Object Detection on Android using TensorFlow Lite

Getting started with TensorFlow Lite on Android, basic object detection.

A short tutorial showcasing some of the functionality for object detection using TensorFlow Lite on a Samsung Galaxy Note Android device.

TensorFlow Lite Github:
https://github.com/tensorflow/examples​

Android Studio:
https://developer.android.com/studio

Subscribe: https://www.youtube.com/channel/UC6WJoKEufFQT87BX1l_IRVQ

#tensorflow

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Build an Android application with Kivy Python framework

If you’re a Python developer thinking about getting started with mobile development, then the Kivy framework is your best bet. With Kivy, you can develop platform-independent applications that compile for iOS, Android, Windows, macOS, and Linux. In this article, we’ll cover Android specifically because it is the most used.

We’ll build a simple random number generator app that you can install on your phone and test when you are done. To follow along with this article, you should be familiar with Python. Let’s get started!

Getting started with Kivy

First, you’ll need a new directory for your app. Make sure you have Python installed on your machine and open a new Python file. You’ll need to install the Kivy module from your terminal using either of the commands below. To avoid any package conflicts, be sure you’re installing Kivy in a virtual environment:

pip install kivy 
//
pip3 install kivy 

Once you have installed Kivy, you should see a success message from your terminal that looks like the screenshots below:

Kivy installation

Successful Kivy installation

 

Next, navigate into your project folder. In the main.py file, we’ll need to import the Kivy module and specify which version we want. You can use Kivy v2.0.0, but if you have a smartphone that is older than Android 8.0, I recommend using Kivy v1.9.0. You can mess around with the different versions during the build to see the differences in features and performance.

Add the version number right after the import kivy line as follows:

kivy.require('1.9.0')

Now, we’ll create a class that will basically define our app; I’ll name mine RandomNumber. This class will inherit the app class from Kivy. Therefore, you need to import the app by adding from kivy.app import App:

class RandomNumber(App): 

In the RandomNumber class, you’ll need to add a function called build, which takes a self parameter. To actually return the UI, we’ll use the build function. For now, I have it returned as a simple label. To do so, you’ll need to import Label using the line from kivy.uix.label import Label:

import kivy
from kivy.app import App
from kivy.uix.label import Label

class RandomNumber(App):
  def build(self):
    return Label(text="Random Number Generator")

Now, our app skeleton is complete! Before moving forward, you should create an instance of the RandomNumber class and run it in your terminal or IDE to see the interface:

import kivy from kivy.app import App from kivy.uix.label import Label class RandomNumber(App):  def build(self):    return Label(text="Random Number Generator") randomApp = RandomNumber() randomApp.run()

When you run the class instance with the text Random Number Generator, you should see a simple interface or window that looks like the screenshot below:

 

Simple interface after running the code

You won’t be able to run the text on Android until you’ve finished building the whole thing.

Outsourcing the interface

Next, we’ll need a way to outsource the interface. First, we’ll create a Kivy file in our directory that will house most of our design work. You’ll want to name this file the same name as your class using lowercase letters and a .kv extension. Kivy will automatically associate the class name and the file name, but it may not work on Android if they are exactly the same.

Inside that .kv file, you need to specify the layout for your app, including elements like the label, buttons, forms, etc. To keep this demonstration simple, I’ll add a label for the title Random Number, a label that will serve as a placeholder for the random number that is generated _, and a Generate button that calls the generate function.

My .kv file looks like the code below, but you can mess around with the different values to fit your requirements:

<boxLayout>:
    orientation: "vertical"
    Label:
        text: "Random Number"
        font_size: 30
        color: 0, 0.62, 0.96

    Label:
        text: "_"
        font_size: 30

    Button:
        text: "Generate"
        font_size: 15 

In the main.py file, you no longer need the Label import statement because the Kivy file takes care of your UI. However, you do need to import boxlayout, which you will use in the Kivy file.

In your main file, you need to add the import statement and edit your main.py file to read return BoxLayout() in the build method:

from kivy.uix.boxlayout import BoxLayout

If you run the command above, you should see a simple interface that has the random number title, the _ place holder, and the clickable generate button:

Random Number app rendered

Notice that you didn’t have to import anything for the Kivy file to work. Basically, when you run the app, it returns boxlayout by looking for a file inside the Kivy file with the same name as your class. Keep in mind, this is a simple interface, and you can make your app as robust as you want. Be sure to check out the Kv language documentation.

Generate the random number function

Now that our app is almost done, we’ll need a simple function to generate random numbers when a user clicks the generate button, then render that random number into the app interface. To do so, we’ll need to change a few things in our files.

First, we’ll import the module that we’ll use to generate a random number with import random. Then, we’ll create a function or method that calls the generated number. For this demonstration, I’ll use a range between 0 and 2000. Generating the random number is simple with the random.randint(0, 2000) command. We’ll add this into our code in a moment.

Next, we’ll create another class that will be our own version of the box layout. Our class will have to inherit the box layout class, which houses the method to generate random numbers and render them on the interface:

class MyRoot(BoxLayout):
    def __init__(self):
        super(MyRoot, self).__init__()

Within that class, we’ll create the generate method, which will not only generate random numbers but also manipulate the label that controls what is displayed as the random number in the Kivy file.

To accommodate this method, we’ll first need to make changes to the .kv file . Since the MyRoot class has inherited the box layout, you can make MyRoot the top level element in your .kv file:

<MyRoot>:
    BoxLayout:
        orientation: "vertical"
        Label:
            text: "Random Number"
            font_size: 30
            color: 0, 0.62, 0.96

        Label:
            text: "_"
            font_size: 30

        Button:
            text: "Generate"
            font_size: 15

Notice that you are still keeping all your UI specifications indented in the Box Layout. After this, you need to add an ID to the label that will hold the generated numbers, making it easy to manipulate when the generate function is called. You need to specify the relationship between the ID in this file and another in the main code at the top, just before the BoxLayout line:

<MyRoot>:
    random_label: random_label
    BoxLayout:
        orientation: "vertical"
        Label:
            text: "Random Number"
            font_size: 30
            color: 0, 0.62, 0.96

        Label:
            id: random_label
            text: "_"
            font_size: 30

        Button:
            text: "Generate"
            font_size: 15

The random_label: random_label line basically means that the label with the ID random_label will be mapped to random_label in the main.py file, meaning that any action that manipulates random_label will be mapped on the label with the specified name.

We can now create the method to generate the random number in the main file:

def generate_number(self):
    self.random_label.text = str(random.randint(0, 2000))

# notice how the class method manipulates the text attributre of the random label by a# ssigning it a new random number generate by the 'random.randint(0, 2000)' funcion. S# ince this the random number generated is an integer, typecasting is required to make # it a string otherwise you will get a typeError in your terminal when you run it.

The MyRoot class should look like the code below:

class MyRoot(BoxLayout):
    def __init__(self):
        super(MyRoot, self).__init__()

    def generate_number(self):
        self.random_label.text = str(random.randint(0, 2000))

Congratulations! You’re now done with the main file of the app. The only thing left to do is make sure that you call this function when the generate button is clicked. You need only add the line on_press: root.generate_number() to the button selection part of your .kv file:

<MyRoot>:
    random_label: random_label
    BoxLayout:
        orientation: "vertical"
        Label:
            text: "Random Number"
            font_size: 30
            color: 0, 0.62, 0.96

        Label:
            id: random_label
            text: "_"
            font_size: 30

        Button:
            text: "Generate"
            font_size: 15
            on_press: root.generate_number()

Now, you can run the app.

Compiling our app on Android

Before compiling our app on Android, I have some bad news for Windows users. You’ll need Linux or macOS to compile your Android application. However, you don’t need to have a separate Linux distribution, instead, you can use a virtual machine.

To compile and generate a full Android .apk application, we’ll use a tool called Buildozer. Let’s install Buildozer through our terminal using one of the commands below:

pip3 install buildozer
//
pip install buildozer

Now, we’ll install some of Buildozer’s required dependencies. I am on Linux Ergo, so I’ll use Linux-specific commands. You should execute these commands one by one:

sudo apt update
sudo apt install -y git zip unzip openjdk-13-jdk python3-pip autoconf libtool pkg-config zlib1g-dev libncurses5-dev libncursesw5-dev libtinfo5 cmake libffi-dev libssl-dev

pip3 install --upgrade Cython==0.29.19 virtualenv 

# add the following line at the end of your ~/.bashrc file
export PATH=$PATH:~/.local/bin/

After executing the specific commands, run buildozer init. You should see an output similar to the screenshot below:

Buildozer successful initialization

The command above creates a Buildozer .spec file, which you can use to make specifications to your app, including the name of the app, the icon, etc. The .spec file should look like the code block below:

[app]

# (str) Title of your application
title = My Application

# (str) Package name
package.name = myapp

# (str) Package domain (needed for android/ios packaging)
package.domain = org.test

# (str) Source code where the main.py live
source.dir = .

# (list) Source files to include (let empty to include all the files)
source.include_exts = py,png,jpg,kv,atlas

# (list) List of inclusions using pattern matching
#source.include_patterns = assets/*,images/*.png

# (list) Source files to exclude (let empty to not exclude anything)
#source.exclude_exts = spec

# (list) List of directory to exclude (let empty to not exclude anything)
#source.exclude_dirs = tests, bin

# (list) List of exclusions using pattern matching
#source.exclude_patterns = license,images/*/*.jpg

# (str) Application versioning (method 1)
version = 0.1

# (str) Application versioning (method 2)
# version.regex = __version__ = \['"\](.*)['"]
# version.filename = %(source.dir)s/main.py

# (list) Application requirements
# comma separated e.g. requirements = sqlite3,kivy
requirements = python3,kivy

# (str) Custom source folders for requirements
# Sets custom source for any requirements with recipes
# requirements.source.kivy = ../../kivy

# (list) Garden requirements
#garden_requirements =

# (str) Presplash of the application
#presplash.filename = %(source.dir)s/data/presplash.png

# (str) Icon of the application
#icon.filename = %(source.dir)s/data/icon.png

# (str) Supported orientation (one of landscape, sensorLandscape, portrait or all)
orientation = portrait

# (list) List of service to declare
#services = NAME:ENTRYPOINT_TO_PY,NAME2:ENTRYPOINT2_TO_PY

#
# OSX Specific
#

#
# author = © Copyright Info

# change the major version of python used by the app
osx.python_version = 3

# Kivy version to use
osx.kivy_version = 1.9.1

#
# Android specific
#

# (bool) Indicate if the application should be fullscreen or not
fullscreen = 0

# (string) Presplash background color (for new android toolchain)
# Supported formats are: #RRGGBB #AARRGGBB or one of the following names:
# red, blue, green, black, white, gray, cyan, magenta, yellow, lightgray,
# darkgray, grey, lightgrey, darkgrey, aqua, fuchsia, lime, maroon, navy,
# olive, purple, silver, teal.
#android.presplash_color = #FFFFFF

# (list) Permissions
#android.permissions = INTERNET

# (int) Target Android API, should be as high as possible.
#android.api = 27

# (int) Minimum API your APK will support.
#android.minapi = 21

# (int) Android SDK version to use
#android.sdk = 20

# (str) Android NDK version to use
#android.ndk = 19b

# (int) Android NDK API to use. This is the minimum API your app will support, it should usually match android.minapi.
#android.ndk_api = 21

# (bool) Use --private data storage (True) or --dir public storage (False)
#android.private_storage = True

# (str) Android NDK directory (if empty, it will be automatically downloaded.)
#android.ndk_path =

# (str) Android SDK directory (if empty, it will be automatically downloaded.)
#android.sdk_path =

# (str) ANT directory (if empty, it will be automatically downloaded.)
#android.ant_path =

# (bool) If True, then skip trying to update the Android sdk
# This can be useful to avoid excess Internet downloads or save time
# when an update is due and you just want to test/build your package
# android.skip_update = False

# (bool) If True, then automatically accept SDK license
# agreements. This is intended for automation only. If set to False,
# the default, you will be shown the license when first running
# buildozer.
# android.accept_sdk_license = False

# (str) Android entry point, default is ok for Kivy-based app
#android.entrypoint = org.renpy.android.PythonActivity

# (str) Android app theme, default is ok for Kivy-based app
# android.apptheme = "@android:style/Theme.NoTitleBar"

# (list) Pattern to whitelist for the whole project
#android.whitelist =

# (str) Path to a custom whitelist file
#android.whitelist_src =

# (str) Path to a custom blacklist file
#android.blacklist_src =

# (list) List of Java .jar files to add to the libs so that pyjnius can access
# their classes. Don't add jars that you do not need, since extra jars can slow
# down the build process. Allows wildcards matching, for example:
# OUYA-ODK/libs/*.jar
#android.add_jars = foo.jar,bar.jar,path/to/more/*.jar

# (list) List of Java files to add to the android project (can be java or a
# directory containing the files)
#android.add_src =

# (list) Android AAR archives to add (currently works only with sdl2_gradle
# bootstrap)
#android.add_aars =

# (list) Gradle dependencies to add (currently works only with sdl2_gradle
# bootstrap)
#android.gradle_dependencies =

# (list) add java compile options
# this can for example be necessary when importing certain java libraries using the 'android.gradle_dependencies' option
# see https://developer.android.com/studio/write/java8-support for further information
# android.add_compile_options = "sourceCompatibility = 1.8", "targetCompatibility = 1.8"

