Tips on how to Construct a Machine Studying Challenge in Elixir

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Construct a machine studying challenge in Elixir.

Introduction to Machine Studying

Machine studying is an ever-growing space of curiosity for builders, companies, tech fans, and most people alike. From agile start-ups to trendsetting trade leaders, companies know that profitable implementation of the appropriate machine studying product might give them a considerable aggressive benefit.

We now have already seen companies reap important advantages of machine studying in manufacturing via automated chatbots and customised procuring experiences.

Given we not too long ago demonstrated learn how to full internet scraping in Elixir, we thought we would take it one step additional and present you learn how to apply this in a machine studying challenge.

You might also like:  A Easy Machine Studying Challenge in JavaScript

The Traditional Algorithmic Method vs Machine Studying

The standard strategy has all the time been algorithm centric. To do that, it’s worthwhile to design an environment friendly algorithm to repair edge instances and meet your information manipulation wants. The extra sophisticated your dataset, the more durable it turns into to cowl all of the angles, and in some unspecified time in the future, an algorithm is now not the easiest way to go.

Fortunately, machine studying affords an alternate. If you’re constructing a machine learning-based system, the objective is to search out dependencies in your information. You want the appropriate info to coach this system to resolve the questions it’s more likely to be requested. To offer the appropriate info, incoming information is important for the machine studying system. You should present satisfactory coaching datasets to realize success.

So with out additional adieu, we’ll present an instance tutorial for a machine studying challenge and present how we achieved success. Be at liberty to comply with alongside.

The Challenge Description

For this challenge, we’ll take a look at an e-commerce platform that provides real-time worth comparisons and solutions. The core performance of any e-commerce machine studying challenge is to:

  1. Extract information from web sites
  2. Course of this information
  3. Present intelligence and solutions to the client
  4. Variable step relying on actions and learnings
  5. Revenue

Some of the widespread issues is the necessity to group information in a constant method. For instance, to illustrate we wish to unify the classes of merchandise from all males’s vogue manufacturers (so we will render all merchandise inside a given class, throughout a number of information sources). Every website (and due to this fact information supply) will doubtless have inconsistent constructions and names, these should be unified and matched earlier than we will run an correct comparability.

For the aim of this information, we’ll construct a challenge that:

  1. Extracts the information from a gaggle of internet sites (on this case we’ll reveal learn how to extract information from the harveynorman.ie store)
  2. Trains a neural community to acknowledge a product class from the product picture
  3. Integrates the neural community into the Elixir code so it completes the picture recognition and suggests merchandise
  4. Builds an online app that glues all the pieces collectively.

Extracting the Information

As we talked about at the start, information is the cornerstone of any profitable machine studying system. The important thing to success at this step is to extract real-world-data that’s publicly out there after which put together it into coaching units. For our instance, we have to collect the essential details about the merchandise (title, description, SKU and picture URL, and so on.). We’ll use the extracted photographs and their classes to carry out the machine studying coaching.

The standard of the educated neural community mannequin is immediately associated to the standard of datasets you are offering. So it is necessary to ensure that the extracted information really is smart.

We’ll use a library referred to as Crawly to carry out the information extraction.

Crawly is an software framework for crawling internet sites and extracting structured information that can be utilized for a variety of helpful purposes, like information mining, info processing or historic archival. You’ll find out extra about it on the documentation web page. Or you possibly can go to our information on learn how to full internet scraping in Elixir.

Now that’s defined, let’s get began! To begin with, we’ll create a brand new Elixir challenge:

combine new products_advisor --sup 

Now the challenge is created, modify the deps operate of the combine.exs file, so it appears like this:

# Run "combine assist deps" to study dependencies. defp deps do [ {:crawly, "~> 0.1"}, ] finish

Now, fetch all of the dependencies: combine deps.get, and we’re able to go. For the subsequent step, implement the module accountable for crawling harveynorman.ie web site. Save the next code beneath the lib/products_advisor/spiders/harveynorman.ex

