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Handwritten digit recognizer using a feed-forward neural network and the MNIST dataset of 70,000 human-labeled handwritten digits.

Home Page: https://rubixml.com

License: MIT License

PHP 100.00%

neural-network computer-vision mnist mnist-dataset mnist-handwriting-recognition image-classification php machine-learning deep-learning deep-neural-networks

mnist's Introduction

Rubix ML

A high-level machine learning and deep learning library for the PHP language.

Developer-friendly API is delightful to use
40+ supervised and unsupervised learning algorithms
Support for ETL, preprocessing, and cross-validation
Open source and free to use commercially

Installation

Install Rubix ML into your project using Composer:

$ composer require rubix/ml

Requirements

PHP 7.4 or above

Optional

GD extension for image support
Mbstring extension for fast multibyte string manipulation
SVM extension for Support Vector Machine engine (libsvm)
PDO extension for relational database support
GraphViz for graph visualization

Documentation

Read the latest docs here.

What is Rubix ML?

Rubix ML is a free open-source machine learning (ML) library that allows you to build programs that learn from your data using the PHP language. We provide tools for the entire machine learning life cycle from ETL to training, cross-validation, and production with over 40 supervised and unsupervised learning algorithms. In addition, we provide tutorials and other educational content to help you get started using ML in your projects.

Getting Started

If you are new to machine learning, we recommend taking a look at the What is Machine Learning? section to get started. If you are already familiar with basic ML concepts, you can browse the basic introduction for a brief look at a typical Rubix ML project. From there, you can browse the official tutorials below which range from beginner to advanced skill level.

Tutorials & Example Projects

Check out these example projects using the Rubix ML library. Many come with instructions and a pre-cleaned dataset.

Interact With The Community

Join Our Telegram Channel

Contributing

See CONTRIBUTING.md for guidelines.

License

The code is licensed MIT and the documentation is licensed CC BY-NC 4.0.

mnist's People

Contributors

Stargazers

Watchers

Forkers

qa1 dovidezra cliffordchan yrslv khalilcharfi vuthaihoc johnnyduo andrewdalpino

mnist's Issues

training is very slow (>8h already) and has no sensible progress indicator

In the README.md it says that training takes less than 3 hours. I let the pc work overnight for over 8 hours and it reached only epoch 6. Is there any way to reduce the training time (in exchange for worse accuracy)? Is there any chance you could provide the pre-trained mnist.model data?

getcomposer link in readme leads to phishing site

the correct link to get composer is

https://getcomposer.org/

and not

getcomposer com

dont recognize

hello,

after train and validate the model can not recognize a png hand written numeric image.why?
what need i to set yet for fine working?

