SparkDL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Learning With Apache Spark"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deep Learning Pipelines is an open source library created by Databricks that\n",
    "provides high-level APIs for scalable deep learning in Python with Apache Spark.\n",
    "\n",
    "It is an awesome effort and it won’t be long until is merged into the official\n",
    "API, so is worth taking a look of it.\n",
    "\n",
    "Some of the advantages of this library compared to the ones that joins Spark\n",
    "with DL are:\n",
    "\n",
    "* In the spirit of Spark and [Spark MLlib](https://spark.apache.org/mllib/), it\n",
    "provides easy-to-use APIs that enable deep learning in very few lines of code.\n",
    "* It focuses on ease of use and integration, without sacrificing performace.\n",
    "* It’s build by the creators of Apache Spark (which are also the main\n",
    "contributors) so it’s more likely for it to be merged as an official API than\n",
    "others.\n",
    "* It is written in Python, so it will integrate with all of its famous libraries,\n",
    "and right now it uses the power of TensorFlow and Keras, the two main libraries\n",
    "of the moment to do DL.\n",
    "\n",
    "Deep Learning Pipelines builds on Apache Spark’s [ML\n",
    "Pipelines](https://spark.apache.org/docs/latest/ml-pipeline.html) for training,\n",
    "and with Spark DataFrames and SQL for deploying models. It includes high-level\n",
    "APIs for common aspects of deep learning so they can be done efficiently in a\n",
    "few lines of code:\n",
    "\n",
    "* Image loading\n",
    "* Applying pre-trained models as transformers in a Spark ML pipeline\n",
    "* Transfer learning\n",
    "* Applying Deep Learning models at scale\n",
    "* Distributed hyperparameter tuning **(I will talk about this later)**\n",
    "* Deploying models in DataFrames and SQL\n",
    "\n",
    "I will describe each of these features in detail with examples. These examples\n",
    "comes from the official\n",
    "[notebook](https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/5669198905533692/3647723071348946/3983381308530741/latest.html)\n",
    "by Databricks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apache Spark on Deep Cognition\n",
    "\n",
    "To run and test the codes in this article you will need to create an account in\n",
    "[Deep Cognition](http://deepcognition.ai/register/). \n",
    "\n",
    "Is very easy and then you can access all of their features. When you log in this\n",
    "is what you should be seeing:\n",
    "\n",
    "![](https://cdn-images-1.medium.com/max/2000/1*8ijqj85Tjscv9xpXjHfEFA.png)\n",
    "\n",
    "Now just click on the left part, the Notebook button:\n",
    "\n",
    "![](https://cdn-images-1.medium.com/max/1600/1*mnMlcYuO2U-KzRvYSuj-Cw.png)\n",
    "\n",
    "And you will be on the Jupyter Notebook with all the installed packages :). Oh!\n",
    "A note here: The Spark Notebook (DLS SPARK) is an upcoming feature which will be\n",
    "released to public sometime next month and tell that it is still in private beta\n",
    "(just for this post).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Spark-DL into the notebook\n",
    "\n",
    "Until now there's no pip installation for the package so we will need to use this workaround to use it inside the notebook. After that we will start the `SparkSession` and `SparkContext`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "SUBMIT_ARGS = \"--packages databricks:spark-deep-learning:1.0.0-spark2.3-s_2.11 pyspark-shell\"\n",
    "os.environ[\"PYSPARK_SUBMIT_ARGS\"] = SUBMIT_ARGS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "\n",
    "spark = SparkSession.builder.appName(\"DL with Spark Deep Cognition\").getOrCreate()\n",
    "sc = spark.sparkContext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <div>\n",
       "            <p><b>SparkContext</b></p>\n",
       "\n",
       "            <p><a href=\"http://bf770e624de0:4040\">Spark UI</a></p>\n",
       "\n",
       "            <dl>\n",
       "              <dt>Version</dt>\n",
       "                <dd><code>v2.3.0</code></dd>\n",
       "              <dt>Master</dt>\n",
       "                <dd><code>local[*]</code></dd>\n",
       "              <dt>AppName</dt>\n",
       "                <dd><code>DL with Spark Deep Cognition</code></dd>\n",
       "            </dl>\n",
       "        </div>\n",
       "        "
      ],
      "text/plain": [
