch_x: {'words': tensor([[ 13, 830, 7746, 174, 3, 47, 6, 83, 5752, 15, + 2177, 15, 63, 57, 406, 84, 1009, 4973, 27, 17, + 13785, 3, 533, 3687, 15623, 39, 375, 8, 15624, 8, + 1323, 4398, 7], + [ 1045, 11113, 16, 104, 5, 4, 176, 1824, 1704, 3, + 2, 18, 11, 4, 1018, 432, 143, 33, 245, 308, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, - -1, -1, -1], - [ 13, 831, 7747, 175, 3, 46, 6, 84, 5753, 15, - 2178, 15, 62, 56, 407, 85, 1010, 4974, 26, 17, - 13786, 3, 534, 3688, 15624, 38, 376, 8, 15625, 8, - 1324, 4399, 7]]), 'seq_len': tensor([21, 33])} - batch_y: {'target': tensor([0, 1])} - batch_x: {'words': tensor([[ 14, 10, 4, 312, 5, 155, 1419, 610, 7], - [ 14, 10, 438, 31, 78, 3, 78, 438, 7]]), 'seq_len': tensor([9, 9])} + -1, -1, -1]]), 'seq_len': tensor([33, 21])} + batch_y: {'target': tensor([1, 0])} + batch_x: {'words': tensor([[ 14, 10, 4, 311, 5, 154, 1418, 609, 7], + [ 14, 10, 437, 32, 78, 3, 78, 437, 7]]), 'seq_len': tensor([9, 9])} batch_y: {'target': tensor([0, 1])} - batch_x: {'words': tensor([[ 2, 156, 3, 4427, 3, 240, 3, 740, 5, 1137, - 40, 42, 2428, 737, 2, 649, 10, 15621, 2286, 7], - [ 24, 96, 27, 45, 8, 337, 37, 240, 8, 2134, - 2, 18, 10, 15623, 1422, 6, 60, 5, 388, 7]]), 'seq_len': tensor([20, 20])} + batch_x: {'words': tensor([[ 2, 155, 3, 4426, 3, 239, 3, 739, 5, 1136, + 41, 43, 2427, 736, 2, 648, 10, 15620, 2285, 7], + [ 24, 95, 28, 46, 8, 336, 38, 239, 8, 2133, + 2, 18, 10, 15622, 1421, 6, 61, 5, 387, 7]]), 'seq_len': tensor([20, 20])} batch_y: {'target': tensor([0, 0])} - batch_x: {'words': tensor([[ 44, 753, 328, 181, 10, 15622, 16, 71, 8905, 9, - 1218, 7, -1, -1, -1, -1, -1, -1, -1, -1], - [ 880, 97, 8, 1027, 12, 8068, 11, 13624, 8, 15620, - 4, 674, 663, 15, 4, 1155, 241, 640, 418, 7]]), 'seq_len': tensor([12, 20])} - batch_y: {'target': tensor([1, 0])} + batch_x: {'words': tensor([[ 4, 277, 685, 18, 7], + [15618, 3204, 5, 1675, -1]]), 'seq_len': tensor([5, 4])} + batch_y: {'target': tensor([1, 1])} + batch_x: {'words': tensor([[ 879, 96, 8, 1026, 12, 8067, 11, 13623, 8, 15619, + 4, 673, 662, 15, 4, 1154, 240, 639, 417, 7], + [ 45, 752, 327, 180, 10, 15621, 16, 72, 8904, 9, + 1217, 7, -1, -1, -1, -1, -1, -1, -1, -1]]), 'seq_len': tensor([20, 12])} + batch_y: {'target': tensor([0, 1])} 可以看到使用了-1进行padding。 Dataset个性化padding - 如果我们希望对某一些 :mod:`~fastNLP.core.field` 进行个性化padding，可以自己构造Padder类，并使用 :meth:`~fastNLP.core.Dataset.set_padder` 函数修改padder来实现。下面通 过构造一个将数据padding到固定长度的padder进行展示： + 如果我们希望对某一些 :mod:`~fastNLP.core.field` 进行个性化padding，可以自己构造Padder类，并使用 :meth:`~fastNLP.core.Dataset.set_padder` 函数修改padder来实现。下面通过构造一个将数据padding到固定长度的padder进行展示： .. code-block:: python @@ -265,53 +265,53 @@ Dataset个性化padding 输出结果如下:: - batch_x: {'words': tensor([[ 4, 278, 686, 18, 7, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + batch_x: {'words': tensor([[ 45, 752, 327, 180, 10, 15621, 16, 72, 8904, 9, + 1217, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [15619, 3205, 5, 1676, 0, 0, 0, 0, 0, 0, + [ 879, 96, 8, 1026, 12, 8067, 11, 13623, 8, 15619, + 4, 673, 662, 15, 4, 1154, 240, 639, 417, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([5, 4])} - batch_y: {'target': tensor([1, 1])} - batch_x: {'words': tensor([[ 2, 156, 3, 4427, 3, 240, 3, 740, 5, 1137, - 40, 42, 2428, 737, 2, 649, 10, 15621, 2286, 7, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [ 24, 96, 27, 45, 8, 337, 37, 240, 8, 2134, - 2, 18, 10, 15623, 1422, 6, 60, 5, 388, 7, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([20, 20])} - batch_y: {'target': tensor([0, 0])} - batch_x: {'words': tensor([[ 13, 831, 7747, 175, 3, 46, 6, 84, 5753, 15, - 2178, 15, 62, 56, 407, 85, 1010, 4974, 26, 17, - 13786, 3, 534, 3688, 15624, 38, 376, 8, 15625, 8, - 1324, 4399, 7, 0, 0, 0, 0, 0, 0, 0], - [ 1046, 11114, 16, 105, 5, 4, 177, 1825, 1705, 3, - 2, 18, 11, 4, 1019, 433, 144, 32, 246, 309, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([12, 20])} + batch_y: {'target': tensor([1, 