train_cifar10.py

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# Todo: resolve convergence problem


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 sys
sys.path.append('/home/i-chenyunpeng/zhoubin/incubator-mxnet/python')
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)
    parser.add_argument('--fsym', type=str, help='location of symbol json file')
    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    = '3,32,32',
        pad_size       = 4,
        # train
        batch_size     = 128,
        num_epochs     = 300,
        lr             = .05,
        lr_step_epochs = '200,250',
        optimizer      = 'adam',
    )
    args = parser.parse_args()

    # load network
    sym = mx.sym.load(args.fsym)

    # train
    fit.fit(args, sym, data.get_rec_iter)