       "<SparkContext master=local[*] appName=DL with Spark Deep Cognition>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Image Loading\n",
    "\n",
    "The first step to applying deep learning on images is the ability to load the\n",
    "images. Deep Learning Pipelines created the utility functions that can load\n",
    "millions of images into a DataFrame and decode them automatically in a\n",
    "distributed fashion, allowing manipulation at scale. Now the newest version of spark\n",
    "(2.3.0) has this ability too and we will be using it to load the images.\n",
    "\n",
    "We will be using the archive of creative-commons licensed flower photos curated\n",
    "by TensorFlow to test this out. To get the set of flower photos, run these\n",
    "commands from the notebook (we will also create a sample folder):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100  218M  100  218M    0     0  6698k      0  0:00:33  0:00:33 --:--:-- 9651k\n"
     ]
    }
   ],
   "source": [
    "!curl -O http://download.tensorflow.org/example_images/flower_photos.tgz\n",
    "!tar xzf flower_photos.tgz\n",
    "!mkdir flower_photos/sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s copy some photos from the tulips and daisy folders to create a small sample of the photos. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!cp flower_photos/daisy/100080576_f52e8ee070_n.jpg flower_photos/sample/\n",
    "!cp flower_photos/daisy/10140303196_b88d3d6cec.jpg flower_photos/sample/\n",
    "!cp flower_photos/tulips/100930342_92e8746431_n.jpg flower_photos/sample/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To take a look at these images on the notebook you can run this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# collect all .png files in working dir\n",
    "fs = !ls flower_photos/sample/*.jpg\n",
    "\n",
    "\n",
    "import IPython.display as dp\n",
    "\n",
    "# create list of image objects\n",
    "images = []\n",
    "for ea in fs:\n",
    "    images.append(dp.Image(filename=ea, format='png'))\n",
    "\n",
    "# display all images\n",
    "for ea in images:\n",
    "    dp.display_png(ea)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let’s use Spark to load this images as a DataFrame. The method `spark.readImage` lets you read images in common formats (jpg, png, etc.) from HDFS storage into DataFrame. Each image is stored as a row in the imageSchema format. The recursive option allows you to read images from subfolders, for example for positive and negative labeled samples. The sampleRatio parameter allows you to experiment with a smaller sample of images before training a model with full data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyspark.ml.image import ImageSchema\n",
    "# Read images using Spark\n",
    "image_df = ImageSchema.readImages(\"flower_photos/sample/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we take a look at this dataframe we see that it spark created one column, called “image”."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+\n",
      "|               image|\n",
      "+--------------------+\n",
      "|[file:/Users/favi...|\n",
      "|[file:/Users/favi...|\n",
      "|[file:/Users/favi...|\n",
      "+--------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "image_df.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The image column contains a string column contains an image struct with schema == ImageSchema."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transfer learning\n",
    "\n",
    "![](https://cdn-images-1.medium.com/max/1600/1*6eDLqIz2sOtdI-6X87Ervg.png)\n",
    "\n",
    "Deep Learning Pipelines provides utilities to perform [transfer\n",
    "learning](https://en.wikipedia.org/wiki/Transfer_learning) on images, which is\n",
    "one of the fastest (code and run-time -wise) ways to start using deep learning.\n",
    "Using Deep Learning Pipelines, it can be done in just several lines of code.\n",
    "\n",
    "Deep Learning Pipelines enables fast transfer learning with the concept of a\n",
    "*Featurizer*. The following example combines the InceptionV3 model and logistic\n",
    "regression in Spark to adapt InceptionV3 to our specific domain. **The\n",
    "DeepImageFeaturizer automatically peels off the last layer of a pre-trained\n",
    "neural network and uses the output from all the previous layers as features for\n",
    "the logistic regression algorithm**. Since logistic regression is a simple and\n",