# (list) Gradle repositories to add {can be necessary for some android.gradle_dependencies}
# please enclose in double quotes 
# e.g. android.gradle_repositories = "maven { url 'https://kotlin.bintray.com/ktor' }"
#android.add_gradle_repositories =

# (list) packaging options to add 
# see https://google.github.io/android-gradle-dsl/current/com.android.build.gradle.internal.dsl.PackagingOptions.html
# can be necessary to solve conflicts in gradle_dependencies
# please enclose in double quotes 
# e.g. android.add_packaging_options = "exclude 'META-INF/common.kotlin_module'", "exclude 'META-INF/*.kotlin_module'"
#android.add_gradle_repositories =

# (list) Java classes to add as activities to the manifest.
#android.add_activities = com.example.ExampleActivity

# (str) OUYA Console category. Should be one of GAME or APP
# If you leave this blank, OUYA support will not be enabled
#android.ouya.category = GAME

# (str) Filename of OUYA Console icon. It must be a 732x412 png image.
#android.ouya.icon.filename = %(source.dir)s/data/ouya_icon.png

# (str) XML file to include as an intent filters in <activity> tag
#android.manifest.intent_filters =

# (str) launchMode to set for the main activity
#android.manifest.launch_mode = standard

# (list) Android additional libraries to copy into libs/armeabi
#android.add_libs_armeabi = libs/android/*.so
#android.add_libs_armeabi_v7a = libs/android-v7/*.so
#android.add_libs_arm64_v8a = libs/android-v8/*.so
#android.add_libs_x86 = libs/android-x86/*.so
#android.add_libs_mips = libs/android-mips/*.so

# (bool) Indicate whether the screen should stay on
# Don't forget to add the WAKE_LOCK permission if you set this to True
#android.wakelock = False

# (list) Android application meta-data to set (key=value format)
#android.meta_data =

# (list) Android library project to add (will be added in the
# project.properties automatically.)
#android.library_references =

# (list) Android shared libraries which will be added to AndroidManifest.xml using <uses-library> tag
#android.uses_library =

# (str) Android logcat filters to use
#android.logcat_filters = *:S python:D

# (bool) Copy library instead of making a libpymodules.so
#android.copy_libs = 1

# (str) The Android arch to build for, choices: armeabi-v7a, arm64-v8a, x86, x86_64
android.arch = armeabi-v7a

# (int) overrides automatic versionCode computation (used in build.gradle)
# this is not the same as app version and should only be edited if you know what you're doing
# android.numeric_version = 1

#
# Python for android (p4a) specific
#

# (str) python-for-android fork to use, defaults to upstream (kivy)
#p4a.fork = kivy

# (str) python-for-android branch to use, defaults to master
#p4a.branch = master

# (str) python-for-android git clone directory (if empty, it will be automatically cloned from github)
#p4a.source_dir =

# (str) The directory in which python-for-android should look for your own build recipes (if any)
#p4a.local_recipes =

# (str) Filename to the hook for p4a
#p4a.hook =

# (str) Bootstrap to use for android builds
# p4a.bootstrap = sdl2

# (int) port number to specify an explicit --port= p4a argument (eg for bootstrap flask)
#p4a.port =


#
# iOS specific
#

# (str) Path to a custom kivy-ios folder
#ios.kivy_ios_dir = ../kivy-ios
# Alternately, specify the URL and branch of a git checkout:
ios.kivy_ios_url = https://github.com/kivy/kivy-ios
ios.kivy_ios_branch = master

# Another platform dependency: ios-deploy
# Uncomment to use a custom checkout
#ios.ios_deploy_dir = ../ios_deploy
# Or specify URL and branch
ios.ios_deploy_url = https://github.com/phonegap/ios-deploy
ios.ios_deploy_branch = 1.7.0

# (str) Name of the certificate to use for signing the debug version
# Get a list of available identities: buildozer ios list_identities
#ios.codesign.debug = "iPhone Developer: <lastname> <firstname> (<hexstring>)"

# (str) Name of the certificate to use for signing the release version
#ios.codesign.release = %(ios.codesign.debug)s


[buildozer]

# (int) Log level (0 = error only, 1 = info, 2 = debug (with command output))
log_level = 2

# (int) Display warning if buildozer is run as root (0 = False, 1 = True)
warn_on_root = 1

# (str) Path to build artifact storage, absolute or relative to spec file
# build_dir = ./.buildozer

# (str) Path to build output (i.e. .apk, .ipa) storage
# bin_dir = ./bin

#    -----------------------------------------------------------------------------
#    List as sections
#
#    You can define all the "list" as [section:key].
#    Each line will be considered as a option to the list.
#    Let's take [app] / source.exclude_patterns.
#    Instead of doing:
#
#[app]
#source.exclude_patterns = license,data/audio/*.wav,data/images/original/*
#
#    This can be translated into:
#
#[app:source.exclude_patterns]
#license
#data/audio/*.wav
#data/images/original/*
#


#    -----------------------------------------------------------------------------
#    Profiles
#
#    You can extend section / key with a profile
#    For example, you want to deploy a demo version of your application without
#    HD content. You could first change the title to add "(demo)" in the name
#    and extend the excluded directories to remove the HD content.
#
#[app@demo]
#title = My Application (demo)
#
#[app:source.exclude_patterns@demo]
#images/hd/*
#
#    Then, invoke the command line with the "demo" profile:
#
#buildozer --profile demo android debug

If you want to specify things like the icon, requirements, loading screen, etc., you should edit this file. After making all the desired edits to your application, run buildozer -v android debug from your app directory to build and compile your application. This may take a while, especially if you have a slow machine.

After the process is done, your terminal should have some logs, one confirming that the build was successful:

Android successful build

You should also have an APK version of your app in your bin directory. This is the application executable that you will install and run on your phone:

Android .apk in the bin directory

Conclusion

Congratulations! If you have followed this tutorial step by step, you should have a simple random number generator app on your phone. Play around with it and tweak some values, then rebuild. Running the rebuild will not take as much time as the first build.

As you can see, building a mobile application with Python is fairly straightforward, as long as you are familiar with the framework or module you are working with. Regardless, the logic is executed the same way.

Get familiar with the Kivy module and it’s widgets. You can never know everything all at once. You only need to find a project and get your feet wet as early as possible. Happy coding.

Link: https://blog.logrocket.com/build-android-application-kivy-python-framework/

#python 

Cree Una Aplicación De Android Con El Marco Kivy Python

Si es un desarrollador de Python que está pensando en comenzar con el desarrollo móvil, entonces el marco Kivy es su mejor opción. Con Kivy, puede desarrollar aplicaciones independientes de la plataforma que compilan para iOS, Android, Windows, macOS y Linux. En este artículo, cubriremos Android específicamente porque es el más utilizado.

Construiremos una aplicación generadora de números aleatorios simple que puede instalar en su teléfono y probar cuando haya terminado. Para continuar con este artículo, debe estar familiarizado con Python. ¡Empecemos!

Primeros pasos con Kivy

Primero, necesitará un nuevo directorio para su aplicación. Asegúrese de tener Python instalado en su máquina y abra un nuevo archivo de Python. Deberá instalar el módulo Kivy desde su terminal usando cualquiera de los comandos a continuación. Para evitar conflictos de paquetes, asegúrese de instalar Kivy en un entorno virtual:

pip install kivy 
//
pip3 install kivy 

Una vez que haya instalado Kivy, debería ver un mensaje de éxito de su terminal que se parece a las capturas de pantalla a continuación:

Instalación decepcionada

Instalación exitosa de Kivy

 

A continuación, navegue a la carpeta de su proyecto. En el main.pyarchivo, necesitaremos importar el módulo Kivy y especificar qué versión queremos. Puede usar Kivy v2.0.0, pero si tiene un teléfono inteligente anterior a Android 8.0, le recomiendo usar Kivy v1.9.0. Puede jugar con las diferentes versiones durante la compilación para ver las diferencias en las características y el rendimiento.

Agregue el número de versión justo después de la import kivylínea de la siguiente manera:

kivy.require('1.9.0')

Ahora, crearemos una clase que básicamente definirá nuestra aplicación; Voy a nombrar el mío RandomNumber. Esta clase heredará la appclase de Kivy. Por lo tanto, debe importar appagregando from kivy.app import App:

class RandomNumber(App): 

En la RandomNumberclase, deberá agregar una función llamada build, que toma un selfparámetro. Para devolver la interfaz de usuario, usaremos la buildfunción. Por ahora, lo tengo devuelto como una simple etiqueta. Para hacerlo, deberá importar Labelusando la línea from kivy.uix.label import Label:

import kivy
from kivy.app import App
from kivy.uix.label import Label

class RandomNumber(App):
  def build(self):
    return Label(text="Random Number Generator")

¡Ahora, el esqueleto de nuestra aplicación está completo! Antes de continuar, debe crear una instancia de la RandomNumberclase y ejecutarla en su terminal o IDE para ver la interfaz:

importar kivy de kivy.app importar aplicación de kivy.uix.label clase de etiqueta de importación RandomNumber(App): def build(self): return Label(text="Generador de números aleatorios") randomApp = RandomNumber() randomApp.run()

Cuando ejecuta la instancia de clase con el texto Random Number Generator, debería ver una interfaz o ventana simple que se parece a la siguiente captura de pantalla:

 

Interfaz simple después de ejecutar el código.

No podrá ejecutar el texto en Android hasta que haya terminado de construir todo.

Externalización de la interfaz

A continuación, necesitaremos una forma de subcontratar la interfaz. Primero, crearemos un archivo Kivy en nuestro directorio que albergará la mayor parte de nuestro trabajo de diseño. Querrá nombrar este archivo con el mismo nombre que su clase usando letras minúsculas y una .kvextensión. Kivy asociará automáticamente el nombre de la clase y el nombre del archivo, pero es posible que no funcione en Android si son exactamente iguales.

Dentro de ese .kvarchivo, debe especificar el diseño de su aplicación, incluidos elementos como la etiqueta, los botones, los formularios, etc. Para simplificar esta demostración, agregaré una etiqueta para el título Random Number, una etiqueta que servirá como marcador de posición. para el número aleatorio que se genera _, y un Generatebotón que llama a la generatefunción.

Mi .kvarchivo se parece al siguiente código, pero puede jugar con los diferentes valores para que se ajusten a sus requisitos:

<boxLayout>:
    orientation: "vertical"
    Label:
        text: "Random Number"
        font_size: 30
        color: 0, 0.62, 0.96

    Label:
        text: "_"
        font_size: 30

    Button:
        text: "Generate"
        font_size: 15 

En el main.pyarchivo, ya no necesita la Labeldeclaración de importación porque el archivo Kivy se encarga de su interfaz de usuario. Sin embargo, necesita importar boxlayout, que utilizará en el archivo Kivy.

En su archivo principal, debe agregar la declaración de importación y editar su main.pyarchivo para leer return BoxLayout()el buildmétodo:

from kivy.uix.boxlayout import BoxLayout

Si ejecuta el comando anterior, debería ver una interfaz simple que tiene el título del número aleatorio, el _marcador de posición y el generatebotón en el que se puede hacer clic:

Aplicación de números aleatorios renderizada

Tenga en cuenta que no tuvo que importar nada para que funcione el archivo Kivy. Básicamente, cuando ejecuta la aplicación, regresa boxlayoutbuscando un archivo dentro del archivo Kivy con el mismo nombre que su clase. Tenga en cuenta que esta es una interfaz simple y puede hacer que su aplicación sea tan robusta como desee. Asegúrese de consultar la documentación del idioma Kv .

Generar la función de números aleatorios

Ahora que nuestra aplicación está casi terminada, necesitaremos una función simple para generar números aleatorios cuando un usuario haga clic en el generatebotón y luego mostrar ese número aleatorio en la interfaz de la aplicación. Para hacerlo, necesitaremos cambiar algunas cosas en nuestros archivos.

Primero, importaremos el módulo que usaremos para generar un número aleatorio con import random. Luego, crearemos una función o método que llame al número generado. Para esta demostración, usaré un rango entre 0y 2000. Generar el número aleatorio es simple con el random.randint(0, 2000)comando. Agregaremos esto a nuestro código en un momento.

A continuación, crearemos otra clase que será nuestra propia versión del box layout. Nuestra clase tendrá que heredar la box layoutclase, que alberga el método para generar números aleatorios y representarlos en la interfaz:

class MyRoot(BoxLayout):
    def __init__(self):
        super(MyRoot, self).__init__()

Dentro de esa clase, crearemos el generatemétodo, que no solo generará números aleatorios, sino que también manipulará la etiqueta que controla lo que se muestra como número aleatorio en el archivo Kivy.

