bananovs / php-neural-network

AI, neural, network, neural network, нейросеть, нейросети

php-neural-network

AI, neural, network, neural network, нейросеть, нейросети

Мануал:

1. Импортируем файл:

require_once "Neuralink/Neuralink.php";

2. Инициализируем

$Neural = new Neuralink();

3. Устанавливаем входящие данные (обычно, линейный массив)

$Neural->setInputArray($LinearArray);

4. Инициализируем нейроны скрытого слоя (любое количество слоёв через запятую с указанием количество нейронов в слое)

$Neural->setHiddenLayersCount(400, 200, 100);

5. Устанавливаем количество выходных нейронов (должно быть равно количеству возможных ответов)

$Neural->setOutputNeuronsCount(10);

6. Устанавливаем тренировочные данные (то, каким должен быть результат на выходных нейронах - линейный массив)

$Neural->setTrainingArray($TrainingArray);

7. После инициализации необходимо выбрать функцию активации

$Neural->setActivationFunc(ActivationFunction::SIGMOID);

8. Задать разброс генерации весов, указав минимум, максимум и кол-во знаков после запятой

$Neural->setWeightGeneration(-5, 5, 5);

9. Задать коэффициент обучения

$Neural->setIQ(0.3);

10. После процесса инициализации - генерируем веса

$Neural->generateWeights();

11. Тренируем (100 раз, например)

for ($i=0; $i<100; $i++) $Neural->train();

12. Выводим результат тренировки

$Neural->isTrained();

13. Смотрим квадраты ошибок выходного слоя нейронов

$Neural->squareError();

Пример работы.

1. Есть картинка, размером 30х30 пикселей. Преобразуем её в ЧБ-вариант

2. Преобразуем в линейный массив, где 0 - черный, 1 - белый (произвольная функция)

$imgLinear = getLinearArray($IMG);

Функция getLinearArray

/**
 * Преобразование изображения в линейный массив
 *
 * @param $img image
 * @return array
 */
function getLinearArray($img)
{
    $arr = array();

    $imgW = imagesx($img);
    $imgH = imagesy($img);

    for ($y=0; $y<$imgH; $y++)
    {
        for ($x=0; $x<$imgW; $x++)
        {
            $rgb = imagecolorat($img, $x, $y);
            $r = ($rgb >> 16) & 0xFF;
            $g = ($rgb >> 8) & 0xFF;
            $b = $rgb & 0xFF;
            if ($r == 0 && $g == 0 && $b == 0) $arr[] = 0;
            else $arr[] = 1;
        }
    }

    if ( count($arr) == 900 ) return $arr;
    else return false;
}

Результат работы функции:

