Merge pull request #143 from astonzhang/rnn

idx md

chapter_recurrent-neural-networks/gru-scratch.md

@@ -79,19 +79,9 @@ vocab_size = len(char_to_idx)
 print('vocab size:', vocab_size)
 ```
 
-```{.json .output n=1}
-[
- {
-  "name": "stdout",
-  "output_type": "stream",
-  "text": "vocab size: 1465\n"
- }
-]
-```
-
 我们使用onehot来将字符索引表示成向量。
 
-```{.python .input  n=2}
+```{.python .input}
 def get_inputs(data):
     return [nd.one_hot(X, vocab_size) for X in data.T]
 ```
@@ -100,7 +90,7 @@ def get_inputs(data):
 
 以下部分对模型参数进行初始化。参数`hidden_dim`定义了隐含状态的长度。
 
-```{.python .input  n=3}
+```{.python .input  n=5}
 import mxnet as mx
 
 # 尝试使用GPU
@@ -141,16 +131,6 @@ def get_params():
     return params
 ```
 
-```{.json .output n=3}
-[
- {
-  "name": "stdout",
-  "output_type": "stream",
-  "text": "Will use gpu(0)\n"
- }
-]
-```
-
 ## 定义模型
 
 我们将前面的模型公式翻译成代码。
@@ -192,16 +172,6 @@ utils.train_and_predict_rnn(rnn=gru_rnn, is_random_iter=False, epochs=200,
                             idx_to_char=idx_to_char, char_to_idx=char_to_idx)
 ```
 
-```{.json .output n=None}
-[
- {
-  "name": "stdout",
-  "output_type": "stream",
-  "text": "Epoch 20. Training perplexity 275.049316\n -  \u5206\u5f00 \u6211\u4e0d\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\n -  \u4e0d\u5206\u5f00 \u6211\u4e0d\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\n -  \u6218\u4e89\u4e2d\u90e8\u961f \u6211\u4e0d\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\u7684\u6211\n\nEpoch 40. Training perplexity 106.707302\n -  \u5206\u5f00 \u6211\u60f3\u4f60\u8fd9\u6837\u6211 \u4f60\u4e0d\u662f\u6211\u60f3\u4f60\u5f00\u4f60 \u6211\u4e0d\u8981\u6211\u60f3\u4f60\u5f00\u4f60 \u6211\u4e0d\u8981\u6211\u60f3\u4f60\u5f00\u4f60 \u6211\u4e0d\u8981\u6211\u60f3\u4f60\u5f00\u4f60 \u6211\u4e0d\u8981\u6211\u60f3\u4f60\u5f00\u4f60 \u6211\u4e0d\u8981\u6211\u60f3\u4f60\u5f00\u4f60 \u6211\u4e0d\u8981\u6211\u60f3\u4f60\u5f00\u4f60 \u6211\u4e0d\u8981\u6211\u60f3\u4f60\u5f00\u4f60 \u6211\u4e0d\u8981\u6211\u60f3\u4f60\u5f00\u4f60 \u6211\u4e0d\u8981\u6211\u60f3\u4f60\u5f00\u4f60 \u6211\u4e0d\u8981\n -  \u4e0d\u5206\u5f00 \u6211\u60f3\u4f60\u8fd9\u6837\u6211\u7684\u53ef\u7231\u4eba \u6211\u60f3\u4f60\u4f60\u60f3\u4f60\u5f00\u7740\u6211\u8981\u4f60 \u6211\u4e0d\u8981\u6211\u60f3\u4f60\u5f00\u7740\u6211\u4e0d\u8981\u6211\u60f3\u4f60 \u6211\u4e0d\u8981\u6211\u60f3\u4f60\u5f00\u7740\u6211\u4e0d\u8981\u6211\u60f3\u4f60 \u6211\u4e0d\u8981\u6211\u60f3\u4f60\u5f00\u7740\u6211\u4e0d\u8981\u6211\u60f3\u4f60 \u6211\u4e0d\u8981\u6211\u60f3\u4f60\u5f00\u7740\u6211\u4e0d\u8981\u6211\u60f3\u4f60 \u6211\u4e0d\u8981\u6211\u60f3\u4f60\u5f00\u7740\u6211\u4e0d\u8981\u6211\u60f3\u4f60 \u6211\u4e0d\n -  \u6218\u4e89\u4e2d\u90e8\u961f \u6211\u60f3\u4f60\u8fd9\u6837\u6211\u7684\u53ef\u7231\u5973\u4eba \u6211\u60f3\u4f60\u4f60\u60f3\u4f60\u5f00\u7740\u6211\u8981\u4f60 \u6211\u4e0d\u8981\u6211\u60f3\u4f60\u5f00\u7740\u6211\u4e0d\u8981\u6211\u60f3\u4f60 \u6211\u4e0d\u8981\u6211\u60f3\u4f60\u5f00\u7740\u6211\u4e0d\u8981\u6211\u60f3\u4f60 \u6211\u4e0d\u8981\u6211\u60f3\u4f60\u5f00\u7740\u6211\u4e0d\u8981\u6211\u60f3\u4f60 \u6211\u4e0d\u8981\u6211\u60f3\u4f60\u5f00\u7740\u6211\u4e0d\u8981\u6211\u60f3\u4f60 \u6211\u4e0d\u8981\u6211\u60f3\u4f60\u5f00\u7740\u6211\u4e0d\u8981\u6211\u60f3\u4f60 \u6211\n\n"
- }
-]
-```
-
 可以看到一开始学到简单的字符，然后简单的词，接着是复杂点的词，然后看上去似乎像个句子了。
 
 ## 结论

chapter_recurrent-neural-networks/index.md

 # 循环神经网络
 
-```{.python .input .eval_rst}
+
+```eval_rst
 
 .. toctree::
    :maxdepth: 2