If you train ANN network on the CBSD, please run
python3 ann_train.py -n CBSD -T
If you test ANN results on the CBSD, please run
python3 ann_train.py -n CBSD
The format:
python3 ann_train.py -n dataset_name [-s1 seed1] [-s2 seed2] [-s3 seed3] [-b batch_size] [-e epochs] [-op optimizer] [-lr leanring_rate] [-T]
The parameters of ann_train.py are as follow:
-n: BSD / CBSD
-s1: int
-s2: int
-s3: int
-b: int
-e: int
-op: adam / sgd / rms / adadelta
-lr: float
-T: '-T' represents train mode
python3 ann_snn_denoising.py -n dataset_name -t timesteps [-m method] [-s scale_method] [-d]
The parameters of ann_snn_denoising.py are as follow:
-n: Set12 / BSD / CBSD
-t: int
-m: layer_wise / connection_wise
-s: robust / max (robust: 99.9 percentile of activations; max: max of activations)
-d: '-d' represents SNN with "reduce by subtraction mechanism"
-neuron: multi / IF
python3 snn_train.py -n dataset_name -t timesteps [-m method] [-s1 seed1] [-s2 seed2] [-s3 seed3] [-b batch_size] [-e epochs] [-lr leanring_rate] [-s scale_method] [-op optimizer] [-T] [-d]
The parameters of snn_train.py are as follow:
-n: BSD / CBSD
-t: int
-s1: int
-s2: int
-s3: int
-b: int
-e: int
-lr: float
-op: adam / sgd / rms / adadelta
-T: '-T' represents train mode
-s: robust / max (robust: 99.9 percentile of activations; max: max of activations)
-m: layer_wise / connection_wise
-d: '-d' represents SNN with "reduce by subtraction mechanism"
-neuron: multi / IF