outf = args["--of"] tarform = args["--tf"] ttype = args["--tt"] nn = args["--mlp"] gpu = not bool(args["--cpu"]) mbsize = int(args["--mb"]) epoch = int(args["--epoch"]) lr = float(args["--lr"]) mm = float(args["--mm"]) re = float(args["--re"]) dr = float(args["--dr"]) seed = int(args["--seed"]) tarf = args["<tar>"] trainf = args["<file>"] model, actfs = dataio.loadnn(nn) if gpu: cuda.get_device(0).use() model.to_gpu() optimizer = optimizers.MomentumSGD(lr=lr, momentum=mm) optimizer.setup(model.collect_parameters()) nlayer = len(actfs) idim = model.l_0.W.data.shape[1] odim = getattr(model, "l_" + str(nlayer - 1)).W.data.shape[0] data = dataio.dataio(trainf, dataform, idim).astype(np.float32) if ttype == "c": forward = forward_cross_entoropy
import re from docopt import docopt import numpy as np import util,dataio if __name__=='__main__': args = docopt(__doc__) of = args["--of"] pick = args["--pick"] file = args["<file>"] nn, actfs = dataio.loadnn(file) p = [] if re.match(r"^\d$", pick): p = [ int(pick) ] elif re.match(r"^\d:\d$", pick): r = pick.rstrip().split(':') assert len(r) == 2 r = map(int, r) p = range(r[0], r[1]) else: util.panic("Pick up argument is Unknown format: %s" % pick) d = {} j = 0 for i in p: