ML-400 Converted back Network interface

CHANGELOG.md

@@ -6,9 +6,10 @@
     - Exportable Extractors now append by default with option to overwrite
     - Added validation interval parameter to MLPs and GBM Learners
     - Removed output layer L2 Penalty parameter from MLP Learners
-    - Remove Network interface
     - RBX Serializer only tracks major library version number
-
+    - Convert NeuralNet classes to use NDArray instead of Matrix
+    - Converted back Network interface
+
 - 2.5.0
     - Added Vantage Point Spatial tree
     - Blob Generator can now `simulate()` a Dataset object

src/Classifiers/LogisticRegression.php

@@ -2,6 +2,7 @@
 
 namespace Rubix\ML\Classifiers;
 
+use Rubix\ML\NeuralNet\FeedForward;
 use Rubix\ML\Online;
 use Rubix\ML\Learner;
 use Rubix\ML\Verbose;
@@ -289,7 +290,7 @@ public function train(Dataset $dataset) : void
 
         $classes = $dataset->possibleOutcomes();
 
-        $this->network = new Network(
+        $this->network = new FeedForward(
             new Placeholder1D($dataset->numFeatures()),
             [new Dense(1, $this->l2Penalty, true, new Xavier1())],
             new Binary($classes, $this->costFn),

src/Classifiers/MultilayerPerceptron.php

@@ -2,6 +2,7 @@
 
 namespace Rubix\ML\Classifiers;
 
+use Rubix\ML\NeuralNet\FeedForward;
 use Rubix\ML\Online;
 use Rubix\ML\Learner;
 use Rubix\ML\Verbose;
@@ -370,7 +371,7 @@ public function train(Dataset $dataset) : void
 
         $hiddenLayers[] = new Dense(count($classes), 0.0, true, new Xavier1());
 
-        $this->network = new Network(
+        $this->network = new FeedForward(
             new Placeholder1D($dataset->numFeatures()),
             $hiddenLayers,
             new Multiclass($classes, $this->costFn),

src/Classifiers/SoftmaxClassifier.php

@@ -2,6 +2,7 @@
 
 namespace Rubix\ML\Classifiers;
 
+use Rubix\ML\NeuralNet\FeedForward;
 use Rubix\ML\Online;
 use Rubix\ML\Learner;
 use Rubix\ML\Verbose;
@@ -285,7 +286,7 @@ public function train(Dataset $dataset) : void
 
         $classes = $dataset->possibleOutcomes();
 
-        $this->network = new Network(
+        $this->network = new FeedForward(
             new Placeholder1D($dataset->numFeatures()),
             [new Dense(count($classes), $this->l2Penalty, true, new Xavier1())],
             new Multiclass($classes, $this->costFn),

src/NeuralNet/FeedForward.php

@@ -27,7 +27,7 @@
  * @package     Rubix/ML
  * @author      Andrew DalPino
  */
-class FeedForward extends Network
+class FeedForward implements Network
 {
     /**
      * The input layer to the network.

src/NeuralNet/Network.php

@@ -2,270 +2,24 @@
 
 namespace Rubix\ML\NeuralNet;
 
-use Tensor\Matrix;
-use Rubix\ML\Encoding;
-use Rubix\ML\Datasets\Dataset;
-use Rubix\ML\Datasets\Labeled;
-use Rubix\ML\NeuralNet\Layers\Input;
-use Rubix\ML\NeuralNet\Layers\Output;
-use Rubix\ML\NeuralNet\Layers\Parametric;
-use Rubix\ML\NeuralNet\Optimizers\Adaptive;
-use Rubix\ML\NeuralNet\Optimizers\Optimizer;
 use Traversable;
 
