The IPDE package is comprised of four parts/sub-packages - cnn(define CNN layers), data(provide datasets), ip(ip encoding and decoding) and pso(pso related methods).
The package is developed in Python 3 and there are several packages used - tensorflow, scipy and numpy, so in order to use the ISPSO package Python 3 and the two dependent packages have to be set up first.
Once the environment is set up, the IPDE package can be imported and called from any python code.
However, to make it easier for debug it would be better to import the whole package into an IDE like pycharm.
Go to the root folder of the repository
python3 de_main.py -h
usage: de_main.py [-h] [-d DATASET] [-m MODE] [-s POP_SIZE] [-l PARTICLE_LENGTH]
[--max_steps MAX_STEPS] [-e TRAINING_EPOCH] [-g MAX_GPU]
[-o OPTIMISE]
optional arguments:
-h, --help show this help message and exit
-d DATASET, --dataset DATASET
choose a dataset among mb, mdrbi, or convex
-m MODE, --mode MODE default:None, 1: production (load full data)
-s POP_SIZE, --pop_size POP_SIZE
population size
-l PARTICLE_LENGTH, --particle_length PARTICLE_LENGTH
particle max length
--max_steps MAX_STEPS
max fly steps
-e TRAINING_EPOCH, --training_epoch TRAINING_EPOCH
training epoch for the evaluation
-f FIRST_GPU_ID, --first_gpu_id FIRST_GPU_ID
first gpu id
-g MAX_GPU, --max_gpu MAX_GPU
max number of gpu
-o OPTIMISE, --optimise OPTIMISE
optimise the learned CNN architecture. Default: None.
1: optimise; otherwise IPDE search
--log_file LOG_FILE the path of log file
--gbest_file GBEST_FILE
the path of gbest file
-r REGULARISE, --regularise REGULARISE
weight regularisation hyper-parameter.
--dropout DROPOUT enable dropout and set dropout rate
--ip_structure IP_STRUCTURE
IP structure. default: 5 bytes, 1: 3 bytes, 2: 2 bytes
with xavier weight initialisation
--partial_dataset PARTIAL_DATASET
Use partial dataset for learning CNN architecture to
speed up the learning process.
python3 de_main.py -d mb -s 30 -l 10 --max_steps 30 -e 5 -f 0 -g 1
python3 de_main.py -d mb -m 1 -s 30 -l 10 --max_steps 30 -e 5 -f 0 -g 1
nohup python3 de_main.py -d mb -m 1 -s 30 -l 10 --max_steps 30 -e 5 -f 0 -g 1 --log_file=log/ippso_cnn.log --gbest_file=log/gbest.pkl &
After the program run, all the main steps can be checked in the log/ippso_cnn.log file and the global best particle will be persisted into log/gbest.pkl file.
nohup python3 de_main.py -d mb -m 1 --w 0.1 --c1 0.02,0.1,0.1,0.1,0.1 --c2 0.02,0.1,0.1,0.1,0.1 -s 30 -l 10 --max_steps 30 -e 5 -f 0 -g 1 --log_file=log/ippso_cnn.log --gbest_file=log/gbest.pkl &
python3 de_main.py -d mb -m 1 -e 30 -g 1 -o 1
nohup python3 de_main.py -d mb -m 1 -e 30 -g 1 -o 1 &
tensorboard --logdir log/ippso_cnn_optimise/tensorboard/train