public void SoftmaxFunctionReturnsCorrectResult()
{
List<Node> nodes = new List<Node>()
{
new Node(new SoftmaxActivationFunction()),
new Node(new SoftmaxActivationFunction()),
new Node(new SoftmaxActivationFunction())
};
nodes[0].Input = 2.0;
nodes[1].Input = -1.0;
nodes[2].Input = 4.0;
Layer layer = new Layer(LayerType.Hidden, new SoftmaxActivationFunction(), 1, 3);
layer.Nodes = nodes;
double scalingFactor = Math.Exp(2 - 4) + Math.Exp(-1 - 4) + Math.Exp(4 - 4);
double result = Math.Exp(2 - 4) / scalingFactor;
List<double> inputs = new List<double>();
foreach(Node node in nodes)
{
inputs.Add(node.Input);
}
layer.ActivateNodes();
//nodes[0].Softmax(inputs.ToArray());
Console.WriteLine("expected {0}, actual {1}", result, nodes[0].ActivatedSum);
Assert.AreEqual(result, nodes[0].ActivatedSum);
}
public void ActivateTanhSettingNodeValuesCorrectly()
{
Layer layer = new Layer(LayerType.Hidden, new HyperbolicTangentActivationFunction(), 1, 3);
layer.Nodes[0].Input = 2.0;
layer.Nodes[1].Input = -1.0;
layer.Nodes[2].Input = 4.0;
double result1 = Math.Tanh(layer.Nodes[0].Input);
double result2 = Math.Tanh(layer.Nodes[1].Input);
double result3 = Math.Tanh(layer.Nodes[2].Input);
layer.ActivateNodes();
Assert.AreEqual(result1, layer.Nodes[0].ActivatedSum);
Assert.AreEqual(result2, layer.Nodes[1].ActivatedSum);
Assert.AreEqual(result3, layer.Nodes[2].ActivatedSum);
}
public void ActivateSigmoidSettingNodeValuesCorrectly()
{
Layer layer = new Layer(LayerType.Hidden, new LogisticSigmoidActivationFunction(), 1, 3);
layer.Nodes[0].Input = 2.0;
layer.Nodes[1].Input = -1.0;
layer.Nodes[2].Input = 4.0;
double result1 = (1.0 / (1.0 + Math.Exp(-(layer.Nodes[0].Input))));
double result2 = (1.0 / (1.0 + Math.Exp(-(layer.Nodes[1].Input))));
double result3 = (1.0 / (1.0 + Math.Exp(-(layer.Nodes[2].Input))));
layer.ActivateNodes();
Assert.AreEqual(result1, layer.Nodes[0].ActivatedSum);
Assert.AreEqual(result2, layer.Nodes[1].ActivatedSum);
Assert.AreEqual(result3, layer.Nodes[2].ActivatedSum);
}
public void ActivateSoftmaxSettingNodeValuesCorrectly()
{
Layer layer = new Layer(LayerType.Hidden, new SoftmaxActivationFunction(), 1, 3);
layer.Nodes[0].Input = 2.0;
layer.Nodes[1].Input = -1.0;
layer.Nodes[2].Input = 4.0;
double scalingFactor = Math.Exp(2 - 4) + Math.Exp(-1 - 4) + Math.Exp(4 - 4);
double result1 = Math.Exp(2 - 4) / scalingFactor;
double result2 = Math.Exp(-1 - 4) / scalingFactor;
double result3 = Math.Exp(4 - 4) / scalingFactor;
layer.ActivateNodes();
Assert.AreEqual(result1, layer.Nodes[0].ActivatedSum);
Assert.AreEqual(result2, layer.Nodes[1].ActivatedSum);
Assert.AreEqual(result3, layer.Nodes[2].ActivatedSum);
}
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);
}