-use function array_reverse;
-
 /**
  * Network
  *
- * A  neural network implementation consisting of an input and output layer and any number
- * of intermediate hidden layers.
- *
  * @internal
  *
  * @category    Machine Learning
  * @package     Rubix/ML
  * @author      Andrew DalPino
+ * @author      Samuel Akopyan <leumas.a@gmail.com>
  */
-class Network
+interface Network
 {
     /**
-     * The input layer to the network.
-     *
-     * @var Input
-     */
-    protected Input $input;
-
-    /**
-     * The hidden layers of the network.
-     *
-     * @var list<Layers\Hidden>
-     */
-    protected array $hidden = [
-        //
-    ];
-
-    /**
-     * The pathing of the backward pass through the hidden layers.
-     *
-     * @var list<Layers\Hidden>
-     */
-    protected array $backPass = [
-        //
-    ];
-
-    /**
-     * The output layer of the network.
-     *
-     * @var Output
-     */
-    protected Output $output;
-
-    /**
-     * The gradient descent optimizer used to train the network.
-     *
-     * @var Optimizer
-     */
-    protected Optimizer $optimizer;
-
-    /**
-     * @param Input $input
-     * @param Layers\Hidden[] $hidden
-     * @param Output $output
-     * @param Optimizer $optimizer
-     */
-    public function __construct(Input $input, array $hidden, Output $output, Optimizer $optimizer)
-    {
-        $hidden = array_values($hidden);
-
-        $backPass = array_reverse($hidden);
-
-        $this->input = $input;
-        $this->hidden = $hidden;
-        $this->output = $output;
-        $this->optimizer = $optimizer;
-        $this->backPass = $backPass;
-    }
-
-    /**
-     * Return the input layer.
-     *
-     * @return Input
-     */
-    public function input() : Input
-    {
-        return $this->input;
-    }
-
-    /**
-     * Return an array of hidden layers indexed left to right.
-     *
-     * @return list<Layers\Hidden>
-     */
-    public function hidden() : array
-    {
-        return $this->hidden;
-    }
-
-    /**
-     * Return the output layer.
-     *
-     * @return Output
-     */
-    public function output() : Output
-    {
-        return $this->output;
-    }
-
-    /**
-     * Return all the layers in the network.
+     * Return the layers of the network.
      *
      * @return Traversable<Layers\Layer>
      */
-    public function layers() : Traversable
-    {
-        yield $this->input;
-
-        yield from $this->hidden;
-
-        yield $this->output;
-    }
-
-    /**
-     * Return the number of trainable parameters in the network.
-     *
-     * @return int
-     */
-    public function numParams() : int
-    {
-        $numParams = 0;
-
-        foreach ($this->layers() as $layer) {
-            if ($layer instanceof Parametric) {
-                foreach ($layer->parameters() as $parameter) {
-                    $numParams += $parameter->param()->size();
-                }
-            }
-        }
-
-        return $numParams;
-    }
-
-    /**
-     * Initialize the parameters of the layers and warm the optimizer cache.
-     */
-    public function initialize() : void
-    {
-        $fanIn = 1;
-
-        foreach ($this->layers() as $layer) {
-            $fanIn = $layer->initialize($fanIn);
-        }
-
-        if ($this->optimizer instanceof Adaptive) {
-            foreach ($this->layers() as $layer) {
-                if ($layer instanceof Parametric) {
-                    foreach ($layer->parameters() as $param) {
-                        $this->optimizer->warm($param);
-                    }
-                }
-            }
-        }
-    }
-
-    /**
-     * Run an inference pass and return the activations at the output layer.
-     *
-     * @param Dataset $dataset
-     * @return Matrix
-     */
-    public function infer(Dataset $dataset) : Matrix
-    {
-        $input = Matrix::quick($dataset->samples())->transpose();
-
-        foreach ($this->layers() as $layer) {
-            $input = $layer->infer($input);
-        }
-
-        return $input->transpose();
-    }
-
-    /**
-     * Perform a forward and backward pass of the network in one call. Returns
-     * the loss from the backward pass.
-     *
-     * @param Labeled $dataset
-     * @return float
-     */
-    public function roundtrip(Labeled $dataset) : float
-    {
-        $input = Matrix::quick($dataset->samples())->transpose();
-
-        $this->feed($input);
-
-        $loss = $this->backpropagate($dataset->labels());
-
-        return $loss;
-    }
-
-    /**
-     * Feed a batch through the network and return a matrix of activations at the output later.
-     *
-     * @param Matrix $input
-     * @return Matrix
-     */
-    public function feed(Matrix $input) : Matrix
-    {
-        foreach ($this->layers() as $layer) {
-            $input = $layer->forward($input);
-        }
-
-        return $input;
-    }
-
-    /**
-     * Backpropagate the gradient of the cost function and return the loss.
-     *
-     * @param list<string|int|float> $labels
-     * @return float
-     */
-    public function backpropagate(array $labels) : float
-    {
-        [$gradient, $loss] = $this->output->back($labels, $this->optimizer);
-
-        foreach ($this->backPass as $layer) {
-            $gradient = $layer->back($gradient, $this->optimizer);
-        }
-
-        return $loss;
-    }
-
-    /**
-     * Export the network architecture as a graph in dot format.
-     *
-     * @return Encoding
-     */
-    public function exportGraphviz() : Encoding
-    {
-        $dot = 'digraph Tree {' . PHP_EOL;
-        $dot .= '  node [shape=box, fontname=helvetica];' . PHP_EOL;
-
-        $layerNum = 0;
-
-        foreach ($this->layers() as $layer) {
-            ++$layerNum;
-
-            $dot .= "  N$layerNum [label=\"$layer\",style=\"rounded\"]" . PHP_EOL;
-
-            if ($layerNum > 1) {
-                $parentId = $layerNum - 1;
-
-                $dot .= "  N{$parentId} -> N{$layerNum};" . PHP_EOL;
-            }
-        }
-
-        $dot .= '}';
-
-        return new Encoding($dot);
-    }
+    public function layers() : Traversable;
 }