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    "# CS 20 : TensorFlow for Deep Learning Research\n",
    "## Lecture 05 : Variable sharing and managing experiments\n",
    "### Randomization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup"
   ]
  },
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.12.0\n"
     ]
    }
   ],
   "source": [
    "from __future__ import absolute_import, division, print_function\n",
    "import tensorflow as tf\n",
    "\n",
    "print(tf.__version__)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 1 : Session keeps track of the random state"
   ]
  },
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   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.85811085\n",
      "-0.20793143\n"
     ]
    }
   ],
   "source": [
    "c = tf.random_normal(shape = [], seed = 2)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    print(sess.run(c))\n",
    "    print(sess.run(c))"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 2 : Each new session will start the random state all over again"
   ]
  },
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   "execution_count": 3,
   "metadata": {},
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     "name": "stdout",
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      "-0.85811085\n",
      "-0.85811085\n",
      "-0.20793143\n"
     ]
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   ],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "c = tf.random_normal(shape = [], seed = 2)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    print(sess.run(c))\n",
    "    \n",
    "with tf.Session() as sess:\n",
    "    print(sess.run(c))\n",
    "    print(sess.run(c))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 3 : With operation level random seed, each op keeps its own seed"
   ]
  },
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   "execution_count": 4,
   "metadata": {},
   "outputs": [
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     "text": [
      "-0.85811085\n",
      "-0.85811085\n"
     ]
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   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "c = tf.random_normal(shape = [], seed = 2)\n",
    "d = tf.random_normal(shape = [], seed = 2)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    print(sess.run(c))\n",
    "    print(sess.run(d))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example 4 : Graph level random seed"
   ]
  },
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   "execution_count": 5,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "-1.4236197\n",
      "0.8052349\n"
     ]
    }
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   "source": [
    "tf.reset_default_graph()\n",
    "tf.set_random_seed(seed = 2)\n",
    "\n",
    "c = tf.random_normal(shape = [])\n",
    "d = tf.random_normal(shape = [])\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    print(sess.run(c))\n",
    "    print(sess.run(d))"
   ]
  },
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   "execution_count": 6,
   "metadata": {},
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     "output_type": "stream",
     "text": [
      "-1.4236197\n",
      "0.8052349\n"
     ]
    }
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   "source": [
    "tf.reset_default_graph()\n",
    "tf.set_random_seed(seed = 2)\n",
    "\n",
    "c = tf.random_normal(shape = [])\n",
    "d = tf.random_normal(shape = [])\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    print(sess.run(c))\n",
    "    print(sess.run(d))"
   ]
  }
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