imgLinear

Array ( [0] => 1 [1] => 0 [2] => 0 [3] => 0 [4] => 0 [5] => 1 [6] => 1 [7] => 1 [8] => 1 [9] => 1 [10] => 1 [11] => 1 [12] => 1 [13] => 1 [14] => 1 [15] => 1 [16] => 1 [17] => 1 [18] => 1 [19] => 1 [20] => 1 [21] => 1 [22] => 1 [23] => 1 [24] => 1 [25] => 1 [26] => 1 [27] => 1 [28] => 1 [29] => 1 [30] => 0 [31] => 0 [32] => 0 [33] => 0 [34] => 0 [35] => 1 [36] => 1 [37] => 1 [38] => 1 [39] => 1 [40] => 1 [41] => 1 [42] => 1 [43] => 1 [44] => 1 [45] => 1 [46] => 1 [47] => 1 [48] => 1 [49] => 1 [50] => 1 [51] => 1 [52] => 1 [53] => 1 [54] => 1 [55] => 1 [56] => 1 [57] => 1 [58] => 1 [59] => 1 [60] => 0 [61] => 0 [62] => 1 [63] => 1 [64] => 1 [65] => 0 [66] => 1 [67] => 1 [68] => 1 [69] => 1 [70] => 1 [71] => 1 [72] => 1 [73] => 1 [74] => 1 [75] => 1 [76] => 1 [77] => 1 [78] => 1 [79] => 1 [80] => 1 [81] => 1 [82] => 1 [83] => 1 [84] => 1 [85] => 1 [86] => 1 [87] => 1 [88] => 1 [89] => 1 [90] => 0 [91] => 1 [92] => 0 [93] => 0 [94] => 0 [95] => 1 [96] => 1 [97] => 1 [98] => 1 [99] => 1 [100] => 1 [101] => 1 [102] => 1 [103] => 1 [104] => 1 [105] => 1 [106] => 1 [107] => 1 [108] => 1 [109] => 1 [110] => 1 [111] => 1 [112] => 1 [113] => 1 [114] => 1 [115] => 1 [116] => 1 [117] => 1 [118] => 1 [119] => 1 [120] => 0 [121] => 1 [122] => 0 [123] => 0 [124] => 0 [125] => 0 [126] => 0 [127] => 1 [128] => 1 [129] => 1 [130] => 1 [131] => 1 [132] => 1 [133] => 1 [134] => 1 [135] => 1 [136] => 1 [137] => 1 [138] => 1 [139] => 1 [140] => 1 [141] => 1 [142] => 1 [143] => 1 [144] => 1 [145] => 1 [146] => 1 [147] => 1 [148] => 1 [149] => 1 [150] => 0 [151] => 1 [152] => 0 [153] => 0 [154] => 1 [155] => 0 [156] => 1 [157] => 0 [158] => 1 [159] => 1 [160] => 1 [161] => 1 [162] => 1 [163] => 1 [164] => 1 [165] => 1 [166] => 1 [167] => 1 [168] => 1 [169] => 1 [170] => 1 [171] => 1 [172] => 1 [173] => 1 [174] => 1 [175] => 1 [176] => 1 [177] => 1 [178] => 1 [179] => 1 [180] => 0 [181] => 1 [182] => 1 [183] => 0 [184] => 1 [185] => 0 [186] => 1 [187] => 0 [188] => 0 [189] => 1 [190] => 1 [191] => 1 [192] => 1 [193] => 1 [194] => 1 [195] => 1 [196] => 1 [197] => 1 [198] => 1 [199] => 1 [200] => 1 [201] => 1 [202] => 1 [203] => 1 [204] => 1 [205] => 1 [206] => 1 [207] => 1 [208] => 1 [209] => 1 [210] => 0 [211] => 0 [212] => 1 [213] => 0 [214] => 1 [215] => 1 [216] => 0 [217] => 1 [218] => 0 [219] => 1 [220] => 1 [221] => 1 [222] => 1 [223] => 1 [224] => 1 [225] => 1 [226] => 1 [227] => 1 [228] => 1 [229] => 1 [230] => 1 [231] => 1 [232] => 1 [233] => 1 [234] => 1 [235] => 1 [236] => 1 [237] => 1 [238] => 1 [239] => 1 [240] => 1 [241] => 0 [242] => 0 [243] => 0 [244] => 0 [245] => 1 [246] => 0 [247] => 1 [248] => 1 [249] => 1 [250] => 1 [251] => 1 [252] => 1 [253] => 1 [254] => 1 [255] => 1 [256] => 1 [257] => 1 [258] => 1 [259] => 1 [260] => 1 [261] => 1 [262] => 1 [263] => 1 [264] => 1 [265] => 1 [266] => 1 [267] => 1 [268] => 1 [269] => 1 [270] => 1 [271] => 1 [272] => 0 [273] => 1 [274] => 0 [275] => 0 [276] => 0 [277] => 1 [278] => 0 [279] => 1 [280] => 1 [281] => 1 [282] => 1 [283] => 1 [284] => 1 [285] => 1 [286] => 1 [287] => 1 [288] => 1 [289] => 1 [290] => 1 [291] => 1 [292] => 1 [293] => 1 [294] => 1 [295] => 1 [296] => 1 [297] => 1 [298] => 1 [299] => 1 [300] => 1 [301] => 1 [302] => 0 [303] => 0 [304] => 1 [305] => 