def main(args): highres = utils.matrix_extract(args.chromosome, 10000, args.inputfile) logging.debug('dividing, filtering and downsampling files...') highres_sub, index = utils.divide(highres) logging.debug(str(highres_sub.shape)) #np.save(infile+"highres",highres_sub) lowres = utils.genDownsample(highres, 1 / float(args.scalerate)) lowres_sub, index = utils.divide(lowres) logging.debug(str(lowres_sub.shape)) #np.save(infile+"lowres",lowres_sub) logging.debug('start training...') trainConvNet.train(lowres_sub, highres_sub, args.outmodel, args.checkpoint) logging.debug('finished...')
def main(args): highres = utils.matrix_extract(args.chromosome, args.chromosome, 10000, args.inputfile) print('dividing, filtering and downsampling files...', flush=True) highres_sub, index = utils.train_divide(highres) print(highres_sub.shape) #np.save(infile+"highres",highres_sub) lowres = utils.genDownsample(highres, 1 / float(args.scalerate)) lowres_sub, index = utils.train_divide(lowres) print(lowres_sub.shape) #np.save(infile+"lowres",lowres_sub) print('start training...', flush=True) trainConvNet.train(lowres_sub, highres_sub, args.outmodel) print('finished...', flush=True)