fix 1.7 en doc, test=develop (#1897)

doc/fluid/advanced_guide/addon_development/design_idea/fluid_design_idea_en.md

@@ -59,40 +59,41 @@ The blocks contain:
 The concept of block is the same with that in generic programs. For example, there are three blocks in the following C++ code:
 
 ``` cpp
-int main(){ //block 0
-    int i = 0;
-    if (i<10){ //block 1
-        for (int j=0;j<10;j++){ //block 2
-        }
-    }
-    return 0;
+#include <iostream>
+
+int main() {
+	int x = 5; // block 0
+	int y = 4; // block 0
+	int out;   // block 0
+	
+	if (x < y) { // block 0
+	    out = 1; // block 1
+	} else {
+	    out = 0; // block 2
+	}
+	
+	std::cout << out << std::endl;
+	return 0;
 }
 ```
 
 Similarly, the following Program contains 3 blocks:
 
 ```python
-import paddle.fluid as fluid # block 0
-
-limit = fluid.layers.fill_constant_batch_size_like(
-    Input=label, dtype='int64', shape=[1], value=5.0)
-cond = fluid.layers.less_than(x=label, y=limit)
-
-ie = fluid.layers.IfElse(cond)
-with ie.true_block(): # block 1
-    true_image = ie.input(image)
-    hidden = fluid.layers.fc(input=true_image, size=100, act='tanh')
-    prob = fluid.layers.fc(input=hidden, size=10, act='softmax')
-    ie.output(prob)
-
-with ie.false_block(): # block 2
-    false_image = ie.input(image)
-    hidden = fluid.layers.fc(
-        input=false_image, size=200, act='tanh')
-    prob = fluid.layers.fc(input=hidden, size=10, act='softmax')
-    ie.output(prob)
-
-prob = ie()
+import paddle.fluid as fluid
+
+x = fluid.data(name='x', shape=[1], dtype='int64') # block 0
+y = fluid.data(name='y', shape=[1], dtype='int64') # block 0
+
+def true_block():
+    return fluid.layers.fill_constant(dtype='int64', value=1, shape=[1]) # block 1
+    
+def false_block():
+    return fluid.layers.fill_constant(dtype='int64', value=0, shape=[1]) # block 2
+
+condition = fluid.layers.less_than(x, y) # block 0
+
+out = fluid.layers.cond(condition, true_block, false_block) # block 0
 ```
 ### BlockDesc and ProgramDesc
 
@@ -229,8 +230,8 @@ import numpy
 train_data=numpy.array([[1.0],[2.0],[3.0],[4.0]]).astype('float32')
 y_true = numpy.array([[2.0],[4.0],[6.0],[8.0]]).astype('float32')
 # Define the network
-x = fluid.layers.data(name="x",shape=[1],dtype='float32')
-y = fluid.layers.data(name="y",shape=[1],dtype='float32')
+x = fluid.data(name="x",shape=[None, 1],dtype='float32')
+y = fluid.data(name="y",shape=[None, 1],dtype='float32')
 y_predict = fluid.layers.fc(input=x,size=1,act=None)
 #definition loss function
 cost = fluid.layers.square_error_cost(input=y_predict,label=y)
@@ -299,7 +300,7 @@ As you can see from the output, the entire definition process is transformed int
 
 BlockDesc contains defined vars and a series of ops. Take input x as an example. In python code, x is 1D data of data type "float 32":
 ```python
-x = fluid.layers.data(name="x",shape=[1],dtype='float32')
+x = fluid.data(name="x",shape=[None, 1],dtype='float32')
 ```
 In BlockDesc, the variable x is described as:
 ```
@@ -348,7 +349,7 @@ Since there are multiple columns of incoming and outgoing data, fluid defines tr
 ```python
 # Start training
  outs = exe.run(
-      feed={'x':train_data,'y':y_true},
+     feed={'x':train_data,'y':y_true},
      fetch_list=[y_predict.name,avg_cost.name])
 ```
 The above code defines that train_data is to be passed into the x variable, y_true is to be passed into the y variable, and output the predicted value of y and the last round value of cost.

doc/fluid/advanced_guide/data_preparing/reader.md

@@ -193,14 +193,3 @@ def image_reader_creator(image_path, label_path, n):
 reader = image_reader_creator("/path/to/image_file", "/path/to/label_file", 1024)
 paddle.train(paddle.batch(reader, 128), {"image":0, "label":1}, ...)
 ```
-
-### How is `paddle.train` implemented
-
-An example implementation of paddle.train is:
-
-```python
-def train(batch_reader, mapping, batch_size, total_pass):
-    for pass_idx in range(total_pass):
-        for mini_batch in batch_reader(): # this loop will never end in online learning.
-            do_forward_backward(mini_batch, mapping)
-```