Para acomodar este método, primero necesitaremos hacer cambios en el .kvarchivo. Dado que la MyRootclase ha heredado el box layout, puede crear MyRootel elemento de nivel superior en su .kvarchivo:

<MyRoot>:
    BoxLayout:
        orientation: "vertical"
        Label:
            text: "Random Number"
            font_size: 30
            color: 0, 0.62, 0.96

        Label:
            text: "_"
            font_size: 30

        Button:
            text: "Generate"
            font_size: 15

Tenga en cuenta que todavía mantiene todas las especificaciones de la interfaz de usuario con sangría en el archivo Box Layout. Después de esto, debe agregar una identificación a la etiqueta que contendrá los números generados, lo que facilita la manipulación cuando generatese llama a la función. Debe especificar la relación entre la ID en este archivo y otra en el código principal en la parte superior, justo antes de la BoxLayoutlínea:

<MyRoot>:
    random_label: random_label
    BoxLayout:
        orientation: "vertical"
        Label:
            text: "Random Number"
            font_size: 30
            color: 0, 0.62, 0.96

        Label:
            id: random_label
            text: "_"
            font_size: 30

        Button:
            text: "Generate"
            font_size: 15

La random_label: random_labellínea básicamente significa que la etiqueta con el ID random_labelse asignará a random_labelen el main.pyarchivo, lo que significa que cualquier acción que manipula random_labelserán mapeados en la etiqueta con el nombre especificado.

Ahora podemos crear el método para generar el número aleatorio en el archivo principal:

def generate_number(self):
    self.random_label.text = str(random.randint(0, 2000))

# notice how the class method manipulates the text attributre of the random label by a# ssigning it a new random number generate by the 'random.randint(0, 2000)' funcion. S# ince this the random number generated is an integer, typecasting is required to make # it a string otherwise you will get a typeError in your terminal when you run it.

La MyRootclase debería parecerse al siguiente código:

class MyRoot(BoxLayout):
    def __init__(self):
        super(MyRoot, self).__init__()

    def generate_number(self):
        self.random_label.text = str(random.randint(0, 2000))

¡Felicidades! Ya ha terminado con el archivo principal de la aplicación. Lo único que queda por hacer es asegurarse de llamar a esta función cuando se haga generateclic en el botón. Solo necesita agregar la línea on_press: root.generate_number()a la parte de selección de botones de su .kvarchivo:

<MyRoot>:
    random_label: random_label
    BoxLayout:
        orientation: "vertical"
        Label:
            text: "Random Number"
            font_size: 30
            color: 0, 0.62, 0.96

        Label:
            id: random_label
            text: "_"
            font_size: 30

        Button:
            text: "Generate"
            font_size: 15
            on_press: root.generate_number()

Ahora, puede ejecutar la aplicación.

Compilando nuestra aplicación en Android

Antes de compilar nuestra aplicación en Android, tengo malas noticias para los usuarios de Windows. Necesitará Linux o macOS para compilar su aplicación de Android. Sin embargo, no necesita tener una distribución de Linux separada, en su lugar, puede usar una máquina virtual.

Para compilar y generar una .apkaplicación Android completa , usaremos una herramienta llamada Buildozer . Instalemos Buildozer a través de nuestra terminal usando uno de los siguientes comandos:

pip3 install buildozer
//
pip install buildozer

Ahora, instalaremos algunas de las dependencias requeridas de Buildozer. Estoy en Linux Ergo, así que usaré comandos específicos de Linux. Debe ejecutar estos comandos uno por uno:

sudo apt update
sudo apt install -y git zip unzip openjdk-13-jdk python3-pip autoconf libtool pkg-config zlib1g-dev libncurses5-dev libncursesw5-dev libtinfo5 cmake libffi-dev libssl-dev

pip3 install --upgrade Cython==0.29.19 virtualenv 

# add the following line at the end of your ~/.bashrc file
export PATH=$PATH:~/.local/bin/

Después de ejecutar los comandos específicos, ejecute buildozer init. Debería ver un resultado similar a la captura de pantalla a continuación:

Inicialización exitosa de Buildozer

El comando anterior crea un .specarchivo Buildozer , que puede usar para hacer especificaciones para su aplicación, incluido el nombre de la aplicación, el ícono, etc. El .specarchivo debe verse como el bloque de código a continuación:

[app]

# (str) Title of your application
title = My Application

# (str) Package name
package.name = myapp

# (str) Package domain (needed for android/ios packaging)
package.domain = org.test

# (str) Source code where the main.py live
source.dir = .

# (list) Source files to include (let empty to include all the files)
source.include_exts = py,png,jpg,kv,atlas

# (list) List of inclusions using pattern matching
#source.include_patterns = assets/*,images/*.png

# (list) Source files to exclude (let empty to not exclude anything)
#source.exclude_exts = spec

# (list) List of directory to exclude (let empty to not exclude anything)
#source.exclude_dirs = tests, bin

# (list) List of exclusions using pattern matching
#source.exclude_patterns = license,images/*/*.jpg

# (str) Application versioning (method 1)
version = 0.1

# (str) Application versioning (method 2)
# version.regex = __version__ = \['"\](.*)['"]
# version.filename = %(source.dir)s/main.py

# (list) Application requirements
# comma separated e.g. requirements = sqlite3,kivy
requirements = python3,kivy

# (str) Custom source folders for requirements
# Sets custom source for any requirements with recipes
# requirements.source.kivy = ../../kivy

# (list) Garden requirements
#garden_requirements =

# (str) Presplash of the application
#presplash.filename = %(source.dir)s/data/presplash.png

# (str) Icon of the application
#icon.filename = %(source.dir)s/data/icon.png

# (str) Supported orientation (one of landscape, sensorLandscape, portrait or all)
orientation = portrait

# (list) List of service to declare
#services = NAME:ENTRYPOINT_TO_PY,NAME2:ENTRYPOINT2_TO_PY

#
# OSX Specific
#

#
# author = © Copyright Info

# change the major version of python used by the app
osx.python_version = 3

# Kivy version to use
osx.kivy_version = 1.9.1

#
# Android specific
#

# (bool) Indicate if the application should be fullscreen or not
fullscreen = 0

# (string) Presplash background color (for new android toolchain)
# Supported formats are: #RRGGBB #AARRGGBB or one of the following names:
# red, blue, green, black, white, gray, cyan, magenta, yellow, lightgray,
# darkgray, grey, lightgrey, darkgrey, aqua, fuchsia, lime, maroon, navy,
# olive, purple, silver, teal.
#android.presplash_color = #FFFFFF

# (list) Permissions
#android.permissions = INTERNET

# (int) Target Android API, should be as high as possible.
#android.api = 27

# (int) Minimum API your APK will support.
#android.minapi = 21

# (int) Android SDK version to use
#android.sdk = 20

# (str) Android NDK version to use
#android.ndk = 19b

# (int) Android NDK API to use. This is the minimum API your app will support, it should usually match android.minapi.
#android.ndk_api = 21

# (bool) Use --private data storage (True) or --dir public storage (False)
#android.private_storage = True

# (str) Android NDK directory (if empty, it will be automatically downloaded.)
#android.ndk_path =

# (str) Android SDK directory (if empty, it will be automatically downloaded.)
#android.sdk_path =

# (str) ANT directory (if empty, it will be automatically downloaded.)
#android.ant_path =

# (bool) If True, then skip trying to update the Android sdk
# This can be useful to avoid excess Internet downloads or save time
# when an update is due and you just want to test/build your package
# android.skip_update = False

# (bool) If True, then automatically accept SDK license
# agreements. This is intended for automation only. If set to False,
# the default, you will be shown the license when first running
# buildozer.
# android.accept_sdk_license = False

# (str) Android entry point, default is ok for Kivy-based app
#android.entrypoint = org.renpy.android.PythonActivity

# (str) Android app theme, default is ok for Kivy-based app
# android.apptheme = "@android:style/Theme.NoTitleBar"

# (list) Pattern to whitelist for the whole project
#android.whitelist =

# (str) Path to a custom whitelist file
#android.whitelist_src =

# (str) Path to a custom blacklist file
#android.blacklist_src =

# (list) List of Java .jar files to add to the libs so that pyjnius can access
# their classes. Don't add jars that you do not need, since extra jars can slow
# down the build process. Allows wildcards matching, for example:
# OUYA-ODK/libs/*.jar
#android.add_jars = foo.jar,bar.jar,path/to/more/*.jar

# (list) List of Java files to add to the android project (can be java or a
# directory containing the files)
#android.add_src =

# (list) Android AAR archives to add (currently works only with sdl2_gradle
# bootstrap)
#android.add_aars =

# (list) Gradle dependencies to add (currently works only with sdl2_gradle
# bootstrap)
#android.gradle_dependencies =

# (list) add java compile options
# this can for example be necessary when importing certain java libraries using the 'android.gradle_dependencies' option
# see https://developer.android.com/studio/write/java8-support for further information
# android.add_compile_options = "sourceCompatibility = 1.8", "targetCompatibility = 1.8"

# (list) Gradle repositories to add {can be necessary for some android.gradle_dependencies}
# please enclose in double quotes 
# e.g. android.gradle_repositories = "maven { url 'https://kotlin.bintray.com/ktor' }"
#android.add_gradle_repositories =

# (list) packaging options to add 
# see https://google.github.io/android-gradle-dsl/current/com.android.build.gradle.internal.dsl.PackagingOptions.html
# can be necessary to solve conflicts in gradle_dependencies
# please enclose in double quotes 
# e.g. android.add_packaging_options = "exclude 'META-INF/common.kotlin_module'", "exclude 'META-INF/*.kotlin_module'"
#android.add_gradle_repositories =

# (list) Java classes to add as activities to the manifest.
#android.add_activities = com.example.ExampleActivity

# (str) OUYA Console category. Should be one of GAME or APP
# If you leave this blank, OUYA support will not be enabled
#android.ouya.category = GAME

# (str) Filename of OUYA Console icon. It must be a 732x412 png image.
#android.ouya.icon.filename = %(source.dir)s/data/ouya_icon.png

# (str) XML file to include as an intent filters in <activity> tag
#android.manifest.intent_filters =

# (str) launchMode to set for the main activity
#android.manifest.launch_mode = standard

# (list) Android additional libraries to copy into libs/armeabi
#android.add_libs_armeabi = libs/android/*.so
#android.add_libs_armeabi_v7a = libs/android-v7/*.so
#android.add_libs_arm64_v8a = libs/android-v8/*.so
#android.add_libs_x86 = libs/android-x86/*.so
#android.add_libs_mips = libs/android-mips/*.so

# (bool) Indicate whether the screen should stay on
# Don't forget to add the WAKE_LOCK permission if you set this to True
#android.wakelock = False

# (list) Android application meta-data to set (key=value format)
#android.meta_data =

# (list) Android library project to add (will be added in the
# project.properties automatically.)
#android.library_references =

# (list) Android shared libraries which will be added to AndroidManifest.xml using <uses-library> tag
#android.uses_library =

# (str) Android logcat filters to use
#android.logcat_filters = *:S python:D

# (bool) Copy library instead of making a libpymodules.so
#android.copy_libs = 1

# (str) The Android arch to build for, choices: armeabi-v7a, arm64-v8a, x86, x86_64
android.arch = armeabi-v7a

# (int) overrides automatic versionCode computation (used in build.gradle)
# this is not the same as app version and should only be edited if you know what you're doing
# android.numeric_version = 1

#
# Python for android (p4a) specific
#

# (str) python-for-android fork to use, defaults to upstream (kivy)
#p4a.fork = kivy

# (str) python-for-android branch to use, defaults to master
#p4a.branch = master

# (str) python-for-android git clone directory (if empty, it will be automatically cloned from github)
#p4a.source_dir =

# (str) The directory in which python-for-android should look for your own build recipes (if any)
#p4a.local_recipes =

# (str) Filename to the hook for p4a
#p4a.hook =

# (str) Bootstrap to use for android builds
# p4a.bootstrap = sdl2

# (int) port number to specify an explicit --port= p4a argument (eg for bootstrap flask)
#p4a.port =


#
# iOS specific
#

# (str) Path to a custom kivy-ios folder
#ios.kivy_ios_dir = ../kivy-ios
# Alternately, specify the URL and branch of a git checkout:
ios.kivy_ios_url = https://github.com/kivy/kivy-ios
ios.kivy_ios_branch = master

# Another platform dependency: ios-deploy
# Uncomment to use a custom checkout
#ios.ios_deploy_dir = ../ios_deploy
# Or specify URL and branch
ios.ios_deploy_url = https://github.com/phonegap/ios-deploy
ios.ios_deploy_branch = 1.7.0

# (str) Name of the certificate to use for signing the debug version
# Get a list of available identities: buildozer ios list_identities
#ios.codesign.debug = "iPhone Developer: <lastname> <firstname> (<hexstring>)"

# (str) Name of the certificate to use for signing the release version
#ios.codesign.release = %(ios.codesign.debug)s


[buildozer]

# (int) Log level (0 = error only, 1 = info, 2 = debug (with command output))
log_level = 2

# (int) Display warning if buildozer is run as root (0 = False, 1 = True)
warn_on_root = 1

# (str) Path to build artifact storage, absolute or relative to spec file
# build_dir = ./.buildozer

# (str) Path to build output (i.e. .apk, .ipa) storage
# bin_dir = ./bin

#    -----------------------------------------------------------------------------
#    List as sections
#
#    You can define all the "list" as [section:key].
#    Each line will be considered as a option to the list.
#    Let's take [app] / source.exclude_patterns.
#    Instead of doing:
#
#[app]
#source.exclude_patterns = license,data/audio/*.wav,data/images/original/*
#
#    This can be translated into:
#
#[app:source.exclude_patterns]
#license
#data/audio/*.wav
#data/images/original/*
#


#    -----------------------------------------------------------------------------
#    Profiles
#
#    You can extend section / key with a profile
#    For example, you want to deploy a demo version of your application without
#    HD content. You could first change the title to add "(demo)" in the name
#    and extend the excluded directories to remove the HD content.
#
#[app@demo]
#title = My Application (demo)
#
#[app:source.exclude_patterns@demo]
#images/hd/*
#
#    Then, invoke the command line with the "demo" profile:
#
#buildozer --profile demo android debug

Si desea especificar cosas como el ícono, los requisitos, la pantalla de carga, etc., debe editar este archivo. Después de realizar todas las ediciones deseadas en su aplicación, ejecute buildozer -v android debugdesde el directorio de su aplicación para construir y compilar su aplicación. Esto puede llevar un tiempo, especialmente si tiene una máquina lenta.

