Gussie  Hansen

Gussie Hansen

1617357438

Save Machine Learning And Deep Learning Models

Saving machine learning (ML) and deep learning (DL) models is an inevitable step in building a ML/DL model to save the state of an object whether for making predictions or reuse it for other purposes when needed.
Serialization (saving) is the process of translating a data structure or object into a format that can be transmitted/stored in a database or file while de-serializing (loading) is the reverse process in which stored objects are loaded from a database or a file.
In this post, you will discover how to save your model using python libraries using SK-Learn and Keras.

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Save Machine Learning And Deep Learning Models
Sasha  Roberts

Sasha Roberts

1659500100

Reform: Form Objects Decoupled From Models In Ruby

Reform

Form objects decoupled from your models.

Reform gives you a form object with validations and nested setup of models. It is completely framework-agnostic and doesn't care about your database.

Although reform can be used in any Ruby framework, it comes with Rails support, works with simple_form and other form gems, allows nesting forms to implement has_one and has_many relationships, can compose a form from multiple objects and gives you coercion.

Full Documentation

Reform is part of the Trailblazer framework. Full documentation is available on the project site.

Reform 2.2

Temporary note: Reform 2.2 does not automatically load Rails files anymore (e.g. ActiveModel::Validations). You need the reform-rails gem, see Installation.

Defining Forms

Forms are defined in separate classes. Often, these classes partially map to a model.

class AlbumForm < Reform::Form
  property :title
  validates :title, presence: true
end

Fields are declared using ::property. Validations work exactly as you know it from Rails or other frameworks. Note that validations no longer go into the model.

The API

Forms have a ridiculously simple API with only a handful of public methods.

  1. #initialize always requires a model that the form represents.
  2. #validate(params) updates the form's fields with the input data (only the form, not the model) and then runs all validations. The return value is the boolean result of the validations.
  3. #errors returns validation messages in a classic ActiveModel style.
  4. #sync writes form data back to the model. This will only use setter methods on the model(s).
  5. #save (optional) will call #save on the model and nested models. Note that this implies a #sync call.
  6. #prepopulate! (optional) will run pre-population hooks to "fill out" your form before rendering.

In addition to the main API, forms expose accessors to the defined properties. This is used for rendering or manual operations.

Setup

In your controller or operation you create a form instance and pass in the models you want to work on.

class AlbumsController
  def new
    @form = AlbumForm.new(Album.new)
  end

This will also work as an editing form with an existing album.

def edit
  @form = AlbumForm.new(Album.find(1))
end

Reform will read property values from the model in setup. In our example, the AlbumForm will call album.title to populate the title field.

Rendering Forms

Your @form is now ready to be rendered, either do it yourself or use something like Rails' #form_for, simple_form or formtastic.

= form_for @form do |f|
  = f.input :title

Nested forms and collections can be easily rendered with fields_for, etc. Note that you no longer pass the model to the form builder, but the Reform instance.

Optionally, you might want to use the #prepopulate! method to pre-populate fields and prepare the form for rendering.

Validation

After form submission, you need to validate the input.

class SongsController
  def create
    @form = SongForm.new(Song.new)

    #=> params: {song: {title: "Rio", length: "366"}}

    if @form.validate(params[:song])

The #validate method first updates the values of the form - the underlying model is still treated as immutuable and remains unchanged. It then runs all validations you provided in the form.

It's the only entry point for updating the form. This is per design, as separating writing and validation doesn't make sense for a form.

This allows rendering the form after validate with the data that has been submitted. However, don't get confused, the model's values are still the old, original values and are only changed after a #save or #sync operation.

Syncing Back

After validation, you have two choices: either call #save and let Reform sort out the rest. Or call #sync, which will write all the properties back to the model. In a nested form, this works recursively, of course.

It's then up to you what to do with the updated models - they're still unsaved.

Saving Forms

The easiest way to save the data is to call #save on the form.

if @form.validate(params[:song])
  @form.save  #=> populates album with incoming data
              #   by calling @form.album.title=.
else
  # handle validation errors.
end

This will sync the data to the model and then call album.save.

Sometimes, you need to do saving manually.

Default values

Reform allows default values to be provided for properties.

class AlbumForm < Reform::Form
  property :price_in_cents, default: 9_95
end

Saving Forms Manually

Calling #save with a block will provide a nested hash of the form's properties and values. This does not call #save on the models and allows you to implement the saving yourself.

The block parameter is a nested hash of the form input.

  @form.save do |hash|
    hash      #=> {title: "Greatest Hits"}
    Album.create(hash)
  end

You can always access the form's model. This is helpful when you were using populators to set up objects when validating.

  @form.save do |hash|
    album = @form.model

    album.update_attributes(hash[:album])
  end

Nesting

Reform provides support for nested objects. Let's say the Album model keeps some associations.

class Album < ActiveRecord::Base
  has_one  :artist
  has_many :songs
end

The implementation details do not really matter here, as long as your album exposes readers and writes like Album#artist and Album#songs, this allows you to define nested forms.

class AlbumForm < Reform::Form
  property :title
  validates :title, presence: true

  property :artist do
    property :full_name
    validates :full_name, presence: true
  end

  collection :songs do
    property :name
  end
end

You can also reuse an existing form from elsewhere using :form.

property :artist, form: ArtistForm

Nested Setup

Reform will wrap defined nested objects in their own forms. This happens automatically when instantiating the form.

album.songs #=> [<Song name:"Run To The Hills">]

form = AlbumForm.new(album)
form.songs[0] #=> <SongForm model: <Song name:"Run To The Hills">>
form.songs[0].name #=> "Run To The Hills"

Nested Rendering

When rendering a nested form you can use the form's readers to access the nested forms.

