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import os
import argparse
import logging
logging.basicConfig(level=logging.DEBUG)
from common import find_mxnet, data, fit
from common.util import download_file
import mxnet as mx
def download_cifar10():
data_dir="data"
fnames = (os.path.join(data_dir, "cifar10_train.rec"),
os.path.join(data_dir, "cifar10_val.rec"))
download_file('http://data.mxnet.io/data/cifar10/cifar10_val.rec', fnames[1])
download_file('http://data.mxnet.io/data/cifar10/cifar10_train.rec', fnames[0])
return fnames
if __name__ == '__main__':
# download data
(train_fname, val_fname) = download_cifar10()
# parse args
parser = argparse.ArgumentParser(description="train cifar10",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
fit.add_fit_args(parser)
data.add_data_args(parser)
data.add_data_aug_args(parser)
data.set_data_aug_level(parser, 2)
parser.set_defaults(
# network
network = 'resnet',
num_layers = 110,
# data
data_train = train_fname,
data_val = val_fname,
num_classes = 10,
num_examples = 50000,
image_shape = '3,28,28',
pad_size = 4,
# train
batch_size = 128,
num_epochs = 300,
lr = .05,
lr_step_epochs = '200,250',
)
args = parser.parse_args()
# load network
from importlib import import_module
net = import_module('symbols.'+args.network)
sym = net.get_symbol(**vars(args))
# train
fit.fit(args, sym, data.get_rec_iter)