Una vez finalizado el proceso, su terminal debería tener algunos registros, uno que confirme que la compilación fue exitosa:

Construcción exitosa de Android

También debe tener una versión APK de su aplicación en su directorio bin. Este es el ejecutable de la aplicación que instalará y ejecutará en su teléfono:

Android .apk en el directorio bin

Conclusión

¡Felicidades! Si ha seguido este tutorial paso a paso, debería tener una aplicación simple de generador de números aleatorios en su teléfono. Juega con él y ajusta algunos valores, luego reconstruye. Ejecutar la reconstrucción no llevará tanto tiempo como la primera compilación.

Como puede ver, crear una aplicación móvil con Python es bastante sencillo , siempre que esté familiarizado con el marco o módulo con el que está trabajando. Independientemente, la lógica se ejecuta de la misma manera.

Familiarícese con el módulo Kivy y sus widgets. Nunca se puede saber todo a la vez. Solo necesita encontrar un proyecto y mojarse los pies lo antes posible. Codificación feliz.

Enlace: https://blog.logrocket.com/build-android-application-kivy-python-framework/

#python 

KivyPythonフレームワークを使用してAndroidアプリケーションを構築する

あなたがモバイル開発を始めることを考えているPython開発者なら、Kivyフレームワークが最善の策です。Kivyを使用すると、iOS、Android、Windows、macOS、およびLinux用にコンパイルされるプラットフォームに依存しないアプリケーションを開発できます。この記事では、Androidが最も使用されているため、特にAndroidについて説明します。

簡単な乱数ジェネレーターアプリを作成します。このアプリを携帯電話にインストールして、完了したらテストできます。この記事を続けるには、Pythonに精通している必要があります。始めましょう！

Kivyを使い始める

まず、アプリ用の新しいディレクトリが必要になります。マシンにPythonがインストールされていることを確認し、新しいPythonファイルを開きます。以下のコマンドのいずれかを使用して、ターミナルからKivyモジュールをインストールする必要があります。パッケージの競合を避けるために、Kivyを仮想環境にインストールしていることを確認してください。

pip install kivy 
//
pip3 install kivy 

Kivyをインストールすると、以下のスクリーンショットのような成功メッセージがターミナルから表示されます。

がっかりしたインストール

Kivyのインストールに成功

 

次に、プロジェクトフォルダに移動します。このmain.pyファイルで、Kivyモジュールをインポートし、必要なバージョンを指定する必要があります。Kivy v2.0.0を使用できますが、Android 8.0より古いスマートフォンを使用している場合は、Kivyv1.9.0を使用することをお勧めします。ビルド中にさまざまなバージョンをいじって、機能とパフォーマンスの違いを確認できます。

import kivy次のように、行の直後にバージョン番号を追加します。

kivy.require('1.9.0')

次に、基本的にアプリを定義するクラスを作成します。私の名前を付けますRandomNumber。このクラスはappKivyからクラスを継承します。したがって、次appを追加してインポートする必要がありますfrom kivy.app import App。

class RandomNumber(App): 

ではRandomNumberクラスは、呼び出された関数を追加する必要がありますbuildとり、selfパラメータを。実際にUIを返すには、このbuild関数を使用します。今のところ、単純なラベルとして返送しています。そのためには、次Labelの行を使用してインポートする必要がありますfrom kivy.uix.label import Label。

import kivy
from kivy.app import App
from kivy.uix.label import Label

class RandomNumber(App):
  def build(self):
    return Label(text="Random Number Generator")

これで、アプリのスケルトンが完成しました。先に進む前に、RandomNumberクラスのインスタンスを作成し、ターミナルまたはIDEで実行して、インターフェイスを確認する必要があります。

import kivy from kivy.app import App from kivy.uix.label import Label class RandomNumber（App）：def build（self）：return Label（text = "Random Number Generator"）randomApp = RandomNumber（）randomApp.run（）

テキストを使用してクラスインスタンスを実行すると、Random Number Generator次のスクリーンショットのような単純なインターフェイスまたはウィンドウが表示されます。

 

コードを実行した後のシンプルなインターフェイス

すべての構築が完了するまで、Androidでテキストを実行することはできません。

インターフェースのアウトソーシング

次に、インターフェースをアウトソーシングする方法が必要になります。まず、ディレクトリにKivyファイルを作成します。このファイルには、ほとんどの設計作業が含まれています。このファイルには、小文字と.kv拡張子を使用して、クラスと同じ名前を付けることができます。Kivyはクラス名とファイル名を自動的に関連付けますが、それらがまったく同じである場合、Androidでは機能しない可能性があります。

その.kvファイル内で、ラベル、ボタン、フォームなどの要素を含むアプリのレイアウトを指定する必要があります。このデモを簡単にするために、タイトルRandom Numberのラベル、プレースホルダーとして機能するラベルを追加します。生成される乱数_、および関数Generateを呼び出すボタンgenerate。

私の.kvファイルは以下のコードのように見えますが、要件に合わせてさまざまな値をいじることができます。

<boxLayout>:
    orientation: "vertical"
    Label:
        text: "Random Number"
        font_size: 30
        color: 0, 0.62, 0.96

    Label:
        text: "_"
        font_size: 30

    Button:
        text: "Generate"
        font_size: 15 

このmain.pyファイルではLabel、KivyファイルがUIを処理するため、importステートメントは不要になりました。ただし、boxlayoutKivyファイルで使用するをインポートする必要があります。

メインファイルで、importステートメントを追加し、main.pyファイルを編集return BoxLayout()してbuildメソッドで読み取る必要があります。

from kivy.uix.boxlayout import BoxLayout

上記のコマンドを実行すると、乱数のタイトル、_プレースホルダー、およびクリック可能なgenerateボタンを備えたシンプルなインターフェイスが表示されます。

レンダリングされた乱数アプリ

Kivyファイルを機能させるために何もインポートする必要がなかったことに注意してください。基本的に、アプリを実行するboxlayoutと、クラスと同じ名前のKivyファイル内のファイルを検索して戻ります。これはシンプルなインターフェースであり、アプリを必要に応じて堅牢にすることができます。Kv言語のドキュメントを必ず確認してください。

乱数関数を生成する

アプリがほぼ完成したので、ユーザーがgenerateボタンをクリックしたときに乱数を生成し、その乱数をアプリのインターフェイスにレンダリングする簡単な関数が必要になります。そのためには、ファイル内のいくつかの変更を行う必要があります。

まず、で乱数を生成するために使用するモジュールをインポートしますimport random。次に、生成された番号を呼び出す関数またはメソッドを作成します。このデモでは、私は間の範囲を使用します0と2000。このrandom.randint(0, 2000)コマンドを使用すると、乱数を簡単に生成できます。これをすぐにコードに追加します。

次に、独自のバージョンとなる別のクラスを作成しますbox layout。このbox layoutクラスは、乱数を生成してインターフェイス上でレンダリングするメソッドを含むクラスを継承する必要があります。

class MyRoot(BoxLayout):
    def __init__(self):
        super(MyRoot, self).__init__()

そのクラス内で、generate乱数を生成するだけでなく、Kivyファイルに乱数として表示されるものを制御するラベルを操作するメソッドを作成します。

この方法に対応するには、最初に.kvファイルに変更を加える必要があります。以来MyRootクラスが継承しているbox layout、あなたが作ることができるMyRootあなたのトップレベルの要素.kvファイルを：

<MyRoot>:
    BoxLayout:
        orientation: "vertical"
        Label:
            text: "Random Number"
            font_size: 30
            color: 0, 0.62, 0.96

        Label:
            text: "_"
            font_size: 30

        Button:
            text: "Generate"
            font_size: 15

でインデントされたすべてのUI仕様を保持していることに注意してくださいBox Layout。この後、生成された番号を保持するIDをラベルに追加して、generate関数が呼び出されたときに簡単に操作できるようにする必要があります。このファイルのIDと、上部のメインコードの別のIDとの関係を、次のBoxLayout行の直前に指定する必要があります。

<MyRoot>:
    random_label: random_label
    BoxLayout:
        orientation: "vertical"
        Label:
            text: "Random Number"
            font_size: 30
            color: 0, 0.62, 0.96

        Label:
            id: random_label
            text: "_"
            font_size: 30

        Button:
            text: "Generate"
            font_size: 15

このrandom_label: random_label行は基本的に、IDrandom_labelを持つラベルがファイルrandom_label内にマップされることをmain.py意味します。つまり、操作random_labelするアクションはすべて、指定された名前のラベルにマップされます。

これで、メインファイルに乱数を生成するメソッドを作成できます。

def generate_number(self):
    self.random_label.text = str(random.randint(0, 2000))

# notice how the class method manipulates the text attributre of the random label by a# ssigning it a new random number generate by the 'random.randint(0, 2000)' funcion. S# ince this the random number generated is an integer, typecasting is required to make # it a string otherwise you will get a typeError in your terminal when you run it.

MyRootこのクラスは、以下のコードのようになります。

class MyRoot(BoxLayout):
    def __init__(self):
        super(MyRoot, self).__init__()

    def generate_number(self):
        self.random_label.text = str(random.randint(0, 2000))

おめでとう！これで、アプリのメインファイルが完成しました。あとは、generateボタンがクリックされたときに必ずこの関数を呼び出すようにしてください。ファイルのon_press: root.generate_number()ボタン選択部分に行を追加するだけで済み.kvます。

<MyRoot>:
    random_label: random_label
    BoxLayout:
        orientation: "vertical"
        Label:
            text: "Random Number"
            font_size: 30
            color: 0, 0.62, 0.96

        Label:
            id: random_label
            text: "_"
            font_size: 30

        Button:
            text: "Generate"
            font_size: 15
            on_press: root.generate_number()

これで、アプリを実行できます。

Androidでアプリをコンパイルする

Androidでアプリをコンパイルする前に、Windowsユーザーにとって悪いニュースがあります。Androidアプリケーションをコンパイルするには、LinuxまたはmacOSが必要です。ただし、個別のLinuxディストリビューションを用意する必要はなく、代わりに仮想マシンを使用できます。

完全なAndroid.apkアプリケーションをコンパイルして生成するには、Buildozerというツールを使用します。以下のコマンドのいずれかを使用して、ターミナルからBuildozerをインストールしましょう。

pip3 install buildozer
//
pip install buildozer

次に、Buildozerに必要な依存関係のいくつかをインストールします。私はLinuxErgoを使用しているので、Linux固有のコマンドを使用します。これらのコマンドを1つずつ実行する必要があります。

sudo apt update
sudo apt install -y git zip unzip openjdk-13-jdk python3-pip autoconf libtool pkg-config zlib1g-dev libncurses5-dev libncursesw5-dev libtinfo5 cmake libffi-dev libssl-dev

pip3 install --upgrade Cython==0.29.19 virtualenv 

# add the following line at the end of your ~/.bashrc file
export PATH=$PATH:~/.local/bin/

特定のコマンドを実行した後、を実行しbuildozer initます。以下のスクリーンショットのような出力が表示されます。

Buildozerの初期化が成功しました

上記のコマンドはBuildozer.specファイルを作成します。このファイルを使用して、アプリの名前やアイコンなどをアプリに指定.specできます。ファイルは次のコードブロックのようになります。

[app]

# (str) Title of your application
title = My Application

# (str) Package name
package.name = myapp

# (str) Package domain (needed for android/ios packaging)
package.domain = org.test

# (str) Source code where the main.py live
source.dir = .

# (list) Source files to include (let empty to include all the files)
source.include_exts = py,png,jpg,kv,atlas

# (list) List of inclusions using pattern matching
#source.include_patterns = assets/*,images/*.png

# (list) Source files to exclude (let empty to not exclude anything)
#source.exclude_exts = spec

# (list) List of directory to exclude (let empty to not exclude anything)
#source.exclude_dirs = tests, bin

# (list) List of exclusions using pattern matching
#source.exclude_patterns = license,images/*/*.jpg

# (str) Application versioning (method 1)
version = 0.1

# (str) Application versioning (method 2)
# version.regex = __version__ = \['"\](.*)['"]
# version.filename = %(source.dir)s/main.py

# (list) Application requirements
# comma separated e.g. requirements = sqlite3,kivy
requirements = python3,kivy

# (str) Custom source folders for requirements
# Sets custom source for any requirements with recipes
# requirements.source.kivy = ../../kivy

