machinelearning_notebook/6_pytorch/0_basic/ref_dynamic-graph.ipynb

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   "source": [
    "# 动态图和静态图\n",
    "目前神经网络框架分为[静态图框架和动态图框架](https://blog.csdn.net/qq_36653505/article/details/87875279)，PyTorch 和 TensorFlow、Caffe 等框架最大的区别就是他们拥有不同的计算图表现形式。 TensorFlow 使用静态图，这意味着我们先定义计算图，然后不断使用它，而在 PyTorch 中，每次都会重新构建一个新的计算图。通过这次课程，我们会了解静态图和动态图之间的优缺点。\n",
    "\n",
    "对于使用者来说，两种形式的计算图有着非常大的区别，同时静态图和动态图都有他们各自的优点，比如动态图比较方便debug，使用者能够用任何他们喜欢的方式进行debug，同时非常直观，而静态图是通过先定义后运行的方式，之后再次运行的时候就不再需要重新构建计算图，所以速度会比动态图更快。"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://ws3.sinaimg.cn/large/006tNc79ly1fmai482qumg30rs0fmq6e.gif)"
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   "cell_type": "markdown",
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   "source": [
    "## PyTorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pytorch\n",
    "import torch\n",
    "first_counter = torch.Tensor([0])\n",
    "second_counter = torch.Tensor([10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "while (first_counter < second_counter):\n",
    "    first_counter += 2\n",
    "    second_counter += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([20.])\n",
      "tensor([20.])\n"
     ]
    }
   ],
   "source": [
    "print(first_counter)\n",
    "print(second_counter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到 PyTorch 的写法跟 Python 的写法是完全一致的，没有任何额外的学习成本\n",
    "\n",
    "上面的例子展示如何使用静态图和动态图构建 while 循环，看起来动态图的方式更加简单且直观，你觉得呢？"
   ]
  }
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