shawngraham/notes-on-dockerized-torch-rnn.md

## notes-on-dockerized-torch-rnn.md

      
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              notes-on-dockerized-torch-rnn.md
            
          
    ##setting up torch-rnn using docker
using this: https://github.com/crisbal/docker-torch-rnn
start docker (quickstart docker terminal)
get the image:
docker run --rm -ti crisbal/torch-rnn:base bash
ok, great, now, tie my data to the image (but do this from the bash line, not from inside docker):
docker run -i -t -v /Users/shawngraham/generativenovel/data:/root/torch-rnn/data crisbal/torch-rnn:base
then preprocess:
python scripts/preprocess.py \
--input_txt data/input.txt \
--output_h5 data/input.h5 \
--output_json data/input.json
and train:
th train.lua \
-input_h5 data/input.h5 \
-input_json data/input.json \
-gpu -1

I let it run for several thousands of iterations, but being impatient, I then stopped it. Now to sample!
th sample.lua -checkpoint cv/checkpoint_10000.t7 -length 2000 -gpu -1
or in my case:
th sample.lua -checkpoint cv/checkpoint_23000.t7 -length 200000 -gpu -1 -start_text "a novel of adventure of the tiny archaeologists" -temperature 0.675 -sample 1 > data/novel.txt which generates roughly 50 000 words of text.
aslo handy:
 cp -R cv data/cv

came back and retrained it with this:
th train.lua -input_h5 input.h5 -input_json input.json -model_type rnn -num_layers 3 -rnn_size 256 -gpu -1