doc/fluid/beginners_guide/coding_practice/single_node.rst

@@ -60,7 +60,7 @@
 
 执行单卡训练可以使用 :code:`fluid.Executor()` 中的 :code:`run()` 方法，运行训练\
 :code:`fluid.Program` 即可。在运行的时候，用户可以通过 :code:`run(feed=...)`\
-参数传入数据；用户可以通过 :code:`run(fetch=...)` 获取持久的数据。例如:\
+参数传入数据；用户可以通过 :code:`run(fetch=...)` 获取输出数据。例如:\
 
 .. code-block:: python
 

doc/fluid/beginners_guide/coding_practice/single_node_en.rst

@@ -51,7 +51,7 @@ Single-card Training
 #####################
 
 Single-card training can be performed through calling :code:`run()` of :code:`fluid.Executor()` to run training :code:`fluid.Program` .
-In the runtime, feed data with :code:`run(feed=...)` and get persistable data with :code:`run(fetch=...)` . For example:
+In the runtime, users can feed data with :code:`run(feed=...)` and get output data with :code:`run(fetch=...)` . For example:
 
 .. code-block:: python
 
@@ -81,17 +81,12 @@ In the runtime, feed data with :code:`run(feed=...)` and get persistable data wi
     loss_data, = exe.run(train_program,
                          feed={"X": x},
                          fetch_list=[loss.name])
-    # Or 
-    # compiled_prog = fluid.CompiledProgram(train_program)
-    # loss_data, = exe.run(compiled_prog,
-    #              feed={"X": x},
-    #              fetch_list=[loss.name])
-
-Notes:
+    # Or use CompiledProgram:
+    compiled_prog = fluid.CompiledProgram(train_program)
+    loss_data, = exe.run(compiled_prog,
+                 feed={"X": x},
+                 fetch_list=[loss.name])
 
-1. About data type supported by feed, please refer to the article :ref:`user_guide_feed_data_to_executor_en`.
-2. The return value of :code:`Executor.run` is the variable value of :code:`fetch_list=[...]` .The fetched Variable must be persistable. :code:`fetch_list` can be fed with either Variable list or name list of variables . :code:`Executor.run` returns Fetch result list.
-3. If the fetched data contain sequence information,  you can set :code:`exe.run(return_numpy=False, ...)` to directly get :code:`fluid.LoDTensor` . You can directly access the information in :code:`fluid.LoDTensor` .
 
 Multi-card Training
 #######################

doc/fluid/beginners_guide/coding_practice/test_while_training.rst

@@ -79,7 +79,7 @@ PaddlePaddle Fluid中使用 :code:`fluid.unique_name` 包来随机初始化用
 
    test_program = fluid.Program()
    with fluid.unique_name.guard():
-       with fluid.program_gurad(test_program, fluid.Program()):
+       with fluid.program_guard(test_program, fluid.Program()):
            test_loss = network(is_test=True)
 
    # fluid.default_main_program() is the train program

doc/fluid/beginners_guide/coding_practice/test_while_training_en.rst

@@ -70,7 +70,7 @@ For example:
 
    test_program = fluid.Program()
    with fluid.unique_name.guard():
-       with fluid.program_gurad(test_program, fluid.Program()):
+       with fluid.program_guard(test_program, fluid.Program()):
            test_loss = network(is_test=True)
 
    # fluid.default_main_program() is the train program