simple_math_tf.ipynb

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  {
   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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   "source": [
    "x = tf.placeholder(shape=(None,), dtype=tf.float32)\n",
    "c = tf.placeholder(shape=(None,), dtype=tf.float32)"
   ]
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   "source": [
    "def my_multiply(c, x):\n",
    "    return tf.multiply(c,x)"
   ]
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   "execution_count": 10,
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    {
     "data": {
      "text/plain": [
       "array([ 1.,  4.,  9.], dtype=float32)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
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    }
   ],
   "source": [
    "sess = tf.Session()\n",
    "sess.run(my_multiply(c,x), feed_dict={c : np.asarray([1,2,3], dtype=np.float32), x : np.asarray([1,2,3])})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "y = tf.gradients([my_multiply(c,x)], x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 1.,  2.,  3.], dtype=float32)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess.run(y, feed_dict={c : np.asarray([1,2,3], dtype=np.float32), x : np.asarray([4,5,6])})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  }
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