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Implement of MAS on MXNet

This is an implement of MAS on MXNet.

Origin MAS on pytorch

what does this project finish

standard setup and training on several task.
finally calculate accuracy on each task.

environment

mxnet-cu80 on version 1.1.0.post0
python 2.7

how to use

clone the project

$ git clone https://github.com/mingzhang96/MAS-mxnet.git
$ cd MAS-mxnet
$ mkdir ckpt && mkdir data && mkdir reg_params

We assume that you are in the $MAS-mxnet directory, and in $MAS-mxnet/data the mnist (.gz) data stays there.

python train_mnist.py

result

we use mlp instead of AlexNet as our base model.

notice: we use model trained on last task to test other tasks.

100 epoch, update_lr = 0.05, train_lr = 0.05

task	accuracy
01	0.6274231678486998
23	0.9417238001958864
45	0.9797225186766275
67	0.972306143001007
89	0.9389813414019162

200 epoch, update_lr = 0.0001, train_lr = 0.0008

task	accuracy
01	0.9952718676122931
23	0.8805093046033301
45	0.955709711846318
67	0.9823766364551864
89	0.9536056480080686

200 epoch, update_lr = 0.0001, train_lr = 0.005, fc2.output = 256

task	accuracy
01	0.9933806146572104
23	0.9299706170421156
45	0.9802561366061899
67	0.9914400805639476
89	0.9646999495713565

tips

the more tasks are, the more epoch need to train.
use small train_lr to finetune.
the last fc performs well if it has large output.