use terminology *data reader creator* instead of *data reader*, and remove...

use terminology *data reader creator* instead of *data reader*, and remove lambda usage from design doc

doc/design/reader/README.md

-# Python Data Reader Design Doc
+# Python Data Reading Design Doc
 
-Paddle reads data from data reader during training. It will be passed into `paddle.train` as a parameter.
+Paddle reads data from *data reader* during training. *data reader creator* (or *reader creator*) creates a *data reader* when invoked. *reader creator* will be passed into `paddle.train` as a parameter.
 
-## Data Reader Interface
+## Data Reader Creator Interface
 
-Data reader is a function with no parameter that creates a iterable (anything can be used in `for x in iterable`):
+Data reader creator is a function with no parameter that creates a iterable (anything can be used in `for x in iterable`):
 
 ```
-iterable = data_reader()
+iterable = data_reader_creator()
 ```
 
 Element produced for the iterable should be a **single** entry of data, **not** a mini batch. That entry of data could be a single item, or a tuple of items. Item should be of [supported type](http://www.paddlepaddle.org/doc/ui/data_provider/pydataprovider2.html?highlight=dense_vector#input-types) (e.g., numpy 1d array of float32, int, list of int)
 
-An example implementation for single item data reader:
+An example implementation for single item data reader creator:
 
 ```python
-def data_reader_fake_image():
+def data_reader_creator_fake_image():
 	while True:
 		yield numpy.random.uniform(-1, 1, size=20*20)
 ```
 
-An example implementation for multiple item data reader:
+An example implementation for multiple item data reader creator:
 ```python
-def data_reader_fake_image_and_label():
+def data_reader_creator_fake_image_and_label():
 	while True:
 		yield numpy.random.uniform(-1, 1, size=20*20), False
 ```
 
 ## Usage
 
-data reader, mapping from item(s) read to data layer, batch size and number of total pass will be passed into `paddle.train`:
+data reader creator, mapping from item(s) read to data layer, batch size and number of total pass will be passed into `paddle.train`:
 
 ```python
 # two data layer is created:
@@ -41,11 +41,11 @@ label_layer = paddle.layer.data("label", ...)
 paddle.train(paddle.dataset.mnist, {"image":0, "label":1}, 128, 10, ...)
 ```
 
-## Data Reader Decorators
+## Data Reader Creator Decorator
 
-Data reader decorators takes a single or multiple data reader, returns a new data reader. It is similar to a [python decorator](https://wiki.python.org/moin/PythonDecorators), but it does not use `@` syntax.
+*Data reader creator decorator* (or *reader creator decorator*) takes a single or multiple data reader creator, returns a new data reader creator. It is similar to a [python decorator](https://wiki.python.org/moin/PythonDecorators), but it does not use `@` syntax.
 
-Since we have a strict interface for data readers (no parameter, return a single data item). Data reader can be used flexiable via data reader decorators. Following are a few examples:
+Since we have a strict interface for data readers creators (no parameter, return a single data item). Data reader creators can be used flexiable via data reader creator decorators. Following are a few examples:
 
 ### Prefetch Data
 
@@ -54,74 +54,77 @@ Since reading data may take time and training can not proceed without data. It i
 Use `paddle.reader.buffered` to prefetch data:
 
 ```python
-buffered_reader = paddle.reader.buffered(paddle.dataset.mnist, 100)
+buffered_reader_creator = paddle.reader.buffered(paddle.dataset.mnist, 100)
 ```
 
-`buffered_reader` will try to buffer (prefetch) `100` data entries.
+`buffered_reader_creator` will try to buffer (prefetch) `100` data entries.
 
-### Compose Multiple Data Readers
+### Compose Multiple Data Reader Creators
 
 For example, we want to use a source of real images (reusing mnist dataset), and a source of fake images as input for [Generative Adversarial Networks](https://arxiv.org/abs/1406.2661).
 
 We can do:
 
 ```python
-def data_reader_fake_image():
+def data_reader_creator_fake_image():
 	while True:
 		yield numpy.random.uniform(-1, 1, size=20*20)
 
-def data_reader_bool(t):
-	while True:
-		yield t
+def data_reader_creator_bool(t):
+	def creator:
+		while True:
+			yield t
+	return creator
 
-true_reader = lambda : data_reader_bool(True)
-false_reader = lambda : data_reader_bool(False)
+true_reader_creator = data_reader_creator_bool(True)
+false_reade_creatorr = data_reader_creator_bool(False)
 
-reader = paddle.reader.combine(paddle.dataset.mnist, data_reader_fake_image, true_reader, false_reader)
+reader_creator = paddle.reader.compose(paddle.dataset.mnist, data_reader_creator_fake_image, true_reader_creator, false_reader_creator)
 # Skipped 1 because paddle.dataset.mnist produces two items per data entry.
 # And we don't care second item at this time.
-paddle.train(reader, {"true_image":0, "fake_image": 2, "true_label": 3, "false_label": 4}, ...)
+paddle.train(reader_creator, {"true_image":0, "fake_image": 2, "true_label": 3, "false_label": 4}, ...)
 ```
 
