End-To-End Memory Networks
Replicate study for End-To-End Memory Networks paper.
Still to-do (see Issues):
- Linear start
- Random noise
- Langauage model
Usage
$ python train.py --help
usage: train.py [-h] [--embedding_size EMBEDDING_SIZE]
[--sentence_length SENTENCE_LENGTH]
[--memory_size MEMORY_SIZE] [--task_id TASK_ID]
[--epoch EPOCH] [--batch_size BATCH_SIZE] [--hops HOPS]
[--learning_rate LEARNING_RATE] [--train_dir TRAIN_DIR]
[--log_dir LOG_DIR] [--ckpt_dir CKPT_DIR] [--pe [PE]] [--nope]
[--joint [JOINT]] [--nojoint]
optional arguments:
-h, --help show this help message and exit
--embedding_size EMBEDDING_SIZE
Dimension for word embedding
--sentence_length SENTENCE_LENGTH
Sentence length. Provide to redefine automatically
calculated (max would be taken).
--memory_size MEMORY_SIZE
Memory size. Provide to redefine automatically
calculated (max would be taken).
--task_id TASK_ID Task number to test and train or (in case of
independent train)
--epoch EPOCH Epoch count
--batch_size BATCH_SIZE
Batch size
--hops HOPS Hops (layers) count
--learning_rate LEARNING_RATE
Starting learning rate
--train_dir TRAIN_DIR
Directory with training files
--log_dir LOG_DIR Directory for tensorboard logs
--ckpt_dir CKPT_DIR Directory for saving/restoring checkpoints
--pe [PE] Enable position encoding
--nope
--joint [JOINT] Train model jointly (that is on all tasks instead of
one).
--nojoint
For example, to train and test model with 5th bAbI task use
python train.py --train_dir data/tasks_1-20_v1-2/en/ --epoch 15 --learning_rate 0.01 --task_id 5 --hops 3
To train model jointly use --joint flag. Model would be trained on all bAbI tasks (1th-20th) and tested on given with --task_id task.
python train.py --train_dir data/tasks_1-20_v1-2/en/ --epoch 15 --learning_rate 0.01 --task_id 5 --hops 3 --joint