Best practices for backstitch

[danpovey]

@freewym, I want to get to a situation where most of our frequently used example scripts have suitable backstitch settings, as it does seem to give a reliable improvement.

I think rather than relying on you to do that, it may be a good idea to just make everyone aware of what the recommended settings are, with guidance for tuning it if applicable-- with some idea of how much it's expected to improve the results. Can you please comment on this issue with answers to those questions?

[freewym]

To turn on the backstitch training, there are just a few lines to add/change to the shell script:

pass the following options to steps/nnet3/chain/train.py: --trainer.optimization.backstitch-training-scale $alpha \ --trainer.optimization.backstitch-training-interval $back_interval \

where a typical setting is: $alpha=0.3 $back_interval=1

or to get speed-up at the cost of potentially a small degradation (which is observed in our swbd experiments): $alpha=1.0 $back_interval=4

Meanwhile, we need to double the value of num-epochs when doing backstitch training (e.g, if num-epochs=4 with normal SGD training, then num-epochs=8 with backstitch training). If the the valid objf has not converged after doubling num-epochs, further increase it until convergence.

For TDNN-LSTM recipes of the chain model, backstitch obtains ~10% relative WER improvement on SWBD, AMI-SDM and tedlium. For TDNN-LSTM cross-entropy models, the improvement is smaller (2-4%). For non-recurrent architectures (e.g., TDNN), the improvement may be even smaller.

Note that the recommended settings above apply to our ASR tasks with chain/cross-entropy models. It may be different for other tasks like image classification (e.g., in CIFAR Resnet recipes, alpha=0.5, back-interval=1, and num-epochs is around 30% more than the one in the normal SGD training) .

[danpovey]

Hm. Nice improvement, but it seems like the impact on training time is substantial, what with the increased num-epochs and the fact the backstitch necessitates processing each minibatch twice (at least if back_interval=1). I'm wondering whether we should have different 'XX_back.sh' versions of each recipe XX.sh. But I'm concerned that this will explode the number of recipes and create more burden on testing. Do you get any improvement without increasing the num-epochs? And I wonder if you ever tried increasing the initial learning rate a bit... this might make it learn faster and have a similar effect to more epochs.

On Tue, Oct 17, 2017 at 2:50 PM, Yiming Wang notifications@github.com wrote:

To turn on the backstitch training, there are just a few lines to add/change to the shell script:

pass the following options to steps/nnet3/chain/train.py: --trainer.optimization.backstitch-training-scale $alpha \ --trainer.optimization.backstitch-training-interval $back_interval \

where a typical setting is: $alpha=0.3 $back_interval=1

or to get speed-up at the cost of potentially a small degradation (which is observed in our swbd experiments): $alpha=1.0 $back_interval=4

Meanwhile, we need to double the value of num-epochs when doing backstitch training (e.g, if num-epochs=4 with normal SGD training, then num-epochs=8 with backstitch training). If the the valid objf has not converged after doubling num-epochs, further increase it until convergence.

For TDNN-LSTM recipes of the chain model, backstitch obtains ~10% relative WER improvement on SWBD, AMI-SDM and tedlium. For TDNN-LSTM cross-entropy models, the improvement is smaller (2-4%). For non-recurrent architectures (e.g., TDNN), the improvement may be even smaller.

Note that the recommended settings above apply to our ASR tasks with chain/cross-entropy models. It may be different for other tasks like image classification (e.g., in CIFAR Resnet recipes, alpha=0.5, back-interval=1, and num-epochs is around 30% more than the one in the normal SGD training) .

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[freewym]

Most of the time with the same num-epochs backstitch is worse. I can try increasing the init-learning-rate.

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