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NN_planning .txt
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NN_planning .txt
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Default version:
NeuralNetwork nn = NeuralNetwork(numberOfLayers);
nn[0].populateNeurons(numberOfNeurons1);
nn[1].populateNeurons(numberOfNeurons2);
nn[2].populateNeurons(numberOfNeurons3);
...
...
...
nn[numberOfLayers - 1].populateNeurons(numberOfNeuronsN);
nn.buildWeightConnections();
vector<double> inputValues; // Size of this should be equal to the number of neurons in input layer of nn
vector<double> outputValues = nn.predict(inputValues);
//////////////////////////////////////////////////////////////
Regular version:
NeuralNetwork* neuralNetwork = new NeuralNetwork();
Layer* inputLayer = new Layer();
Layer* hiddenLayer = new Layer();
Layer* outputLayer = new Layer();
inputLayer->populateNeurons(2);
hiddenLayer->populateNeurons(4);
outputLayer->populateNeurons(3);
neuralNetwork->addLayer(inputLayer);
neuralNetwork->addLayer(hiddenLayer);
neuralNetwork->addLayer(outputLayer);
neuralNetwork->buildWeightConnections(); // Initialized weights and biases
// <Training>
neuralNetwork->train(trainingData);
- trainingData is a two dimensional vector; (trainingData[cases][sample_data_of_the_case])
- train takes samples vector by vector
- runs it through predict function
- takes the results and calculate the cost
- run backpropogation (updates weights and biases)
// </Training>
vector<double> results = neuralNetwork->predict(input);
cout << neuralNetwork->toString();
//////////////////////////////////////////////////////////////
Logo
Name
--------------
Small Description
Links to ReadMe Headers
Image of NN (GIF)
Key Features
-----------------------------------------
- LivePreview - Make changes, see changes
- Instantly see what thr Markdown look like
- Sync Scrolling
- Automatic scroll
How to Use
-----------------------------------------
Example
-----------------------------------------
Structure
-----------------------------------------
Class Diagrams
-------------------------------------
Visualization
-----------------------------------------
Credits
-----------------------------------------
This software uses the following open source package:
- SFML
License
-----------------------------------------
- MIT
Contact Links
-----------------------------------------
- Github links
//////////////////////////////////////////////////////////////