A new kind of pooling layer for faster and sharper convergence
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README.md

loss-exps

Experiments with a different pooling layer for image classification

Run cluttered mnist experiments

python train.py --result cluttered-mnist/pool-1 --pool-range 1 # val_cross_entropy 0.259
python train.py --result cluttered-mnist/pool-2 --pool-range 2 # val_cross_entropy 0.202
python train.py --result cluttered-mnist/pool-3 --pool-range 3 # val_cross_entropy 0.196
python train.py --result cluttered-mnist/pool-4 --pool-range 4 # val_cross_entropy 0.196

Run fashion mnist experiments

python train.py --result fashion-mnist/pool-1 --pool-range 1 --dataset fashion-mnist # val_cross_entropy 0.291
python train.py --result fashion-mnist/pool-2 --pool-range 2 --dataset fashion-mnist # val_cross_entropy 0.282
python train.py --result fashion-mnist/pool-3 --pool-range 3 --dataset fashion-mnist # val_cross_entropy 0.268
python train.py --result fashion-mnist/pool-4 --pool-range 4 --dataset fashion-mnist # val_cross_entropy 0.276

To access all command line arguments

python train.py -h