diff --git a/02.recognize_digits/README.md b/02.recognize_digits/README.md
index 869059a43c378fe32f014e911c8b3f723ad808f5..66e526bded114f84adfa3ad8d1beeea982ac333e 100644
--- a/02.recognize_digits/README.md
+++ b/02.recognize_digits/README.md
@@ -340,7 +340,10 @@ def event_handler_plot(event):
         lists.append((event.epoch, avg_cost, acc))
 ```
 
+#### Start training
+
 Now that we setup the event_handler and the reader, we can start training the model. `feed_order` is used to map the data dict to the train_program
+
 ```python
 # Train the model now
 trainer.train(
@@ -381,6 +384,24 @@ Usually, with MNIST data, the softmax regression model achieves an accuracy arou
 
 After training, users can use the trained model to classify images. The following code shows how to inference MNIST images through `fluid.Inferencer`.
 
+### Create Inferencer
+
+The `Inferencer` takes an `infer_func` and `param_path` to setup the network and the trained parameters.
+We can simply plug-in the classifier defined earlier here.
+
+```python
+inferencer = fluid.Inferencer(
+    # infer_func=softmax_regression, # uncomment for softmax regression
+    # infer_func=multilayer_perceptron, # uncomment for MLP
+    infer_func=convolutional_neural_network,  # uncomment for LeNet5
+    param_path=params_dirname,
+    place=place)
+```
+
+#### Generate input data for inferring
+
+`infer_3.png` is an example image of the digit `3`. Turn it into an numpy array to match the data feeder format.
+
 ```python
 # Prepare the test image
 import os
@@ -395,15 +416,13 @@ def load_image(file):
 
 cur_dir = cur_dir = os.getcwd()
 img = load_image(cur_dir + '/image/infer_3.png')
+```
 
+### Inference
 
-inferencer = fluid.Inferencer(
-    # infer_func=softmax_regression, # uncomment for softmax regression
-    # infer_func=multilayer_perceptron, # uncomment for MLP
-    infer_func=convolutional_neural_network,  # uncomment for LeNet5
-    param_path=params_dirname,
-    place=place)
+Now we are ready to do inference.
 
+```python
 results = inferencer.infer({'img': img})
 lab = np.argsort(results)  # probs and lab are the results of one batch data
 print "Label of image/infer_3.png is: %d" % lab[0][0][-1]
diff --git a/02.recognize_digits/index.html b/02.recognize_digits/index.html
index 40d21186a6eeefe731576c8303e7b0059c0aa8d5..c83d5bcea3676d731af7d73fc409fe804a6b993e 100644
--- a/02.recognize_digits/index.html
+++ b/02.recognize_digits/index.html
@@ -382,7 +382,10 @@ def event_handler_plot(event):
         lists.append((event.epoch, avg_cost, acc))
 ```
 
+#### Start training
+
 Now that we setup the event_handler and the reader, we can start training the model. `feed_order` is used to map the data dict to the train_program
+
 ```python
 # Train the model now
 trainer.train(
@@ -423,6 +426,24 @@ Usually, with MNIST data, the softmax regression model achieves an accuracy arou
 
 After training, users can use the trained model to classify images. The following code shows how to inference MNIST images through `fluid.Inferencer`.
 
+### Create Inferencer
+
+The `Inferencer` takes an `infer_func` and `param_path` to setup the network and the trained parameters.
+We can simply plug-in the classifier defined earlier here.
+
+```python
+inferencer = fluid.Inferencer(
+    # infer_func=softmax_regression, # uncomment for softmax regression
+    # infer_func=multilayer_perceptron, # uncomment for MLP
+    infer_func=convolutional_neural_network,  # uncomment for LeNet5
+    param_path=params_dirname,
+    place=place)
+```
+
+#### Generate input data for inferring
+
+`infer_3.png` is an example image of the digit `3`. Turn it into an numpy array to match the data feeder format.
+
 ```python
 # Prepare the test image
 import os
@@ -437,15 +458,13 @@ def load_image(file):
 
 cur_dir = cur_dir = os.getcwd()
 img = load_image(cur_dir + '/image/infer_3.png')
+```
 
+### Inference
 
-inferencer = fluid.Inferencer(
-    # infer_func=softmax_regression, # uncomment for softmax regression
-    # infer_func=multilayer_perceptron, # uncomment for MLP
-    infer_func=convolutional_neural_network,  # uncomment for LeNet5
-    param_path=params_dirname,
-    place=place)
+Now we are ready to do inference.
 
+```python
 results = inferencer.infer({'img': img})
 lab = np.argsort(results)  # probs and lab are the results of one batch data
 print "Label of image/infer_3.png is: %d" % lab[0][0][-1]