问题
I am writing a calibration pipeline to learn the hyperparameters for neural networks to detect properties of DNA sequences*. This therefore requires training a large number of models on the same dataset with different hyperparameters.
I am trying to optimise this to run on GPU. DNA sequence datasets are quite small compared to image datasets (typically 10s or 100s of base-pairs in 4 'channels' to represent the 4 DNA bases, A, C, G and T, compared to 10,000s of pixels in 3 RGB channels), and consequently cannot make full use of the parallelisation on a GPU unless multiple models are trained at the same time.
Is there a way to do this in nolearn, lasagne or, at worst, Theano?
* It's based on the DeepBind model for detecting where transcription factors bind to DNA, if you're interested.
来源：https://stackoverflow.com/questions/35135331/how-can-you-train-multiple-neural-networks-simultaneously-in-nolearn-lasagne-the