# (list) Garden requirements
#garden_requirements =

# (str) Presplash of the application
#presplash.filename = %(source.dir)s/data/presplash.png

# (str) Icon of the application
#icon.filename = %(source.dir)s/data/icon.png

# (str) Supported orientation (one of landscape, sensorLandscape, portrait or all)
orientation = portrait

# (list) List of service to declare
#services = NAME:ENTRYPOINT_TO_PY,NAME2:ENTRYPOINT2_TO_PY

#
# OSX Specific
#

#
# author = © Copyright Info

# change the major version of python used by the app
osx.python_version = 3

# Kivy version to use
osx.kivy_version = 1.9.1

#
# Android specific
#

# (bool) Indicate if the application should be fullscreen or not
fullscreen = 0

# (string) Presplash background color (for new android toolchain)
# Supported formats are: #RRGGBB #AARRGGBB or one of the following names:
# red, blue, green, black, white, gray, cyan, magenta, yellow, lightgray,
# darkgray, grey, lightgrey, darkgrey, aqua, fuchsia, lime, maroon, navy,
# olive, purple, silver, teal.
#android.presplash_color = #FFFFFF

# (list) Permissions
#android.permissions = INTERNET

# (int) Target Android API, should be as high as possible.
#android.api = 27

# (int) Minimum API your APK will support.
#android.minapi = 21

# (int) Android SDK version to use
#android.sdk = 20

# (str) Android NDK version to use
#android.ndk = 19b

# (int) Android NDK API to use. This is the minimum API your app will support, it should usually match android.minapi.
#android.ndk_api = 21

# (bool) Use --private data storage (True) or --dir public storage (False)
#android.private_storage = True

# (str) Android NDK directory (if empty, it will be automatically downloaded.)
#android.ndk_path =

# (str) Android SDK directory (if empty, it will be automatically downloaded.)
#android.sdk_path =

# (str) ANT directory (if empty, it will be automatically downloaded.)
#android.ant_path =

# (bool) If True, then skip trying to update the Android sdk
# This can be useful to avoid excess Internet downloads or save time
# when an update is due and you just want to test/build your package
# android.skip_update = False

# (bool) If True, then automatically accept SDK license
# agreements. This is intended for automation only. If set to False,
# the default, you will be shown the license when first running
# buildozer.
# android.accept_sdk_license = False

# (str) Android entry point, default is ok for Kivy-based app
#android.entrypoint = org.renpy.android.PythonActivity

# (str) Android app theme, default is ok for Kivy-based app
# android.apptheme = "@android:style/Theme.NoTitleBar"

# (list) Pattern to whitelist for the whole project
#android.whitelist =

# (str) Path to a custom whitelist file
#android.whitelist_src =

# (str) Path to a custom blacklist file
#android.blacklist_src =

# (list) List of Java .jar files to add to the libs so that pyjnius can access
# their classes. Don't add jars that you do not need, since extra jars can slow
# down the build process. Allows wildcards matching, for example:
# OUYA-ODK/libs/*.jar
#android.add_jars = foo.jar,bar.jar,path/to/more/*.jar

# (list) List of Java files to add to the android project (can be java or a
# directory containing the files)
#android.add_src =

# (list) Android AAR archives to add (currently works only with sdl2_gradle
# bootstrap)
#android.add_aars =

# (list) Gradle dependencies to add (currently works only with sdl2_gradle
# bootstrap)
#android.gradle_dependencies =

# (list) add java compile options
# this can for example be necessary when importing certain java libraries using the 'android.gradle_dependencies' option
# see https://developer.android.com/studio/write/java8-support for further information
# android.add_compile_options = "sourceCompatibility = 1.8", "targetCompatibility = 1.8"

# (list) Gradle repositories to add {can be necessary for some android.gradle_dependencies}
# please enclose in double quotes 
# e.g. android.gradle_repositories = "maven { url 'https://kotlin.bintray.com/ktor' }"
#android.add_gradle_repositories =

# (list) packaging options to add 
# see https://google.github.io/android-gradle-dsl/current/com.android.build.gradle.internal.dsl.PackagingOptions.html
# can be necessary to solve conflicts in gradle_dependencies
# please enclose in double quotes 
# e.g. android.add_packaging_options = "exclude 'META-INF/common.kotlin_module'", "exclude 'META-INF/*.kotlin_module'"
#android.add_gradle_repositories =

# (list) Java classes to add as activities to the manifest.
#android.add_activities = com.example.ExampleActivity

# (str) OUYA Console category. Should be one of GAME or APP
# If you leave this blank, OUYA support will not be enabled
#android.ouya.category = GAME

# (str) Filename of OUYA Console icon. It must be a 732x412 png image.
#android.ouya.icon.filename = %(source.dir)s/data/ouya_icon.png

# (str) XML file to include as an intent filters in <activity> tag
#android.manifest.intent_filters =

# (str) launchMode to set for the main activity
#android.manifest.launch_mode = standard

# (list) Android additional libraries to copy into libs/armeabi
#android.add_libs_armeabi = libs/android/*.so
#android.add_libs_armeabi_v7a = libs/android-v7/*.so
#android.add_libs_arm64_v8a = libs/android-v8/*.so
#android.add_libs_x86 = libs/android-x86/*.so
#android.add_libs_mips = libs/android-mips/*.so

# (bool) Indicate whether the screen should stay on
# Don't forget to add the WAKE_LOCK permission if you set this to True
#android.wakelock = False

# (list) Android application meta-data to set (key=value format)
#android.meta_data =

# (list) Android library project to add (will be added in the
# project.properties automatically.)
#android.library_references =

# (list) Android shared libraries which will be added to AndroidManifest.xml using <uses-library> tag
#android.uses_library =

# (str) Android logcat filters to use
#android.logcat_filters = *:S python:D

# (bool) Copy library instead of making a libpymodules.so
#android.copy_libs = 1

# (str) The Android arch to build for, choices: armeabi-v7a, arm64-v8a, x86, x86_64
android.arch = armeabi-v7a

# (int) overrides automatic versionCode computation (used in build.gradle)
# this is not the same as app version and should only be edited if you know what you're doing
# android.numeric_version = 1

#
# Python for android (p4a) specific
#

# (str) python-for-android fork to use, defaults to upstream (kivy)
#p4a.fork = kivy

# (str) python-for-android branch to use, defaults to master
#p4a.branch = master

# (str) python-for-android git clone directory (if empty, it will be automatically cloned from github)
#p4a.source_dir =

# (str) The directory in which python-for-android should look for your own build recipes (if any)
#p4a.local_recipes =

# (str) Filename to the hook for p4a
#p4a.hook =

# (str) Bootstrap to use for android builds
# p4a.bootstrap = sdl2

# (int) port number to specify an explicit --port= p4a argument (eg for bootstrap flask)
#p4a.port =


#
# iOS specific
#

# (str) Path to a custom kivy-ios folder
#ios.kivy_ios_dir = ../kivy-ios
# Alternately, specify the URL and branch of a git checkout:
ios.kivy_ios_url = https://github.com/kivy/kivy-ios
ios.kivy_ios_branch = master

# Another platform dependency: ios-deploy
# Uncomment to use a custom checkout
#ios.ios_deploy_dir = ../ios_deploy
# Or specify URL and branch
ios.ios_deploy_url = https://github.com/phonegap/ios-deploy
ios.ios_deploy_branch = 1.7.0

# (str) Name of the certificate to use for signing the debug version
# Get a list of available identities: buildozer ios list_identities
#ios.codesign.debug = "iPhone Developer: <lastname> <firstname> (<hexstring>)"

# (str) Name of the certificate to use for signing the release version
#ios.codesign.release = %(ios.codesign.debug)s


[buildozer]

# (int) Log level (0 = error only, 1 = info, 2 = debug (with command output))
log_level = 2

# (int) Display warning if buildozer is run as root (0 = False, 1 = True)
warn_on_root = 1

# (str) Path to build artifact storage, absolute or relative to spec file
# build_dir = ./.buildozer

# (str) Path to build output (i.e. .apk, .ipa) storage
# bin_dir = ./bin

#    -----------------------------------------------------------------------------
#    List as sections
#
#    You can define all the "list" as [section:key].
#    Each line will be considered as a option to the list.
#    Let's take [app] / source.exclude_patterns.
#    Instead of doing:
#
#[app]
#source.exclude_patterns = license,data/audio/*.wav,data/images/original/*
#
#    This can be translated into:
#
#[app:source.exclude_patterns]
#license
#data/audio/*.wav
#data/images/original/*
#


#    -----------------------------------------------------------------------------
#    Profiles
#
#    You can extend section / key with a profile
#    For example, you want to deploy a demo version of your application without
#    HD content. You could first change the title to add "(demo)" in the name
#    and extend the excluded directories to remove the HD content.
#
#[app@demo]
#title = My Application (demo)
#
#[app:source.exclude_patterns@demo]
#images/hd/*
#
#    Then, invoke the command line with the "demo" profile:
#
#buildozer --profile demo android debug

アイコン、要件、ロード画面などを指定する場合は、このファイルを編集する必要があります。アプリケーションに必要なすべての編集を行った後buildozer -v android debug、アプリディレクトリから実行して、アプリケーションをビルドおよびコンパイルします。特に低速のマシンを使用している場合は、これに時間がかかることがあります。

プロセスが完了すると、端末にいくつかのログが表示され、ビルドが成功したことを確認できます。

Androidの成功したビルド

また、binディレクトリにアプリのAPKバージョンが必要です。これは、携帯電話にインストールして実行するアプリケーションの実行可能ファイルです。

binディレクトリのAndroid.apk

結論

おめでとう！このチュートリアルをステップバイステップで実行した場合は、電話に単純な乱数ジェネレーターアプリがインストールされているはずです。それをいじって、いくつかの値を微調整してから、再構築してください。再構築の実行は、最初のビルドほど時間はかかりません。

ご覧のとおり、Pythonを使用したモバイルアプリケーションの構築は、使用しているフレームワークまたはモジュールに精通している限り、かなり簡単です。とにかく、ロジックは同じ方法で実行されます。

Kivyモジュールとそのウィジェットに慣れてください。すべてを一度に知ることはできません。プロジェクトを見つけて、できるだけ早く足を濡らすだけです。ハッピーコーディング。

リンク：https：//blog.logrocket.com/build-android-application-kivy-python-framework/

#python 

Dl4clj: Clojure Wrapper for Deeplearning4j.

dl4clj

Port of deeplearning4j to clojure

Contact info

If you have any questions,

• my email is will@yetanalytics.com
• I'm will_hoyt in the clojurians slack
• twitter is @FeLungz (don't check very often)

TODO

• update examples dir
• finish README
  • add in examples using Transfer Learning
• finish tests
  • eval is missing regression tests, roc tests
  • nn-test is missing regression tests
  • spark tests need to be redone
  • need dl4clj.core tests
• revist spark for updates
• write specs for user facing functions
  • this is very important, match isnt strict for maps
  • provides 100% certianty of the input -> output flow
  • check the args as they come in, dispatch once I know its safe, test the pure output
• collapse overlapping api namespaces
• add to core use case flows

Features

Stable Features with tests

• Neural Networks DSL
• Early Stopping Training
• Transfer Learning
• Evaluation
• Data import

Features being worked on for 0.1.0

• Clustering (testing in progress)
• Spark (currently being refactored)
• Front End (maybe current release, maybe future release. Not sure yet)
• Version of dl4j is 0.0.8 in this project. Current dl4j version is 0.0.9
• Parallelism
• Kafka support
• Other items mentioned in TODO

Features being worked on for future releases

• NLP
• Computational Graphs
• Reinforement Learning
• Arbiter

Artifacts

NOT YET RELEASED TO CLOJARS

• fork or clone to try it out

If using Maven add the following repository definition to your pom.xml:

<repository>
  <id>clojars.org</id>
  <url>http://clojars.org/repo</url>
</repository>

Latest release

With Leiningen:

n/a

With Maven:

n/a

<dependency>
  <groupId>_</groupId>
  <artifactId>_</artifactId>
  <version>_</version>
</dependency>

Usage

Things you need to know

All functions for creating dl4j objects return code by default

• All of these functions have an option to return the dl4j object
  • :as-code? = false
• This because all builders require the code representation of dl4j objects
  • this requirement is not going to change
• INDarray creation fns default to objects, this is for convenience
  • :as-code? is still respected

API functions return code when all args are provided as code

API functions return the value of calling the wrapped method when args are provided as a mixture of objects and code or just objects

The tests are there to help clarify behavior, if you are unsure of how to use a fn, search the tests

• for questions about spark, refer to the spark section bellow

Example of obj/code duality

(ns my.ns
  (:require [dl4clj.nn.conf.builders.layers :as l]))

;; as code (the default)

(l/dense-layer-builder
 :activation-fn :relu
 :learning-rate 0.006
 :weight-init :xavier
 :layer-name "example layer"
 :n-in 10
 :n-out 1)

;; =>

(doto
 (org.deeplearning4j.nn.conf.layers.DenseLayer$Builder.)
 (.nOut 1)
 (.activation (dl4clj.constants/value-of {:activation-fn :relu}))
 (.weightInit (dl4clj.constants/value-of {:weight-init :xavier}))
 (.nIn 10)
 (.name "example layer")
 (.learningRate 0.006))

