PreKnowledge/1.第一个tf程序.ipynb

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    "# 第一个tf程序\n",
    "\n",
    "在这里，我们开启第一个tf程序\n",
    "\n",
    "tf的运行方式是先构造计算图，因此，主要流程为：\n",
    "\n",
    "1. 将整个计算流程图完全搭建好。\n",
    "2. 构建session\n",
    "3. 通过session运行流程图\n",
    "\n",
    "**清注意：** 在构建图的过程中，所有的内容对你都是不可见的，只有用session运行之后，才能看到整个数据流真实的过程。\n",
    "\n",
    "**建议：** 在写tf代码时，尽可能将关键结点写上注释，例如变量的形状、数据类型之类的，不然回头看时会很痛苦。"
   ]
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 通过tf.constant创建一个常量，这就是我们的图\n",
    "message = tf.constant('Hello World!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建session\n",
    "sess = tf.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Welcome to the exciting world of Deep Neural Networks!'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run，看一下message结点的结果\n",
    "sess.run(message)"
   ]
  }
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