0 [306] => 0 [307] => 1 [308] => 0 [309] => 0 [310] => 1 [311] => 1 [312] => 1 [313] => 1 [314] => 1 [315] => 1 [316] => 1 [317] => 1 [318] => 1 [319] => 1 [320] => 1 [321] => 1 [322] => 1 [323] => 1 [324] => 1 [325] => 1 [326] => 1 [327] => 1 [328] => 1 [329] => 1 [330] => 1 [331] => 1 [332] => 1 [333] => 0 [334] => 0 [335] => 1 [336] => 1 [337] => 1 [338] => 1 [339] => 0 [340] => 1 [341] => 1 [342] => 1 [343] => 1 [344] => 1 [345] => 1 [346] => 1 [347] => 1 [348] => 1 [349] => 1 [350] => 1 [351] => 1 [352] => 1 [353] => 1 [354] => 1 [355] => 1 [356] => 1 [357] => 1 [358] => 1 [359] => 1 [360] => 1 [361] => 1 [362] => 1 [363] => 1 [364] => 0 [365] => 0 [366] => 0 [367] => 1 [368] => 1 [369] => 0 [370] => 1 [371] => 1 [372] => 1 [373] => 1 [374] => 1 [375] => 1 [376] => 1 [377] => 1 [378] => 1 [379] => 1 [380] => 1 [381] => 1 [382] => 1 [383] => 1 [384] => 1 [385] => 1 [386] => 1 [387] => 1 [388] => 1 [389] => 1 [390] => 1 [391] => 1 [392] => 1 [393] => 1 [394] => 1 [395] => 1 [396] => 0 [397] => 0 [398] => 0 [399] => 0 [400] => 1 [401] => 1 [402] => 1 [403] => 1 [404] => 1 [405] => 1 [406] => 1 [407] => 1 [408] => 1 [409] => 1 [410] => 1 [411] => 1 [412] => 1 [413] => 1 [414] => 1 [415] => 1 [416] => 1 [417] => 1 [418] => 1 [419] => 1 [420] => 1 [421] => 1 [422] => 1 [423] => 1 [424] => 0 [425] => 0 [426] => 0 [427] => 0 [428] => 0 [429] => 0 [430] => 1 [431] => 1 [432] => 1 [433] => 1 [434] => 1 [435] => 1 [436] => 1 [437] => 1 [438] => 1 [439] => 1 [440] => 1 [441] => 1 [442] => 1 [443] => 1 [444] => 1 [445] => 1 [446] => 1 [447] => 1 [448] => 1 [449] => 1 [450] => 1 [451] => 1 [452] => 1 [453] => 0 [454] => 0 [455] => 1 [456] => 0 [457] => 1 [458] => 1 [459] => 0 [460] => 1 [461] => 1 [462] => 1 [463] => 1 [464] => 1 [465] => 1 [466] => 1 [467] => 1 [468] => 1 [469] => 1 [470] => 1 [471] => 1 [472] => 1 [473] => 1 [474] => 1 [475] => 1 [476] => 1 [477] => 1 [478] => 1 [479] => 1 [480] => 1 [481] => 1 [482] => 1 [483] => 0 [484] => 1 [485] => 1 [486] => 0 [487] => 0 [488] => 0 [489] => 1 [490] => 1 [491] => 1 [492] => 1 [493] => 1 [494] => 1 [495] => 1 [496] => 1 [497] => 1 [498] => 1 [499] => 1 [500] => 1 [501] => 1 [502] => 1 [503] => 1 [504] => 1 [505] => 1 [506] => 1 [507] => 1 [508] => 1 [509] => 1 [510] => 1 [511] => 1 [512] => 1 [513] => 0 [514] => 0 [515] => 0 [516] => 0 [517] => 0 [518] => 0 [519] => 1 [520] => 1 [521] => 1 [522] => 1 [523] => 1 [524] => 1 [525] => 1 [526] => 1 [527] => 1 [528] => 1 [529] => 1 [530] => 1 [531] => 1 [532] => 1 [533] => 1 [534] => 1 [535] => 1 [536] => 1 [537] => 1 [538] => 1 [539] => 1 [540] => 1 [541] => 1 [542] => 1 [543] => 1 [544] => 1 [545] => 0 [546] => 0 [547] => 0 [548] => 1 [549] => 1 [550] => 1 [551] => 1 [552] => 1 [553] => 1 [554] => 1 [555] => 1 [556] => 1 [557] => 1 [558] => 1 [559] => 1 [560] => 1 [561] => 1 [562] => 1 [563] => 1 [564] => 1 [565] => 1 [566] => 1 [567] => 1 [568] => 1 [569] => 1 [570] => 1 [571] => 1 [572] => 1 [573] => 1 [574] => 1 [575] => 1 [576] => 1 [577] => 1 [578] => 1 [579] => 1 [580] => 1 [581] => 1 [582] => 1 [583] => 1 [584] => 1 [585] => 1 [586] => 1 [587] => 1 [588] => 1 [589] => 1 [590] => 1 [591] => 1 [592] => 1 [593] => 1 [594] => 1 [595] => 1 [596] => 1 [597] => 1 [598] => 1 [599] => 1 [600] => 1 [601] => 1 [602] => 1 [603] => 1 [604] => 1 [605] => 1 [606] => 1 [607] => 1 [608] => 1 [609] => 1 [610] => 1 [611] => 1 [612] => 1 [613] => 1 [614] => 1 [615] => 1 [616] => 1 [617] => 1 [618] => 1 [619] => 1 [620] => 1 [621] => 1 [622] => 1 [623] => 1 [624] => 1 [625] => 1 [626] => 1 [627] => 1 [628] => 1 [629] => 1 [630] => 1 [631] => 1 [632] => 1 [633] => 1 [634] => 1 [635] => 1 [636] => 1 [637] => 1 [638] => 1 [639] => 1 [640] => 1 [641] => 1 [642] => 1 [643] => 1 [644] => 1 [645] => 1 [646] => 1 [647] => 1 [648] => 1 [649] => 1 [650] => 1 [651] => 1 [652] => 1 [653] => 1 [654] => 1 [655] => 1 [656] => 1 [657] => 1 [658] => 1 [659] => 1 [660] => 1 [661] => 1 [662] => 1 [663] => 1 [664] => 1 [665] => 1 [666] => 1 [667] => 1 [668] => 1 [669] => 1 [670] => 1 [671] => 1 [672] => 1 [673] => 1 [674] => 1 [675] => 1 [676] => 1 [677] => 1 [678] => 1 [679] => 1 [680] => 1 [681] => 1 [682] => 1 [683] => 1 [684] => 1 [685] => 1 [686] => 1 [687] => 1 [688] => 1 [689] => 1 [690] => 1 [691] => 1 [692] => 1 [693] => 1 [694] => 1 [695] => 1 [696] => 1 [697] => 1 [698] => 1 [699] => 1 [700] => 1 [701] => 1 [702] => 1 [703] => 1 [704] => 1 [705] => 1 [706] => 1 [707] => 1 [708] => 1 [709] => 1 [710] => 1 [711] => 1 [712] => 1 [713] => 1 [714] => 1 [715] => 1 [716] => 1 [717] => 1 [718] => 1 [719] => 1 [720] => 1 [721] => 1 [722] => 1 [723] => 1 [724] => 1 [725] => 1 [726] => 1 [727] => 1 [728] => 1 [729] => 1 [730] => 1 [731] => 1 [732] => 1 [733] => 1 [734] => 1 [735] => 1 [736] => 1 [737] => 1 [738] => 1 [739] => 1 [740] => 1 [741] => 1 [742] => 1 [743] => 1 [744] => 1 [745] => 1 [746] => 1 [747] => 1 [748] => 1 [749] => 1 [750] => 1 [751] => 1 [752] => 1 [753] => 1 [754] => 1 [755] => 1 [756] => 1 [757] => 1 [758] => 1 [759] => 1 [760] => 1 [761] => 1 [762] => 1 [763] => 1 [764] => 1 [765] => 1 [766] => 1 [767] => 1 [768] => 1 [769] => 1 [770] => 1 [771] => 1 [772] => 1 [773] => 1 [774] => 1 [775] => 1 [776] => 1 [777] => 1 [778] => 1 [779] => 1 [780] => 1 [781] => 1 [782] => 1 [783] => 1 [784] => 1 [785] => 1 [786] => 1 [787] => 1 [788] => 1 [789] => 1 [790] => 1 [791] => 1 [792] => 1 [793] => 1 [794] => 1 [795] => 1 [796] => 1 [797] => 1 [798] => 1 [799] => 1 [800] => 1 [801] => 1 [802] => 1 [803] => 1 [804] => 1 [805] => 1 [806] => 1 [807] => 1 [808] => 1 [809] => 1 [810] => 1 [811] => 1 [812] => 1 [813] => 1 [814] => 1 [815] => 1 [816] => 1 [817] => 1 [818] => 1 [819] => 1 [820] => 1 [821] => 1 [822] => 1 [823] => 1 [824] => 1 [825] => 1 [826] => 1 [827] => 1 [828] => 1 [829] => 1 [830] => 1 [831] => 1 [832] => 1 [833] => 1 [834] => 1 [835] => 1 [836] => 1 [837] => 1 [838] => 1 [839] => 1 [840] => 1 [841] => 1 [842] => 1 [843] => 1 [844] => 1 [845] => 1 [846] => 1 [847] => 1 [848] => 1 [849] => 1 [850] => 1 [851] => 1 [852] => 1 [853] => 1 [854] => 1 [855] => 1 [856] => 1 [857] => 1 [858] => 1 [859] => 1 [860] => 1 [861] => 1 [862] => 1 [863] => 1 [864] => 1 [865] => 1 [866] => 1 [867] => 1 [868] => 1 [869] => 1 [870] => 1 [871] => 1 [872] => 1 [873] => 1 [874] => 1 [875] => 1 [876] => 1 [877] => 1 [878] => 1 [879] => 1 [880] => 1 [881] => 1 [882] => 1 [883] => 1 [884] => 1 [885] => 1 [886] => 1 [887] => 1 [888] => 1 [889] => 1 [890] => 1 [891] => 1 [892] => 1 [893] => 1 [894] => 1 [895] => 1 [896] => 1 [897] => 1 [898] => 1 [899] => 1 )