Regards,
Gabor

installation error

Hello!
I am trying to install but pecl gives me the following error:
libtool: compile: cc -I. -I/tmp/pear/temp/tensor/ext -I/tmp/pear/temp/pear-build-rootgoI8i3/tensor-3.0.00/include -I/tmp/pear/temp/pear-build-rootgoI8i3/tensor-3.0.00/main -I/tmp/pear/temp/tensor/ext -I/usr/include/php/20210902 -I/usr/include/php/20210902/main -I/usr/include/php/20210902/TSRM -I/usr/include/php/20210902/Zend -I/usr/include/php/20210902/ext -I/usr/include/php/20210902/ext/date/lib -DHAVE_CONFIG_H -g -O2 -O3 -ffast-math -c /tmp/pear/temp/tensor/ext/kernel/main.c -MMD -MF kernel/main.dep -MT kernel/main.lo -fPIC -DPIC -o kernel/.libs/main.o /tmp/pear/temp/tensor/ext/kernel/main.c: In function ‘zephir_fast_count’: /tmp/pear/temp/tensor/ext/kernel/main.c:154:45: error: ‘spl_ce_Countable’ undeclared (first use in this function); did you mean ‘zend_ce_countable’? 154 | if (instanceof_function(Z_OBJCE_P(value), spl_ce_Countable)) { | ^~~~~~~~~~~~~~~~ | zend_ce_countable /tmp/pear/temp/tensor/ext/kernel/main.c:154:45: note: each undeclared identifier is reported only once for each function it appears in /tmp/pear/temp/tensor/ext/kernel/main.c: In function ‘zephir_fast_count_ev’: /tmp/pear/temp/tensor/ext/kernel/main.c:204:45: error: ‘spl_ce_Countable’ undeclared (first use in this function); did you mean ‘zend_ce_countable’? 204 | if (instanceof_function(Z_OBJCE_P(value), spl_ce_Countable)) { | ^~~~~~~~~~~~~~~~ | zend_ce_countable /tmp/pear/temp/tensor/ext/kernel/main.c: In function ‘zephir_fast_count_int’: /tmp/pear/temp/tensor/ext/kernel/main.c:252:45: error: ‘spl_ce_Countable’ undeclared (first use in this function); did you mean ‘zend_ce_countable’? 252 | if (instanceof_function(Z_OBJCE_P(value), spl_ce_Countable)) { | ^~~~~~~~~~~~~~~~ | zend_ce_countable /tmp/pear/temp/tensor/ext/kernel/main.c: In function ‘zephir_function_exists’: /tmp/pear/temp/tensor/ext/kernel/main.c:285:101: warning: comparison between pointer and integer 285 | if (zend_hash_str_exists(CG(function_table), Z_STRVAL_P(function_name), Z_STRLEN_P(function_name)) != NULL) { | ^~ /tmp/pear/temp/tensor/ext/kernel/main.c: In function ‘zephir_function_exists_ex’: /tmp/pear/temp/tensor/ext/kernel/main.c:301:76: warning: comparison between pointer and integer 301 | if (zend_hash_str_exists(CG(function_table), function_name, function_len) != NULL) { | ^~ make: *** [Makefile:205: kernel/main.lo] Error 1 ERROR: make' failed
`

my system is ubuntu server 20.04 with php 8.0.14
Thanks!

PHP Fatal Error while I try to run train.php

PHP Fatal Error while I try to run train.php using 0.0.18-beta version of this library, XAMPP and PHP 7.3.8 on Windows 10

C:\xampp\php\php.exe D:\[projects]\PhpStorm\MNIST\train.php
Loading data into memory ...
Training ...
[2020-02-02 16:47:36] MNIST.INFO: Fitted ZScaleStandardizer

Fatal error: Uncaught InvalidArgumentException: The number of input nodes must be greater than 0, 0 given. in D:\[projects]\PhpStorm\MNIST\vendor\rubix\ml\src\NeuralNet\Layers\Placeholder1D.php:34
Stack trace:
#0 D:\[projects]\PhpStorm\MNIST\vendor\rubix\ml\src\Classifiers\MultilayerPerceptron.php(316): Rubix\ML\NeuralNet\Layers\Placeholder1D->__construct(0)
#1 D:\[projects]\PhpStorm\MNIST\vendor\rubix\ml\src\Pipeline.php(150): Rubix\ML\Classifiers\MultilayerPerceptron->train(Object(Rubix\ML\Datasets\Labeled))
#2 D:\[projects]\PhpStorm\MNIST\vendor\rubix\ml\src\PersistentModel.php(125): Rubix\ML\Pipeline->train(Object(Rubix\ML\Datasets\Labeled))
#3 D:\[projects]\PhpStorm\MNIST\train.php(61): Rubix\ML\PersistentModel->train(Object(Rubix\ML\Datasets\Labeled))
#4 {main}
  thrown in D:\[projects]\PhpStorm\MNIST\vendor\rubix\ml\src\NeuralNet\Layers\Placeholder1D.php on line 34
PHP Fatal error:  Uncaught InvalidArgumentException: The number of input nodes must be greater than 0, 0 given. in D:\[projects]\PhpStorm\MNIST\vendor\rubix\ml\src\NeuralNet\Layers\Placeholder1D.php:34
Stack trace:
#0 D:\[projects]\PhpStorm\MNIST\vendor\rubix\ml\src\Classifiers\MultilayerPerceptron.php(316): Rubix\ML\NeuralNet\Layers\Placeholder1D->__construct(0)
#1 D:\[projects]\PhpStorm\MNIST\vendor\rubix\ml\src\Pipeline.php(150): Rubix\ML\Classifiers\MultilayerPerceptron->train(Object(Rubix\ML\Datasets\Labeled))
#2 D:\[projects]\PhpStorm\MNIST\vendor\rubix\ml\src\PersistentModel.php(125): Rubix\ML\Pipeline->train(Object(Rubix\ML\Datasets\Labeled))
#3 D:\[projects]\PhpStorm\MNIST\train.php(61): Rubix\ML\PersistentModel->train(Object(Rubix\ML\Datasets\Labeled))
#4 {main}
  thrown in D:\[projects]\PhpStorm\MNIST\vendor\rubix\ml\src\NeuralNet\Layers\Placeholder1D.php on line 34

