A Beginner’s Guide to Deep Learning in PHP

In recent years, the rapid development of deep learning technology has had a huge impact on many fields. As a popular programming language, PHP is also gradually integrating with deep learning. In this article, we will provide beginners with a simple introductory guide to deep learning in PHP to help them understand how deep learning is implemented in PHP and benefit from it.

First of all, we need to understand what deep learning is. In the field of artificial intelligence, deep learning is a machine learning technology that aims to enable computers to learn and perform tasks on their own, rather than being programmed by humans. Deep learning represents human efforts to simulate human thinking and behavior.

To implement deep learning in PHP, we need some important tools and libraries. The following are some noteworthy tools and libraries:

1. TensorFlow: It is a very popular deep learning library that supports Python and C programming languages. It is rich in detailed documentation and application examples, and is suitable for learners who are just getting started. Very friendly.
2. Keras: Keras is a high-level deep learning library. It is an interface to TensorFlow and provides an easier-to-understand API.
3. Theano: Theano is a library for defining, optimizing, and evaluating mathematical expressions, often used in high-performance computing.

For PHP developers, Keras is a good choice. We can use Keras to build deep learning models.

Next we will demonstrate how to use Keras for deep learning in PHP.

First, we need to install Keras in the PHP environment. We can use Composer to install PHP's Keras library from Packagist. In the command line, enter the following command:

composer require php-ai/php-ml

After the installation is complete, we can start building the deep learning model.

Here, we will use an example to demonstrate the construction process of a deep learning model. We want to train a model for digital recognition.

First, we need to prepare training data. We can use the MNIST dataset, which is a very popular numeric dataset and is already included in Keras.

use PhpmlDatasetMnistDataset;
$dataset = new MnistDataset();
$dataset->load();

Next, we need to split the data into training data and test data.

use PhpmlCrossValidationStratifiedRandomSplit;
$sampler = new StratifiedRandomSplit($dataset->getSamples(), $dataset->getTargets(), 0.5);

In this example, we use StratifiedRandomSplit, which is a data splitting method in the Phpml library.

Next, we will use Keras to build a deep learning model. We will use the Sequential model, which is a simple deep learning model.

use PhpmlNeuralNetworkLayerDense;
use PhpmlNeuralNetworkLayerFlatten;
use PhpmlNeuralNetworkLayerActivation;
use PhpmlNeuralNetworkLayerDropout;
use PhpmlNeuralNetworkClassifierKeras;
use PhpmlNeuralNetworkOptimizerAdam;
use PhpmlNeuralNetworkActivationFunctionSigmoid;

$model = new Sequential();

$model->add(new Flatten());
$model->add(new Dense(800, new Sigmoid()));
$model->add(new Dropout(0.2));
$model->add(new Dense(10, new Sigmoid()));
$model->add(new Activation('softmax'));

$optimizer = new Adam();
$model->compile($optimizer, 'categorical_crossentropy', ['accuracy']);

Here we create a sequential model and add some layers. Specifically, we added a Flatten layer, a dense layer of 800 nodes, a 20% dropout layer, a dense layer of 10 nodes, and an Activation layer with Softmax activation.

Next, we need to fit the model to the training data.

$keras = new Keras([
    'input_shape' => [1, 28, 28],
    'output_shape' => [10],
    'loss' => 'categorical_crossentropy',
    'metrics' => ['accuracy'],
    'epochs' => 3,
    'batch_size' => 128,
]);

$keras->fit($sampler->getTrainSamples(), $sampler->getTrainLabels());

Here, we instantiate the Keras object and train it for 3 epochs with a batch size of 128.

Finally, we can use the test data set to evaluate our model.

$score = $keras->score($sampler->getTestSamples(), $sampler->getTestLabels());
echo 'Test Accuracy: ' . $score['accuracy'] . PHP_EOL;

Here, we use the score method provided in Keras to evaluate the accuracy of the test data set.

This is a simple introductory guide to deep learning in PHP. Now, we have seen how to build a deep learning model in PHP using Keras, as well as how to train and evaluate the model. Through this example, we hope to help beginners better understand and apply deep learning technology.

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