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* Multiple training data sets allowed * Tests with multiple training data sets * Updating docs according to #38 Documenting all models which predictions will be based on all training data provided. Some models already supported multiple training data sets.
31 lines
898 B
Markdown
31 lines
898 B
Markdown
# Backpropagation
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Backpropagation, an abbreviation for "backward propagation of errors", is a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent.
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## Constructor Parameters
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* $network (Network) - network to train (for example MultilayerPerceptron instance)
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* $theta (int) - network theta parameter
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```
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use Phpml\NeuralNetwork\Network\MultilayerPerceptron;
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use Phpml\NeuralNetwork\Training\Backpropagation;
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$network = new MultilayerPerceptron([2, 2, 1]);
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$training = new Backpropagation($network);
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```
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## Training
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Example of XOR training:
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```
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$training->train(
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$samples = [[1, 0], [0, 1], [1, 1], [0, 0]],
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$targets = [[1], [1], [0], [0]],
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$desiredError = 0.2,
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$maxIteraions = 30000
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);
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```
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You can train the neural network using multiple data sets, predictions will be based on all the training data.
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