Process finished with exit code 255

Running train.php ends up with PHP Fatal Error

I`m using 0.0.18-beta version of this library with PHP Version 7.3.12 bundled in XAMPP Version: 7.3.12 on Windows 10.
Whenever I try to run train.php, I get the following error:

C:\xampp\php\php.exe D:\Projects\MNIST\train.php
Loading data into memory ...
Training ...
[2020-02-02 12:01:26] MNIST.INFO: Fitted ZScaleStandardizer
PHP Fatal error:  Uncaught InvalidArgumentException: Classifiers require categorical labels, continuous given. in D:\Projects\MNIST\vendor\rubix\ml\src\Other\Specifications\LabelsAreCompatibleWithLearner.php:27
Stack trace:
#0 D:\Projects\MNIST\vendor\rubix\ml\src\Classifiers\MultilayerPerceptron.php(347): Rubix\ML\Other\Specifications\LabelsAreCompatibleWithLearner::check(Object(Rubix\ML\Datasets\Labeled), Object(Rubix\ML\Classifiers\MultilayerPerceptron))
#1 D:\Projects\MNIST\vendor\rubix\ml\src\Classifiers\MultilayerPerceptron.php(324): Rubix\ML\Classifiers\MultilayerPerceptron->partial(Object(Rubix\ML\Datasets\Labeled))
#2 D:\Projects\MNIST\vendor\rubix\ml\src\Pipeline.php(150): Rubix\ML\Classifiers\MultilayerPerceptron->train(Object(Rubix\ML\Datasets\Labeled))
#3 D:\Projects\MNIST\vendor\rubix\ml\src\PersistentModel.php(125): Rubix\ML\Pipeline->train(Object(Rubix\ML\Datasets\Labeled))
#4 D:\Projects\MNIST\train.php(61): Rubix\ML\PersistentModel->train(Object(Rubix\ML\Datasets\Labeled))
#5 {main}
  thrown in D:\Projects\MNIST\vendor\rubix\ml\src\Other\Specifications\LabelsAreCompatibleWithLearner.php on line 27

Fatal error: Uncaught InvalidArgumentException: Classifiers require categorical labels, continuous given. in D:\Projects\MNIST\vendor\rubix\ml\src\Other\Specifications\LabelsAreCompatibleWithLearner.php:27
Stack trace:
#0 D:\Projects\MNIST\vendor\rubix\ml\src\Classifiers\MultilayerPerceptron.php(347): Rubix\ML\Other\Specifications\LabelsAreCompatibleWithLearner::check(Object(Rubix\ML\Datasets\Labeled), Object(Rubix\ML\Classifiers\MultilayerPerceptron))
#1 D:\Projects\MNIST\vendor\rubix\ml\src\Classifiers\MultilayerPerceptron.php(324): Rubix\ML\Classifiers\MultilayerPerceptron->partial(Object(Rubix\ML\Datasets\Labeled))
#2 D:\Projects\MNIST\vendor\rubix\ml\src\Pipeline.php(150): Rubix\ML\Classifiers\MultilayerPerceptron->train(Object(Rubix\ML\Datasets\Labeled))
#3 D:\Projects\MNIST\vendor\rubix\ml\src\PersistentModel.php(125): Rubix\ML\Pipeline->train(Object(Rubix\ML\Datasets\Labeled))
#4 D:\Projects\MNIST\train.php(61): Rubix\ML\PersistentModel->train(Object(Rubix\ML\Datasets\Labeled))
#5 {main}
  thrown in D:\Projects\MNIST\vendor\rubix\ml\src\Other\Specifications\LabelsAreCompatibleWithLearner.php on line 27

