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:
