From 43d7d03c3045b04239998f2e8db058bee9ce01d5 Mon Sep 17 00:00:00 2001
From: jiangzhonglian <jiang-s@163.com>
Date: Mon, 29 Jul 2019 17:17:41 +0800
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diff --git a/docs/chatbot.md b/docs/chatbot.md
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+++ b/docs/chatbot.md
@@ -102,3 +102,58 @@ u0 +++$+++ u2 +++$+++ m0 +++$+++ ['L207', 'L208']
 4. 打印输出 pairs
 5. voc.trim, 过滤掉词频数据（有利于让训练更快收敛的策略是去除词汇表中很少使用的单词。减少特征空间也会降低模型学习目标函数的难度）
 
+```
+['there .', 'where ?']
+['you have my word . as a gentleman', 'you re sweet .']
+['hi .', 'looks like things worked out tonight huh ?']
+['you know chastity ?', 'i believe we share an art instructor']
+['have fun tonight ?', 'tons']
+['well no . . .', 'then that s all you had to say .']
+['then that s all you had to say .', 'but']
+['but', 'you always been this selfish ?']
+['do you listen to this crap ?', 'what crap ?']
+['what good stuff ?', 'the real you .']
+```
+
+### 为模型格式化数据
+
+1. 加速训练，利用GPU并行计算能力，则需要使用小批量 `mini-batches`
+2. 为了保证数据长短一致，设置 `(max_length，batch_size)`, 短于 max_length 的句子在 EOS_token 之后进行零填充 `(zero padded)`
+3. 矩阵转置（以便跨第一维的索引返回批处理中所有句子的时间步长）
+
+![](https://pytorch.apachecn.org/docs/1.0/img/b2f1969c698070d055c23fc81ab07b1b.jpg)
+
+## 定义模型
+
+Seq2seq模型的目标是将可变长度序列作为输入，并使用固定大小的模型将可变长度序列作为输出返回。
+
+* Seq2Seq模型: 
+    1. 编码器，其将可变长度输入序列编码为固定长度上下文向量。 
+    2. 解码器，它接收输入文字和上下文矢量，并返回序列中下一句文字的概率和在下一次迭代中使用的隐藏状态。
+
+![](https://pytorch.apachecn.org/docs/1.0/img/32a87cf8d0353ceb0037776f833b92a7.jpg)
+
+
+* 编码器:
+
+如果将填充的一批序列传递给RNN模块，我们必须分别使用torch.nn.utils.rnn.pack_padded_sequence和torch.nn.utils.rnn.pad_packed_sequence在RNN传递时分别进行填充和反填充。
+
+```py
+def forward(self, input_seq, input_lengths, hidden=None):
+    # Convert word indexes to embeddings
+    embedded = self.embedding(input_seq)
+    # Pack padded batch of sequences for RNN module
+    packed = torch.nn.utils.rnn.pack_padded_sequence(embedded, input_lengths)
+    # Forward pass through GRU
+    outputs, hidden = self.gru(packed, hidden)
+    # Unpack padding
+    outputs, _ = torch.nn.utils.rnn.pad_packed_sequence(outputs)
+    # Sum bidirectional GRU outputs
+    outputs = outputs[:, :, :self.hidden_size] + outputs[:, : ,self.hidden_size:]
+    # Return output and final hidden state
+    return outputs, hidden
+```
+
+![](https://pytorch.apachecn.org/docs/1.0/img/c653271eb5fb762482bceb5e2464e680.jpg)
+
+* 解码器:
\ No newline at end of file
-- 
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