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https://github.com/loleg/poetrnn.jl
https://github.com/loleg/poetrnn.jl
Last synced: 4 days ago
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- Host: GitHub
- URL: https://github.com/loleg/poetrnn.jl
- Owner: loleg
- Created: 2018-01-01T13:22:06.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2018-01-01T13:23:06.000Z (about 7 years ago)
- Last Synced: 2024-12-29T16:37:24.865Z (5 days ago)
- Language: Julia
- Size: 8.79 KB
- Stars: 0
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# PoetRNN.jl
This project is a *Julia* implementation of a LSTM RNN (recurrent neural network) designed to learn and produce short verse poetry. This is inspired from [PoetRNN](https://github.com/sballas8/PoetRNN), written by Sam Ballas in Python.
The code is derived from a Julia LSTM/RNN implementation of `char-rnn` that avoids explicit unrolling and deals with variable-length sequences, forked from the `char-lstm` example [in MXNet.jl](https://github.com/dmlc/MXNet.jl/tree/master/examples/char-lstm).
An excellent explanation of how this works is documented in the [LSTM RNN tutorial](http://dmlc.ml/MXNet.jl/latest/tutorial/char-lstm/) - itself adapted from the
[MXNet example in Python binding](https://github.com/dmlc/mxnet/blob/master/example/rnn/char_lstm.ipynb).
## Usage
To run this project, you will need to install (Pkg.add) three Julia packages:
- MXNet.jl
- Iterators.jl
- StatsBase.jl
### Training
An example dataset (default filename in `config.jl` is `input.txt`) can be downloaded [here](https://github.com/dmlc/web-data/tree/master/mxnet/tinyshakespeare) and placed in the `data` folder.
Copy [config.jl.default](config.jl.default) to `config.jl` and modify parameters, and then run [train.jl](train.jl).
An example output of training looks like this:
```
...
INFO: Speed: 357.72 samples/sec
INFO: == Epoch 020 ==========
INFO: ## Training summary
INFO: NLL = 1.4672
INFO: perplexity = 4.3373
INFO: time = 87.2631 seconds
INFO: ## Validation summary
INFO: NLL = 1.6374
INFO: perplexity = 5.1418
INFO: Saved checkpoint to 'char-lstm/checkpoints/ptb-0020.params'
INFO: Speed: 368.74 samples/sec
INFO: Speed: 361.04 samples/sec
INFO: Speed: 360.02 samples/sec
. . .
INFO: Speed: 360.24 samples/sec
INFO: Speed: 360.32 samples/sec
INFO: Speed: 362.38 samples/sec
INFO: == Epoch 021 ==========
INFO: ## Training summary
INFO: NLL = 1.4655
INFO: perplexity = 4.3297
INFO: time = 86.9243 seconds
INFO: ## Validation summary
INFO: NLL = 1.6366
INFO: perplexity = 5.1378
INFO: Saved checkpoint to 'examples/char-lstm/checkpoints/ptb-0021.params'
```
### Sampling
Run [sampler.jl](sampler.jl) to generate sample sentences from the trained model. Some example sentences:
```
## Sample 1
all have sir,
Away will fill'd in His time, I'll keep her, do not madam, if they here? Some more ha?
## Sample 2
am.
CLAUDIO:
Hone here, let her, the remedge, and I know not slept a likely, thou some soully free?
## Sample 3
arrel which noble thing
The exchnachsureding worns: I ne'er drunken Biancas, fairer, than the lawfu?
## Sample 4
augh assalu, you'ld tell me corn;
Farew. First, for me of a loved. Has thereat I knock you presents?
## Sample 5
ame the first answer.
MARIZARINIO:
Door of Angelo as her lord, shrield liken Here fellow the fool ?
## Sample 6
ad well.
CLAUDIO:
Soon him a fellows here; for her fine edge in a bogms' lord's wife.
LUCENTIO:
I?
## Sample 7
adrezilian measure.
LUCENTIO:
So, help'd you hath nes have a than dream's corn, beautio, I perchas?
## Sample 8
as eatter me;
The girlly: and no other conciolation!
BISTRUMIO:
I have be rest girl. O, that I a h?
## Sample 9
and is intend you sort:
What held her all 'clama's for maffice. Some servant.' what I say me the cu?
## Sample 10
an thoughts will said in our pleasue,
Not scanin on him that you live; believaries she.
ISABELLLLL?
```