fix URLError when removing duplicated tutorials

doc/howto/deep_model/rnn/rnn_config_cn.rst

@@ -21,7 +21,7 @@ wmt14数据的提供文件在 `python/paddle/v2/dataset/wmt14.py <https://github
 
 循环神经网络在每个时间步骤顺序地处理序列。下面列出了 LSTM 的架构的示例。
 
-.. image:: ../../../tutorials/sentiment_analysis/bi_lstm.jpg
+.. image:: src/bi_lstm.jpg
       :align: center
 
 一般来说，循环网络从 :math:`t=1` 到 :math:`t=T` 或者反向地从 :math:`t=T` 到 :math:`t=1` 执行以下操作。
@@ -96,7 +96,7 @@ Sequence to Sequence Model with Attention
 我们将使用 sequence to sequence model with attention
 作为例子演示如何配置复杂的循环神经网络模型。该模型的说明如下图所示。
 
-.. image:: ../../../tutorials/text_generation/encoder-decoder-attention-model.png
+.. image:: src/encoder-decoder-attention-model.png
       :align: center
 
 在这个模型中，源序列 :math:`S = \{s_1, \dots, s_T\}` 

doc/howto/deep_model/rnn/rnn_config_en.rst

@@ -19,7 +19,7 @@ Simple Gated Recurrent Neural Network
 
 Recurrent neural network process a sequence at each time step sequentially. An example of the architecture of LSTM is listed below.
 
-.. image:: ../../../tutorials/sentiment_analysis/src/bi_lstm.jpg
+.. image:: src/bi_lstm.jpg
      :align: center
 
 Generally speaking, a recurrent network perform the following operations from :math:`t=1` to :math:`t=T`, or reversely from :math:`t=T` to :math:`t=1`.
@@ -78,7 +78,7 @@ Sequence to Sequence Model with Attention
 -----------------------------------------
 We will use the sequence to sequence model with attention as an example to demonstrate how you can configure complex recurrent neural network models. An illustration of the sequence to sequence model with attention is shown in the following figure.
 
-.. image:: ../../../tutorials/text_generation/encoder-decoder-attention-model.png
+.. image:: src/encoder-decoder-attention-model.png
       :align: center
 
 In this model, the source sequence :math:`S = \{s_1, \dots, s_T\}` is encoded with a bidirectional gated recurrent neural networks. The hidden states of the bidirectional gated recurrent neural network :math:`H_S = \{H_1, \dots, H_T\}` is called *encoder vector* The decoder is a gated recurrent neural network. When decoding each token :math:`y_t`, the gated recurrent neural network generates a set of weights :math:`W_S^t = \{W_1^t, \dots, W_T^t\}`, which are used to compute a weighted sum of the encoder vector. The weighted sum of the encoder vector is utilized to condition the generation of the token :math:`y_t`.