export_and_evaluate_azure_tfestimator_model.ipynb

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   "source": [
    "## first downlaod the output of your model from azure blob storage\n",
    "ds = ws.get_default_datastore()\n",
    "\n",
    "ds.download(target_path='outputs',\n",
    "                   prefix='founder-classifier/outputs/model',\n",
    "                   show_progress=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from outputs/founder-classifier/outputs/model/1570442423\\variables\\variables\n"
     ]
    }
   ],
   "source": [
    "## restore your tf.estimator\n",
    "\n",
    "from tensorflow.contrib import predictor\n",
    "\n",
    "saved_model_location='outputs/founder-classifier/outputs/model/1570442423'\n",
    "\n",
    "loaded_model = predictor.from_saved_model(saved_model_location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy on the test set is:  0.8847352024922118\n"
     ]
    }
   ],
   "source": [
    "## load your test set\n",
    "import json\n",
    "import numpy as np\n",
    "\n",
    "with open('./data/test.json', 'rb') as f:\n",
    "    test_data=json.load(f)\n",
    "\n",
    "features=np.reshape(test_data['images'], (-1,28,28,1))\n",
    "\n",
    "## make predictions\n",
    "predictions = loaded_model({'x': features})['classes']\n",
    "\n",
    "true_labels=test_data['labels']\n",
    "\n",
    "## compute the accuracy\n",
    "count=0\n",
    "for i in range(len(predictions)):\n",
    "    if predictions[i]==true_labels[i]:\n",
    "        count+=1\n",
    "accuracy=count/len(predictions)\n",
    "print('The accuracy on the test set is: ', accuracy)"
   ]
  }
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