public BackPropNetwork(NetworkProperties props, BackPropProperties backProps, Random rnd)
{
this.rnd = rnd; // for Shuffle()
this.backProps = backProps;
this.Network = new NeuralNetwork(props, rnd);
// back-prop related arrays below
this.hGrads = new double[props.NumHidden];
this.oGrads = new double[props.NumOutput];
this.ihPrevWeightsDelta = NetworkData.MakeMatrix(props.NumInput, props.NumHidden);
this.hPrevBiasesDelta = new double[props.NumHidden];
this.hoPrevWeightsDelta = NetworkData.MakeMatrix(props.NumHidden, props.NumOutput);
this.oPrevBiasesDelta = new double[props.NumOutput];
}
public NetworkData Clone()
{
var ret = new NetworkData(this.Props);
Buffer.BlockCopy(this.hBiases, 0, ret.hBiases, 0, Buffer.ByteLength(this.hBiases));
Buffer.BlockCopy(this.oBiases, 0, ret.oBiases, 0, Buffer.ByteLength(this.oBiases));
for (int i = 0; i < this.ihWeights.Length; i++)
{
Buffer.BlockCopy(this.ihWeights[i], 0, ret.ihWeights[i], 0, Buffer.ByteLength(this.ihWeights[0]));
}
for (int i = 0; i < this.hoWeights.Length; i++)
{
Buffer.BlockCopy(this.hoWeights[i], 0, ret.hoWeights[i], 0, Buffer.ByteLength(this.hoWeights[0]));
}
return ret;
}
public NetworkData Clone()
{
var ret = new NetworkData(this.Props);
Buffer.BlockCopy(this.hBiases, 0, ret.hBiases, 0, Buffer.ByteLength(this.hBiases));
Buffer.BlockCopy(this.oBiases, 0, ret.oBiases, 0, Buffer.ByteLength(this.oBiases));
for (int i = 0; i < this.ihWeights.Length; i++)
{
Buffer.BlockCopy(this.ihWeights[i], 0, ret.ihWeights[i], 0, Buffer.ByteLength(this.ihWeights[0]));
}
for (int i = 0; i < this.hoWeights.Length; i++)
{
Buffer.BlockCopy(this.hoWeights[i], 0, ret.hoWeights[i], 0, Buffer.ByteLength(this.hoWeights[0]));
}
return(ret);
}
public bool IsEqual(NetworkData other)
{
if (this.Props.NumInput != other.Props.NumInput)
{
return(false);
}
if (this.Props.NumHidden != other.Props.NumHidden)
{
return(false);
}
if (this.Props.NumOutput != other.Props.NumOutput)
{
return(false);
}
if (!this.hBiases.SequenceEqual(other.hBiases))
{
return(false);
}
if (!this.oBiases.SequenceEqual(other.oBiases))
{
return(false);
}
for (var i = 0; i < this.ihWeights.Length; i++)
{
if (!this.ihWeights[i].SequenceEqual(other.ihWeights[i]))
{
return(false);
}
}
for (var i = 0; i < this.hoWeights.Length; i++)
{
if (!this.hoWeights[i].SequenceEqual(other.hoWeights[i]))
{
return(false);
}
}
return(true);
}
private NeuralNetwork(NetworkData data)
{
this.Data = data;
}
private NeuralNetwork(NetworkData data)
{
this.Data = data;
}
public bool IsEqual(NetworkData other)
{
if (this.Props.NumInput != other.Props.NumInput) return false;
if (this.Props.NumHidden != other.Props.NumHidden) return false;
if (this.Props.NumOutput != other.Props.NumOutput) return false;
if (!this.hBiases.SequenceEqual(other.hBiases)) return false;
if (!this.oBiases.SequenceEqual(other.oBiases)) return false;
for (var i = 0; i < this.ihWeights.Length; i++)
{
if (!this.ihWeights[i].SequenceEqual(other.ihWeights[i])) return false;
}
for (var i = 0; i < this.hoWeights.Length; i++)
{
if (!this.hoWeights[i].SequenceEqual(other.hoWeights[i])) return false;
}
return true;
}