MakeRecordFloat.ipynb

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     "text": [
      "<class 'numpy.ndarray'>\n",
      "240000\n"
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     "text": [
      "100%|████████████████████████████████████████████████████████████████████████| 239999/239999 [01:16<00:00, 3151.44it/s]\n"
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   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import csv\n",
    "from tqdm import tqdm\n",
    "import scipy\n",
    "\n",
    "MNIST_DATA = np.loadtxt(open(\"./data/EMNIST.csv\", \"rb\"), delimiter=\",\", skiprows=0)\n",
    "print(type(MNIST_DATA))\n",
    "print(len(MNIST_DATA))\n",
    "\n",
    "record_path = './data/trainE.tfrecords'\n",
    "tf_writer = tf.python_io.TFRecordWriter(record_path)\n",
    "\n",
    "for row in tqdm(range(1,len(MNIST_DATA))):\n",
    "    class_num = int(MNIST_DATA[row][0])\n",
    "    img_vec = MNIST_DATA[row][1:]\n",
    "    label_vec = np.zeros(10)\n",
    "    label_vec[class_num] = 1    \n",
    "    \n",
    "    example = tf.train.Example(\n",
    "        features = tf.train.Features(\n",
    "            feature = { \n",
    "                'label':tf.train.Feature(float_list=tf.train.FloatList(value=[float(x) for x in label_vec])),\n",
    "                'image':tf.train.Feature(float_list=tf.train.FloatList(value=[float(x) for x in img_vec]))\n",
    "            }\n",
    "        )    \n",
    "    )\n",
    "    tf_writer.write(example.SerializeToString())\n",
    "tf_writer.close()"
   ]
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