Refactoring and commenting the code.

README.rst

@@ -1,9 +1,9 @@
 Neural Doodle
 =============
 
-Do you want to use a deep neural network to borrow the skills of other artists and turn your two-bit doodles into fine artworks? Look no further! This project is an implementation of `Semantic Style Transfer <http://arxiv.org/abs/1603.01768>`_ (Champandard, 2016), based on the `Neural Patches <http://arxiv.org/abs/1601.04589>`_ algorithm (Li 2016).
+Use a deep neural network to borrow the skills of real artists and turn your two-bit doodles into masterpieces! This project is an implementation of `Semantic Style Transfer <http://arxiv.org/abs/1603.01768>`_ (Champandard, 2016), based on the `Neural Patches <http://arxiv.org/abs/1601.04589>`_ algorithm (Li 2016).
 
-Using the ``doodle.py`` script, you can generate an image by using three images as inputs: the original style, its annotated-version, your own doodle, and optionally a target image. The algorithm will then extract annotated patches from the style image, and incrementally transfer them over to the target image based on how closery they match.
+The ``doodle.py`` script generates an image by using three or four images as inputs: the original style and its annotation, and a target content image (optional) with its annotation (a.k.a. your doodle). The algorithm then extracts annotated patches from the style image, and incrementally transfers them over to the target image based on how closely they match.
 
 **NOTE**: This project is possible thanks to the `nucl.ai Conference <http://nucl.ai/>`_ on **July 18-20**. Join us in **Vienna**!
 
@@ -51,9 +51,9 @@ Frequent Questions
 ------------------
 
 Q: How is semantic style transfer different to neural analogies?
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-It's still too early to say definitively, both approaches were discovered independently by `@alexjc <https://twitter.com/alexjc>`_ and `@awentzonline <https://twitter.com/awentzonline>`_ (respectively). Here are some early impressions:
+It's still too early to say definitively, both approaches were discovered independently in 2016 by `@alexjc <https://twitter.com/alexjc>`_ and `@awentzonline <https://twitter.com/awentzonline>`_ (respectively). Here are some early impressions:
 
 1. One algorithm is style transfer that happens to do analogies, and the other is analogies that happens to do style transfer now. Adam extended his implementation to use a content loss after the `Semantic Style Transfer <http://arxiv.org/abs/1603.01768>`_ paper was published, so now they're even more similar under the hood!
 
@@ -63,7 +63,7 @@ It's still too early to say definitively, both approaches were discovered indepe
 
 4. Neural analogies is designed to work with images, and can only support the RGB format for its masks. Semantic style transfer was designed to **integrate with other neural networks** (for pixel labeling and semantic segmentation), and can use any format for its maps, including RGBA or many channels per label masks.
 
-5. Semantic style transfer is **about 25% faster and uses less memory** too.  For neural analogies, the extra computation is effectively the analogy prior — which could improve the quality of the results in theory. In practice, it's hard to tell at this stage.
+5. Semantic style transfer is **about 25% faster and uses less memory** too.  For neural analogies, the extra computation is effectively the analogy prior — which could improve the quality of the results in theory. In practice, it's hard to tell at this stage and more testing is needed.
 
 If you have any comparisons or insights, be sure to let us know!