Change to use optimizer func in markdown

02.recognize_digits/README.md

@@ -245,6 +245,15 @@ def train_program():
     return [avg_cost, acc]
 ```
 
+#### Optimizer Function Configuration
+
+In the following `Adam` optimizer, `learning_rate` means the speed at which the network training converges.
+
+```python
+def optimizer_program():
+    return fluid.optimizer.Adam(learning_rate=0.001)
+```
+
 ### Data Feeders Configuration
 
 Then we specify the training data `paddle.dataset.mnist.train()` and testing data `paddle.dataset.mnist.test()`. These two methods are *reader creators*. Once called, a reader creator returns a *reader*.  A reader is a Python method, which, once called, returns a Python generator, which yields instances of data.
@@ -266,15 +275,13 @@ test_reader = paddle.batch(
 ### Trainer Configuration
 
 Now, we need to setup the trainer. The trainer need to take in `train_program`, `place`, and `optimizer`.
-In the following `Adam` optimizer, `learning_rate` means the speed at which the network training converges.
 
 ```python
 use_cuda = False # set to True if training with GPU
 place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-optimizer = fluid.optimizer.Adam(learning_rate=0.001)
 
 trainer = fluid.Trainer(
-    train_func=train_program, place=place, optimizer=optimizer)
+    train_func=train_program, place=place, optimizer_func=optimizer_program)
  ```
 
 #### Event Handler

02.recognize_digits/index.html

@@ -287,6 +287,15 @@ def train_program():
     return [avg_cost, acc]
 ```
 
+#### Optimizer Function Configuration
+
+In the following `Adam` optimizer, `learning_rate` means the speed at which the network training converges.
+
+```python
+def optimizer_program():
+    return fluid.optimizer.Adam(learning_rate=0.001)
+```
+
 ### Data Feeders Configuration
 
 Then we specify the training data `paddle.dataset.mnist.train()` and testing data `paddle.dataset.mnist.test()`. These two methods are *reader creators*. Once called, a reader creator returns a *reader*.  A reader is a Python method, which, once called, returns a Python generator, which yields instances of data.
@@ -308,15 +317,13 @@ test_reader = paddle.batch(
 ### Trainer Configuration
 
 Now, we need to setup the trainer. The trainer need to take in `train_program`, `place`, and `optimizer`.
-In the following `Adam` optimizer, `learning_rate` means the speed at which the network training converges.
 
 ```python
 use_cuda = False # set to True if training with GPU
 place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-optimizer = fluid.optimizer.Adam(learning_rate=0.001)
 
 trainer = fluid.Trainer(
-    train_func=train_program, place=place, optimizer=optimizer)
+    train_func=train_program, place=place, optimizer_func=optimizer_program)
  ```
 
 #### Event Handler