About the project

Please see blog post for details: https://blog.asjadk.com/deepprocess/

How to run experiments:

1. Data for each experiment can be found on ./data/BusinessProcess folder
2. File presuf_run.py contains code for 3 experiments
3. In presuf_run.py: there are train and test funtions for each task. Just call the appropriate one

How to tune hyper parameters:

1. In each function, hyper parameters are hard-coded
2. Just edit directly

Type of hyper parameters:

1. Method type (edit in constructer's arguments):

• LSTM seq2seq: use_mem=False
• DNC: use_mem=True, decoder_mode=True/False, dual_controller=False, write_protect=False
• DC-MANN: use_mem=True, decoder_mode=True, dual_controller=True, write_protect=False
• DCw_MANN: use_mem=True, decoder_mode=True/False, dual_controller=True, write_protect=True

2. Model parameters (edit in constructer's arguments):

• use_emb=True/False: use embedding layer or not
• dual_emb=True/False: if use emedding layer, use one share or two embeddings for encoder and decoder
• hidden_controller_dim: dimension of controller hidden state

3. Memory parameters (if use memory):

• words_count: number of memory slots
• word_size: size of each memory slots
• read_heads: number of reading heads

4. Training parameters:

• batch_size: number of sequence sampled per batch
• iterations: max number of training step
• lm_train=True/False: training by the language model's way (edit in prepare_sample_batch function)
• optimizer: in file dnc.py, function build_loss_function_mask (default is adam)

Notes:

1. The current hyper-parameters are picked by experience from other projects
2. Except from different method types, I have not tried with other hyper-parameters