;; as an object

(l/dense-layer-builder
 :activation-fn :relu
 :learning-rate 0.006
 :weight-init :xavier
 :layer-name "example layer"
 :n-in 10
 :n-out 1
 :as-code? false)

;; =>

#object[org.deeplearning4j.nn.conf.layers.DenseLayer 0x69d7d160 "DenseLayer(super=FeedForwardLayer(super=Layer(layerName=example layer, activationFn=relu, weightInit=XAVIER, biasInit=NaN, dist=null, learningRate=0.006, biasLearningRate=NaN, learningRateSchedule=null, momentum=NaN, momentumSchedule=null, l1=NaN, l2=NaN, l1Bias=NaN, l2Bias=NaN, dropOut=NaN, updater=null, rho=NaN, epsilon=NaN, rmsDecay=NaN, adamMeanDecay=NaN, adamVarDecay=NaN, gradientNormalization=null, gradientNormalizationThreshold=NaN), nIn=10, nOut=1))"]

General usage examples

Importing data

Loading data from a file (here its a csv)

(ns my.ns
 (:require [dl4clj.datasets.input-splits :as s]
           [dl4clj.datasets.record-readers :as rr]
           [dl4clj.datasets.api.record-readers :refer :all]
           [dl4clj.datasets.iterators :as ds-iter]
           [dl4clj.datasets.api.iterators :refer :all]
           [dl4clj.helpers :refer [data-from-iter]]))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; file splits (convert the data to records)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def poker-path "resources/poker-hand-training.csv")
;; this is not a complete dataset, it is just here to sever as an example

(def file-split (s/new-filesplit :path poker-path))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; record readers, (read the records created by the file split)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def csv-rr (initialize-rr! :rr (rr/new-csv-record-reader :skip-n-lines 0 :delimiter ",")
                                 :input-split file-split))

;; lets look at some data
(println (next-record! :rr csv-rr :as-code? false))
;; => #object[java.util.ArrayList 0x2473e02d [1, 10, 1, 11, 1, 13, 1, 12, 1, 1, 9]]
;; this is our first line from the csv


;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; record readers dataset iterators (turn our writables into a dataset)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def rr-ds-iter (ds-iter/new-record-reader-dataset-iterator
                 :record-reader csv-rr
                 :batch-size 1
                 :label-idx 10
                 :n-possible-labels 10))

;; we use our record reader created above
;; we want to see one example per dataset obj returned (:batch-size = 1)
;; we know our label is at the last index, so :label-idx = 10
;; there are 10 possible types of poker hands so :n-possible-labels = 10
;; you can also set :label-idx to -1 to use the last index no matter the size of the seq

(def other-rr-ds-iter (ds-iter/new-record-reader-dataset-iterator
                       :record-reader csv-rr
                       :batch-size 1
                       :label-idx -1
                       :n-possible-labels 10))

(str (next-example! :iter rr-ds-iter :as-code? false))
;; =>
;;===========INPUT===================
;;[1.00, 10.00, 1.00, 11.00, 1.00, 13.00, 1.00, 12.00, 1.00, 1.00]
;;=================OUTPUT==================
;;[0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 1.00]


;; and to show that :label-idx = -1 gives us the same output

(= (next-example! :iter rr-ds-iter :as-code? false)
   (next-example! :iter other-rr-ds-iter :as-code? false)) ;; => true

INDArrays and Datasets from clojure data structures

(ns my.ns
  (:require [nd4clj.linalg.factory.nd4j :refer [vec->indarray matrix->indarray
                                                indarray-of-zeros indarray-of-ones
                                                indarray-of-rand vec-or-matrix->indarray]]
            [dl4clj.datasets.new-datasets :refer [new-ds]]
            [dl4clj.datasets.api.datasets :refer [as-list]]
            [dl4clj.datasets.iterators :refer [new-existing-dataset-iterator]]
            [dl4clj.datasets.api.iterators :refer :all]
            [dl4clj.datasets.pre-processors :as ds-pp]
            [dl4clj.datasets.api.pre-processors :refer :all]
            [dl4clj.core :as c]))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; INDArray creation
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;;TODO: consider defaulting to code

;; can create from a vector

(vec->indarray [1 2 3 4])
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x269df212 [1.00, 2.00, 3.00, 4.00]]

;; or from a matrix

(matrix->indarray [[1 2 3 4] [2 4 6 8]])
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x20aa7fe1
;; [[1.00, 2.00, 3.00, 4.00], [2.00, 4.00, 6.00, 8.00]]]


;; will fill in spareness with zeros

(matrix->indarray [[1 2 3 4] [2 4 6 8] [10 12]])
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x8b7796c
;;[[1.00, 2.00, 3.00, 4.00],
;; [2.00, 4.00, 6.00, 8.00],
;; [10.00, 12.00, 0.00, 0.00]]]

;; can create an indarray of all zeros with specified shape
;; defaults to :rows = 1 :columns = 1

(indarray-of-zeros :rows 3 :columns 2)
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x6f586a7e
;;[[0.00, 0.00],
;; [0.00, 0.00],
;; [0.00, 0.00]]]

(indarray-of-zeros) ;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0xe59ffec 0.00]

;; and if only one is supplied, will get a vector of specified length

(indarray-of-zeros :rows 2)
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x2899d974 [0.00, 0.00]]

(indarray-of-zeros :columns 2)
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0xa5b9782 [0.00, 0.00]]

;; same considerations/defaults for indarray-of-ones and indarray-of-rand

(indarray-of-ones :rows 2 :columns 3)
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x54f08662 [[1.00, 1.00, 1.00], [1.00, 1.00, 1.00]]]

(indarray-of-rand :rows 2 :columns 3)
;; all values are greater than 0 but less than 1
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x2f20293b [[0.85, 0.86, 0.13], [0.94, 0.04, 0.36]]]



;; vec-or-matrix->indarray is built into all functions which require INDArrays
;; so that you can use clojure data structures
;; but you still have the option of passing existing INDArrays

(def example-array (vec-or-matrix->indarray [1 2 3 4]))
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x5c44c71f [1.00, 2.00, 3.00, 4.00]]

(vec-or-matrix->indarray example-array)
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x607b03b0 [1.00, 2.00, 3.00, 4.00]]

(vec-or-matrix->indarray (indarray-of-rand :rows 2))
;; => #object[org.nd4j.linalg.cpu.nativecpu.NDArray 0x49143b08 [0.76, 0.92]]

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; data-set creation
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def ds-with-single-example (new-ds :input [1 2 3 4]
                                    :output [0.0 1.0 0.0]))

(as-list :ds ds-with-single-example :as-code? false)
;; =>
;; #object[java.util.ArrayList 0x5d703d12
;;[===========INPUT===================
;;[1.00, 2.00, 3.00, 4.00]
;;=================OUTPUT==================
;;[0.00, 1.00, 0.00]]]

(def ds-with-multiple-examples (new-ds
                                :input [[1 2 3 4] [2 4 6 8]]
                                :output [[0.0 1.0 0.0] [0.0 0.0 1.0]]))

(as-list :ds ds-with-multiple-examples :as-code? false)
;; =>
;;#object[java.util.ArrayList 0x29c7a9e2
;;[===========INPUT===================
;;[1.00, 2.00, 3.00, 4.00]
;;=================OUTPUT==================
;;[0.00, 1.00, 0.00],
;;===========INPUT===================
;;[2.00, 4.00, 6.00, 8.00]
;;=================OUTPUT==================
;;[0.00, 0.00, 1.00]]]

;; we can create a dataset iterator from the code which creates datasets
;; and set the labels for our outputs (optional)

(def ds-with-multiple-examples
  (new-ds
   :input [[1 2 3 4] [2 4 6 8]]
   :output [[0.0 1.0 0.0] [0.0 0.0 1.0]]))

;; iterator
(def training-rr-ds-iter
  (new-existing-dataset-iterator
   :dataset ds-with-multiple-examples
   :labels ["foo" "baz" "foobaz"]))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; data-set normalization
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; this gathers statistics on the dataset and normalizes the data
;; and applies the transformation to all dataset objects in the iterator
(def train-iter-normalized
  (c/normalize-iter! :iter training-rr-ds-iter
                     :normalizer (ds-pp/new-standardize-normalization-ds-preprocessor)
                     :as-code? false))

;; above returns the normalized iterator
;; to get fit normalizer

(def the-normalizer
  (get-pre-processor train-iter-normalized))

Model configuration

Creating a neural network configuration with singe and multiple layers

(ns my.ns
  (:require [dl4clj.nn.conf.builders.layers :as l]
            [dl4clj.nn.conf.builders.nn :as nn]
            [dl4clj.nn.conf.distributions :as dist]
            [dl4clj.nn.conf.input-pre-processor :as pp]
            [dl4clj.nn.conf.step-fns :as s-fn]))

;; nn/builder has 3 types of args
;; 1) args which set network configuration params
;; 2) args which set default values for layers
;; 3) args which set multi layer network configuration params

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; single layer nn configuration
;; here we are setting network configuration
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(nn/builder :optimization-algo :stochastic-gradient-descent
            :seed 123
            :iterations 1
            :minimize? true
            :use-drop-connect? false
            :lr-score-based-decay-rate 0.002
            :regularization? false
            :step-fn :default-step-fn
            :layers {:dense-layer {:activation-fn :relu
                                   :updater :adam
                                   :adam-mean-decay 0.2
                                   :adam-var-decay 0.1
                                   :learning-rate 0.006
                                   :weight-init :xavier
                                   :layer-name "single layer model example"
                                   :n-in 10
                                   :n-out 20}})

;; there are several options within a nn-conf map which can be configuration maps
;; or calls to fns
;; It doesn't matter which option you choose and you don't have to stay consistent
;; the list of params which can be passed as config maps or fn calls will
;; be enumerated at a later date

(nn/builder :optimization-algo :stochastic-gradient-descent
            :seed 123
            :iterations 1
            :minimize? true
            :use-drop-connect? false
            :lr-score-based-decay-rate 0.002
            :regularization? false
            :step-fn (s-fn/new-default-step-fn)
            :build? true
            ;; dont need to specify layer order, theres only one
            :layers (l/dense-layer-builder
                    :activation-fn :relu
                    :updater :adam
                    :adam-mean-decay 0.2
                    :adam-var-decay 0.1
                    :dist (dist/new-normal-distribution :mean 0 :std 1)
                    :learning-rate 0.006
                    :weight-init :xavier
                    :layer-name "single layer model example"
                    :n-in 10
                    :n-out 20))

;; these configurations are the same

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; multi-layer configuration
;; here we are also setting layer defaults
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; defaults will apply to layers which do not specify those value in their config

(nn/builder
 :optimization-algo :stochastic-gradient-descent
 :seed 123
 :iterations 1
 :minimize? true
 :use-drop-connect? false
 :lr-score-based-decay-rate 0.002
 :regularization? false
 :default-activation-fn :sigmoid
 :default-weight-init :uniform

 ;; we need to specify the layer order
 :layers {0 (l/activation-layer-builder
             :activation-fn :relu
             :updater :adam
             :adam-mean-decay 0.2
             :adam-var-decay 0.1
             :learning-rate 0.006
             :weight-init :xavier
             :layer-name "example first layer"
             :n-in 10
             :n-out 20)
          1 {:output-layer {:n-in 20
                            :n-out 2
                            :loss-fn :mse
                            :layer-name "example output layer"}}})

;; specifying multi-layer config params

(nn/builder
 ;; network args
 :optimization-algo :stochastic-gradient-descent
 :seed 123
 :iterations 1
 :minimize? true
 :use-drop-connect? false
 :lr-score-based-decay-rate 0.002
 :regularization? false

 ;; layer defaults
 :default-activation-fn :sigmoid
 :default-weight-init :uniform

 ;; the layers
 :layers {0 (l/activation-layer-builder
             :activation-fn :relu
             :updater :adam
             :adam-mean-decay 0.2
             :adam-var-decay 0.1
             :learning-rate 0.006
             :weight-init :xavier
             :layer-name "example first layer"
             :n-in 10
             :n-out 20)
          1 {:output-layer {:n-in 20
                            :n-out 2
                            :loss-fn :mse
                            :layer-name "example output layer"}}}
 ;; multi layer network args
 :backprop? true
 :backprop-type :standard
 :pretrain? false
 :input-pre-processors {0 (pp/new-zero-mean-pre-pre-processor)
                        1 {:unit-variance-processor {}}})

Configuration to Trained models

Multi Layer models

• an implementation of the dl4j mnist classification example

(ns my.ns
  (:require [dl4clj.datasets.iterators :as iter]
            [dl4clj.datasets.input-splits :as split]
            [dl4clj.datasets.record-readers :as rr]
            [dl4clj.optimize.listeners :as listener]
            [dl4clj.nn.conf.builders.nn :as nn]
            [dl4clj.nn.multilayer.multi-layer-network :as mln]
            [dl4clj.nn.api.model :refer [init! set-listeners!]]
            [dl4clj.nn.api.multi-layer-network :refer [evaluate-classification]]
            [dl4clj.datasets.api.record-readers :refer [initialize-rr!]]
            [dl4clj.eval.api.eval :refer [get-stats get-accuracy]]
            [dl4clj.core :as c]))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; nn-conf -> multi-layer-network
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def nn-conf
  (nn/builder
   ;; network args
   :optimization-algo :stochastic-gradient-descent
   :seed 123 :iterations 1 :regularization? true

