Comments (1)
I would probably try the following things, but I don't think there is much more to be gained this way either.
- Make the networks deeper where possible.
- Add another convnet to the ensemble.
- Try using different rectifier units. Notably the
elu
unit that is now available in lasagne sounds promising. - You might be able to boost the score slightly if you try to compute optimal thresholds at the end rather than using
0.5, 1.5, 2.5, 3.5
. - I think you can at least save some time during training if you remove the fully connected layers and/or add
'nonlinearity': None
to the parameters for the output layer. The default linearity isrectify
so the minimum the convnets can output at the moment is 0. You might have to tune the learning rate schedule again for this to work well though.
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Related Issues (19)
- Some errors in data.py file HOT 1
- Objective class in lasagne.objectives is deprecated and is removed HOT 4
- Error when using CPU mode HOT 12
- Error with CudaConvnet mode HOT 5
- Some questions on your conv. n.n. architecture and blend network HOT 1
- Please help me, I'm obviously an indiot
- error in pip install -r requirements.txt HOT 4
- lasagne and nolearn version HOT 6
- ValueError: "non finite loss" while running make_kaggle_solution.sh HOT 17
- ImportError: No module named configs.c_128_5x5_32 HOT 8
- Can I Run this Code? HOT 1
- when i change the number of train pictures,I got error
- confusion about 'right_eye_indicator' feature
- AttributeError: mode HOT 7
- why blur? HOT 1
- Dump of model?
- Cuda and CuDNN version HOT 1
- how much time it takes to run the code? HOT 1
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