    "fast algorithm, this transfer learning training can converge quickly using far\n",
    "fewer images than are typically required to train a deep learning model from\n",
    "ground-up.\n",
    "\n",
    "Firstly, we need to create training & test DataFrames for transfer learning. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyspark.ml.image import ImageSchema\n",
    "from pyspark.sql.functions import lit\n",
    "from sparkdl.image import imageIO\n",
    "\n",
    "tulips_df = ImageSchema.readImages(\"flower_photos/tulips\").withColumn(\"label\", lit(1))\n",
    "daisy_df = imageIO.readImagesWithCustomFn(\"flower_photos/daisy\", decode_f=imageIO.PIL_decode).withColumn(\"label\", lit(0))\n",
    "tulips_train, tulips_test, _ = tulips_df.randomSplit([0.1, 0.05, 0.85])  # use larger training sets (e.g. [0.6, 0.4] for getting more images)\n",
    "daisy_train, daisy_test, _ = daisy_df.randomSplit([0.1, 0.05, 0.85])     # use larger training sets (e.g. [0.6, 0.4] for getting more images)\n",
    "train_df = tulips_train.unionAll(daisy_train)\n",
    "test_df = tulips_test.unionAll(daisy_test)\n",
    "\n",
    "# Under the hood, each of the partitions is fully loaded in memory, which may be expensive.\n",
    "# This ensure that each of the paritions has a small size.\n",
    "train_df = train_df.repartition(100)\n",
    "test_df = test_df.repartition(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now let’s train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyspark.ml.classification import LogisticRegression\n",
    "from pyspark.ml import Pipeline\n",
    "from sparkdl import DeepImageFeaturizer \n",
    "\n",
    "featurizer = DeepImageFeaturizer(inputCol=\"image\", outputCol=\"features\", modelName=\"InceptionV3\")\n",
    "lr = LogisticRegression(maxIter=10, regParam=0.05, elasticNetParam=0.3, labelCol=\"label\")\n",
    "p = Pipeline(stages=[featurizer, lr])\n",
    "\n",
    "p_model = p.fit(train_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s see how well the model does:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy = 0.9753086419753086\n"
     ]
    }
   ],
   "source": [
    "from pyspark.ml.evaluation import MulticlassClassificationEvaluator\n",
    "\n",
    "tested_df = p_model.transform(test_df)\n",
    "evaluator = MulticlassClassificationEvaluator(metricName=\"accuracy\")\n",
    "print(\"Test set accuracy = \" + str(evaluator.evaluate(tested_df.select(\"prediction\", \"label\"))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not so bad for an example and with no tunning at all!\n",
    "\n",
    "We can take look at where we are making mistakes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+-------------------+-----+\n",
      "|              origin|                  p|label|\n",
      "+--------------------+-------------------+-----+\n",
      "|file:/Users/favio...| 0.6765519606599172|    0|\n",
      "|file:/Users/favio...| 0.6488288065425233|    0|\n",
      "|file:/Users/favio...| 0.2990478006959378|    0|\n",
      "|file:/Users/favio...|0.24766667971694895|    0|\n",
      "|file:/Users/favio...|0.20110581819745543|    0|\n",
      "|file:/Users/favio...|  0.829475249733301|    1|\n",
      "|file:/Users/favio...| 0.8513949370932291|    1|\n",
      "|file:/Users/favio...|0.13291511982428988|    0|\n",
      "|file:/Users/favio...| 0.1311275271810219|    0|\n",
      "|file:/Users/favio...|0.12805711589185384|    0|\n",
      "+--------------------+-------------------+-----+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pyspark.sql.types import DoubleType\n",
    "from pyspark.sql.functions import expr\n",
    "from pyspark.sql.functions import *\n",
    "from pyspark.sql.types import *\n",
    "\n",
    "def _p1(v):\n",
    "    return float(v.array[1])\n",
    "take_one = udf(_p1, DoubleType())\n",
    "\n",
    "df = tested_df.withColumn(\"p\", take_one(tested_df.probability))\n",
    "wrong_df = df.orderBy(expr(\"abs(p - label)\"), ascending=False)\n",
    "wrong_df.select(\"image.origin\", \"p\", \"label\").show(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Applying Deep Learning models at scale\n",
    "\n",
    "Deep Learning Pipelines supports running pre-trained models in a distributed\n",
    "manner with Spark, available in both batch and [streaming data\n",
    "processing](https://databricks.com/blog/2016/07/28/structured-streaming-in-apache-spark.html).\n",
    "\n",
    "\n",
    "It houses some of the most popular models, enabling users to start using deep\n",
