book info

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diff --git a/README.assets/GRU_stru_bp_ori.JPG b/README.assets/GRU_stru_bp_ori.JPG
diff --git a/README.assets/LSTM_02_stru.jpg b/README.assets/LSTM_02_stru.jpg
diff --git a/README.assets/book.jpg b/README.assets/book.jpg
diff --git a/README.assets/bp_layers.jpg b/README.assets/bp_layers.jpg
diff --git a/README.md b/README.md
@@ -1,5 +1,21 @@
 # 《实战深度学习算法：基于Python和Numpy实现》
-### 配套的示例源码与数据下载说明。
+<img src="./img/book.jpg" alt="实战深度学习算法" style="zoom:50%;" />
+
+
+
+#### 促销比价
+
+##### 实体书
+
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+
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+
+#### 电子书
+
+[亚马逊电子书]([https://www.amazon.cn/dp/B07ZJ5LD1C/ref=sr_1_1?__mk_zh_CN=%E4%BA%9A%E9%A9%AC%E9%80%8A%E7%BD%91%E7%AB%99&keywords=%E5%AE%9E%E6%88%98%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%AE%97%E6%B3%95&qid=1586619079&sr=8-1](https://www.amazon.cn/dp/B07ZJ5LD1C/ref=sr_1_1?__mk_zh_CN=亚马逊网站&keywords=实战深度学习算法&qid=1586619079&sr=8-1)"kindle电子书")     [知乎电子书](https://www.zhihu.com/pub/book/119647254 "知乎电子书")
+
+### 配套示例源码与数据下载说明。
 
 不借助**深度学习框架**，零起点实现神经网络的重要算法。
 
@@ -35,6 +51,20 @@
 | 8    | 指数分析            | 长短时记忆网络LSTM前向计算、反向传播推导、梯度裁剪           | 选择1，原始数据，需要自行预处理：./data/沪深300历史数据.csv ；<br>选择2，预处理后数据，可直接用于训练和验证：./data/hs300_data_seq_nodate.csv |
 | 9    | 情感分析            | 双向门控循环单元BiGRU前向计算、反向传播推导、Dropout正则化   | 选择1，原始数据，需自行预处理：[IMDB原始数据](http://ai.stanford.edu/~amaas/data/sentiment)、[nltk停用词表](http://www.nltk.org)、[50维40万英文词嵌入矩阵](https://nlp.stanford.edu/projects/glove)。<br>选择2，预处理后的词向量数据（一半数据量），可直接用于训练和验证。https://pan.baidu.com/s/1VZaUCceA6oEmkDaUB9oFJw 提取码 9xdu |
 
+Batch Norm结构
+
+<img src="./img/bp_layers.jpg" style="zoom:50%;" />
+
+LSTM结构
+
+<img src="./img/LSTM_02_stru.jpg" style="zoom:50%;" />
+
+
+
+GRU结构
+
+<img src="./img/GRU_stru_bp_ori.JPG" style="zoom:50%;" />
+
 ### 勘误
 
 ./勘误_实战深度学习算法.pdf	

diff --git a/img/GRU_stru_bp_ori.JPG b/img/GRU_stru_bp_ori.JPG
diff --git a/img/LSTM_02_stru.jpg b/img/LSTM_02_stru.jpg
diff --git a/img/book.jpg b/img/book.jpg
diff --git a/img/bp_layers.jpg b/img/bp_layers.jpg
diff --git a/src/chapter6_conv_bn_mnist.py b/src/chapter6_conv_bn_mnist.py
@@ -36,7 +36,7 @@
 trace_file = trace_file_path + exec_name + ".data"
 
 # 训练数据
-path_minst_unpack = 'F:\cwork\Project\TF_py3.6\MNIST_data_unpack'
+path_minst_unpack = 'D:\data\MNIST\MNIST_data_unpack'
 
 # General params
 class Params: