Build a new ResNext-101 on CIFAR-10 with AdaDelta

loadResnextAdadelta.py

import mxnet as mx
import numpy as np
import cv2, cPickle, logging
from symbols import resnext

logging.basicConfig(level=logging.DEBUG)

path="cifar-10-batches-py/"
examples=50000
batch=128
epochs=300

train_iter = mx.io.ImageRecordIter(
  path_imgrec="cifar10_train.rec", data_name="data", label_name="softmax_label", batch_size=batch, 
  data_shape=(3,32,32), shuffle=True)

valid_iter = mx.io.ImageRecordIter(
  path_imgrec="cifar10_val.rec", data_name="data", label_name="softmax_label", batch_size=batch, 
  data_shape=(3,32,32))

# Use ResNext-110
sym = resnext.get_symbol(10, 110, "3,32,32")
mod = mx.mod.Module(symbol=sym, context=(mx.gpu(0), mx.gpu(1), mx.gpu(2), mx.gpu(3)))
mod.bind(data_shapes=train_iter.provide_data, label_shapes=train_iter.provide_label)
mod.init_params(initializer=mx.init.Xavier())

mod.fit(train_iter, eval_data=valid_iter, optimizer='adadelta', num_epoch=epochs)

mod.save_checkpoint("resnext-101-symbol", epochs)