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更新 tutorial_5 和 tutorial_6 的实验结果
This commit is contained in:
ChenXin
parent
e3f95c9a24
commit
c4b2a8bac3
@ -232,6 +232,6 @@
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训练过程输出如下::
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Evaluate data in 0.19 seconds!
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[tester]
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AccuracyMetric: acc=0.889109
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Evaluate data in 0.43 seconds!
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[tester]
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AccuracyMetric: acc=0.773333
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@ -1,9 +1,8 @@
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==============================================================================
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动手实现一个文本分类器II-使用DataSetIter实现自定义训练过程
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使用DataSetIter实现自定义训练过程
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==============================================================================
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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）、
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还是消极的（label=1），使用 :class:`~fastNLP.DataSetIter` 类来编写自己的训练过程。
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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。
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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。
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@ -55,18 +54,19 @@ DataSetIter初探之前的内容与 :doc:`/tutorials/tutorial_5_loss_optimizer`
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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` 。
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数据集分割
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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
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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，
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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。
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.. code-block:: python
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train_data = databundle.get_dataset('train')
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train_data = databundle.get_dataset('train')[:5000]
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train_data, test_data = train_data.split(0.015)
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dev_data = databundle.get_dataset('dev')
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print(len(train_data),len(dev_data),len(test_data))
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输出结果为::
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66339 872 1010
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4925 872 75
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数据集 :meth:`~fastNLP.DataSet.set_input` 和 :meth:`~fastNLP.DataSet.set_target` 函数
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: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
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@ -162,33 +162,33 @@ DataSetIter自动padding
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输出结果如下::
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batch_x: {'words': tensor([[ 4, 278, 686, 18, 7],
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[15619, 3205, 5, 1676, 0]]), 'seq_len': tensor([5, 4])}
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batch_y: {'target': tensor([1, 1])}
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batch_x: {'words': tensor([[ 44, 753, 328, 181, 10, 15622, 16, 71, 8905, 9,
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1218, 7, 0, 0, 0, 0, 0, 0, 0, 0],
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[ 880, 97, 8, 1027, 12, 8068, 11, 13624, 8, 15620,
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4, 674, 663, 15, 4, 1155, 241, 640, 418, 7]]), 'seq_len': tensor([12, 20])}
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batch_y: {'target': tensor([1, 0])}
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batch_x: {'words': tensor([[ 1046, 11114, 16, 105, 5, 4, 177, 1825, 1705, 3,
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2, 18, 11, 4, 1019, 433, 144, 32, 246, 309,
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batch_x: {'words': tensor([[ 13, 830, 7746, 174, 3, 47, 6, 83, 5752, 15,
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2177, 15, 63, 57, 406, 84, 1009, 4973, 27, 17,
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13785, 3, 533, 3687, 15623, 39, 375, 8, 15624, 8,
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1323, 4398, 7],
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[ 1045, 11113, 16, 104, 5, 4, 176, 1824, 1704, 3,
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2, 18, 11, 4, 1018, 432, 143, 33, 245, 308,
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7, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0],
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[ 13, 831, 7747, 175, 3, 46, 6, 84, 5753, 15,
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2178, 15, 62, 56, 407, 85, 1010, 4974, 26, 17,
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13786, 3, 534, 3688, 15624, 38, 376, 8, 15625, 8,
