hellomotor / tf_rnnlm

Language Modeling with RNN using TensorFlow

Recurrent Neural Network Language Model using TensorFlow

Content

• Original Work
• Motivations
• Quickstart
• Continue Training
• Getting a text perplexity with regard to the LM
• Line by line loglikes
• Results on PTB Dataset

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Original Work

Our work is based on the RNN LM tutorial on tensorflow.org following the paper from Zaremba et al., 2014.

The tutorial uses the PTB dataset (tgz). We intend to work with various dataset, that's why we made names more generic by removing several "PTB" prefix initially present in the code.

Original sources: TensorFlow v0.11 - PTB

See also:

• Nelken's "tf" repo inspired our work by the way it implements features we are interested in.
• Benoit Favre's tf rnn lm

## Motivations

• Getting started with TensorFlow
• Make RNN LM manipulation easy in practice (easily look/edit configs, cancel/resume training, multiple outputs...)
• Train RNN LM for ASR using Kaldi (especially using loglikes mode)

Quickstart

git clone https://github.com/pltrdy/tf_rnnlm
cd tf_rnnlm
./train.py --help

Downloading PTB dataset:

chmod +x tools/get_ptb.sh
./tools/get_ptb.sh

Training small model:

mkdir small_model
./train.py --data_path ./simple-examples/data --model_dir=./small_model --config small

Training custom model:

mkdir custom_model

# Generating new config file
chmod +x gen_config.py
./gen_config.py small custom_model/config

# Edit whatever you want
vi custom_model/config

# Train it. (it will automatically look for 'config' file in the model directory as no --config is set).
# It will look for 'train.txt', 'test.txt' and 'valid.txt' in --data_path
# These files must be present.
./train.py --data_path=./simple-examples/data --model_dir=./custom_model

note: data files are expected to be called train.txt, test.txt and valid.txt. Note that get_ptb.sh creates symlinks for that purpose

Training all models and reporting results

./run.sh

Yes, that's all. It will train small, medium and large then generate a report like this using report.sh.
Feel free to share the report with us!

Continue Training

One can continue an interrupted training with the following command:

./train.py --data_path=./simple-examples/data --model_dir=./model

Where ./model must contain config, word_to_id, checkpoint and the corresponding .cktp files.

Getting a text perplexity with regard to the LM

# Compute and outputs the perplexity of ./simple-examples/data/test.txt using LM in ./model
./test.py --data_path=./simple-examples/data --model_dir=./model

Line by line loglikes

Running the model on each stdin line and returning its 'loglikes' (i.e. -costs/log(10)).

Note: in particular, it is meant to be used for Kaldi's rescoring.

cat ./data/test.txt | ./loglikes.py --model_dir=./model

Text generation

Not documented yet

Results on PTB dataset

Configuration small medium and large are defined in config.py and are the same as in tensorflow.models.rnn.ptb.ptb_word_lm.py:200

Using batch_size=32

config	train	valid	test	speed	training_time
small	24.608	118.848	113.366	~49kWPS	4m17s
medium	26.068	91.305	87.152	~25kWPS	24m50s
large	18.245	84.603	79.515	~6kWPS	135m15s
small	27.913	123.896	119.496	~42kWPS	4m56s
medium	28.533	98.105	94.576	~23kWPS	26m51s
large	21.635	91.916	87.110	~6kWPS	140m675

Using batch_size=64

config	train	valid	test	speed	training_time
small	32.202	119.802	115.209	~44kWPS	4m40s
medium	31.591	97.219	93.450	~24kWPS	25m0s
large	18.198	88.675	83.143	~9kWPS	95m25s
small	39.031	127.949	124.292	~94kWPS	3m9s
medium	33.130	102.652	99.381	~29kWPS	21m7s
large	21.122	95.310	90.658	~7kWPS	112m48s

kWPS: processing speed, i.e. thousands word per seconds.
Reported time are real times (see What do 'real', 'user' and 'sys' mean in the output of time(1)?
Testing is done using softmax on transposed weights. (docs/transpose.md)
For faster results increasing batch_size should speed up the process, with a small perplexity increase as a side effect and an increased GPU Memory consumption. (which can fire Out Of Memory exception)

## Contributing Please do!
Fork the repo -> edit the code -> commit with descriptive commit message -> open a pull request
You can also open an issue for any discussion about bugs, performance or results.
Please also share your results with us! (see sharing your results)