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_posts/2017-07-11-generating-random-poems-with-python.md
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@ -4,13 +4,13 @@ layout: post
hidden: true hidden: true
--- ---
In this post, I will demonstrate how to begin generating random text using a few In this post, I will demonstrate how to generate random text using a few lines
lines of standard python and then progressively refining the output until it of standard python and then progressively refine the output until it looks
looks poem-like. poem-like.
If you would like to follow along with this post and actually run the code If you would like to follow along with this post and run the code snippets
snippets mentioned here, you can clone [my NLP yourself, you can clone [my NLP repository](https://github.com/thallada/nlp/)
repository](https://github.com/thallada/nlp/) and run [the Jupyter and run [the Jupyter
notebook](https://github.com/thallada/nlp/blob/master/edX%20Lightning%20Talk.ipynb). notebook](https://github.com/thallada/nlp/blob/master/edX%20Lightning%20Talk.ipynb).
You might not realize it, but you probably use an app everyday that can generate You might not realize it, but you probably use an app everyday that can generate
@ -19,7 +19,7 @@ random text that sounds like you: your phone keyboard.
![Suggested next words UI feature on the iOS ![Suggested next words UI feature on the iOS
keyboard](/img/blog/phone_keyboard.jpg) keyboard](/img/blog/phone_keyboard.jpg)
So how does it work? Just by tapping the next suggested word over and over, you can generate text. So how does it work?
## Corpus ## Corpus
@ -329,8 +329,7 @@ does: 1
To see this in action, try out a haiku generator I created that uses Buzzfeed To see this in action, try out a haiku generator I created that uses Buzzfeed
article titles as a corpus. It does not incorporate rhyming, it just counts the article titles as a corpus. It does not incorporate rhyming, it just counts the
syllables to make sure it's 5-7-5 [as it syllables to make sure it's [5-7-5]((https://en.wikipedia.org/wiki/Haiku). You can view the full code
should](https://en.wikipedia.org/wiki/Haiku). You can view the full code
[here](https://github.com/thallada/nlp/blob/master/generate_poem.py). [here](https://github.com/thallada/nlp/blob/master/generate_poem.py).
![Buzzfeed Haiku Generator](/img/blog/buzzfeed.jpg) ![Buzzfeed Haiku Generator](/img/blog/buzzfeed.jpg)
@ -424,7 +423,6 @@ from syntax_aware_generate import generate
generate('trump.txt', word_limit=10) generate('trump.txt', word_limit=10)
``` ```
``` ```
(SBARQ (SBARQ
(SQ (SQ
(NP (PRP I)) (NP (PRP I))
@ -476,14 +474,14 @@ have connections to other nodes in other layers of the network. These
connections have weights which each node multiplies by the corresponding input connections have weights which each node multiplies by the corresponding input
and enters into a particular [activation and enters into a particular [activation
function](https://en.wikipedia.org/wiki/Activation_function) to output a single function](https://en.wikipedia.org/wiki/Activation_function) to output a single
number. The optimal weights of every connection for solving a particular problem number. The optimal weights for solving a particular problem with the network
with the network are learned by training the network using are learned by training the network using
[backpropagation](https://en.wikipedia.org/wiki/Backpropagation) to perform [backpropagation](https://en.wikipedia.org/wiki/Backpropagation) to perform
[gradient descent](https://en.wikipedia.org/wiki/Gradient_descent) on a [gradient descent](https://en.wikipedia.org/wiki/Gradient_descent) on a
particular [cost function](https://en.wikipedia.org/wiki/Loss_function) that particular [cost function](https://en.wikipedia.org/wiki/Loss_function) that
tries to balance getting the correct answer while also tries to balance getting the correct answer while also
[generalizing](https://en.wikipedia.org/wiki/Regularization_(mathematics)) the network [generalizing](https://en.wikipedia.org/wiki/Regularization_(mathematics)) the
enough to perform well on data the network hasn't seen before. network enough to perform well on data the network hasn't seen before.
[Long short-term memory [Long short-term memory
(LSTM)](https://en.wikipedia.org/wiki/Long_short-term_memory) is a type of (LSTM)](https://en.wikipedia.org/wiki/Long_short-term_memory) is a type of