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1324, 4399, 7]]), 'seq_len': tensor([21, 33])}
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batch_y: {'target': tensor([0, 1])}
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batch_x: {'words': tensor([[ 14, 10, 438, 31, 78, 3, 78, 438, 7],
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[ 14, 10, 4, 312, 5, 155, 1419, 610, 7]]), 'seq_len': tensor([9, 9])}
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0, 0, 0]]), 'seq_len': tensor([33, 21])}
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batch_y: {'target': tensor([1, 0])}
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batch_x: {'words': tensor([[ 24, 96, 27, 45, 8, 337, 37, 240, 8, 2134,
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2, 18, 10, 15623, 1422, 6, 60, 5, 388, 7],
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[ 2, 156, 3, 4427, 3, 240, 3, 740, 5, 1137,
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40, 42, 2428, 737, 2, 649, 10, 15621, 2286, 7]]), 'seq_len': tensor([20, 20])}
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batch_x: {'words': tensor([[ 14, 10, 4, 311, 5, 154, 1418, 609, 7],
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[ 14, 10, 437, 32, 78, 3, 78, 437, 7]]), 'seq_len': tensor([9, 9])}
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batch_y: {'target': tensor([0, 1])}
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batch_x: {'words': tensor([[ 4, 277, 685, 18, 7],
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[15618, 3204, 5, 1675, 0]]), 'seq_len': tensor([5, 4])}
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batch_y: {'target': tensor([1, 1])}
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batch_x: {'words': tensor([[ 2, 155, 3, 4426, 3, 239, 3, 739, 5, 1136,
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41, 43, 2427, 736, 2, 648, 10, 15620, 2285, 7],
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[ 24, 95, 28, 46, 8, 336, 38, 239, 8, 2133,
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2, 18, 10, 15622, 1421, 6, 61, 5, 387, 7]]), 'seq_len': tensor([20, 20])}
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batch_y: {'target': tensor([0, 0])}
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batch_x: {'words': tensor([[ 879, 96, 8, 1026, 12, 8067, 11, 13623, 8, 15619,
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4, 673, 662, 15, 4, 1154, 240, 639, 417, 7],
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[ 45, 752, 327, 180, 10, 15621, 16, 72, 8904, 9,
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1217, 7, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([20, 12])}
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batch_y: {'target': tensor([0, 1])}
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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。
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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。
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Dataset改变padding值
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可以通过 :meth:`~fastNLP.core.Dataset.set_pad_val` 方法修改默认的pad值，代码如下：
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@ -203,36 +203,36 @@ Dataset改变padding值
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输出结果如下::
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batch_x: {'words': tensor([[15619, 3205, 5, 1676, -1],
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[ 4, 278, 686, 18, 7]]), 'seq_len': tensor([4, 5])}
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batch_y: {'target': tensor([1, 1])}
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batch_x: {'words': tensor([[ 1046, 11114, 16, 105, 5, 4, 177, 1825, 1705, 3,
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2, 18, 11, 4, 1019, 433, 144, 32, 246, 309,
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batch_x: {'words': tensor([[ 13, 830, 7746, 174, 3, 47, 6, 83, 5752, 15,
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2177, 15, 63, 57, 406, 84, 1009, 4973, 27, 17,
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13785, 3, 533, 3687, 15623, 39, 375, 8, 15624, 8,
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1323, 4398, 7],
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[ 1045, 11113, 16, 104, 5, 4, 176, 1824, 1704, 3,
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2, 18, 11, 4, 1018, 432, 143, 33, 245, 308,
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7, -1, -1, -1, -1, -1, -1, -1, -1, -1,
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-1, -1, -1],
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[ 13, 831, 7747, 175, 3, 46, 6, 84, 5753, 15,
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2178, 15, 62, 56, 407, 85, 1010, 4974, 26, 17,
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13786, 3, 534, 3688, 15624, 38, 376, 8, 15625, 8,
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1324, 4399, 7]]), 'seq_len': tensor([21, 33])}
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batch_y: {'target': tensor([0, 1])}
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batch_x: {'words': tensor([[ 14, 10, 4, 312, 5, 155, 1419, 610, 7],
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[ 14, 10, 438, 31, 78, 3, 78, 438, 7]]), 'seq_len': tensor([9, 9])}
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batch_y: {'target': tensor([0, 1])}
