Ninluc/Reseaux-de-neurones-artificiels-Perceptrons
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Added Limited Epoch Event Buffer

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for better frontend performance when using big  max epoch number
This commit is contained in:
Matthias Guillitte
2026-03-21 09:42:05 +01:00
parent f0e7be4476
commit 6abb417430
10 changed files with 149 additions and 22 deletions
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15
app/Http/Controllers/PerceptronController.php
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@@ -8,6 +8,7 @@ use App\Models\SimpleBinaryPerceptronTraining;
use App\Services\DataSetReader;
use App\Services\ISynapticWeightsProvider;
use App\Services\PerceptronIterationEventBuffer;
use App\Services\PerceptronLimitedEpochEventBuffer;
use App\Services\ZeroSynapticWeights;
use Illuminate\Http\Request;
@@ -91,8 +92,15 @@ class PerceptronController extends Controller
case 'simple':
$dataset['defaultLearningRate'] = 0.015;
break;
case 'gradientdescent':
$dataset['defaultLearningRate'] = 0.001;
$dataset['defaultMinError'] = 2.0;
break;
}
break;
case 'table_2_11':
$dataset['defaultMinError'] = 1.0;
break;
}
$datasets[] = $dataset;
}
@@ -121,9 +129,14 @@ class PerceptronController extends Controller
$synapticWeightsProvider = new ZeroSynapticWeights();
}
$iterationEventBuffer = new PerceptronIterationEventBuffer($sessionId, $trainingId);
if ($maxIterations > config('perceptron.limited_broadcast_iterations')) {
$iterationsInterval = (int)($maxIterations / config('perceptron.limited_broadcast_iterations'));
$iterationEventBuffer = new PerceptronLimitedEpochEventBuffer($sessionId, $trainingId, $iterationsInterval);
}
$dataSetReader = $this->getDataSetReader($dataSet);
$iterationEventBuffer = new PerceptronIterationEventBuffer($sessionId, $trainingId);
$networkTraining = match ($perceptronType) {
'simple' => new SimpleBinaryPerceptronTraining($dataSetReader, $learningRate, $maxIterations, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId),

10
app/Services/DataSetReader.php
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@@ -51,6 +51,16 @@ class DataSetReader {
return $randomLine;
}
public function getNextLine(): array | null {
if (!isset($this->currentLines[0])) {
return null; // No more lines to read
}
$this->lastReadLineIndex = array_search($this->currentLines[0], $this->lines, true);
return array_shift($this->currentLines);
}
public function getInputSize(): int
{
return count($this->lines[0]) - 1; // Don't count the label

10
app/Services/IPerceptronIterationEventBuffer.php Normal file
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@@ -0,0 +1,10 @@
<?php
namespace App\Services;
interface IPerceptronIterationEventBuffer {
public function flush(): void ;
public function addIteration(int $iteration, int $exampleIndex, float $error, array $synaptic_weights): void ;
}

14
app/Services/PerceptronIterationEventBuffer.php
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@@ -2,15 +2,11 @@
namespace App\Services;
use Illuminate\Support\Facades\Log;
class PerceptronIterationEventBuffer {
class PerceptronIterationEventBuffer implements IPerceptronIterationEventBuffer {
private $data;
private int $nextSizeIncreaseThreshold;
private int $underSizeIncreaseCount = 0;
private int $MAX_SIZE = 50;
public function __construct(
private string $sessionId,
private string $trainingId,
@@ -26,9 +22,9 @@ class PerceptronIterationEventBuffer {
$this->data = [];
}
public function addIteration(int $iteration, int $exampleIndex, float $error, array $synaptic_weights): void {
public function addIteration(int $epoch, int $exampleIndex, float $error, array $synaptic_weights): void {
$this->data[] = [
"iteration" => $iteration,
"epoch" => $epoch,
"exampleIndex" => $exampleIndex,
"error" => $error,
"weights" => $synaptic_weights,
@@ -42,8 +38,8 @@ class PerceptronIterationEventBuffer {
$this->flush();
$this->nextSizeIncreaseThreshold *= $this->sizeIncreaseFactor;
if ($this->nextSizeIncreaseThreshold > $this->MAX_SIZE) {
$this->nextSizeIncreaseThreshold = $this->MAX_SIZE; // Cap the threshold to the maximum size
if ($this->nextSizeIncreaseThreshold > config('perceptron.broadcast_iteration_size')) {
$this->nextSizeIncreaseThreshold = config('perceptron.broadcast_iteration_size'); // Cap the threshold to the maximum size
}
}
}