0])} + batch_x: {'words': tensor([[ 13, 830, 7746, 174, 3, 47, 6, 83, 5752, 15, + 2177, 15, 63, 57, 406, 84, 1009, 4973, 27, 17, + 13785, 3, 533, 3687, 15623, 39, 375, 8, 15624, 8, + 1323, 4398, 7, 0, 0, 0, 0, 0, 0, 0], + [ 1045, 11113, 16, 104, 5, 4, 176, 1824, 1704, 3, + 2, 18, 11, 4, 1018, 432, 143, 33, 245, 308, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([33, 21])} batch_y: {'target': tensor([1, 0])} - batch_x: {'words': tensor([[ 14, 10, 4, 312, 5, 155, 1419, 610, 7, 0, 0, 0, + batch_x: {'words': tensor([[ 14, 10, 4, 311, 5, 154, 1418, 609, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [ 14, 10, 438, 31, 78, 3, 78, 438, 7, 0, 0, 0, + [ 14, 10, 437, 32, 78, 3, 78, 437, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([9, 9])} batch_y: {'target': tensor([0, 1])} - batch_x: {'words': tensor([[ 44, 753, 328, 181, 10, 15622, 16, 71, 8905, 9, - 1218, 7, 0, 0, 0, 0, 0, 0, 0, 0, + batch_x: {'words': tensor([[ 2, 155, 3, 4426, 3, 239, 3, 739, 5, 1136, + 41, 43, 2427, 736, 2, 648, 10, 15620, 2285, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], - [ 880, 97, 8, 1027, 12, 8068, 11, 13624, 8, 15620, - 4, 674, 663, 15, 4, 1155, 241, 640, 418, 7, + [ 24, 95, 28, 46, 8, 336, 38, 239, 8, 2133, + 2, 18, 10, 15622, 1421, 6, 61, 5, 387, 7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([12, 20])} - batch_y: {'target': tensor([1, 0])} + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([20, 20])} + batch_y: {'target': tensor([0, 0])} + batch_x: {'words': tensor([[ 4, 277, 685, 18, 7, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [15618, 3204, 5, 1675, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([5, 4])} + batch_y: {'target': tensor([1, 1])} - 在这里所有的`words`都被pad成了长度为40的list。 + 在这里所有的 `words` 都被pad成了长度为40的list。 使用DataSetIter自己编写训练过程 @@ -375,39 +375,38 @@ Dataset个性化padding -----start training----- - Evaluate data in 0.2 seconds! - Epoch 0 Avg Loss: 0.33 AccuracyMetric: acc=0.825688 48895ms + Evaluate data in 2.68 seconds! + Epoch 0 Avg Loss: 0.66 AccuracyMetric: acc=0.708716 29307ms - Evaluate data in 0.19 seconds! - Epoch 1 Avg Loss: 0.16 AccuracyMetric: acc=0.829128 102081ms + Evaluate data in 0.38 seconds! + Epoch 1 Avg Loss: 0.41 AccuracyMetric: acc=0.770642 52200ms - Evaluate data in 0.18 seconds! - Epoch 2 Avg Loss: 0.10 AccuracyMetric: acc=0.822248 152853ms + Evaluate data in 0.51 seconds! + Epoch 2 Avg Loss: 0.16 AccuracyMetric: acc=0.747706 70268ms - Evaluate data in 0.17 seconds! - Epoch 3 Avg Loss: 0.08 AccuracyMetric: acc=0.821101 200184ms + Evaluate data in 0.96 seconds! + Epoch 3 Avg Loss: 0.06 AccuracyMetric: acc=0.741972 90349ms - Evaluate data in 0.17 seconds! - Epoch 4 Avg Loss: 0.06 AccuracyMetric: acc=0.827982 253097ms + Evaluate data in 1.04 seconds! + Epoch 4 Avg Loss: 0.03 AccuracyMetric: acc=0.740826 114250ms - Evaluate data in 0.27 seconds! - Epoch 5 Avg Loss: 0.05 AccuracyMetric: acc=0.806193 303883ms + Evaluate data in 0.8 seconds! + Epoch 5 Avg Loss: 0.02 AccuracyMetric: acc=0.738532 134742ms - Evaluate data in 0.26 seconds! - Epoch 6 Avg Loss: 0.04 AccuracyMetric: acc=0.803899 392315ms + Evaluate data in 0.65 seconds! + Epoch 6 Avg Loss: 0.01 AccuracyMetric: acc=0.731651 154503ms - Evaluate data in 0.36 seconds! - Epoch 7 Avg Loss: 0.04 AccuracyMetric: acc=0.802752 527211ms + Evaluate data in 0.8 seconds! + Epoch 7 Avg Loss: 0.01 AccuracyMetric: acc=0.738532 175397ms - Evaluate data in 0.15 seconds! - Epoch 8 Avg Loss: 0.03 AccuracyMetric: acc=0.809633 661533ms + Evaluate data in 0.36 seconds! + Epoch 8 Avg Loss: 0.01 AccuracyMetric: acc=0.733945 192384ms - Evaluate data in 0.31 seconds! - Epoch 9 Avg Loss: 0.03 AccuracyMetric: acc=0.797018 812232ms + Evaluate data in 0.84 seconds! + Epoch 9 Avg Loss: 0.01 AccuracyMetric: acc=0.744266 214417ms - Evaluate data in 0.25 seconds! - [tester] - AccuracyMetric: acc=0.917822 - + Evaluate data in 0.04 seconds! + [tester] + AccuracyMetric: acc=0.786667