Escaping the Gradient Vanishing: Periodic Alternatives of Softmax in Attention Mechanism

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Deep LearningPeriod-alternatives-of-Softmax
Overview

Period-alternatives-of-Softmax

Experimental Demo for our paper

'Escaping the Gradient Vanishing: Periodic Alternatives of Softmax in Attention Mechanism'

We suggest that replacing the exponential function by periodic functions. Through experiments on a simply designed demo referenced to LeViT, our method is proved to be able to alleviate the gradient problem and yield substantial improvements compared to Softmax and its variants.

** Create your own 'dataset' fold, and maybe need to modify the demo.py file for your own dataset except for cifar-10, cifar-100 and Tiny-imageNet.

Function available:

softmax , norm_softmax
sinmax, norm_sinmax
cosmax, norm_cosmax
sin_2_max, norm_sin_2_max
sin_2_max_move, norm_sin_2_max_move
sirenmax, norm_sirenmax
sin_softmax, norm_sin_softmax

mode available:

search:
        Random search for a suitable set of learning rate and weight decay, and record the results in 
        Attention_test/*functions/lr_wd_search.txt
run:
        Train the demo, and there will be four .npy files created in root.
        (1) 'record_val_acc.npy' for val acc record every 100 iter;
        (2) 'record_train_acc.npy' for train acc record every batch;
        (3) 'record_loss.npy' for train loss record every batch;
        (4) 'kq_value.npy' for Q.K record *before sclaled*.
att_run:
        Same as the run mode but:
        (1) No kq_value record;
        (2) Every 5 epoch, input a test image and record the attention score map of each head of each layer.
            Saved in 'Attention_test/attention_maps.npy'
Owner
slwang9353
The last punk on the North China Plain.
slwang9353
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