51
app/Services/PerceptronLimitedEpochEventBuffer.php Normal file
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@@ -0,0 +1,51 @@
<?php
namespace App\Services;
class PerceptronLimitedEpochEventBuffer implements IPerceptronIterationEventBuffer {
private array $data;
private int $underSizeIncreaseCount = 0;
public function __construct(
private string $sessionId,
private string $trainingId,
private int $epochInterval,
private int $sizeIncreaseStart = 10,
) {
$this->data = [];
}
public function flush(): void {
event(new \App\Events\PerceptronTrainingIteration($this->data, $this->sessionId, $this->trainingId));
$this->data = [];
}
public function addIteration(int $epoch, int $exampleIndex, float $error, array $synaptic_weights): void {
$newData = [
"epoch" => $epoch,
"exampleIndex" => $exampleIndex,
"error" => $error,
"weights" => $synaptic_weights,
];
if ($this->underSizeIncreaseCount <= $this->sizeIncreaseStart) { // Special case where we need to send each iteration separately
$this->underSizeIncreaseCount++;
$this->data[] = $newData;
$this->flush();
return;
}
$lastEpoch = $this->data[0]['epoch'] ?? null;
if ($this->data && $lastEpoch !== $epoch) { // Current Epoch has changed from the last one
if ($lastEpoch % $this->epochInterval === 0) { // The last epoch need to be sent
$this->flush(); // Flush all data from the previous epoch
}
else {
$this->data = [];
}
$lastEpoch = $epoch;
}
$this->data[] = $newData;
}
}

19
config/perceptron.php Normal file
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@@ -0,0 +1,19 @@
<?php
return [
/**
* Minimum number of iterations for which the broadcast of the training progress is allowed in full.
* Beyond this number of iterations, the broadcast will be splitted every x iterations,
* x is limited_broadcast_number
*/
'limited_broadcast_iterations' => 200,
/**
* How much broadcasts is sent when in limmited broadcast mode
*/
'limited_broadcast_number' => 200,
'broadcast_iteration_size' => 75,
];

4
public/data_sets/logic_or.csv Normal file
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@@ -0,0 +1,4 @@
0, 0, -1
0, 1, 1
1, 0, 1
1, 1, 1
1 0 0 -1
2 0 1 1
3 1 0 1
4 1 1 1

28
resources/js/components/IterationTable.vue
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@@ -15,14 +15,29 @@ const allWeightPerIteration: ComputedRef<number[][]> = computed(() => {
return iteration.weights.flat(2);
});
});
const rowBgDark = computed(() => {
let isEven = false;
return props.iterations.map((iteration, index, arr) => {
if (index > 0 && arr[index - 1].epoch !== iteration.epoch) {
isEven = !isEven;
}
return isEven;
});
});
</script>
<template>
<table class="table w-full border-collapse border border-gray-300">
<tr class="text-left" v-if="props.iterations.length > 0">
<th>Itération</th>
<th>Époch</th>
<th>Exemple</th>
<th v-for="(weight, index) in allWeightPerIteration[allWeightPerIteration.length - 1]" v-bind:key="index">
<th
v-for="(weight, index) in allWeightPerIteration[
allWeightPerIteration.length - 1
]"
v-bind:key="index"
>
X<sub>{{ index }}</sub>
</th>
<th>Erreur</th>
@@ -31,12 +46,15 @@ const allWeightPerIteration: ComputedRef<number[][]> = computed(() => {
v-for="(iteration, index) in props.iterations"
v-bind:key="index"
:class="{
'bg-gray-900': iteration.iteration % 2 === 0,
'bg-gray-900': rowBgDark[index],
}"
>
<td>{{ iteration.iteration }}</td>
<td>{{ iteration.epoch }}</td>
<td>{{ iteration.exampleIndex }}</td>
<td v-for="(weight, index) in allWeightPerIteration[index]" v-bind:key="index">
<td
v-for="(weight, index) in allWeightPerIteration[index]"
v-bind:key="index"
>
{{ weight.toFixed(2) }}
</td>
<td>{{ iteration.error.toFixed(2) }}</td>

2
resources/js/components/PerceptronDecisionGraph.vue
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@@ -46,7 +46,7 @@ function getPerceptronDecisionBoundaryDataset(
networkWeights[0].length == 1 &&
networkWeights[0][0].length == 3
) { // Unique, 3 weights perceptron
const perceptronWeights = networkWeights[0][0]; // We take the unique
const perceptronWeights = networkWeights[0][0]; // We take the unique perceptron
function perceptronLine(x: number): number {
// w0 + w1*x + w2*y = 0 => y = -(w1/w2)*x - w0/w2

14
resources/js/components/PerceptronSetup.vue
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@@ -47,11 +47,17 @@ watch(selectedDatasetCopy, (newvalue) => {
(dataset) => dataset.label === newvalue
) || null;
let defaultLearningRate = props.defaultLearningRate;
// LearningRate
learningRate.value = props.defaultLearningRate;
if (selectedDatasetCopy && selectedDatasetCopy.defaultLearningRate !== undefined) {
defaultLearningRate = selectedDatasetCopy.defaultLearningRate;
learningRate.value = selectedDatasetCopy.defaultLearningRate;
}
learningRate.value = defaultLearningRate;
// MinError
minError.value = props.minError;
if (selectedDatasetCopy && selectedDatasetCopy.defaultMinError !== undefined) {
minError.value = selectedDatasetCopy.defaultMinError;
}
// MaxIterations
maxIterations.value = props.defaultMaxIterations;
})
@@ -196,7 +202,7 @@ watch(selectedDatasetCopy, (newValue) => {
<!-- MAX ITERATIONS -->
<FormField name="max_iterations">
<FormItem>
<FormLabel>Nombre maximum d'itérations</FormLabel>
<FormLabel>Nombre maximum d'epoch</FormLabel>
<FormControl>
<Input
type="number"