defmodule HarveynormanIe do @behaviour Crawly.Spider require Logger @impl Crawly.Spider def base_url(), do: "https://www.harveynorman.ie" @impl Crawly.Spider def init() do [ start_urls: [ "https://www.harveynorman.ie/tvs-headphones/" ] ] finish @impl Crawly.Spider def parse_item(response) do # Extracting pagination urls pagination_urls = response.physique |> Floki.discover("ol.pager li a") |> Floki.attribute("href") # Extracting product urls product_urls = response.physique |> Floki.discover("a.product-img") |> Floki.attribute("href") all_urls = pagination_urls ++ product_urls # Changing URLs into Crawly requests requests = all_urls |> Enum.map(&build_absolute_url/1) |> Enum.map(&Crawly.Utils.request_from_url/1) # Extracting merchandise fields title = response.physique |> Floki.discover("h1.product-title") |> Floki.textual content() id = response.physique |> Floki.discover(".product-id") |> Floki.textual content() class = response.physique |> Floki.discover(".nav-breadcrumbs :nth-child(3)") |> Floki.textual content() description = response.physique |> Floki.discover(".product-tab-wrapper") |> Floki.textual content() photographs = response.physique |> Floki.discover(" .pict") |> Floki.attribute("src") |> Enum.map(&build_image_url/1) %Crawly.ParsedItem{ :gadgets => [ %{ id: id, title: title, category: category, images: images, description: description } ], :requests => requests } finish defp build_absolute_url(url), do: URI.merge(base_url(), url) |> to_string() defp build_image_url(url) do URI.merge("https://hniesfp.imgix.internet", url) |> to_string() finish finish

Right here we’re implementing a module referred to as HarveynormanIe which triggers a Crawly.Spider conduct by defining its callbacks: init/0 (used to create preliminary request utilized by the spider code to fetch the preliminary pages), base_url/0 (used to filter out unrelated urls, e.g. urls resulting in the surface world) and parse_item/1 (accountable for the conversion of the downloaded request into gadgets and new requests to comply with).

Now for the essential configuration:

Right here we’ll use the next settings to configure Crawly for our platform:

config :crawly, # Shut spider if it extracts lower than 10 gadgets per minute closespider_timeout: 10, # Begin 16 concurrent employees per area concurrent_requests_per_domain: 16, follow_redirects: true, # Outline merchandise construction (required fields) merchandise: [:title, :id, :category, :description], # Outline merchandise identifyer (used to filter out duplicated gadgets) item_id: :id, # Outline merchandise merchandise pipelines pipelines: [ Crawly.Pipelines.Validate, Crawly.Pipelines.DuplicatesFilter, Crawly.Pipelines.JSONEncoder ]

That is it. Our fundamental crawler is prepared, now we will get the information extracted in a JL format, despatched to a folder beneath the title: /tmp/HarveynormanIe.jl

Crawly helps a variety of configuration choices, like base_store_path which lets you retailer gadgets beneath totally different places, see the associated a part of the documentation right here. The complete evaluation of Crawly’s capabilities is exterior of the scope of this weblog publish.

Use the next command to begin the spider:

iex -S combine
Crawly.Engine.start_spider(HarveynormanIe)

You will note the next entries amongst your logs:

6:34:48.639 [debug] Scraped "{"title":"Sony MDR-E9LP In-Ear Headphones | Blue","photographs":["https://hniesfp.imgix.net/8/images/detailed/161/MDRE9LPL.AE.jpg?fit=fill&bg=0FFF&w=833&h=555&auto=format,compress","https://hniesfp.imgix.net/8/images/feature_variant/48/sony_logo_v3.jpg?fit=fill&bg=0FFF&w=264&h=68&auto=format,compress"],"id":"MDRE9LPL.AE","description":"Neodymium Magnet13.5mm driver unit reproduces highly effective bass sound.Pair with a Music PlayerUse your headphones with a Walkman, "<> ...

The above entries point out {that a} crawling course of is efficiently working, and we’re getting gadgets saved in our file system.

TensorFlow Mannequin Coaching

To simplify and pace up the mannequin coaching course of, we’re going to use a pre-trained picture classifier. We’ll use a picture classifier educated on ImageNet to create a brand new classification layer on high of utilizing a switch studying approach. The brand new mannequin will probably be primarily based on MobileNet V2 with a depth multiplier of 0.5 and an enter dimension of 224×224 pixels.