    "learning without the costly step of training a model. The predictions of the\n",
    "model, of course, is done in parallel with all the benefits that come with\n",
    "Spark.\n",
    "\n",
    "In addition to using the built-in models, users can plug in [Keras\n",
    "models](https://keras.io/models/about-keras-models/) and TensorFlow Graphs in a\n",
    "Spark prediction pipeline. This turns any single-node models on single-node\n",
    "tools into one that can be applied in a distributed fashion, on a large amount\n",
    "of data.\n",
    "\n",
    "The following code creates a Spark prediction pipeline using InceptionV3, a\n",
    "state-of-the-art convolutional neural network (CNN) model for image\n",
    "classification, and predicts what objects are in the images that we just loaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Froze 378 variables.\n",
      "Converted 378 variables to const ops.\n",
      "INFO:tensorflow:Froze 0 variables.\n",
      "Converted 0 variables to const ops.\n"
     ]
    }
   ],
   "source": [
    "from sparkdl import DeepImagePredictor\n",
    "# Read images using Spark\n",
    "image_df = ImageSchema.readImages(\"flower_photos/sample/\")\n",
    "\n",
    "predictor = DeepImagePredictor(inputCol=\"image\", outputCol=\"predicted_labels\", modelName=\"InceptionV3\", decodePredictions=True, topK=10)\n",
    "predictions_df = predictor.transform(image_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look to the predictions dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
      "|predicted_labels                                                                                                                                                                                                                                                                                                                                                        |\n",
      "+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
      "|[[n03930313, picket_fence, 0.1424783], [n11939491, daisy, 0.10951301], [n03991062, pot, 0.04505], [n02206856, bee, 0.03734662], [n02280649, cabbage_butterfly, 0.019011213], [n13133613, ear, 0.017185668], [n02219486, ant, 0.014198389], [n02281406, sulphur_butterfly, 0.013113698], [n12620546, hip, 0.012272579], [n03457902, greenhouse, 0.011370744]]            |\n",
      "|[[n11939491, daisy, 0.9532104], [n02219486, ant, 6.175268E-4], [n02206856, bee, 5.1203516E-4], [n02190166, fly, 4.0093894E-4], [n02165456, ladybug, 3.70687E-4], [n02281406, sulphur_butterfly, 3.0587992E-4], [n02112018, Pomeranian, 2.9011074E-4], [n01795545, black_grouse, 2.5667972E-4], [n02177972, weevil, 2.4875381E-4], [n07745940, strawberry, 2.3729511E-4]]|\n",
      "|[[n11939491, daisy, 0.89181453], [n02219486, ant, 0.0012404523], [n02206856, bee, 8.13047E-4], [n02190166, fly, 6.03804E-4], [n02165456, ladybug, 6.005444E-4], [n02281406, sulphur_butterfly, 5.32096E-4], [n04599235, wool, 4.6653638E-4], [n02112018, Pomeranian, 4.625338E-4], [n07930864, cup, 4.400617E-4], [n02177972, weevil, 4.2434104E-4]]                    |\n",
      "+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions_df.select(\"predicted_labels\").show(truncate=False,n=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the `predicted_labels` column shows \"daisy\" as a high probability\n",
    "class for all of sample flowers using this base model,** for some reason the\n",
    "tulip was closer to a picket fence than to a flower (maybe because of the\n",
    "background of the photo)**. \n",
    "\n",
    "However, as can be seen from the differences in the probability values, the\n",
    "neural network has the information to discern the two flower types. Hence our\n",
    "transfer learning example above was able to properly learn the differences\n",
    "between daisies and tulips starting from the base model.\n",
    "\n",
    "Let’s see how well our model discern the type of the flower:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = p_model.transform(image_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import *\n",
    "from pyspark.sql.types import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def _p1(v):\n",
    "    return float(v.array[1])\n",
    "take_one = udf(_p1, DoubleType())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------------------------------------------------------------------+--------------------+\n",
      "|origin                                                                          |p_daisy             |\n",
