{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.6.9 64-bit",
   "metadata": {
    "interpreter": {
     "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os,sys,math\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "import mnist_dataloader\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "torch.Size([])\ntorch.Size([])\n[1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "a = torch.randn(10,14)\n",
    "b = a.shape[1:1]\n",
    "print(b)\n",
    "b.numel()\n",
    "\n",
    "print(b)\n",
    "\n",
    "b = torch.Size([1])\n",
    "c = torch.Size([2,3])\n",
    "d = torch.Size(torch.cat([torch.tensor(b),torch.tensor(c)]))\n",
    "\n",
    "d = [*b,*c]\n",
    "\n",
    "print(d)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}