I have recreated karpathy/char-rnn with my own RNN package. It works fairly well, and I have used it to generate some cool results.
First, gather a folder with a bunch of text files in it (or with one big text file in it). Let's call this path/to/text.
Now, install Go and setup a GOPATH. Once this is done, you are ready to install char-rnn itself:
$ go get -u github.com/unixpickle/char-rnn
$ cd $GOPATH/src/github.com/unixpickle/char-rnn/char-rnn
$ go build
This will generate an executable called char-rnn in your current directory.
If you have CUDA bindings setup (instructions here), you can use CUDA like so:
$ go get -u -tags cuda github.com/unixpickle/char-rnn/...
$ cd $GOPATH/src/github.com/unixpickle/char-rnn/char-rnn
$ go build -tags cuda
You can train char-rnn on some data as follows:
$ ./char-rnn train lstm /path/to/lstm /path/to/sample/directory
2016/06/22 17:52:53 Loaded model from file.
2016/06/22 17:52:53 Training LSTM on 22308 samples...
2016/06/22 17:59:29 Epoch 0: cost=1857807.936353
...
You can set the GOMAXPROCS environment variable to specify how many CPU threads to use for training.
If the /path/to/lstm file already exists, it will be loaded as an LSTM and training will resume where it left off. Otherwise, a new LSTM will be created.
It may take a while to train the LSTM reasonably well. On karpathy's tinyshakespeare, it took my Intel NUC (quad-core i3 with 1.7GHz) roughly 18 hours to train reasonably well (although for much of that time I was only using a single CPU core).
To pause or stop training, press Ctrl+C exactly once. This will finish the current mini-batch and then terminate the program. Once the program has terminated, a trained LSTM will be saved to /path/to/lstm. Note: if you hit Ctrl+C more than once, the program will terminate without saving.
Once you have trained an LSTM, you can use it to generate a block of text. You must decide how much text to generate (e.g. 1000 characters, like below):
$ ./char-rnn gen /path/to/lstm 1000
I ran a GRU on the output of ls -l /usr/bin and then generated some dir listings:
-rwxr-xr-x 35 root wheel 821 Aug 23 2015 iptab5.18
-r-xr-xr-x 1 root wheel 3659 Sep 28 2015 instmodse
-rwxr-xr-x 1 root wheel 75 Oct 25 2015 info3eal -> /System/Library/Frameworks/JavaVM.framework/Versions/Current/Commands/rmic
lrwxr-xr-x 1 root wheel 84 Oct 25 2015 javmap -> /System/Library/Frameworks/JavaVM.framework/Versions/Current/Commands/kchase
-rwxr-xr-x 1 root wheel 59576 Oct 17 2015 anplrac
-rwxr-xr-x 1 root wheel 77 Oct 25 2015 edbsc -> cling
-r-xr-xr-x 1 root wheel 18176 Oct 17 2015 nv5.16
-rwxr-xr-x 1 root wheel 17204 Aug 22 2015 pod2readme5.16
-rwxr-xr-x 35 root wheel 811 Aug 23 2015 lwp-download5.16
-r-xr-xr-x 1 root wheel 3573 Aug 22 2015 dbiprof5.18
-rwxr-xr-x 1 root wheel 23368 Oct 17 2015 enice
-rwxr-xr-x 1 root wheel 43 Oct 25 2015 jstat -> /System/Library/Frameworks/JavaVM.framework/Versions/Current/Commands/intext
-rwxr-xr-x 1 root wheel 77 Oct 25 2015 netalloc.5 -> ../../System/Library/Frameworks/Python.framework/Versions/-arwervim
lrwxr-xr-x 1 root wheel 82 Oct 25 2015 j0 -> vmeadsrad
-rwxr-xr-x 1 root wheel 18176 Oct 17 2015 gzeratex
-rwxr-xr-x 1 root wheel 1947 Aug 22 2015 config_data5.16
-rwxr-xr-x 1 root wheel 9151 Aug 23 2015 ifstroc5.16
-rwxr-xr-x 1 root wheel 2 Oct 25 2015 viaevketat-cvisthar -> 2toc2.6