   ;; setting layer defaults
   :default-activation-fn :relu :default-l2 7.5e-6
   :default-weight-init :xavier :default-learning-rate 0.0015
   :default-updater :nesterovs :default-momentum 0.98

   ;; setting layer configuration
   :layers {0 {:dense-layer
               {:layer-name "example first layer"
                :n-in 784 :n-out 500}}
            1 {:dense-layer
               {:layer-name "example second layer"
                :n-in 500 :n-out 100}}
            2 {:output-layer
               {:n-in 100 :n-out 10
                ;; layer specific params
                :loss-fn :negativeloglikelihood
                :activation-fn :softmax
                :layer-name "example output layer"}}}

   ;; multi layer args
   :backprop? true
   :pretrain? false))

(def multi-layer-network (c/model-from-conf nn-conf))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; local cpu training with dl4j pre-built iterators
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; lets use the pre-built Mnist data set iterator

(def train-mnist-iter
  (iter/new-mnist-data-set-iterator
   :batch-size 64
   :train? true
   :seed 123))

(def test-mnist-iter
  (iter/new-mnist-data-set-iterator
   :batch-size 64
   :train? false
   :seed 123))

;; and lets set a listener so we can know how training is going

(def score-listener (listener/new-score-iteration-listener :print-every-n 5))

;; and attach it to our model

;; TODO: listeners are broken, look into log4j warnning
(def mln-with-listener (set-listeners! :model multi-layer-network
                                       :listeners [score-listener]))

(def trained-mln (mln/train-mln-with-ds-iter! :mln mln-with-listener
                                              :iter train-mnist-iter
                                              :n-epochs 15
                                              :as-code? false))

;; training happens because :as-code? = false
;; if it was true, we would still just have a data structure
;; we now have a trained model that has seen the training dataset 15 times
;; time to evaluate our model

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;Create an evaluation object
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def eval-obj (evaluate-classification :mln trained-mln
                                       :iter test-mnist-iter))

;; always remember that these objects are stateful, dont use the same eval-obj
;; to eval two different networks
;; we trained the model on a training dataset.  We evaluate on a test set

(println (get-stats :evaler eval-obj))
;; this will print the stats to standard out for each feature/label pair

;;Examples labeled as 0 classified by model as 0: 968 times
;;Examples labeled as 0 classified by model as 1: 1 times
;;Examples labeled as 0 classified by model as 2: 1 times
;;Examples labeled as 0 classified by model as 3: 1 times
;;Examples labeled as 0 classified by model as 5: 1 times
;;Examples labeled as 0 classified by model as 6: 3 times
;;Examples labeled as 0 classified by model as 7: 1 times
;;Examples labeled as 0 classified by model as 8: 2 times
;;Examples labeled as 0 classified by model as 9: 2 times
;;Examples labeled as 1 classified by model as 1: 1126 times
;;Examples labeled as 1 classified by model as 2: 2 times
;;Examples labeled as 1 classified by model as 3: 1 times
;;Examples labeled as 1 classified by model as 5: 1 times
;;Examples labeled as 1 classified by model as 6: 2 times
;;Examples labeled as 1 classified by model as 7: 1 times
;;Examples labeled as 1 classified by model as 8: 2 times
;;Examples labeled as 2 classified by model as 0: 3 times
;;Examples labeled as 2 classified by model as 1: 2 times
;;Examples labeled as 2 classified by model as 2: 1006 times
;;Examples labeled as 2 classified by model as 3: 2 times
;;Examples labeled as 2 classified by model as 4: 3 times
;;Examples labeled as 2 classified by model as 6: 3 times
;;Examples labeled as 2 classified by model as 7: 7 times
;;Examples labeled as 2 classified by model as 8: 6 times
;;Examples labeled as 3 classified by model as 2: 4 times
;;Examples labeled as 3 classified by model as 3: 990 times
;;Examples labeled as 3 classified by model as 5: 3 times
;;Examples labeled as 3 classified by model as 7: 3 times
;;Examples labeled as 3 classified by model as 8: 3 times
;;Examples labeled as 3 classified by model as 9: 7 times
;;Examples labeled as 4 classified by model as 2: 2 times
;;Examples labeled as 4 classified by model as 3: 1 times
;;Examples labeled as 4 classified by model as 4: 967 times
;;Examples labeled as 4 classified by model as 6: 4 times
;;Examples labeled as 4 classified by model as 7: 1 times
;;Examples labeled as 4 classified by model as 9: 7 times
;;Examples labeled as 5 classified by model as 0: 2 times
;;Examples labeled as 5 classified by model as 3: 6 times
;;Examples labeled as 5 classified by model as 4: 1 times
;;Examples labeled as 5 classified by model as 5: 874 times
;;Examples labeled as 5 classified by model as 6: 3 times
;;Examples labeled as 5 classified by model as 7: 1 times
;;Examples labeled as 5 classified by model as 8: 3 times
;;Examples labeled as 5 classified by model as 9: 2 times
;;Examples labeled as 6 classified by model as 0: 4 times
;;Examples labeled as 6 classified by model as 1: 3 times
;;Examples labeled as 6 classified by model as 3: 2 times
;;Examples labeled as 6 classified by model as 4: 4 times
;;Examples labeled as 6 classified by model as 5: 4 times
;;Examples labeled as 6 classified by model as 6: 939 times
;;Examples labeled as 6 classified by model as 7: 1 times
;;Examples labeled as 6 classified by model as 8: 1 times
;;Examples labeled as 7 classified by model as 1: 7 times
;;Examples labeled as 7 classified by model as 2: 4 times
;;Examples labeled as 7 classified by model as 3: 3 times
;;Examples labeled as 7 classified by model as 7: 1005 times
;;Examples labeled as 7 classified by model as 8: 2 times
;;Examples labeled as 7 classified by model as 9: 7 times
;;Examples labeled as 8 classified by model as 0: 3 times
;;Examples labeled as 8 classified by model as 2: 3 times
;;Examples labeled as 8 classified by model as 3: 2 times
;;Examples labeled as 8 classified by model as 4: 4 times
;;Examples labeled as 8 classified by model as 5: 3 times
;;Examples labeled as 8 classified by model as 6: 2 times
;;Examples labeled as 8 classified by model as 7: 4 times
;;Examples labeled as 8 classified by model as 8: 947 times
;;Examples labeled as 8 classified by model as 9: 6 times
;;Examples labeled as 9 classified by model as 0: 2 times
;;Examples labeled as 9 classified by model as 1: 2 times
;;Examples labeled as 9 classified by model as 3: 4 times
;;Examples labeled as 9 classified by model as 4: 8 times
;;Examples labeled as 9 classified by model as 6: 1 times
;;Examples labeled as 9 classified by model as 7: 4 times
;;Examples labeled as 9 classified by model as 8: 2 times
;;Examples labeled as 9 classified by model as 9: 986 times

;;==========================Scores========================================
;; Accuracy:        0.9808
;; Precision:       0.9808
;; Recall:          0.9807
;; F1 Score:        0.9807
;;========================================================================

;; can get the stats that are printed via fns in the evaluation namespace
;; after running eval-model-whole-ds

(get-accuracy :evaler evaler-with-stats) ;; => 0.9808

Model Tuning

Early Stopping (controlling training)

it is recommened you start here when designing models

using dl4clj.core

(ns my.ns
  (:require [dl4clj.earlystopping.termination-conditions :refer :all]
            [dl4clj.earlystopping.model-saver :refer [new-in-memory-saver]]
            [dl4clj.nn.api.multi-layer-network :refer [evaluate-classification]]
            [dl4clj.eval.api.eval :refer [get-stats]]
            [dl4clj.nn.conf.builders.nn :as nn]
            [dl4clj.datasets.iterators :as iter]
            [dl4clj.core :as c]))

(def nn-conf
  (nn/builder
   ;; network args
   :optimization-algo :stochastic-gradient-descent
   :seed 123
   :iterations 1
   :regularization? true

   ;; setting layer defaults
   :default-activation-fn :relu
   :default-l2 7.5e-6
   :default-weight-init :xavier
   :default-learning-rate 0.0015
   :default-updater :nesterovs
   :default-momentum 0.98

   ;; setting layer configuration
   :layers {0 {:dense-layer
               {:layer-name "example first layer"
                :n-in 784 :n-out 500}}
            1 {:dense-layer
               {:layer-name "example second layer"
                :n-in 500 :n-out 100}}
            2 {:output-layer
               {:n-in 100 :n-out 10
                ;; layer specific params
                :loss-fn :negativeloglikelihood
                :activation-fn :softmax
                :layer-name "example output layer"}}}

   ;; multi layer args
   :backprop? true
   :pretrain? false))

(def train-iter
  (iter/new-mnist-data-set-iterator
   :batch-size 64
   :train? true
   :seed 123))

(def test-iter
  (iter/new-mnist-data-set-iterator
   :batch-size 64
   :train? false
   :seed 123))

(def invalid-score-condition (new-invalid-score-iteration-termination-condition))

(def max-score-condition (new-max-score-iteration-termination-condition
                          :max-score 20.0))

(def max-time-condition (new-max-time-iteration-termination-condition
                         :max-time-val 10
                         :max-time-unit :minutes))

(def score-doesnt-improve-condition (new-score-improvement-epoch-termination-condition
                                     :max-n-epoch-no-improve 5))

(def target-score-condition (new-best-score-epoch-termination-condition
                             :best-expected-score 0.009))

(def max-number-epochs-condition (new-max-epochs-termination-condition :max-n 20))

(def in-mem-saver (new-in-memory-saver))

(def trained-mln
;; defaults to returning the model
  (c/train-with-early-stopping
   :nn-conf nn-conf
   :training-iter train-mnist-iter
   :testing-iter test-mnist-iter
   :eval-every-n-epochs 1
   :iteration-termination-conditions [invalid-score-condition
                                      max-score-condition
                                      max-time-condition]
   :epoch-termination-conditions [score-doesnt-improve-condition
                                  target-score-condition
                                  max-number-epochs-condition]
   :save-last-model? true
   :model-saver in-mem-saver
   :as-code? false))

(def model-evaler
  (evaluate-classification :mln trained-mln :iter test-mnist-iter))

(println (get-stats :evaler model-evaler))

• explicit, step by step way of doing this

(ns my.ns
  (:require [dl4clj.earlystopping.early-stopping-config :refer [new-early-stopping-config]]
            [dl4clj.earlystopping.termination-conditions :refer :all]
            [dl4clj.earlystopping.model-saver :refer [new-in-memory-saver new-local-file-model-saver]]
            [dl4clj.earlystopping.score-calc :refer [new-ds-loss-calculator]]
            [dl4clj.earlystopping.early-stopping-trainer :refer [new-early-stopping-trainer]]
            [dl4clj.earlystopping.api.early-stopping-trainer :refer [fit-trainer!]]
            [dl4clj.nn.conf.builders.nn :as nn]
            [dl4clj.nn.multilayer.multi-layer-network :as mln]
            [dl4clj.utils :refer [load-model!]]
            [dl4clj.datasets.iterators :as iter]
            [dl4clj.core :as c]))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; start with our network config
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def nn-conf
  (nn/builder
   ;; network args
   :optimization-algo :stochastic-gradient-descent
   :seed 123 :iterations 1 :regularization? true
   ;; setting layer defaults
   :default-activation-fn :relu :default-l2 7.5e-6
   :default-weight-init :xavier :default-learning-rate 0.0015
   :default-updater :nesterovs :default-momentum 0.98
   ;; setting layer configuration
   :layers {0 {:dense-layer
               {:layer-name "example first layer"
                :n-in 784 :n-out 500}}
            1 {:dense-layer
               {:layer-name "example second layer"
                :n-in 500 :n-out 100}}
            2 {:output-layer
               {:n-in 100 :n-out 10
                ;; layer specific params
                :loss-fn :negativeloglikelihood
                :activation-fn :softmax
                :layer-name "example output layer"}}}
   ;; multi layer args
   :backprop? true
   :pretrain? false))

(def mln (c/model-from-conf nn-conf))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; the training/testing data
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def train-iter
  (iter/new-mnist-data-set-iterator
   :batch-size 64
   :train? true
   :seed 123))

(def test-iter
  (iter/new-mnist-data-set-iterator
   :batch-size 64
   :train? false
   :seed 123))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; we are going to need termination conditions
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; these allow us to control when we exit training

;; this can be based off of iterations or epochs

;; iteration termination conditions

(def invalid-score-condition (new-invalid-score-iteration-termination-condition))

(def max-score-condition (new-max-score-iteration-termination-condition
                          :max-score 20.0))

(def max-time-condition (new-max-time-iteration-termination-condition
                         :max-time-val 10
                         :max-time-unit :minutes))

;; epoch termination conditions

(def score-doesnt-improve-condition (new-score-improvement-epoch-termination-condition
                                     :max-n-epoch-no-improve 5))

(def target-score-condition (new-best-score-epoch-termination-condition :best-expected-score 0.009))

