follow comments form Helin

recognize_digits/README.en.md

@@ -136,7 +136,7 @@ import paddle.v2 as paddle
 
 We want to use this program to demonstrate multiple kinds of models.  Let define each of them as a Python function:
 
-- softmax regression: the network has a fully-connection layer and a softmax output layer:
+- softmax regression: the network has a fully-connection layer with softmax activation:
 
 ```python
 def softmax_regression(img):
@@ -146,7 +146,7 @@ def softmax_regression(img):
     return predict
 ```
 
-- multi-layer perceptron: this network has two hidden fully-connected layers, both using ReLU activation.  The output layer is softmax:
+- multi-layer perceptron: this network has two hidden fully-connected layers, one with LeRU and the other with softmax activation:
 
 ```python
 def multilayer_perceptron(img):
@@ -226,7 +226,7 @@ Now, it is time to specify training parameters. The number 0.9 in the following
                                  update_equation=optimizer)
 ```
 
-Then we specify the training data `paddle.dataset.movielens.train()` and testing data `paddle.dataset.movielens.test()`.  These two functions are *reader creators*, once called, returns a *reader*.  A reader is a Python function, which, once called, returns a Python yield generator, which yields instances of data.  
+Then we specify the training data `paddle.dataset.movielens.train()` and testing data `paddle.dataset.movielens.test()`.  These two functions are *reader creators*, once called, returns a *reader*.  A reader is a Python function, which, once called, returns a Python generator, which yields instances of data.  
 
 Here `shuffle` is a reader decorator, which takes a reader A as its parameter, and returns a new reader B, where B calls A to read in `buffer_size` data instances everytime into a buffer, then shuffles and yield instances in the buffer.  If you want very shuffled data, try use a larger buffer size. 
 
@@ -268,7 +268,7 @@ During training, `trainer.train` invokes `event_handler` for certain events. Thi
     # Test with Pass 0, Cost 0.326659, {'classification_error_evaluator': 0.09470000118017197}
 ```
 
-After the training, we can choose the best model:
+After the training, we can check the model's prediction accuracy.
 
 ```
     # find the best pass

recognize_digits/README.md

@@ -271,34 +271,7 @@ def main():
     # Test with Pass 0, Cost 0.326659, {'classification_error_evaluator': 0.09470000118017197}
 ```
 
-训练之后，我们可以选出最佳模型，并评估其效果。
-
-```
-    # find the best pass
-    best = sorted(lists, key=lambda list: float(list[1]))[0]
-    print 'Best pass is %s, testing Avgcost is %s' % (best[0], best[1])
-    print 'The classification accuracy is %.2f%%' % (100 - float(best[2]) * 100)
-```
-- softmax回归模型：分类效果最好的时候是pass-34，分类准确率为92.34%。
-
-```
-    # Best pass is 34, testing Avgcost is 0.275004139346
-    # The classification accuracy is 92.34%
-```
-
-- 多层感知器：最终训练的准确率为97.66%，相比于softmax回归模型有了显著的提升。原因是softmax回归模型较为简单，无法拟合更为复杂的数据，而加入了隐藏层之后的多层感知器则具有更强的拟合能力。
-
-```
-    # Best pass is 85, testing Avgcost is 0.0784368447196
-    # The classification accuracy is 97.66%
-```
-
-- 卷积神经网络：最好分类准确率达到惊人的99.20%。说明对于图像问题而言，卷积神经网络能够比一般的全连接网络达到更好的识别效果，而这与卷积层具有局部连接和共享权重的特性是分不开的。同时，从训练日志中可以看到，卷积神经网络在很早的时候就能达到很好的效果，说明其收敛速度非常快。
-
-```
-    # Best pass is 76, testing Avgcost is 0.0244684
-    # The classification accuracy is 99.20%
-```
+训练之后，检查模型的预测准确度。用 MNIST 训练的时候，一般 softmax回归模型的分类准确率为约为 92.34%，多层感知器为97.66%，卷积神经网络可以达到 99.20%。
 
 ## 总结