From 7756bf3b33e44816949786b927b0a634550a1a6b Mon Sep 17 00:00:00 2001
From: Steffy-zxf <48793257+Steffy-zxf@users.noreply.github.com>
Date: Tue, 21 Apr 2020 17:51:32 +0800
Subject: [PATCH] add define task (update network) docs (#532)

* add define task (update network) docs
---
 docs/tutorial/define_task_example.md | 84 ++++++++++++++++++++++++++++
 1 file changed, 84 insertions(+)
 create mode 100644 docs/tutorial/define_task_example.md

diff --git a/docs/tutorial/define_task_example.md b/docs/tutorial/define_task_example.md
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+# 如何修改Task中的模型网络
+
+在应用中，用户需要更换迁移网络结构以调整模型在数据集上的性能。根据[如何自定义Task](./how_to_define_task.md)，本教程展示如何修改Task中的默认网络。
+以序列标注任务为例，本教程展示如何修改默认网络结构。SequenceLabelTask提供了两种网络选择，一种是FC网络，一种是FC+CRF网络。
+
+此时如果想在这基础之上，添加LSTM网络，组成BiLSTM+CRF的一种序列标注任务常用网络结构。
+此时，需要定义一个Task，继承自SequenceLabelTask，并改写其中build_net()方法。
+
+
+下方代码示例写了一个BiLSTM+CRF的网络。代码如下：
+
+```python
+class SequenceLabelTask_BiLSTMCRF(SequenceLabelTask):
+    def _build_net(self):
+        """
+        自定义序列标注迁移网络结构BiLSTM+CRF
+        """
+        self.seq_len = fluid.layers.data(
+            name="seq_len", shape=[1], dtype='int64', lod_level=0)
+
+        if version_compare(paddle.__version__, "1.6"):
+            self.seq_len_used = fluid.layers.squeeze(self.seq_len, axes=[1])
+        else:
+            self.seq_len_used = self.seq_len
+
+        if self.add_crf:
+            # 迁移网络为BiLSTM+CRF
+
+            # 去padding
+            unpad_feature = fluid.layers.sequence_unpad(
+                self.feature, length=self.seq_len_used)
+
+            # bilstm层
+            hid_dim = 128
+            fc0 = fluid.layers.fc(input=unpad_feature, size=hid_dim * 4)
+            rfc0 = fluid.layers.fc(input=unpad_feature, size=hid_dim * 4)
+            lstm_h, c = fluid.layers.dynamic_lstm(
+                input=fc0, size=hid_dim * 4, is_reverse=False)
+            rlstm_h, c = fluid.layers.dynamic_lstm(
+                input=rfc0, size=hid_dim * 4, is_reverse=True)
+            # 拼接lstm
+            lstm_concat = fluid.layers.concat(input=[lstm_h, rlstm_h], axis=1)
+
+            self.emission = fluid.layers.fc(
+                size=self.num_classes,
+                input=lstm_concat,
+                param_attr=fluid.ParamAttr(
+                    initializer=fluid.initializer.Uniform(low=-0.1, high=0.1),
+                    regularizer=fluid.regularizer.L2DecayRegularizer(
+                        regularization_coeff=1e-4)))
+            size = self.emission.shape[1]
+            fluid.layers.create_parameter(
+                shape=[size + 2, size], dtype=self.emission.dtype, name='crfw')
+            # CRF层
+            self.ret_infers = fluid.layers.crf_decoding(
+                input=self.emission, param_attr=fluid.ParamAttr(name='crfw'))
+            ret_infers = fluid.layers.assign(self.ret_infers)
+            # 返回预测值，list类型
+            return [ret_infers]
+        else:
+            # 迁移网络为FC
+            self.logits = fluid.layers.fc(
+                input=self.feature,
+                size=self.num_classes,
+                num_flatten_dims=2,
+                param_attr=fluid.ParamAttr(
+                    name="cls_seq_label_out_w",
+                    initializer=fluid.initializer.TruncatedNormal(scale=0.02)),
+                bias_attr=fluid.ParamAttr(
+                    name="cls_seq_label_out_b",
+                    initializer=fluid.initializer.Constant(0.)))
+
+            self.ret_infers = fluid.layers.reshape(
+                x=fluid.layers.argmax(self.logits, axis=2), shape=[-1, 1])
+
+            logits = self.logits
+            logits = fluid.layers.flatten(logits, axis=2)
+            logits = fluid.layers.softmax(logits)
+            self.num_labels = logits.shape[1]
+            # 返回预测值，list类型
+            return [logits]
+```
+
+以上代码通过继承PaddleHub已经内置的Task，改写其中_build_net方法即可实现自定义迁移网络结构。
-- 
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