UPD: I found out that label type is determined by determine() method in ...\vendor\rubix\ml\src\DataType.php and that only data of type string is corresponding to categorical type. So I tried converting each $label value to string before writing it to $labels array in train.php:

for ($label = 0; $label < 10; $label++) {
    foreach (glob("training/$label/*.png") as $file) {
        $label = strval($label);
        $samples[] = [imagecreatefrompng($file)];
        $labels[] = $label;
    }
}

This did the trick for me and I was able to run the script from PhpStorm 2019.3 built-in terminal. Nevertheless, for some reason the training only completed 1 epoch and finished. This seems to be a really strange behavior. Below is the output for that run (the prompt for saving the model does not appear on screen, but giving 'y' as the input saves the model):

C:\xampp\php\php.exe D:\Projects\MNIST\train.php
Loading data into memory ...
Training ...
[2020-02-02 17:07:40] MNIST.INFO: Fitted ZScaleStandardizer
[2020-02-02 17:07:45] MNIST.INFO: Learner init hidden=[0=Dense 1=Activation 2=Dropout 3=Dense 4=Activation 5=Dropout 6=Dense 7=Activation 8=Dropout] batch_size=200 optimizer=Adam alpha=0.0001 epochs=1000 min_change=0.0001 window=3 hold_out=0.1 cost_fn=CrossEntropy metric=FBeta
[2020-02-02 17:16:39] MNIST.INFO: Epoch 1 score=0.03078281535439 loss=0
[2020-02-02 17:16:39] MNIST.INFO: Training complete
Progress saved to progress.csv
y

Process finished with exit code 0

But when I try to run the script from Windows Command Prompt or PowerShell I get the following error:

C:\xampp\php\php.exe D:\Projects\MNIST\train.php
Loading data into memory ...
Training ...
[2020-02-02 18:04:09] MNIST.INFO: Fitted ZScaleStandardizer
PHP Fatal error:  Uncaught InvalidArgumentException: The number of input nodes must be greater than 0, 0 given. in D:\Projects\MNIST\vendor\rubix\ml\src\NeuralNet\Layers\Placeholder1D.php:34
Stack trace:
#0 D:\Projects\MNIST\vendor\rubix\ml\src\Classifiers\MultilayerPerceptron.php(316): Rubix\ML\NeuralNet\Layers\Placeholder1D->__construct(0)
#1 D:\Projects\MNIST\vendor\rubix\ml\src\Pipeline.php(150): Rubix\ML\Classifiers\MultilayerPerceptron->train(Object(Rubix\ML\Datasets\Labeled))
#2 D:\Projects\MNIST\vendor\rubix\ml\src\PersistentModel.php(125): Rubix\ML\Pipeline->train(Object(Rubix\ML\Datasets\Labeled))
#3 D:\Projects\MNIST\train.php(71): Rubix\ML\PersistentModel->train(Object(Rubix\ML\Datasets\Labeled))
#4 {main}
  thrown in D:\Projects\MNIST\vendor\rubix\ml\src\NeuralNet\Layers\Placeholder1D.php on line 34

Fatal error: Uncaught InvalidArgumentException: The number of input nodes must be greater than 0, 0 given. in D:\Projects\MNIST\vendor\rubix\ml\src\NeuralNet\Layers\Placeholder1D.php:34
Stack trace:
#0 D:\Projects\MNIST\vendor\rubix\ml\src\Classifiers\MultilayerPerceptron.php(316): Rubix\ML\NeuralNet\Layers\Placeholder1D->__construct(0)
#1 D:\Projects\MNIST\vendor\rubix\ml\src\Pipeline.php(150): Rubix\ML\Classifiers\MultilayerPerceptron->train(Object(Rubix\ML\Datasets\Labeled))
#2 D:\Projects\MNIST\vendor\rubix\ml\src\PersistentModel.php(125): Rubix\ML\Pipeline->train(Object(Rubix\ML\Datasets\Labeled))
#3 D:\Projects\MNIST\train.php(71): Rubix\ML\PersistentModel->train(Object(Rubix\ML\Datasets\Labeled))
#4 {main}
  thrown in D:\Projects\MNIST\vendor\rubix\ml\src\NeuralNet\Layers\Placeholder1D.php on line 34