(def max-number-epochs-condition (new-max-epochs-termination-condition :max-n 20))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; we also need a way to save our model
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; can be in memory or to a local directory

(def in-mem-saver (new-in-memory-saver))

(def local-file-saver (new-local-file-model-saver :directory "resources/tmp/readme/"))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; set up your score calculator
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def score-calcer (new-ds-loss-calculator :iter test-iter
                                          :average? true))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; create an early stopping configuration
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; termination conditions
;; a way to save our model
;; a way to calculate the score of our model on the dataset

(def early-stopping-conf
  (new-early-stopping-config
   :epoch-termination-conditions [score-doesnt-improve-condition
                                  target-score-condition
                                  max-number-epochs-condition]
   :iteration-termination-conditions [invalid-score-condition
                                      max-score-condition
                                      max-time-condition]
   :eval-every-n-epochs 5
   :model-saver local-file-saver
   :save-last-model? true
   :score-calculator score-calcer))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; create an early stopping trainer from our data, model and early stopping conf
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def es-trainer (new-early-stopping-trainer :early-stopping-conf early-stopping-conf
                                            :mln mln
                                            :iter train-iter))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; fit and use our early stopping trainer
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def es-trainer-fitted (fit-trainer! es-trainer :as-code? false))

;; when the trainer terminates, you will see something like this
;;[nREPL-worker-24] BaseEarlyStoppingTrainer INFO  Completed training epoch 14
;;[nREPL-worker-24] BaseEarlyStoppingTrainer INFO  New best model: score = 0.005225599372851298,
;;                                                   epoch = 14 (previous: score = 0.018243224899038346, epoch = 7)
;;[nREPL-worker-24] BaseEarlyStoppingTrainer INFO Hit epoch termination condition at epoch 14.
;;                                           Details: BestScoreEpochTerminationCondition(0.009)

;; and if we look at the es-trainer-fitted object we see

;;#object[org.deeplearning4j.earlystopping.EarlyStoppingResult 0x5ab74f27 EarlyStoppingResult
;;(terminationReason=EpochTerminationCondition,details=BestScoreEpochTerminationCondition(0.009),
;; bestModelEpoch=14,bestModelScore=0.005225599372851298,totalEpochs=15)]

;; and our model has been saved to /resources/tmp/readme/bestModel.bin
;; there we have our model config, model params and our updater state

;; we can then load this model to use it or continue refining it

(def loaded-model (load-model! :path "resources/tmp/readme/bestModel.bin"
                               :load-updater? true))

Transfer Learning (freezing layers)

;; TODO: need to write up examples

Spark Training

dl4j Spark usage

How it is done in dl4clj

• Uses dl4clj.core
  • This example uses a fn which takes care of most steps for you
    • allows you to pass args as code bc the fn accounts for the multiple spark contexts issue encountered when everything is just a data structure

(ns my.ns
  (:require [dl4clj.nn.conf.builders.layers :as l]
            [dl4clj.nn.conf.builders.nn :as nn]
            [dl4clj.datasets.iterators :refer [new-iris-data-set-iterator]]
            [dl4clj.eval.api.eval :refer [get-stats]]
            [dl4clj.spark.masters.param-avg :as master]
            [dl4clj.spark.data.java-rdd :refer [new-java-spark-context
                                                java-rdd-from-iter]]
            [dl4clj.spark.api.dl4j-multi-layer :refer [eval-classification-spark-mln
                                                       get-spark-context]]
            [dl4clj.core :as c]))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 1, create your model config
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def mln-conf
  (nn/builder
   :optimization-algo :stochastic-gradient-descent
   :default-learning-rate 0.006
   :layers {0 (l/dense-layer-builder :n-in 4 :n-out 2 :activation-fn :relu)
            1 {:output-layer
               {:loss-fn :negativeloglikelihood
                :n-in 2 :n-out 3
                :activation-fn :soft-max
                :weight-init :xavier}}}
   :backprop? true
   :backprop-type :standard))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 2, training master
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def training-master
  (master/new-parameter-averaging-training-master
   :build? true
   :rdd-n-examples 10
   :n-workers 4
   :averaging-freq 10
   :batch-size-per-worker 2
   :export-dir "resources/spark/master/"
   :rdd-training-approach :direct
   :repartition-data :always
   :repartition-strategy :balanced
   :seed 1234
   :save-updater? true
   :storage-level :none))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 3, spark context
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def your-spark-context
  (new-java-spark-context :app-name "example app"))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 4, training data
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def iris-iter
  (new-iris-data-set-iterator
   :batch-size 1
   :n-examples 5))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 5, spark mln
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def fitted-spark-mln
  (c/train-with-spark :spark-context your-spark-context
                      :mln-conf mln-conf
                      :training-master training-master
                      :iter iris-iter
                      :n-epochs 1
                      :as-code? false))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 5, use spark context from spark-mln to create rdd
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; TODO: eliminate this step

(def our-rdd
  (let [sc (get-spark-context fitted-spark-mln :as-code? false)]
    (java-rdd-from-iter :spark-context sc
                        :iter iris-iter)))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 6, evaluation model and print stats (poor performance of model expected)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def eval-obj
  (eval-classification-spark-mln
   :spark-mln fitted-spark-mln
   :rdd our-rdd))

(println (get-stats :evaler eval-obj))

• this example demonstrates the dl4j workflow
  • NOTE: unlike the previous example, this one requires dl4j objects to be used
    • this is becaues spark only wants you to have one spark context at a time

(ns my.ns
  (:require [dl4clj.nn.conf.builders.layers :as l]
            [dl4clj.nn.conf.builders.nn :as nn]
            [dl4clj.datasets.iterators :refer [new-iris-data-set-iterator]]
            [dl4clj.eval.api.eval :refer [get-stats]]
            [dl4clj.spark.masters.param-avg :as master]
            [dl4clj.spark.data.java-rdd :refer [new-java-spark-context java-rdd-from-iter]]
            [dl4clj.spark.dl4j-multi-layer :as spark-mln]
            [dl4clj.spark.api.dl4j-multi-layer :refer [fit-spark-mln!
                                                       eval-classification-spark-mln]]))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 1, create your model
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def mln-conf
  (nn/builder
   :optimization-algo :stochastic-gradient-descent
   :default-learning-rate 0.006
   :layers {0 (l/dense-layer-builder :n-in 4 :n-out 2 :activation-fn :relu)
            1 {:output-layer
               {:loss-fn :negativeloglikelihood
                :n-in 2 :n-out 3
                :activation-fn :soft-max
                :weight-init :xavier}}}
   :backprop? true
   :as-code? false
   :backprop-type :standard))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 2, create a training master
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; not all options specified, but most are

(def training-master
  (master/new-parameter-averaging-training-master
   :build? true
   :rdd-n-examples 10
   :n-workers 4
   :averaging-freq 10
   :batch-size-per-worker 2
   :export-dir "resources/spark/master/"
   :rdd-training-approach :direct
   :repartition-data :always
   :repartition-strategy :balanced
   :seed 1234
   :as-code? false
   :save-updater? true
   :storage-level :none))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 3, create a Spark Multi Layer Network
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def your-spark-context
  (new-java-spark-context :app-name "example app" :as-code? false))

;; new-java-spark-context will turn an existing spark-configuration into a java spark context
;; or create a new java spark context with master set to "local[*]" and the app name
;; set to :app-name


(def spark-mln
  (spark-mln/new-spark-multi-layer-network
   :spark-context your-spark-context
   :mln mln-conf
   :training-master training-master
   :as-code? false))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 4, load your data
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; one way is via a dataset-iterator
;; can make one directly from a dataset (iterator data-set)
;; see: nd4clj.linalg.dataset.api.data-set and nd4clj.linalg.dataset.data-set
;; we are going to use a pre-built one

(def iris-iter
  (new-iris-data-set-iterator
   :batch-size 1
   :n-examples 5
   :as-code? false))

;; now lets convert the data into a javaRDD

(def our-rdd
  (java-rdd-from-iter :spark-context your-spark-context
                      :iter iris-iter))

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Step 5, fit and evaluate the model
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(def fitted-spark-mln
  (fit-spark-mln!
   :spark-mln spark-mln
   :rdd our-rdd
   :n-epochs 1))
;; this fn also has the option to supply :path-to-data instead of :rdd
;; that path should point to a directory containing a number of dataset objects

(def eval-obj
  (eval-classification-spark-mln
   :spark-mln fitted-spark-mln
   :rdd our-rdd))
;; we would want to have different testing and training rdd's but here we are using
;; the data we trained on

;; lets get the stats for how our model performed

(println (get-stats :evaler eval-obj))

Terminology

Coming soon

Packages to come back to:

Implement ComputationGraphs and the classes which use them

• https://deeplearning4j.org/doc/org/deeplearning4j/nn/graph/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/nn/conf/ComputationGraphConfiguration.GraphBuilder.html
• https://deeplearning4j.org/doc/org/deeplearning4j/nn/conf/ComputationGraphConfiguration.html
• https://deeplearning4j.org/doc/org/deeplearning4j/nn/conf/graph/package-frame.html
• https://deeplearning4j.org/doc/org/deeplearning4j/nn/conf/graph/rnn/package-frame.html

NLP

• https://deeplearning4j.org/doc/org/deeplearning4j/bagofwords/vectorizer/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/datasets/vectorizer/Vectorizer.html
• https://deeplearning4j.org/doc/org/deeplearning4j/iterator/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/iterator/provider/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/WeightLookupTable.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/inmemory/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/learning/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/learning/impl/elements/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/learning/impl/sequence/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/loader/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/reader/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/reader/impl/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/embeddings/wordvectors/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/glove/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/glove/count/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/node2vec/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/paragraphvectors/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/enums/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/interfaces/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/iterators/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/listeners/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/sequence/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/serialization/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/transformers/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/transformers/impl/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/sequencevectors/transformers/impl/iterables/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/word2vec/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/word2vec/iterator/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/word2vec/wordstore/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/models/word2vec/wordstore/inmemory/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/text/annotator/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/text/corpora/sentiwordnet/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/text/corpora/treeparser/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/text/corpora/treeparser/transformer/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/text/documentiterator/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/text/documentiterator/interoperability/DocumentIteratorConverter.html
• https://deeplearning4j.org/doc/org/deeplearning4j/text/inputsanitation/InputHomogenization.html
• https://deeplearning4j.org/doc/org/deeplearning4j/text/invertedindex/InvertedIndex.html
• https://deeplearning4j.org/doc/org/deeplearning4j/text/labels/LabelsProvider.html
• https://deeplearning4j.org/doc/org/deeplearning4j/text/movingwindow/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/text/sentenceiterator/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/text/sentenceiterator/interoperability/SentenceIteratorConverter.html
• https://deeplearning4j.org/doc/org/deeplearning4j/text/sentenceiterator/labelaware/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/text/stopwords/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/text/tokenization/tokenizer/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/text/tokenization/tokenizer/preprocessor/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/text/tokenization/tokenizer/tokenprepreprocessor/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/text/tokenization/tokenizerfactory/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/text/uima/UimaResource.html

Parallelism

• https://deeplearning4j.org/doc/org/deeplearning4j/parallelism/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/parallelism/factory/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/parallelism/main/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/parallelism/parameterserver/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/parallelism/trainer/package-summary.html

TSNE

• https://deeplearning4j.org/doc/org/deeplearning4j/plot/package-summary.html

UI

• https://deeplearning4j.org/doc/org/deeplearning4j/ui/activation/PathUpdate.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/api/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/chart/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/chart/style/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/component/ComponentDiv.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/component/style/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/decorator/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/decorator/style/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/table/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/table/style/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/text/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/components/text/style/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/flow/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/flow/beans/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/flow/data/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/i18n/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/module/convolutional/ConvolutionalListenerModule.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/module/defaultModule/DefaultModule.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/module/flow/FlowListenerModule.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/module/histogram/HistogramModule.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/module/remote/RemoteReceiverModule.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/module/train/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/module/tsne/TsneModule.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/nearestneighbors/word2vec/NearestNeighborsQuery.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/play/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/play/misc/FunctionUtil.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/play/staticroutes/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/providers/ObjectMapperProvider.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/renders/PathUpdate.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/standalone/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/stats/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/stats/api/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/stats/impl/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/stats/impl/java/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/stats/sbe/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/storage/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/storage/impl/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/storage/mapdb/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/storage/sqlite/J7FileStatsStorage.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/weights/package-summary.html
• https://deeplearning4j.org/doc/org/deeplearning4j/ui/weights/beans/CompactModelAndGradient.html

---

Author: yetanalytics
Source Code: https://github.com/yetanalytics/dl4clj
License: BSD-2-Clause License

#machine-learning #deep-learning 

How to Find Ulimit For user on Linux

How can I find the correct ulimit values for a user account or process on Linux systems?

For proper operation, we must ensure that the correct ulimit values set after installing various software. The Linux system provides means of restricting the number of resources that can be used. Limits set for each Linux user account. However, system limits are applied separately to each process that is running for that user too. For example, if certain thresholds are too low, the system might not be able to server web pages using Nginx/Apache or PHP/Python app. System resource limits viewed or set with the NA command. Let us see how to use the ulimit that provides control over the resources available to the shell and processes.

#[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object] #[object object]