mlp

mlp(
    @activation 2
    @batchsize 50
    @hiddenlayers 3 3
    @learnrate 0.01
    @maxiter 1000
    @momentum 0.9
    @tapin 0
    @tapout -1
    @outputactivation 0
    @validation 0.2
    @mode 0
    @useseed 0
) -> llll

Generates a Multilayer Perceptron Neural Network object for either regression or classification tasks.

---

Arguments

• @activation [int]: Activation function for hidden layers. (default: 2).
  • 0: Identity (-∞ to ∞—i.e., no activation).
  • 1: Sigmoid (0 to 1).
  • 2: ReLU. (0 to ∞).
  • 3: Tanh (-1 to 1).
• @batchsize [int]: Batch size. (default: 50).
• @hiddenlayers [list/int]: A list of integers, each specifying the desired size of every fully connected, hidden layer. (default: 3 3).
• @learnrate [float]: Learning rate. (default: 0.01).
• @maxiter [int]: Maximum number of iterations during training. (default: 1000).
• @momentum [float]: Momentum. (default: 0.9).
• @tapin [int]: The index of the layer to use as input to the neural network for predict (0-based). The default of 0 is the first layer (the original input layer), 1 is the first hidden layer, etc. This can be used to access different parts of a trained neural network such as the encoder or decoder of an autoencoder. (default: 0).
• @tapout [int]: The index of the layer to use as output to the neural network for predict (0-based). The default of -1 is the last layer (the original output layer). This can be used to access different parts of a trained neural network such as the encoder or decoder of an autoencoder. (default: -1).
• @outputactivation [int]: Activation function for output layers. Ignored when @mode is 1. (default: 0).
  • 0: Identity (-∞ to ∞—i.e., no activation).
  • 1: Sigmoid (0 to 1).
  • 2: ReLU. (0 to ∞).
  • 3: Tanh (-1 to 1).
• @validation [float]: Factor of the dataset to use for validation during training. (default: 0.2).
• @mode [int]: MLP prediction mode, determining the type of set required during the fit process, as well as the type of set returned during prediction. (default: 0).
  • 0: Regression—meant to be fitted with a dataset as an output, via the fit function.
  • 1: Classification—meant to be fitted with a labelset as an output, via the fit function.
• @useseed [int]: Use random seed for parameter initialization. (default: 0).
  • 0: Off
  • 1: On

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Output

Multilayer perceptron object [llll]

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Usage

$indata = null;
$outdata = null;
## generate basic dataset based on "less-than" function
for $i in 1...100 do (
    $a = rand(0, 1);
    $b = rand(0, 1);
    $indata _= [$a $b]; ## input point
    $outdata _= [$a < $b] ## expected output (0 or 1)
);
$indataset = dataset($indata); ## input points
$outdataset = dataset($outdata); ## expected outputs
$model = mlp(@outputactivation 1); ## create mlp model
## fit (i.e., train) model to learn input/output mappping from dataset
for $i in 1...10 do ( ## repeat training to minimize loss
    print(fit($model, $indataset, $outdataset), 'Loss:') 
);
writeobject($model, "./mlp.json"); ## write as JSON for future use (optional)
$model = readobject("./mlp.json"); ## read pre-trained model from JSON (optional)
$xpoint = 0.25 0.75; ## sample point
$pred = predict($model, $xpoint); ## generate prediction
print($pred, "prediction:") ## should be (almost) 1.0, as 0.25 < 0.75 is true