Activation neuron computes weighted sum of its inputs, adds threshold value and then applies activation function. The neuron isusually used in multi-layer neural networks.
/// <summary> /// Initializes a new instance of the <see cref="ActivationLayer"/> class. /// </summary> /// /// <param name="neuronsCount">Layer's neurons count.</param> /// <param name="inputsCount">Layer's inputs count.</param> /// <param name="function">Activation function of neurons of the layer.</param> /// /// <remarks>The new layer is randomized (see <see cref="ActivationNeuron.Randomize"/> /// method) after it is created.</remarks> /// public ActivationLayer( int neuronsCount, int inputsCount, IActivationFunction function ) : base( neuronsCount, inputsCount ) { // create each neuron for ( int i = 0; i < neurons.Length; i++ ) neurons[i] = new ActivationNeuron( inputsCount, function ); }
/// <summary> /// Initializes a new instance of the <see cref="ActivationLayer"/> class /// </summary> /// <param name="neuronsCount">Layer's neurons count</param> /// <param name="inputsCount">Layer's inputs count</param> /// <param name="function">Activation function of neurons of the layer</param> /// /// <remarks>The new layet will be randomized (see <see cref="ActivationNeuron.Randomize"/> /// method) after it is created.</remarks> /// public ActivationLayer(int neuronsCount, int inputsCount, IActivationFunction function) : base(neuronsCount, inputsCount) { // create each neuron for (int i = 0; i < neuronsCount; i++) { neurons[i] = new ActivationNeuron(inputsCount, function); } }
/// <summary> /// Initializes a new instance of the <see cref="ActivationMaximization"/> class. /// </summary> /// /// <param name="neuron">The neuron to be visualized.</param> /// public ActivationMaximization(ActivationNeuron neuron) { this.neuron = neuron; }