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Tyler Hallada 2017-07-11 10:56:24 -04:00
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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
---
In this post, I will demonstrate how to begin generating random text using a few
lines of standard python and then progressively refining the output until it
looks poem-like.
In this post, I will demonstrate how to generate random text using a few lines
of standard python and then progressively refine the output until it looks
poem-like.
If you would like to follow along with this post and actually run the code
snippets mentioned here, you can clone [my NLP
repository](https://github.com/thallada/nlp/) and run [the Jupyter
If you would like to follow along with this post and run the code snippets
yourself, you can clone [my NLP repository](https://github.com/thallada/nlp/)
and run [the Jupyter
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
@ -19,7 +19,7 @@ random text that sounds like you: your phone keyboard.
![Suggested next words UI feature on the iOS
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
@ -329,8 +329,7 @@ does: 1
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
syllables to make sure it's 5-7-5 [as it
should](https://en.wikipedia.org/wiki/Haiku). You can view the full code
syllables to make sure it's [5-7-5]((https://en.wikipedia.org/wiki/Haiku). You can view the full code
[here](https://github.com/thallada/nlp/blob/master/generate_poem.py).
![Buzzfeed Haiku Generator](/img/blog/buzzfeed.jpg)
@ -424,7 +423,6 @@ from syntax_aware_generate import generate
generate('trump.txt', word_limit=10)
```
```
(SBARQ
(SQ
(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
and enters into a particular [activation
function](https://en.wikipedia.org/wiki/Activation_function) to output a single
number. The optimal weights of every connection for solving a particular problem
with the network are learned by training the network using
number. The optimal weights for solving a particular problem with the network
are learned by training the network using
[backpropagation](https://en.wikipedia.org/wiki/Backpropagation) to perform
[gradient descent](https://en.wikipedia.org/wiki/Gradient_descent) on a
particular [cost function](https://en.wikipedia.org/wiki/Loss_function) that
tries to balance getting the correct answer while also
[generalizing](https://en.wikipedia.org/wiki/Regularization_(mathematics)) the network
enough to perform well on data the network hasn't seen before.
[generalizing](https://en.wikipedia.org/wiki/Regularization_(mathematics)) the
network enough to perform well on data the network hasn't seen before.
[Long short-term memory
(LSTM)](https://en.wikipedia.org/wiki/Long_short-term_memory) is a type of