= text_field :title,         @form.title
= text_field "artist[name]", @form.artist.name

Or use something like #fields_for in a Rails environment.

= form_for @form do |f|
  = f.text_field :title

  = f.fields_for :artist do |a|
    = a.text_field :name

Nested Processing

validate will assign values to the nested forms. sync and save work analogue to the non-nested form, just in a recursive way.

The block form of #save would give you the following data.

@form.save do |nested|
  nested #=> {title:  "Greatest Hits",
         #    artist: {name: "Duran Duran"},
         #    songs: [{title: "Hungry Like The Wolf"},
         #            {title: "Last Chance On The Stairways"}]
         #   }
  end

The manual saving with block is not encouraged. You should rather check the Disposable docs to find out how to implement your manual tweak with the official API.

Populating Forms

Very often, you need to give Reform some information how to create or find nested objects when validateing. This directive is called populator and documented here.

Installation

Add this line to your Gemfile:

gem "reform"

Reform works fine with Rails 3.1-5.0. However, inheritance of validations with ActiveModel::Validations is broken in Rails 3.2 and 4.0.

Since Reform 2.2, you have to add the reform-rails gem to your Gemfile to automatically load ActiveModel/Rails files.

gem "reform-rails"

Since Reform 2.0 you need to specify which validation backend you want to use (unless you're in a Rails environment where ActiveModel will be used).

To use ActiveModel (not recommended because very out-dated).

require "reform/form/active_model/validations"
Reform::Form.class_eval do
  include Reform::Form::ActiveModel::Validations
end

To use dry-validation (recommended).

require "reform/form/dry"
Reform::Form.class_eval do
  feature Reform::Form::Dry
end

Put this in an initializer or on top of your script.

Compositions

Reform allows to map multiple models to one form. The complete documentation is here, however, this is how it works.

class AlbumForm < Reform::Form
  include Composition

  property :id,    on: :album
  property :title, on: :album
  property :songs, on: :cd
  property :cd_id, on: :cd, from: :id
end

When initializing a composition, you have to pass a hash that contains the composees.

AlbumForm.new(album: album, cd: CD.find(1))

More

Reform comes many more optional features, like hash fields, coercion, virtual fields, and so on. Check the full documentation here.

Reform is part of the Trailblazer project. Please buy my book to support the development and learn everything about Reform - there's two chapters dedicated to Reform!

Security And Strong_parameters

By explicitly defining the form layout using ::property there is no more need for protecting from unwanted input. strong_parameter or attr_accessible become obsolete. Reform will simply ignore undefined incoming parameters.

This is not Reform 1.x!

Temporary note: This is the README and API for Reform 2. On the public API, only a few tiny things have changed. Here are the Reform 1.2 docs.

Anyway, please upgrade and report problems and do not simply assume that we will magically find out what needs to get fixed. When in trouble, join us on Gitter.

Full documentation for Reform is available online, or support us and grab the Trailblazer book. There is an Upgrading Guide to help you migrate through versions.

Attributions!!!

Great thanks to Blake Education for giving us the freedom and time to develop this project in 2013 while working on their project.


Author: trailblazer
Source code: https://github.com/trailblazer/reform
License:  MIT license

#ruby  #ruby-on-rails

sophia tondon

sophia tondon

1620898103

5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

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Visit Blog- https://www.xplace.com/article/8743

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Marget D

Marget D

1618317562

Top Deep Learning Development Services | Hire Deep Learning Developer

View more: https://www.inexture.com/services/deep-learning-development/

We at Inexture, strategically work on every project we are associated with. We propose a robust set of AI, ML, and DL consulting services. Our virtuoso team of data scientists and developers meticulously work on every project and add a personalized touch to it. Because we keep our clientele aware of everything being done associated with their project so there’s a sense of transparency being maintained. Leverage our services for your next AI project for end-to-end optimum services.

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Nora Joy

1604154094

Hire Machine Learning Developers in India

Hire machine learning developers in India ,DxMinds Technologies is the best product engineering company in India making innovative solutions using Machine learning and deep learning. We are among the best to hire machine learning experts in India work in different industry domains like Healthcare retail, banking and finance ,oil and gas, ecommerce, telecommunication ,FMCG, fashion etc.
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Nora Joy

1607006620

Applications of machine learning in different industry domains

Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

  • ML and AI can offer high security in the transportation industry.
  • It offers high reliability of their services or vehicles.
  • The adoption of this technology in the transportation industry can increase the efficiency of the service.
  • In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

  • Identifying Diseases and Diagnosis
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Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

  • Fraud prevention
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Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

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Future of machine learning

Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

  • Improved Unsupervised Algorithms
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**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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