 ### Shuffle
 
-Given shuffle buffer size `n`, `paddle.reader.shuffle` will return a data reader that buffers `n` data entries and shuffle them before a data entry is read.
+Given shuffle buffer size `n`, `paddle.reader.shuffle` will return a data reader creator that buffers `n` data entries and shuffle them before a data entry is read.
 
 Example:
 ```python
-reader = paddle.reader.shuffle(paddle.dataset.mnist, 512)
+reader_creator = paddle.reader.shuffle(paddle.dataset.mnist, 512)
 ```
 
 ## Q & A
 
 ### Why return only a single entry, but not a mini batch?
 
-If a mini batch is returned, data reader need to take care of batch size. But batch size is a concept for training, it makes more sense for user to specify batch size as a parameter for `train`.
+If a mini batch is returned, data reader creator need to take care of batch size. But batch size is a concept for training, it makes more sense for user to specify batch size as a parameter for `train`.
 
-Practically, always return a single entry make reusing existing data reader much easier (e.g., if existing data reader return not a single entry but 3 entries, training code will be more complex because it need to handle cases like batch size 2).
+Practically, always return a single entry make reusing existing data reader creators much easier (e.g., if existing reader creator return not a single entry but 3 entries, training code will be more complex because it need to handle cases like batch size 2).
 
 ### Why use a dictionary but not a list to provide mapping?
 
 We decided to use dictionary (`{"image":0, "label":1}`) instead of list (`["image", "label"]`) is because that user can easily resue item (e.g., using `{"image_a":0, "image_b":0, "label":1}`) or skip item (e.g., using `{"image_a":0, "label":2}`).
 
-### How to create custom data reader
+### How to create custom data reader creator
 
 ```python
-def image_reader(image_path, label_path, n):
-	f = open(image_path)
-	l = open(label_path)
-	images = numpy.fromfile(
-		f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28)).astype('float32')
-	images = images / 255.0 * 2.0 - 1.0
-	labels = numpy.fromfile(l, 'ubyte', count=n).astype("int")
-	for i in xrange(n):
-		yield images[i, :], labels[i] # a single entry of data is created each time
-	f.close()
-	l.close()
-
-# use python lambda to change image_reader into a function with no parameters.
-reader = lambda : image_reader("/path/to/image_file", "/path/to/label_file", 1024)
-paddle.train(reader, {"image":0, "label":1}, ...)
+def image_reader_creator(image_path, label_path, n):
+	def reader_creator():
+		f = open(image_path)
+		l = open(label_path)
+		images = numpy.fromfile(
+			f, 'ubyte', count=n * 28 * 28).reshape((n, 28 * 28)).astype('float32')
+		images = images / 255.0 * 2.0 - 1.0
+		labels = numpy.fromfile(l, 'ubyte', count=n).astype("int")
+		for i in xrange(n):
+			yield images[i, :], labels[i] # a single entry of data is created each time
+		f.close()
+		l.close()
+	return reader_creator
+
+reader_creator = image_reader_creator("/path/to/image_file", "/path/to/label_file", 1024)
+paddle.train(reader_creator, {"image":0, "label":1}, ...)
 ```
 
 ### How is `paddle.train` implemented
@@ -129,17 +132,17 @@ paddle.train(reader, {"image":0, "label":1}, ...)
 An example implementation of paddle.train could be:
 
 ```python
-def minibatch_decorater(reader, minibatch_size):
+def minibatch_decorater(reader_creator, minibatch_size):
 	def ret():
-		r = reader()
+		r = reader_creator()
 		buf = [r.next() for x in xrange(minibatch_size)]
 		while len(buf) > 0:
 			yield buf
 			buf = [r.next() for x in xrange(minibatch_size)]
 	return ret
 
-def train(reader, mapping, batch_size, total_pass):
+def train(reader_creator, mapping, batch_size, total_pass):
 	for pass_idx in range(total_pass):
-		for mini_batch in minibatch_decorater(reader): # this loop will never end in online learning.
+		for mini_batch in minibatch_decorater(reader_creator): # this loop will never end in online learning.
 			do_forward_backward(mini_batch, mapping)
 ```