3. Создадим массив с тренировочными данными, где выходной нейрон должен соответствовать нужному значению:

$TrainingArray = array(
    0 =>    array(1, 0, 0, 0, 0, 0, 0, 0, 0, 0),
    1 =>    array(0, 1, 0, 0, 0, 0, 0, 0, 0, 0),
    2 =>    array(0, 0, 1, 0, 0, 0, 0, 0, 0, 0),
    3 =>    array(0, 0, 0, 1, 0, 0, 0, 0, 0, 0),
    4 =>    array(0, 0, 0, 0, 1, 0, 0, 0, 0, 0),
    5 =>    array(0, 0, 0, 0, 0, 1, 0, 0, 0, 0),
    6 =>    array(0, 0, 0, 0, 0, 0, 1, 0, 0, 0),
    7 =>    array(0, 0, 0, 0, 0, 0, 0, 1, 0, 0),
    8 =>    array(0, 0, 0, 0, 0, 0, 0, 0, 1, 0),
    9 =>    array(0, 0, 0, 0, 0, 0, 0, 0, 0, 1)
);

В данном случае, картинка с числом 1, значит, массив с индексом 1

4. Задаём все выше указанные параметры

$TrainingData = $TrainingArray[$NumberData['number_number']];
$Neural = new Neuralink();
$Neural->setInputArray($imgLinear);
$Neural->setHiddenLayersCount(400, 200, 100);
$Neural->setOutputNeuronsCount(10);
$Neural->setTrainingArray($TrainingData);
$Neural->setActivationFunc(ActivationFunction::SIGMOID);
$Neural->setWeightGeneration(-5,5, 5);
$Neural->setIQ(0.3);
$Neural->generateWeights();
for ($i=0; $i<100; $i++) $Neural->train();
$Neural->isTrained();
debug("Ошибка выходных нейронов: " . $Neural->squareError());

5. Результат работы на скриншоте:

После стократной корректировки весов, нейросеть распознает число.