This half relies on the TensorFlow tutorial on learn how to learn how to retrain a picture classifier for brand spanking new classes. For those who adopted the earlier steps, then the coaching information set has already been downloaded (scraped) right into a configured listing (/tmp/products_advisor by default). All the pictures are situated in response to their class:

/tmp/products_advisor ├── building_&_hardware ├── computer_accessories ├── connected_home ├── headphones ├── hi-fi,_audio_&_speakers ├── home_cinema ├── lighting_&_electrical ├── storage_&_home ├── instruments ├── toughbuilt_24in_wall_organizer ├── tv_&_audio_accessories └── tvs

Earlier than the mannequin may be educated, let’s evaluation the downloaded information set. You may see that some classes include a really small variety of scraped photographs. In instances with lower than 200 photographs, there may be not sufficient information to precisely practice your machine studying program, so we will delete these classes.

discover /tmp/products_advisor -depth 1 -type d  -exec bash -c "echo -ne '{}'; ls '{}' | wc -l" ;  | awk '$2<200 {print $1}'  | xargs -L1 rm -rf

This may depart us with simply 5 classes that can be utilized for the brand new mannequin:

/tmp/products_advisor ├── headphones ├── hi-fi,_audio_&_speakers ├── instruments ├── tv_&_audio_accessories └── tvs

Making a mannequin is as straightforward as working a python script that was created by the TensorFlow authors and may be discovered within the official TensorFlow Github repository:

TFMODULE=https://tfhub.dev/google/imagenet/mobilenet_v2_050_224/classification/2 python bin/retrain.py  --tfhub_module=$TFMODULE  --bottleneck_dir=tf/bottlenecks  --how_many_training_steps=1000  --model_dir=tf/fashions  --summaries_dir=tf/training_summaries  --output_graph=tf/retrained_graph.pb  --output_labels=tf/retrained_labels.txt  --image_dir=/tmp/products_advisor

On the MacBook Professional 2018 2.2 GHz Intel Core i7 this course of takes roughly 5 minutes. Consequently, the retrained graph, together with new label classes, may be discovered within the configured places (tf/retrained_graph.pb and tf/retrained_labels.txt on this instance), these can be utilized for additional picture classification:

IMAGE_PATH="/tmp/products_advisor/hi-fi,_audio_&_speakers/0017c7f1-129f-4fa7-a62b-9766d2cb4486.jpeg" python bin/label_image.py  --graph=tf/retrained_graph.pb  --labels tf/retrained_labels.txt  --image=$IMAGE_PATH  --input_layer=Placeholder  --output_layer=final_result  --input_height=224  --input_width=224 hi-fi audio audio system 0.9721675
instruments 0.01919974
television audio equipment 0.008398962
headphones 0.00015944676
tvs 7.433378e-05

As you possibly can see, the newly educated mannequin categorized the pictures from the coaching set with 0.9721675 likelihood of belonging to the “hi-fi audio audio system” class.

Picture Classification Utilizing Elixir

Utilizing python a tensor may be created utilizing the next code:

import tensorflow as tf def read_tensor_from_image_file(file_name): file_reader = tf.read_file("file_reader", input_name) image_reader = tf.picture.decode_jpeg( file_reader, channels=3, title="jpeg_reader") float_caster = tf.forged(image_reader, tf.float32) dims_expander = tf.expand_dims(float_caster, 0) resized = tf.picture.resize_bilinear(dims_expander, [224, 224]) normalized = tf.divide(resized, [input_std]) sess = tf.Session() return sess.run(normalized)

Now let’s classify the pictures from an Elixir software. TensorFlow offers APIs for the next languages: Python, C++, Java, Go and JavaScript. Clearly, there isn’t any native help for BEAM languages. We might’ve used C++ bindings, although the C++ library is barely designed to work with the bazel construct device.

Let’s depart the combo integration with bazel as an train to a curious reader and as a substitute check out the C API, that can be utilized as native applied features (NIF) for Elixir. Happily, there isn’t any want to write down bindings for Elixir as there is a library that has virtually all the pieces that we’d like: https://github.com/anshuman23/tensorflex.

As we noticed earlier, to provide a picture as an enter for a TensorFlow session, it needs to be transformed to an appropriate format: four-dimensional tensor that accommodates a decoded normalized picture that’s 224×224 in dimension (as outlined within the chosen MobileNet V2 mannequin). The output is a 2-dimensional tensor that may maintain a vector of values. For a newly educated mannequin, the output is acquired as a 5x1 float32 tensor. 5 comes from the variety of courses within the mannequin.

Picture Decoding

Let’s assume that the pictures are going to be supplied encoded in JPEG. We might write a library to decode JPEG in Elixir, nonetheless, there are a number of open supply C libraries that can be utilized from NIFs. The opposite choice can be to seek for an Elixir library, that already offers this performance. Hex.pm exhibits that there is a library referred to as imago that may decode photographs from totally different codecs and carry out some post-processing. It makes use of rust and will depend on different rust libraries to carry out its decoding. Nearly all its performance is redundant in our case. To scale back the variety of dependencies and for academic functions, let’s cut up this into 2 easy Elixir libraries that will probably be accountable for JPEG decoding and picture resizing.

