{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 0.0667530298233 0.0202220570854 100\n",
      "2 0.101294636726 0.0143614476756 100\n",
      "4 0.159001708031 0.0122533362064 100\n",
      "8 0.284222841263 0.019770890117 100\n",
      "16 0.512441396713 0.0245514665774 100\n",
      "32 1.05267119408 0.0333890508251 100\n",
      "64 2.0679615736 0.0327217044203 100\n"
     ]
    }
   ],
   "source": [
    "all_results = {}\n",
    "for batch_size in range(7):\n",
    "    tf.reset_default_graph()\n",
    "    batch_size = 2**batch_size\n",
    "    inputs,outputs = tf.random_normal([batch_size, 299,299,3]), tf.random_normal([batch_size, 1000])\n",
    "    model,_ = nets.inception.inception_v3(inputs=inputs)\n",
    "    loss = tf.losses.mean_squared_error(model, outputs)\n",
    "    opt = tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(loss)\n",
    "    \n",
    "    with tf.train.MonitoredTrainingSession(config=tf.ConfigProto(log_device_placement=True)) as sess:\n",
    "        results = []\n",
    "        for i in range(100):\n",
    "            s = time.time()\n",
    "            sess.run(opt)\n",
    "            e = time.time()\n",
    "            results.append(e-s)\n",
    "        print(batch_size, np.median(results), np.std(results), len(results))\n",
    "        all_results[batch_size] = results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
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&quot;^save/Assign_109&quot;\\n  input: &quot;^save/Assign_110&quot;\\n  input: &quot;^save/Assign_111&quot;\\n  input: &quot;^save/Assign_112&quot;\\n  input: &quot;^save/Assign_113&quot;\\n  input: &quot;^save/Assign_114&quot;\\n  input: &quot;^save/Assign_115&quot;\\n  input: &quot;^save/Assign_116&quot;\\n  input: &quot;^save/Assign_117&quot;\\n  input: &quot;^save/Assign_118&quot;\\n  input: &quot;^save/Assign_119&quot;\\n  input: &quot;^save/Assign_120&quot;\\n  input: &quot;^save/Assign_121&quot;\\n  input: &quot;^save/Assign_122&quot;\\n  input: &quot;^save/Assign_123&quot;\\n  input: &quot;^save/Assign_124&quot;\\n  input: &quot;^save/Assign_125&quot;\\n  input: &quot;^save/Assign_126&quot;\\n  input: &quot;^save/Assign_127&quot;\\n  input: &quot;^save/Assign_128&quot;\\n  input: &quot;^save/Assign_129&quot;\\n  input: &quot;^save/Assign_130&quot;\\n  input: &quot;^save/Assign_131&quot;\\n  input: &quot;^save/Assign_132&quot;\\n  input: &quot;^save/Assign_133&quot;\\n  input: &quot;^save/Assign_134&quot;\\n  input: &quot;^save/Assign_135&quot;\\n  input: &quot;^save/Assign_136&quot;\\n  input: &quot;^save/Assign_137&quot;\\n  input: &quot;^save/Assign_138&quot;\\n  input: &quot;^save/Assign_139&quot;\\n  input: &quot;^save/Assign_140&quot;\\n  input: &quot;^save/Assign_141&quot;\\n  input: &quot;^save/Assign_142&quot;\\n  input: &quot;^save/Assign_143&quot;\\n  input: &quot;^save/Assign_144&quot;\\n  input: &quot;^save/Assign_145&quot;\\n  input: &quot;^save/Assign_146&quot;\\n  input: &quot;^save/Assign_147&quot;\\n  input: &quot;^save/Assign_148&quot;\\n  input: &quot;^save/Assign_149&quot;\\n  input: &quot;^save/Assign_150&quot;\\n  input: &quot;^save/Assign_151&quot;\\n  input: &quot;^save/Assign_152&quot;\\n  input: &quot;^save/Assign_153&quot;\\n  input: &quot;^save/Assign_154&quot;\\n  input: &quot;^save/Assign_155&quot;\\n  input: &quot;^save/Assign_156&quot;\\n  input: &quot;^save/Assign_157&quot;\\n  input: &quot;^save/Assign_158&quot;\\n  input: &quot;^save/Assign_159&quot;\\n  input: &quot;^save/Assign_160&quot;\\n  input: &quot;^save/Assign_161&quot;\\n  input: &quot;^save/Assign_162&quot;\\n  input: &quot;^save/Assign_163&quot;\\n  input: &quot;^save/Assign_164&quot;\\n  input: &quot;^save/Assign_165&quot;\\n  input: &quot;^save/Assign_166&quot;\\n  input: &quot;^save/Assign_167&quot;\\n  input: &quot;^save/Assign_168&quot;\\n  input: &quot;^save/Assign_169&quot;\\n  input: &quot;^save/Assign_170&quot;\\n  input: &quot;^save/Assign_171&quot;\\n  input: &quot;^save/Assign_172&quot;\\n  input: &quot;^save/Assign_173&quot;\\n  input: &quot;^save/Assign_174&quot;\\n  input: &quot;^save/Assign_175&quot;\\n  input: &quot;^save/Assign_176&quot;\\n  input: &quot;^save/Assign_177&quot;\\n  input: &quot;^save/Assign_178&quot;\\n  input: &quot;^save/Assign_179&quot;\\n  input: &quot;^save/Assign_180&quot;\\n  input: &quot;^save/Assign_181&quot;\\n  input: &quot;^save/Assign_182&quot;\\n  input: &quot;^save/Assign_183&quot;\\n  input: &quot;^save/Assign_184&quot;\\n  input: &quot;^save/Assign_185&quot;\\n  input: &quot;^save/Assign_186&quot;\\n  input: &quot;^save/Assign_187&quot;\\n  input: &quot;^save/Assign_188&quot;\\n  input: &quot;^save/Assign_189&quot;\\n  input: &quot;^save/Assign_190&quot;\\n  input: &quot;^save/Assign_191&quot;\\n  input: &quot;^save/Assign_192&quot;\\n  input: &quot;^save/Assign_193&quot;\\n  input: &quot;^save/Assign_194&quot;\\n  input: &quot;^save/Assign_195&quot;\\n}\\nnode {\\n  name: &quot;save/restore_all&quot;\\n  op: &quot;NoOp&quot;\\n  input: &quot;^save/restore_shard&quot;\\n}\\n';\n",
       "          }\n",
       "        </script>\n",
       "        <link rel=&quot;import&quot; href=&quot;https://tensorboard.appspot.com/tf-graph-basic.build.html&quot; onload=load()>\n",
       "        <div style=&quot;height:600px&quot;>\n",
       "          <tf-graph-basic id=&quot;graph0.2412690384660061&quot;></tf-graph-basic>\n",
       "        </div>\n",
       "    \"></iframe>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_graph(tf.get_default_graph())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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59TOL2Jzu3g/r1ZFPHerc+3xjwJckSWoh1m3ZwT1VuvdXn2z3Ph8Z8CVJklqI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\n/x9r3n8YnXgN6gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7f91c4c2e8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 251,
       "width": 380
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot()\n",
    "x=sorted(list(all_results.keys()))\n",
    "y=[np.median(all_results[x]) for x in x]\n",
    "ax.plot(x,y, label='actual')\n",
    "ax.set_xscale(\"log\",basex=2)\n",
    "# ax.plot(x, [y[1]*i for i in x],label='linear')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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w02bn5iInz83eQ1neB/7dnod+79cHj1RpiVt1hQU3/CVwdTnCtcDQOuxPRERE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\nJEnCwkCSJEkS8P9OGmH7XT3VdgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f801034ea20>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 265,
       "width": 387
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot()\n",
    "x=sorted(list(all_results.keys()))\n",
    "y=[x/np.median(all_results[x]) for x in x]\n",
    "y2=[x/np.min(all_results[x]) for x in x]\n",
    "speedup=[i/y[0] for i in y]\n",
    "speedup2=[i/y2[0] for i in y2]\n",
    "ax.plot(x,speedup, 'o-', label='Median')\n",
    "ax.plot(x,speedup2, 'o-', label='Best')\n",
    "ax.set_xscale(\"log\",basex=2)\n",
    "ax.set_xlabel(\"Batch Size\")\n",
    "ax.set_ylabel(\"Throughput (#item/sec) Speed Up\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}