      "+--------------------------------------------------------------------------------+--------------------+\n",
      "|file:/Users/faviovazquez/SparkDL/flower_photos/sample/100930342_92e8746431_n.jpg|0.016760347798379538|\n",
      "|file:/Users/faviovazquez/SparkDL/flower_photos/sample/10140303196_b88d3d6cec.jpg|0.9704259547739851  |\n",
      "|file:/Users/faviovazquez/SparkDL/flower_photos/sample/100080576_f52e8ee070_n.jpg|0.9705190124824862  |\n",
      "+--------------------------------------------------------------------------------+--------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 100930342_92e8746431_n.jpg not a daisy\n",
    "df.select(\"image.origin\",(1-take_one(df.probability)).alias(\"p_daisy\")).show(truncate=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## For Keras users\n",
    "\n",
    "For applying Keras models in a distributed manner using Spark, KerasImageFileTransformer works on TensorFlow-backed Keras models. It \n",
    "\n",
    "- Internally creates a DataFrame containing a column of images by applying the user-specified image loading and processing function to the input DataFrame containing a column of image URIs\n",
    "- Loads a Keras model from the given model file path\n",
    "- Applies the model to the image DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use the transformer, we first need to have a Keras model stored as a file. For this notebook we'll just save the Keras built-in InceptionV3 model instead of training one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.applications import InceptionV3\n",
    "\n",
    "model = InceptionV3(weights=\"imagenet\")\n",
    "model.save('model-full.h5')  # saves to the local filesystem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will create a Keras transformer but first we will preprocess the images to work with it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.applications.inception_v3 import preprocess_input\n",
    "from keras.preprocessing.image import img_to_array, load_img\n",
    "import numpy as np\n",
    "from pyspark.sql.types import StringType\n",
    "from sparkdl import KerasImageFileTransformer\n",
    "\n",
    "def loadAndPreprocessKerasInceptionV3(uri):\n",
    "  # this is a typical way to load and prep images in keras\n",
    "    image = img_to_array(load_img(uri, target_size=(299, 299)))  # image dimensions for InceptionV3\n",
    "    image = np.expand_dims(image, axis=0)\n",
    "    return preprocess_input(image)\n",
    "\n",
    "transformer = KerasImageFileTransformer(inputCol=\"uri\", outputCol=\"predictions\",\n",
    "                                        modelFile='model-full.h5',  # local file path for model\n",
    "                                        imageLoader=loadAndPreprocessKerasInceptionV3,\n",
    "                                        outputMode=\"vector\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will read now the images and load them into a Spark Dataframe and them use our transformer to apply the model into the images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fs = !ls flower_photos/sample/*.jpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['flower_photos/sample/100080576_f52e8ee070_n.jpg',\n",
       " 'flower_photos/sample/100930342_92e8746431_n.jpg',\n",
       " 'flower_photos/sample/10140303196_b88d3d6cec.jpg']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "uri_df = spark.createDataFrame(fs, StringType()).toDF(\"uri\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+\n",
      "|                 uri|\n",
      "+--------------------+\n",
      "|flower_photos/sam...|\n",
      "|flower_photos/sam...|\n",
      "|flower_photos/sam...|\n",
      "+--------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "uri_df.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/faviovazquez/anaconda/lib/python3.6/site-packages/keras/models.py:252: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
      "  warnings.warn('No training configuration found in save file: '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Froze 378 variables.\n",
      "Converted 378 variables to const ops.\n",
      "INFO:tensorflow:Froze 0 variables.\n",
      "Converted 0 variables to const ops.\n"
     ]
    }
   ],
   "source": [
    "keras_pred_df = transformer.transform(uri_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+--------------------+\n",
      "|                 uri|         predictions|\n",
