public void FeedForwardThrowsIfLayerActivatedSumsAreNull()
{
Layer hidden = new Layer(LayerType.Hidden, new LogisticSigmoidActivationFunction(), 1, 4);
Layer output = new Layer(LayerType.Output, new SoftmaxActivationFunction(), 2, 3);
hidden.Nodes[0].Input = 1;
hidden.Nodes[1].Input = 2;
hidden.Nodes[2].Input = 3;
hidden.Nodes[3].Input = 4;
hidden.Biases = new List<double>() { 1, 1, 1 };
hidden.Weights = new List<double>() {1,1,1,1,1,1,1,1,1,1,1,1};
hidden.FeedForward(output);
}
public void FeedForwardSetsNextLayerInputsCorrectly()
{
List<double> biases = new List<double>()
{
1,
1,
1,
1
};
List<double> weights = new List<double>()
{
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
};
Layer input = new Layer(LayerType.Input, null, 0, 3);
Layer hidden = new Layer(LayerType.Hidden, new LogisticSigmoidActivationFunction(), 1, 4);
input.Weights = weights;
input.Biases = biases;
input.Nodes[0].Input = 1;
input.Nodes[1].Input = 2;
input.Nodes[2].Input = 3;
input.Nodes[0].ActivatedSum = 1;
input.Nodes[1].ActivatedSum = 2;
input.Nodes[2].ActivatedSum = 3;
input.ActivateNodes();
input.FeedForward(hidden);
Assert.AreEqual(7, hidden.Nodes[0].Input);
}
public void FeedForwardThrowsIfBiasesDoesNotEqualNextLayerNodes()
{
Layer input = new Layer(LayerType.Input, null, 0, 5);
Layer hidden = new Layer( LayerType.Hidden, new LogisticSigmoidActivationFunction(), 1, 4);
input.Nodes[0].Input = 1;
input.Nodes[1].Input = 2;
input.Nodes[2].Input = 3;
input.Nodes[3].Input = 4;
input.Nodes[0].ActivatedSum = 1;
input.Nodes[1].ActivatedSum = 2;
input.Nodes[2].ActivatedSum = 3;
input.Nodes[3].ActivatedSum = 4;
input.ActivateNodes();
input.FeedForward(hidden);
}