JPEG Decoding

This library will use a JPEG API to decode and supply the picture. This makes the Elixir a part of the library accountable for loading a NIF and documenting the APIs:

defmodule Jaypeg do @moduledoc Easy library for JPEG processing. ## Decoding elixir {:okay, <<104, 146, ...>>, [width: 2000, height: 1333, channels: 3]} = Jaypeg.decode(File.learn!("file/picture.jpg")) @on_load :load_nifs @doc Decode JPEG picture and return details about the decode picture akin to width, peak and variety of channels. ## Examples iex> Jaypeg.decode(File.learn!("file/picture.jpg")) {:okay, <<104, 146, ...>>, [width: 2000, height: 1333, channels: 3]} def decode(_encoded_image) do :erlang.nif_error(:nif_not_loaded) finish def load_nifs do :okay = :erlang.load_nif(Software.app_dir(:jaypeg, "priv/jaypeg"), 0) finish
Finish

The NIF implementation shouldn’t be far more sophisticated. It initializes all the pieces vital for decoding the JPEG variables, passes the supplied content material of the picture as a stream to a JPEG decoder and ultimately cleans up after itself:

static ERL_NIF_TERM decode(ErlNifEnv *env, int argc, const ERL_NIF_TERM argv[]) { ERL_NIF_TERM jpeg_binary_term; jpeg_binary_term = argv[0]; if (!enif_is_binary(env, jpeg_binary_term)) { return enif_make_badarg(env); } ErlNifBinary jpeg_binary; enif_inspect_binary(env, jpeg_binary_term, &jpeg_binary); struct jpeg_decompress_struct cinfo; struct jpeg_error_mgr jerr; cinfo.err = jpeg_std_error(&jerr); jpeg_create_decompress(&cinfo); FILE * img_src = fmemopen(jpeg_binary.information, jpeg_binary.dimension, "rb"); if (img_src == NULL) return enif_make_tuple2(env, enif_make_atom(env, "error"), enif_make_atom(env, "fmemopen")); jpeg_stdio_src(&cinfo, img_src); int error_check; error_check = jpeg_read_header(&cinfo, TRUE); if (error_check != 1) return enif_make_tuple2(env, enif_make_atom(env, "error"), enif_make_atom(env, "bad_jpeg")); jpeg_start_decompress(&cinfo); int width, peak, num_pixels, row_stride; width = cinfo.output_width; peak = cinfo.output_height; num_pixels = cinfo.output_components; unsigned lengthy output_size; output_size = width * peak * num_pixels; row_stride = width * num_pixels; ErlNifBinary bmp_binary; enif_alloc_binary(output_size, &bmp_binary); whereas (cinfo.output_scanline < cinfo.output_height) { unsigned char *buf[1]; buf[0] = bmp_binary.information + cinfo.output_scanline * row_stride; jpeg_read_scanlines(&cinfo, buf, 1); } jpeg_finish_decompress(&cinfo); jpeg_destroy_decompress(&cinfo); fclose(img_src); ERL_NIF_TERM bmp_term; bmp_term = enif_make_binary(env, &bmp_binary); ERL_NIF_TERM properties_term; properties_term = decode_properties(env, width, peak, num_pixels); return enif_make_tuple3( env, enif_make_atom(env, "okay"), bmp_term, properties_term);
}

Now, all that is left to do to make the tooling work is to declare the NIF features and definitions. The complete code is obtainable on GitHub.

Picture Resizing

Despite the fact that it’s potential to reimplement the picture operation algorithm utilizing Elixir, that is out of the scope of this train and we determined to make use of C/C++ stb library, that’s distributed beneath a public area and may be simply built-in as an Elixir NIF. The library is actually only a proxy for a C operate that resizes a picture, the Elixir half is devoted to the NIF load and documentation:

static ERL_NIF_TERM resize(ErlNifEnv *env, int argc, const ERL_NIF_TERM argv[]) { ErlNifBinary in_img_binary; enif_inspect_binary(env, argv[0], &in_img_binary); unsigned in_width, in_height, num_channels; enif_get_uint(env, argv[1], &in_width); enif_get_uint(env, argv[2], &in_height); enif_get_uint(env, argv[3], &num_channels); unsigned out_width, out_height; enif_get_uint(env, argv[4], &out_width); enif_get_uint(env, argv[5], &out_height); unsigned lengthy output_size; output_size = out_width * out_height * num_channels; ErlNifBinary out_img_binary; enif_alloc_binary(output_size, &out_img_binary); if (stbir_resize_uint8( in_img_binary.information, in_width, in_height, 0, out_img_binary.information, out_width, out_height, 0, num_channels) != 1) return enif_make_tuple2( env, enif_make_atom(env, "error"), enif_make_atom(env, "resize")); ERL_NIF_TERM out_img_term; out_img_term = enif_make_binary(env, &out_img_binary); return enif_make_tuple2(env, enif_make_atom(env, "okay"), out_img_term);
}

The picture resizing library is obtainable on GitHub as nicely.