      "+--------------------+--------------------+\n",
      "|flower_photos/sam...|[7.43818964110687...|\n",
      "|flower_photos/sam...|[3.56566015398129...|\n",
      "|flower_photos/sam...|[3.49412839568685...|\n",
      "+--------------------+--------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "keras_pred_df.select(\"uri\", \"predictions\").show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working with general tensors\n",
    "\n",
    "Deep Learning Pipelines also provides ways to apply models with tensor inputs\n",
    "(up to 2 dimensions), written in popular deep learning libraries:\n",
    "\n",
    "* TensorFlow graphs\n",
    "* Keras models\n",
    "\n",
    "In this article we will focus only in the Keras models. The `KerasTransformer`\n",
    "applies a TensorFlow-backed Keras model to tensor inputs of up to 2 dimensions.\n",
    "It loads a Keras model from a given model file path and applies the model to a\n",
    "column of arrays (where an array corresponds to a Tensor), outputting a column\n",
    "of arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Froze 4 variables.\n",
      "Converted 4 variables to const ops.\n",
      "INFO:tensorflow:Froze 0 variables.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/faviovazquez/anaconda/lib/python3.6/site-packages/keras/models.py:252: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
      "  warnings.warn('No training configuration found in save file: '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converted 0 variables to const ops.\n"
     ]
    }
   ],
   "source": [
    "from sparkdl import KerasTransformer\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import numpy as np\n",
    "\n",
    "# Generate random input data\n",
    "num_features = 10\n",
    "num_examples = 100\n",
    "input_data = [{\"features\" : np.random.randn(num_features).astype(float).tolist()} for i in range(num_examples)]\n",
    "schema = StructType([ StructField(\"features\", ArrayType(FloatType()), True)])\n",
    "input_df = spark.createDataFrame(input_data, schema)\n",
    "\n",
    "# Create and save a single-hidden-layer Keras model for binary classification\n",
    "# NOTE: In a typical workflow, we'd train the model before exporting it to disk,\n",
    "# but we skip that step here for brevity\n",
    "model = Sequential()\n",
    "model.add(Dense(units=20, input_shape=[num_features], activation='relu'))\n",
    "model.add(Dense(units=1, activation='sigmoid'))\n",
    "model_path = \"simple-binary-classification\"\n",
    "model.save(model_path)\n",
    "\n",
    "# Create transformer and apply it to our input data\n",
    "transformer = KerasTransformer(inputCol=\"features\", outputCol=\"predictions\", modelFile=model_path)\n",
    "final_df = transformer.transform(input_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+--------------------+\n",
      "|  predictions|            features|\n",
      "+-------------+--------------------+\n",
      "| [0.86104786]|[-0.76344526, 0.2...|\n",
      "| [0.21693115]|[0.41084298, 0.93...|\n",
      "|[0.057743043]|[0.062970825, 0.3...|\n",
      "| [0.43409333]|[-0.43408343, -1....|\n",
      "| [0.43690935]|[-0.89413625, 0.8...|\n",
      "| [0.49984664]|[-0.82052463, -0....|\n",
      "|  [0.6204273]|[-0.5075533, 0.54...|\n",
      "|  [0.2285336]|[0.016106872, -0....|\n",
      "| [0.37478408]|[-1.6756374, 0.84...|\n",
      "|  [0.2997861]|[-0.34952268, 1.2...|\n",
      "|  [0.3885377]|[0.1639214, -0.22...|\n",
      "|  [0.5006814]|[0.91551965, -0.3...|\n",
      "| [0.20518135]|[-1.2620118, -0.4...|\n",
      "| [0.18882117]|[-0.14812712, 0.8...|\n",
      "| [0.49993372]|[1.4617485, -0.33...|\n",
      "| [0.42390883]|[-0.877813, 0.603...|\n",
      "|  [0.5232896]|[-0.031451378, -1...|\n",
      "| [0.45858437]|[0.9310042, -1.77...|\n",
      "| [0.49794272]|[-0.37061003, -1....|\n",
      "|  [0.2543479]|[0.41954428, 1.88...|\n",
      "+-------------+--------------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "final_df.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deploying Models in SQL\n",
    "\n",
    "One way to productionize a model is to deploy it as a Spark SQL User Defined Function, which allows anyone who knows SQL to use it. Deep Learning Pipelines provides mechanisms to take a deep learning model and register a Spark SQL User Defined Function (UDF). In particular, Deep Learning Pipelines 0.2.0 adds support for creating SQL UDFs from Keras models that work on image data.\n",
    "\n",
