{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TensorFlow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyhf\n",
    "from pyhf import Model\n",
    "from pyhf.simplemodels import hepdata_like\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---\n",
      "as tensorflow\n",
      "-----\n",
      "<class 'tensorflow.python.framework.ops.Tensor'> [-22.877851486206055]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.summary.writer.writer.FileWriter at 0x11fe1af90>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source = {\n",
    "  \"binning\": [2,-0.5,1.5],\n",
    "  \"bindata\": {\n",
    "    \"data\":    [120.0, 180.0],\n",
    "    \"bkg\":     [100.0, 150.0],\n",
    "    \"bkgerr\":     [10.0, 10.0],\n",
    "    \"sig\":     [30.0, 95.0]\n",
    "  }\n",
    "}\n",
    "\n",
    "pdf  = hepdata_like(source['bindata']['sig'], source['bindata']['bkg'], source['bindata']['bkgerr'])\n",
    "data = source['bindata']['data'] + pdf.config.auxdata\n",
    "\n",
    "init_pars = pdf.config.suggested_init()\n",
    "par_bounds = pdf.config.suggested_bounds()\n",
    "\n",
    "\n",
    "print '---\\nas tensorflow\\n-----'\n",
    "import tensorflow as tf\n",
    "pyhf.tensorlib = pyhf.tensorflow_backend()\n",
    "v =  pdf.logpdf(init_pars,data)\n",
    "\n",
    "pyhf.tensorlib.session = tf.Session()\n",
    "print type(v),pyhf.tensorlib.tolist(v)\n",
    "\n",
    "\n",
    "import os\n",
    "tf.summary.FileWriter(os.getcwd(), pyhf.tensorlib.session.graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[4.]], dtype=float32), array([[12.]], dtype=float32)]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = tf.Variable([1.])\n",
    "y = tf.Variable([2.])\n",
    "\n",
    "z = x**2*y + y**3*x\n",
    "\n",
    "hessian = tf.hessians(z,[x,y])\n",
    "\n",
    "sess = tf.Session()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "sess.run(hessian)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[4.]]), array([[12.]])]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = tf.cast([1.], tf.float64)\n",
    "y = tf.cast([2.], tf.float64)\n",
    "\n",
    "z = x**2*y + y**3*x\n",
    "\n",
    "hessian = tf.hessians(z,[x,y])\n",
    "\n",
    "sess = tf.Session()\n",
    "sess.run(hessian)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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