Making a Tensor From an Picture

Now it is time to create a tensor from the processed photographs (after it has been decoded and resized). To have the ability to load a processed picture as a tensor, the Tensorflex library ought to be prolonged with 2 features:

  1. Create a matrix from a supplied binary
  2. Create a float32 tensor from a given matrix.

Implementation of the features are very Tensorflex particular and would not make a lot sense to a reader with out an understanding of the context. NIF implementation may be discovered on GitHub and may be discovered beneath features binary_to_matrix and matrix_to_float32_tensor respectively.

Placing All the pieces Collectively

As soon as all vital parts can be found, it is time to put all the pieces collectively. This half is much like what may be seen at the start of the weblog publish, the place the picture was labeled utilizing Python, however this time we’re going to use Elixir to leverage all of the libraries that we have now modified:

def classify_image(picture, graph, labels) do {:okay, decoded, properties} = Jaypeg.decode(picture) in_width = properties[:width] in_height = properties[:height] channels = properties[:channels] peak = width = 224 {:okay, resized} = ImgUtils.resize(decoded, in_width, in_height, channels, width, peak) {:okay, input_tensor} = Tensorflex.binary_to_matrix(resized, width, peak * channels) |> Tensorflex.divide_matrix_by_scalar(255) |> Tensorflex.matrix_to_float32_tensor({1, width, peak, channels}) {:okay, output_tensor} = Tensorflex.create_matrix(1, 2, [[length(labels), 1]]) |> Tensorflex.float32_tensor_alloc() Tensorflex.run_session( graph, input_tensor, output_tensor, "Placeholder", "final_result" ) finish

classify_image operate returns an inventory of chances for every given label:

iex(1)> picture = File.learn!("/tmp/television.jpeg")
<<255, 216, 255, 224, 0, 16, 74, 70, 73, 70, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 255, 219, 0, 132, 0, 9, 6, 7, 19, 19, 18, 21, 18, 19, 17, 18, 22, 19, 21, 21, 21, 22, 18, 23, 19, 23, 18, 19, 21, 23, ...>>
iex(2)> {:okay, graph} = Tensorflex.read_graph("/tmp/retrained_graph.pb")
{:okay, %Tensorflex.Graph{ def: #Reference<0.2581978403.3326476294.49326>, title: "/Customers/grigory/work/image_classifier/priv/retrained_graph.pb" }}
iex(3)> labels = ImageClassifier.read_labels("/tmp/retrained_labels.txt")
["headphones", "hi fi audio speakers", "tools", "tv audio accessories", "tvs"]
iex(4)> probes = ImageClassifier.classify_image(picture, graph, labels)
[ [1.605743818799965e-6, 2.0029481220262824e-6, 3.241990925744176e-4, 3.040388401132077e-4, 0.9993681311607361]
]

retrained_graph.pb and retrained_labels.txt may be discovered within the tf listing of the products-advisor-model-trainer repository that was talked about earlier on within the mannequin coaching step. If the mannequin was educated efficiently, tf listing ought to be much like this tree:

/products-advisor-model-trainer/tf/ ├── bottlenecks ├── retrained_graph.pb ├── retrained_labels.txt └── training_summaries

Essentially the most possible label can simply be discovered by the next line:

iex(6)> Listing.flatten(probes) |> Enum.zip(labels) |> Enum.max()
{0.9993681311607361, "tvs"}

Be taught Extra

So there you’ve it. This can be a fundamental demonstration of how Elixir can be utilized to finish machine studying initiatives. The complete code is obtainable on the GitHub. If you would like to remain up-to-date with extra initiatives like this, why not signal as much as our publication? Or try our detailed weblog on learn how to full internet scraping in Elixir. Or, in the event you’re planning a machine studying challenge, why not speak to us, we would be completely happy to assist.

Additional Studying

6 Causes Why Your Machine Studying Challenge Will Fail to Get Into Manufacturing

What Builders Have to Know About Machine Studying within the SDLC

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