    "The resulting UDF takes a column (formatted as a image struct “SpImage”) and produces the output of the given Keras model; e.g. for Inception V3, it produces a real valued score vector over the ImageNet object categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Froze 0 variables.\n",
      "Converted 0 variables to const ops.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/faviovazquez/anaconda/lib/python3.6/site-packages/keras/models.py:252: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
      "  warnings.warn('No training configuration found in save file: '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Froze 378 variables.\n",
      "Converted 378 variables to const ops.\n",
      "INFO:tensorflow:Froze 0 variables.\n",
      "Converted 0 variables to const ops.\n",
      "INFO:tensorflow:Froze 0 variables.\n",
      "Converted 0 variables to const ops.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sparkdl.graph.builder.GraphFunction at 0x136ac2be0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.applications import InceptionV3\n",
    "from sparkdl.udf.keras_image_model import registerKerasImageUDF\n",
    "\n",
    "registerKerasImageUDF(\"inceptionV3_udf\", InceptionV3(weights=\"imagenet\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Keras workflows dealing with images, it’s common to have preprocessing steps before the model is applied to the image. If our workflow requires preprocessing, we can optionally provide a preprocessing function to UDF registration. The preprocessor should take in a filepath and return an image array; below is a simple example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Froze 0 variables.\n",
      "Converted 0 variables to const ops.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/faviovazquez/anaconda/lib/python3.6/site-packages/keras/models.py:252: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
      "  warnings.warn('No training configuration found in save file: '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Froze 378 variables.\n",
      "Converted 378 variables to const ops.\n",
      "INFO:tensorflow:Froze 0 variables.\n",
      "Converted 0 variables to const ops.\n",
      "INFO:tensorflow:Froze 0 variables.\n",
      "Converted 0 variables to const ops.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sparkdl.graph.builder.GraphFunction at 0x14454be48>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.applications import InceptionV3\n",
    "from sparkdl.udf.keras_image_model import registerKerasImageUDF\n",
    "\n",
    "def keras_load_img(fpath):\n",
    "    from keras.preprocessing.image import load_img, img_to_array\n",
    "    import numpy as np\n",
    "    img = load_img(fpath, target_size=(299, 299))\n",
    "    return img_to_array(img).astype(np.uint8)\n",
    "\n",
    "registerKerasImageUDF(\"inceptionV3_udf_with_preprocessing\", InceptionV3(weights=\"imagenet\"), keras_load_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once a UDF has been registered, it can be used in a SQL query:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pyspark.ml.image import ImageSchema\n",
    "\n",
    "image_df = ImageSchema.readImages(\"flower_photos/sample/\")\n",
    "image_df.registerTempTable(\"sample_images\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+\n",
      "|               image|\n",
      "+--------------------+\n",
      "|[file:/Users/favi...|\n",
      "|[file:/Users/favi...|\n",
      "|[file:/Users/favi...|\n",
      "+--------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "image_df.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
      "|predictions                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |\n",
      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--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "spark.sql(\"SELECT inceptionV3_udf_with_preprocessing(image) as predictions from sample_images\").show(truncate=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "This is very powerful. Once a data scientist builds the desired model, Deep Learning Pipelines makes it simple to expose it as a function in SQL, so anyone in their organization can use it — data engineers, data scientists, business analysts, anybody."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next part I’ll discuss Distributed Hyperparameter Tuning with Spark, and\n",
    "will try new models and examples :)."
   ]
  }
 ],
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