DataLoader.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mxnet import gluon\n",
    "from mxnet.gluon.data.vision import transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data_iter(num_workers, batch_size, shuffle=True):\n",
    "    transformer = transforms.Compose([\n",
    "        transforms.ToTensor()\n",
    "    ])\n",
    "    train = gluon.data.vision.datasets.FashionMNIST(train=True)\n",
    "    train.transform_first(transformer)\n",
    "    test = gluon.data.vision.datasets.FashionMNIST(train=False)\n",
    "    test.transform_first(transformer)\n",
    "    train_iter = gluon.data.DataLoader(train, batch_size, shuffle=shuffle, num_workers=num_workers)\n",
    "    test_iter = gluon.data.DataLoader(train, batch_size, shuffle=shuffle, num_workers=num_workers)\n",
    "    return train_iter, test_iter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_iter, test_iter = load_data_iter(4, 10)"
   ]
  }
 ],
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