Merge pull request #1412 from helinwang/design

use terminology *data reader creator* instead of *data reader*, and r…

doc/design/reader/README.md

 # Python Data Reader Design Doc
 
-Paddle reads data from data reader during training. It will be passed into `paddle.train` as a parameter.
+At training and testing time, PaddlePaddle programs need to read data. To ease the users' work to write data reading code, we define that
+
+- A *reader* is a function that reads data (from file, network, random number generator, etc) and yields data items.
+- A *reader creator* is a function that returns a reader function.
+- A *reader* decorator is a function, which accepts one or more readers, and returns a reader.
+
+and provide frequently used reader creators and reader decorators.
 
 ## Data Reader Interface
 
-Data reader is a function with no parameter that creates a iterable (anything can be used in `for x in iterable`):
+Indeed, *data reader* doesn't have to be a function that reads and yields data items. It can be any function with no parameter that creates a iterable (anything can be used in `for x in iterable`):
 
 ```
 iterable = data_reader()
 ```
 
-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)
+Element produced from 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 reader_creator_random_image(width, height):
+	def reader():
 		while True:
-		yield numpy.random.uniform(-1, 1, size=20*20)
+			yield numpy.random.uniform(-1, 1, size=width*height)
+	return reader
 ```
 
-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 reader_creator_random_imageand_label(widht, height, label):
+	def reader():
 		while True:
-		yield numpy.random.uniform(-1, 1, size=20*20), False
+			yield numpy.random.uniform(-1, 1, size=width*height), label
+	return reader
 ```
 
 ## Usage
@@ -41,9 +51,9 @@ label_layer = paddle.layer.data("label", ...)
 paddle.train(paddle.dataset.mnist, {"image":0, "label":1}, 128, 10, ...)
 ```
 
-## Data Reader Decorators
+## Data Reader 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 decorator* 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.
 
 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:
 
@@ -61,23 +71,27 @@ buffered_reader = paddle.reader.buffered(paddle.dataset.mnist, 100)
 
 ### Compose Multiple Data Readers
 
-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).
+For example, we want to use a source of real images (reusing mnist dataset), and a source of random images as input for [Generative Adversarial Networks](https://arxiv.org/abs/1406.2661).
 
 We can do:
 
 ```python
-def data_reader_fake_image():
+def reader_creator_random_image(width, height):
+	def reader():
 		while True:
-		yield numpy.random.uniform(-1, 1, size=20*20)
+			yield numpy.random.uniform(-1, 1, size=width*height)
+	return reader
 
-def data_reader_bool(t):
+def reader_creator_bool(t):
+	def reader:
 		while True:
 			yield t
+	return reader
 
-true_reader = lambda : data_reader_bool(True)
-false_reader = lambda : data_reader_bool(False)
+true_reader = reader_creator_bool(True)
+false_reader = reader_creator_bool(False)
 
-reader = paddle.reader.combine(paddle.dataset.mnist, data_reader_fake_image, true_reader, false_reader)
+reader = paddle.reader.compose(paddle.dataset.mnist, data_reader_creator_random_image(20, 20), true_reader, false_reader)
 # 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}, ...)
@@ -98,16 +112,17 @@ reader = paddle.reader.shuffle(paddle.dataset.mnist, 512)
 
 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`.
 
-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 readers much easier (e.g., if existing 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).
 
 ### 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):
+def image_reader_creator(image_path, label_path, n):
+	def reader():
 		f = open(image_path)
 		l = open(label_path)
 		images = numpy.fromfile(
@@ -118,9 +133,10 @@ def image_reader(image_path, label_path, n):
 			yield images[i, :], labels[i] # a single entry of data is created each time
 		f.close()
 		l.close()
+	return reader
 
-# 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)
+# images_reader_creator creates a reader
+reader = image_reader_creator("/path/to/image_file", "/path/to/label_file", 1024)
 paddle.train(reader, {"image":0, "label":1}, ...)
 ```
 
@@ -129,7 +145,7 @@ paddle.train(reader, {"image":0, "label":1}, ...)
 An example implementation of paddle.train could be:
 
 ```python
-def minibatch_decorater(reader, minibatch_size):
+def make_minibatch(reader, minibatch_size):
 	def ret():
 		r = reader()
 		buf = [r.next() for x in xrange(minibatch_size)]
@@ -140,6 +156,6 @@ def minibatch_decorater(reader, minibatch_size):
 
 def train(reader, 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 make_minibatch(reader): # this loop will never end in online learning.
 			do_forward_backward(mini_batch, mapping)
 ```