• namespace: Rindow\NeuralNetworks\Data\Dataset
• classname: ClassifiedDirectoryDataset

Reads the files classified according to the file system directory in order and returns them with the class name.

Specify the top directory with the following structure.

-top-+-class1-+-file1
     |        +-file2
     |
     +-class2-+-file3
              +-file4

Methods

constructor

public function __construct(
    object $mo,
    string $path,
    string $pattern=null,
    int $batch_size=32,
    object $crawler=null,
    DatasetFilter $filter=null,
    bool $unclassified=false,
    bool $shuffle=false,
    int $limit=null,
    array $restricted_by_class=null,
)

Arguments

• path: Top directory of classified directories.
• pattern: File name pattern. Specifies the regular expression for preg_match.
• batch_size: Batch size
• crawler: Specifies an instance of the service that crawls the directory tree. By default it uses its own Dir class.
• filter: Specifies the filter for the dataset. Filter will be described later.
• unclassified: It works in unclassified mode. If set to true, the returned value will not include classname.
• shuffle: Shuffles the order of the returned values.
• limit: Sets the maximum number of values to return.
• restricted_by_class: Restricts returning only the values contained in the specified class.

Examples

use Rindow\NeuralNetworks\Data\Dataset\ClassifiedDirectoryDataset;
$dataset = new ClassifiedDirectoryDataset($mo,'/text',pattern:'@.*\\.txt@');
foreach ($dataset as $batchdataset) {
    [$contents,$labels] = $batchdataset;
    foreach ($contents as $key => $value) {
        $content = $value;
        $class   = $labels[$key];
        //....... some processing
    }
}

setFilter

public function setFilter(DatasetFilter $filter) : void

Set a filter to process input data on demand.

Arguments

• filter: Instance of conversion filter.

batchSize

public function batchSize() : int

Dataset batch size.

datasetSize

public function datasetSize() : int

Total size of the dataset.

In the initial state, the file is not read, so the correct size cannot be returned. You can get the size after reading to the end of the dataset

count

public function count() : int

Number of batch steps.

In the initial state, the file is not read, so the correct size cannot be returned. You can get the size after reading to the end of the dataset

How to make a filter

It is loaded by the fgetcsv function and passed a PHP native Array organized by batch size. You need to implement a filter that converts this to an NDArray.

Create a class that implements DatasetFilter and implement the translate method.

translate

public function translate(iterable $inputs, iterable $tests=null, $options=null) : array

Arguments

• inputs: NDArray rows of the specified batch size from the inputs.
• tests: NDArray rows of the specified batch size from the tests.
• options: array of file path list.

Output set

• inputs: Data expected to be used for training input
• tests: Data expected to be used for correct labels

Filter Example

An example of a filter that returns input data in 1/10

use Rindow\NeuralNetworks\Data\Dataset\DatasetFilter;

class TestFilter implements DatasetFilter
{
    public function __construct($mo = null)
    {
        $this->mo = $mo;
    }
    public function translate(
        iterable $inputs, iterable $tests=null, $options=null) : array
    {
        $la = $this->mo->la();
        foreach($inputs as $key => $value) {
        }
        return [$inputs,$tests];
    }
}