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batch_x: {'words': tensor([[ 2, 156, 3, 4427, 3, 240, 3, 740, 5, 1137,
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40, 42, 2428, 737, 2, 649, 10, 15621, 2286, 7],
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[ 24, 96, 27, 45, 8, 337, 37, 240, 8, 2134,
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2, 18, 10, 15623, 1422, 6, 60, 5, 388, 7]]), 'seq_len': tensor([20, 20])}
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batch_y: {'target': tensor([0, 0])}
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batch_x: {'words': tensor([[ 44, 753, 328, 181, 10, 15622, 16, 71, 8905, 9,
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1218, 7, -1, -1, -1, -1, -1, -1, -1, -1],
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[ 880, 97, 8, 1027, 12, 8068, 11, 13624, 8, 15620,
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4, 674, 663, 15, 4, 1155, 241, 640, 418, 7]]), 'seq_len': tensor([12, 20])}
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-1, -1, -1]]), 'seq_len': tensor([33, 21])}
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batch_y: {'target': tensor([1, 0])}
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batch_x: {'words': tensor([[ 14, 10, 4, 311, 5, 154, 1418, 609, 7],
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[ 14, 10, 437, 32, 78, 3, 78, 437, 7]]), 'seq_len': tensor([9, 9])}
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batch_y: {'target': tensor([0, 1])}
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batch_x: {'words': tensor([[ 2, 155, 3, 4426, 3, 239, 3, 739, 5, 1136,
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41, 43, 2427, 736, 2, 648, 10, 15620, 2285, 7],
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[ 24, 95, 28, 46, 8, 336, 38, 239, 8, 2133,
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2, 18, 10, 15622, 1421, 6, 61, 5, 387, 7]]), 'seq_len': tensor([20, 20])}
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batch_y: {'target': tensor([0, 0])}
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batch_x: {'words': tensor([[ 4, 277, 685, 18, 7],
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[15618, 3204, 5, 1675, -1]]), 'seq_len': tensor([5, 4])}
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batch_y: {'target': tensor([1, 1])}
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batch_x: {'words': tensor([[ 879, 96, 8, 1026, 12, 8067, 11, 13623, 8, 15619,
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4, 673, 662, 15, 4, 1154, 240, 639, 417, 7],
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[ 45, 752, 327, 180, 10, 15621, 16, 72, 8904, 9,
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1217, 7, -1, -1, -1, -1, -1, -1, -1, -1]]), 'seq_len': tensor([20, 12])}
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batch_y: {'target': tensor([0, 1])}
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可以看到使用了-1进行padding。
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Dataset个性化padding
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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：
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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：
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.. code-block:: python
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@ -265,53 +265,53 @@ Dataset个性化padding
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输出结果如下::
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batch_x: {'words': tensor([[ 4, 278, 686, 18, 7, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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batch_x: {'words': tensor([[ 45, 752, 327, 180, 10, 15621, 16, 72, 8904, 9,
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1217, 7, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[15619, 3205, 5, 1676, 0, 0, 0, 0, 0, 0,
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[ 879, 96, 8, 1026, 12, 8067, 11, 13623, 8, 15619,
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4, 673, 662, 15, 4, 1154, 240, 639, 417, 7,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([5, 4])}
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batch_y: {'target': tensor([1, 1])}
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batch_x: {'words': tensor([[ 2, 156, 3, 4427, 3, 240, 3, 740, 5, 1137,
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40, 42, 2428, 737, 2, 649, 10, 15621, 2286, 7,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[ 24, 96, 27, 45, 8, 337, 37, 240, 8, 2134,
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2, 18, 10, 15623, 1422, 6, 60, 5, 388, 7,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([20, 20])}
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batch_y: {'target': tensor([0, 0])}
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batch_x: {'words': tensor([[ 13, 831, 7747, 175, 3, 46, 6, 84, 5753, 15,
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2178, 15, 62, 56, 407, 85, 1010, 4974, 26, 17,
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13786, 3, 534, 3688, 15624, 38, 376, 8, 15625, 8,
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1324, 4399, 7, 0, 0, 0, 0, 0, 0, 0],
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[ 1046, 11114, 16, 105, 5, 4, 177, 1825, 1705, 3,
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2, 18, 11, 4, 1019, 433, 144, 32, 246, 309,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([12, 20])}
