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https://github.com/Llewellynvdm/php-ml.git
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c32bf3fe2b
* ability to specify per-layer activation function * some tests for new addition to layer * appease style CI whitespace issue * more flexible addition of layers, and developer can pass Layer object in manually * new test for layer object in mlp constructor * documentation for added MLP functionality
90 lines
2.3 KiB
Markdown
90 lines
2.3 KiB
Markdown
# MLPClassifier
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A multilayer perceptron (MLP) is a feedforward artificial neural network model that maps sets of input data onto a set of appropriate outputs.
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## Constructor Parameters
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* $inputLayerFeatures (int) - the number of input layer features
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* $hiddenLayers (array) - array with the hidden layers configuration, each value represent number of neurons in each layers
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* $classes (array) - array with the different training set classes (array keys are ignored)
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* $iterations (int) - number of training iterations
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* $learningRate (float) - the learning rate
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* $activationFunction (ActivationFunction) - neuron activation function
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```
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use Phpml\Classification\MLPClassifier;
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$mlp = new MLPClassifier(4, [2], ['a', 'b', 'c']);
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// 4 nodes in input layer, 2 nodes in first hidden layer and 3 possible labels.
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```
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An Activation Function may also be passed in with each individual hidden layer. Example:
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```
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use Phpml\NeuralNetwork\ActivationFunction\PReLU;
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use Phpml\NeuralNetwork\ActivationFunction\Sigmoid;
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$mlp = new MLPClassifier(4, [[2, new PReLU], [2, new Sigmoid]], ['a', 'b', 'c']);
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```
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Instead of configuring each hidden layer as an array, they may also be configured with Layer objects. Example:
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```
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use Phpml\NeuralNetwork\Layer;
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use Phpml\NeuralNetwork\Node\Neuron;
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$layer1 = new Layer(2, Neuron::class, new PReLU);
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$layer2 = new Layer(2, Neuron::class, new Sigmoid);
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$mlp = new MLPClassifier(4, [$layer1, $layer2], ['a', 'b', 'c']);
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```
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## Train
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To train a MLP simply provide train samples and labels (as array). Example:
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```
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$mlp->train(
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$samples = [[1, 0, 0, 0], [0, 1, 1, 0], [1, 1, 1, 1], [0, 0, 0, 0]],
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$targets = ['a', 'a', 'b', 'c']
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);
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```
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Use partialTrain method to train in batches. Example:
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```
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$mlp->partialTrain(
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$samples = [[1, 0, 0, 0], [0, 1, 1, 0]],
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$targets = ['a', 'a']
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);
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$mlp->partialTrain(
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$samples = [[1, 1, 1, 1], [0, 0, 0, 0]],
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$targets = ['b', 'c']
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);
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```
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You can update the learning rate between partialTrain runs:
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```
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$mlp->setLearningRate(0.1);
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```
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## Predict
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To predict sample label use predict method. You can provide one sample or array of samples:
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```
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$mlp->predict([[1, 1, 1, 1], [0, 0, 0, 0]]);
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// return ['b', 'c'];
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```
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## Activation Functions
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* BinaryStep
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* Gaussian
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* HyperbolicTangent
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* Parametric Rectified Linear Unit
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* Sigmoid (default)
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* Thresholded Rectified Linear Unit
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