protected virtual void ActionByFull(LayerEventArgs layerEventArgs)
{
_InputSignals = layerEventArgs.OutputsSignalsPrevLayer;
_OutputSignals = Feedforward.GetOutputSignalsOfAllSamples(
_InputSignals,
_NeuronWeights,
layerEventArgs.ActivationFunc,
_BiasWeights);
}
protected virtual void ActionBySelect(LayerEventArgs layerEventArgs)
{
var _numOfActiveDataset = (int)layerEventArgs.NumberOfActiveDataset;
_InputSignals[_numOfActiveDataset] = layerEventArgs.OutputsSignalsPrevLayer[_numOfActiveDataset];
_OutputSignals[_numOfActiveDataset] = Feedforward.GetOutputSignalOfSample(
_InputSignals[_numOfActiveDataset],
_NeuronWeights,
layerEventArgs.ActivationFunc,
_BiasWeights);
}
public void GetOutputsSignals()
{
//Arrange
float[][] inputSignal = new float[][]
{
new float[] { 0, 0 },
new float[] { 0, 1 },
new float[] { 1, 0 },
new float[] { 1, 1 }
};
float[][] neuronsWeights = new float[][]
{
new float[] { 1, 1, 1 },
new float[] { 1, 1, 1 }
};
float[][] expected = new float[][]
{
new float[] { 0.5F, 0.5F, 0.5F },
new float[] { 0.7310586F, 0.7310586F, 0.7310586F },
new float[] { 0.7310586F, 0.7310586F, 0.7310586F },
new float[] { 0.8807971F, 0.8807971F, 0.8807971F }
};
//Act
var actual = Feedforward.GetOutputSignalsOfAllSamples(inputSignal, neuronsWeights, ActivationFunc.Sigmoid, null);
//Assert
for (int i = 0; i < expected.Length; i++)
{
for (int j = 0; j < expected[i].Length; j++)
{
Assert.AreEqual(expected[i][j], actual[i][j]);
}
}
}