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batch_y: {'target': tensor([1, 0])}
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batch_x: {'words': tensor([[ 13, 830, 7746, 174, 3, 47, 6, 83, 5752, 15,
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2177, 15, 63, 57, 406, 84, 1009, 4973, 27, 17,
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13785, 3, 533, 3687, 15623, 39, 375, 8, 15624, 8,
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1323, 4398, 7, 0, 0, 0, 0, 0, 0, 0],
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[ 1045, 11113, 16, 104, 5, 4, 176, 1824, 1704, 3,
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2, 18, 11, 4, 1018, 432, 143, 33, 245, 308,
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7, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([33, 21])}
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batch_y: {'target': tensor([1, 0])}
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batch_x: {'words': tensor([[ 14, 10, 4, 312, 5, 155, 1419, 610, 7, 0, 0, 0,
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batch_x: {'words': tensor([[ 14, 10, 4, 311, 5, 154, 1418, 609, 7, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0],
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[ 14, 10, 438, 31, 78, 3, 78, 438, 7, 0, 0, 0,
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[ 14, 10, 437, 32, 78, 3, 78, 437, 7, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0]]), 'seq_len': tensor([9, 9])}
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batch_y: {'target': tensor([0, 1])}
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batch_x: {'words': tensor([[ 44, 753, 328, 181, 10, 15622, 16, 71, 8905, 9,
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1218, 7, 0, 0, 0, 0, 0, 0, 0, 0,
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batch_x: {'words': tensor([[ 2, 155, 3, 4426, 3, 239, 3, 739, 5, 1136,
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41, 43, 2427, 736, 2, 648, 10, 15620, 2285, 7,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[ 880, 97, 8, 1027, 12, 8068, 11, 13624, 8, 15620,
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4, 674, 663, 15, 4, 1155, 241, 640, 418, 7,
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[ 24, 95, 28, 46, 8, 336, 38, 239, 8, 2133,
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2, 18, 10, 15622, 1421, 6, 61, 5, 387, 7,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([12, 20])}
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batch_y: {'target': tensor([1, 0])}
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([20, 20])}
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batch_y: {'target': tensor([0, 0])}
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batch_x: {'words': tensor([[ 4, 277, 685, 18, 7, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[15618, 3204, 5, 1675, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'seq_len': tensor([5, 4])}
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batch_y: {'target': tensor([1, 1])}
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在这里所有的`words`都被pad成了长度为40的list。
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在这里所有的 `words` 都被pad成了长度为40的list。
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使用DataSetIter自己编写训练过程
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@ -375,39 +375,38 @@ Dataset个性化padding
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-----start training-----
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Evaluate data in 0.2 seconds!
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Epoch 0 Avg Loss: 0.33 AccuracyMetric: acc=0.825688 48895ms
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Evaluate data in 2.68 seconds!
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Epoch 0 Avg Loss: 0.66 AccuracyMetric: acc=0.708716 29307ms
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Evaluate data in 0.19 seconds!
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Epoch 1 Avg Loss: 0.16 AccuracyMetric: acc=0.829128 102081ms
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Evaluate data in 0.38 seconds!
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Epoch 1 Avg Loss: 0.41 AccuracyMetric: acc=0.770642 52200ms
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Evaluate data in 0.18 seconds!
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Epoch 2 Avg Loss: 0.10 AccuracyMetric: acc=0.822248 152853ms
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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 0.8 seconds!
|
||||
Epoch 7 Avg Loss: 0.01 AccuracyMetric: acc=0.738532 175397ms
|
||||
|
||||
Evaluate data in 0.36 seconds!
|
||||
Epoch 7 Avg Loss: 0.04 AccuracyMetric: acc=0.802752 527211ms
|
||||
Epoch 8 Avg Loss: 0.01 AccuracyMetric: acc=0.733945 192384ms
|
||||
|
||||
Evaluate data in 0.15 seconds!
|
||||
Epoch 8 Avg Loss: 0.03 AccuracyMetric: acc=0.809633 661533ms
|
||||
Evaluate data in 0.84 seconds!
|
||||
Epoch 9 Avg Loss: 0.01 AccuracyMetric: acc=0.744266 214417ms
|
||||
|
||||
Evaluate data in 0.31 seconds!
|
||||
Epoch 9 Avg Loss: 0.03 AccuracyMetric: acc=0.797018 812232ms
|
||||
|
||||
Evaluate data in 0.25 seconds!
|
||||
[tester]
|
||||
AccuracyMetric: acc=0.917822
|
||||
|
||||
Evaluate data in 0.04 seconds!
|
||||
[tester]
|
||||
AccuracyMetric: acc=0.786667
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user