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andrewdalpinoandrewdalpino
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Tighter typehinting
1 parent 06ae7ee commit 1f6e804

21 files changed

+67
-60
lines changed

‎CHANGELOG.md

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@@ -1,3 +1,6 @@
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- 1.3.2
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- Optimize Binary output layer
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- 1.3.1
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- Update to Ok Bloomer 1.0 stable
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‎src/NeuralNet/FeedForward.php

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@@ -38,7 +38,7 @@ class FeedForward implements Network
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/**
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* The hidden layers of the network.
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*
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* @var \Rubix\ML\NeuralNet\Layers\Hidden[]
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* @var list<\Rubix\ML\NeuralNet\Layers\Hidden>
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*/
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protected array $hidden = [
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//
@@ -47,7 +47,7 @@ class FeedForward implements Network
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/**
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* The pathing of the backward pass through the hidden layers.
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*
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* @var \Rubix\ML\NeuralNet\Layers\Hidden[]
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* @var list<\Rubix\ML\NeuralNet\Layers\Hidden>
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*/
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protected array $backPass = [
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//
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*/
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public function __construct(Input $input, array $hidden, Output $output, Optimizer $optimizer)
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{
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$hidden = array_values($hidden);
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$backPass = array_reverse($hidden);
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$this->input = $input;
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$this->hidden = $hidden;
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$this->output = $output;
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$this->optimizer = $optimizer;
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$this->backPass = array_reverse($hidden);
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$this->backPass = $backPass;
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}
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/**
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/**
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* Return an array of hidden layers indexed left to right.
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*
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* @return \Rubix\ML\NeuralNet\Layers\Hidden[]
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* @return list<\Rubix\ML\NeuralNet\Layers\Hidden>
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*/
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public function hidden() : array
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{
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*/
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public function initialize() : void
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{
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$fanIn = 0;
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$fanIn = 1;
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foreach ($this->layers() as $layer) {
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$fanIn = $layer->initialize($fanIn);

‎src/NeuralNet/Layers/Activation.php

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@@ -30,7 +30,7 @@ class Activation implements Hidden
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/**
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* The width of the layer.
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*
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* @var int<0,max>|null
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* @var positive-int|null
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*/
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protected ?int $width = null;
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* @internal
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*
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* @throws \Rubix\ML\Exceptions\RuntimeException
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* @return int<0,max>
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* @return positive-int
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*/
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public function width() : int
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{
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*
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* @internal
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*
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* @param int<0,max> $fanIn
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* @return int<0,max>
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* @param positive-int $fanIn
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* @return positive-int
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*/
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public function initialize(int $fanIn) : int
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{

‎src/NeuralNet/Layers/BatchNorm.php

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@@ -57,7 +57,7 @@ class BatchNorm implements Hidden, Parametric
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/**
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* The width of the layer. i.e. the number of neurons.
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*
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* @var int<0,max>|null
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* @var positive-int|null
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*/
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protected ?int $width = null;
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* @internal
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*
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* @throws \Rubix\ML\Exceptions\RuntimeException
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* @return int<0,max>
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* @return positive-int
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*/
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public function width() : int
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{
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*
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* @internal
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*
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* @param int<0,max> $fanIn
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* @return int<0,max>
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* @param positive-int $fanIn
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* @return positive-int
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*/
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public function initialize(int $fanIn) : int
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{

‎src/NeuralNet/Layers/Binary.php

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@@ -91,7 +91,7 @@ public function __construct(array $classes, ?ClassificationLoss $costFn = null)
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/**
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* Return the width of the layer.
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*
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* @return int<0,max>
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* @return positive-int
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*/
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public function width() : int
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{
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* Initialize the layer with the fan in from the previous layer and return
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* the fan out for this layer.
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*
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* @param int<0,max> $fanIn
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* @param positive-int $fanIn
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* @throws \Rubix\ML\Exceptions\InvalidArgumentException
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* @return int<0,max>
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* @return positive-int
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*/
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public function initialize(int $fanIn) : int
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{
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->divide($output->n());
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}
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$dL = $this->costFn->differentiate($output, $expected)
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$dLoss = $this->costFn->differentiate($output, $expected)
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->divide($output->n());
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return $this->sigmoid->differentiate($input, $output)
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->multiply($dL);
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->multiply($dLoss);
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}
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}

‎src/NeuralNet/Layers/Continuous.php

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@@ -48,7 +48,7 @@ public function __construct(?RegressionLoss $costFn = null)
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/**
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* Return the width of the layer.
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*
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* @return int<0,max>
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* @return positive-int
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*/
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public function width() : int
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{
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* Initialize the layer with the fan in from the previous layer and return
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* the fan out for this layer.
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*
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* @param int<0,max> $fanIn
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* @param positive-int $fanIn
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* @throws \Rubix\ML\Exceptions\InvalidArgumentException
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* @return int<0,max>
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* @return positive-int
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*/
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public function initialize(int $fanIn) : int
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{

‎src/NeuralNet/Layers/Dense.php

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@@ -121,7 +121,7 @@ public function __construct(
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*
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* @internal
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*
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* @return int<0,max>
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* @return positive-int
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*/
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public function width() : int
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{
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*
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* @internal
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*
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* @param int<0,max> $fanIn
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* @return int<0,max>
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* @param positive-int $fanIn
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* @return positive-int
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*/
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public function initialize(int $fanIn) : int
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{

‎src/NeuralNet/Layers/Dropout.php

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@@ -43,7 +43,7 @@ class Dropout implements Hidden
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/**
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* The width of the layer.
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*
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* @var int<0,max>|null
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* @var positive-int|null
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*/
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protected ?int $width = null;
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* @internal
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*
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* @throws \Rubix\ML\Exceptions\RuntimeException
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* @return int<0,max>
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* @return positive-int
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*/
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public function width() : int
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{
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*
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* @internal
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*
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* @param int<0,max> $fanIn
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* @return int<0,max>
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* @param positive-int $fanIn
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* @return positive-int
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*/
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public function initialize(int $fanIn) : int
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{

‎src/NeuralNet/Layers/Layer.php

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@@ -11,7 +11,7 @@ interface Layer
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*
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* @internal
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*
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* @return int<0,max>
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* @return positive-int
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*/
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public function width() : int;
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*
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* @internal
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*
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* @param int $fanIn
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* @return int
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* @param positive-int $fanIn
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* @return positive-int
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*/
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public function initialize(int $fanIn) : int;
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‎src/NeuralNet/Layers/Multiclass.php

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@@ -87,20 +87,20 @@ public function __construct(array $classes, ?ClassificationLoss $costFn = null)
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/**
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* Return the width of the layer.
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*
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* @return int<0,max>
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* @return positive-int
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*/
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public function width() : int
9393
{
94-
return count($this->classes);
94+
return max(1, count($this->classes));
9595
}
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/**
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* Initialize the layer with the fan in from the previous layer and return
9999
* the fan out for this layer.
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*
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* @param int<0,max> $fanIn
101+
* @param positive-int $fanIn
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* @throws \Rubix\ML\Exceptions\InvalidArgumentException
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* @return int<0,max>
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* @return positive-int
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*/
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public function initialize(int $fanIn) : int
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{
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199199
->divide($output->n());
200200
}
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202-
$dL = $this->costFn->differentiate($output, $expected)
202+
$dLoss = $this->costFn->differentiate($output, $expected)
203203
->divide($output->n());
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205205
return $this->softmax->differentiate($input, $output)
206-
->multiply($dL);
206+
->multiply($dLoss);
207207
}
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}

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