public FCSuperResolution()
{
superres_enc_front = InputLayer.Create(StartSide, 3);
superres_enc_back = ActivationLayer.Create <LeakyReLU>();
superres_enc_front.Append(
FCLayer.Create(16, 16).Append(
ActivationLayer.Create <LeakyReLU>().Append(
FCLayer.Create(8, 8).Append(
ActivationLayer.Create <LeakyReLU>().Append(
FCLayer.Create(4, 4).Append(
superres_enc_back
))))));
superres_dec_front = InputLayer.Create(4, 4);
superres_dec_back = ActivationLayer.Create <Tanh>();
superres_dec_front.Append(
FCLayer.Create(8, 8).Append(
ActivationLayer.Create <LeakyReLU>().Append(
FCLayer.Create(16, 8).Append(
ActivationLayer.Create <LeakyReLU>().Append(
FCLayer.Create(32, 3).Append(
superres_dec_back
))))));
superres_enc_back.Append(superres_dec_front);
//Initialize Weights
superres_enc_front.SetupInternalState();
superres_enc_front.InitializeWeights(new UniformWeightInitializer(0, 0.001f));
}
public DGAN()
{
discriminator = InputLayer.Create(StartSide, 1);
discriminator_back = ActivationLayer.Create <Sigmoid>();
discriminator.Append(
FCLayer.Create(1, 256).Append(
ActivationLayer.Create <LeakyReLU>().Append(
FCLayer.Create(1, 1).Append(
discriminator_back
))));
generator = InputLayer.Create(1, LatentSize);
generator_back = ActivationLayer.Create <Tanh>();
generator.Append(
FCLayer.Create(1, 256).Append(
ActivationLayer.Create <LeakyReLU>().Append(
DropoutLayer.Create(0.5f).Append(
FCLayer.Create(1, 512).Append(
ActivationLayer.Create <LeakyReLU>().Append(
DropoutLayer.Create(0.5f).Append(
FCLayer.Create(1, OutputSize).Append(
generator_back
))))))));
//Initialize Weights
discriminator.SetupInternalState();
discriminator.InitializeWeights(new UniformWeightInitializer(3, 0));
generator.SetupInternalState();
generator.InitializeWeights(new UniformWeightInitializer(1, 0));
}
private void Awake()
{
_rng = new Rng(1234);
DataManager.LoadFloatData();
// Create convolution layers
_layers = new List <ConvLayer2D>();
var l1 = ConvLayer2D.Create(DataManager.Width, DataManager.Channels, 5, 1, 0, 4).Value;
_layers.Add(l1);
var l2 = ConvLayer2D.Create(l1.OutWidth, l1.NumFilters, 3, 3, 0, 4).Value;
_layers.Add(l2);
var l3 = ConvLayer2D.Create(l2.OutWidth, l2.NumFilters, 3, 1, 0, 4).Value;
_layers.Add(l3);
var last = l3;
int convOutCount = last.OutWidth * last.OutWidth * last.NumFilters;
Debug.Log("Conv out neuron count: " + convOutCount);
_fcLayer = new FCLayer(10, convOutCount);
// Parameter initialization
for (int i = 0; i < _layers.Count; i++)
{
NeuralMath.RandomGaussian(ref _rng, _layers[i].Kernel, 0f, 0.25f);
NeuralMath.RandomGaussian(ref _rng, _layers[i].Bias, 0f, 0.1f);
}
NeuralMath.RandomGaussian(ref _rng, _fcLayer.Biases, 0f, 0.1f);
NeuralMath.RandomGaussian(ref _rng, _fcLayer.Weights, 0f, 0.1f);
// Create debug textures
_layerTex = new List <Conv2DLayerTexture>(_layers.Count);
for (int i = 0; i < _layers.Count; i++)
{
_layerTex.Add(new Conv2DLayerTexture(_layers[i]));
}
// Create the training structure
_batch = new NativeArray <int>(BatchSize, Allocator.Persistent, NativeArrayOptions.ClearMemory);
_targetOutputs = new NativeArray <float>(OutputClassCount, Allocator.Persistent, NativeArrayOptions.UninitializedMemory);
_dCdO = new NativeArray <float>(OutputClassCount, Allocator.Persistent, NativeArrayOptions.UninitializedMemory);
_input = new NativeArray <float>(DataManager.ImgDims * DataManager.Channels, Allocator.Persistent, NativeArrayOptions.UninitializedMemory);
}
/// <summary>
/// Instantiates Layer from LayerData and sets initial values.
/// </summary>
/// <param name="l"></param>
/// <param name="input"></param>
/// <param name="isOutput"></param>
/// <returns></returns>
GameObject InstantiateLayer(LayerData l, GameObject input, bool isOutput)
{
switch (l.type)
{
case LayerType.IMAGE:
{
GameObject go = GetImageLayerPrefab();
GameObject inst = Instantiate(go);
ImageLayer imLayer = inst.GetComponent <ImageLayer>();
imLayer.reducedResolution = new Vector2Int(l.activationShape[1], l.activationShape[2]);
imLayer.fullResolution = new Vector2Int(l.activationShape[1], l.activationShape[2]);
imLayer.pixelSpacing = 0.025f;
imLayer.depth = l.activationShape[3];
GameObject canvas = GameObject.FindGameObjectWithTag("Canvas");
Material pixelMaterial = canvas.GetComponent <GuiManager>().pixelMaterial;
imLayer.rgb = false;
MeshRenderer meshRenderer = inst.GetComponent <MeshRenderer>();
meshRenderer.sharedMaterials[0] = pixelMaterial;
imLayer.SetActivationTensorShape(l.activationShape);
for (int i = 0; i < l.activationTensors.Count; i++)
{
imLayer.SetActivationTensorForEpoch(l.activationTensors[i], i);
}
return(inst);
}
case LayerType.CONV:
{
GameObject go = GetConvLayerPrefab();
GameObject inst = Instantiate(go);
ConvLayer convLayer = inst.GetComponent <ConvLayer>();
convLayer.convShape = new Vector2Int(l.weightShape[0], l.weightShape[1]);
convLayer.reducedDepth = l.weightShape[3];
convLayer.fullDepth = l.weightShape[3];
convLayer.input = input;
convLayer.filterSpread = 1.0f;
convLayer.lineCircleGrid = 2.0f;
convLayer.filterSpacing = 0.025f;
convLayer.SetWeightTensorShape(l.weightShape);
for (int i = 0; i < l.weightTensors.Count; i++)
{
convLayer.SetWeightTensorForEpoch(l.weightTensors[i], i);
}
convLayer.SetActivationTensorShape(l.activationShape);
for (int i = 0; i < l.activationTensors.Count; i++)
{
convLayer.SetActivationTensorForEpoch(l.activationTensors[i], i);
}
return(inst);
}
case LayerType.MAXPOOL:
{
GameObject go = GetMaxPoolLayerPrefab();
GameObject inst = Instantiate(go);
MaxPoolLayer mpLayer = inst.GetComponent <MaxPoolLayer>();
mpLayer.filterSpacing = 0.025f;
mpLayer.zOffset = 0.25f;
mpLayer.input = input;
mpLayer.SetActivationTensorShape(l.activationShape);
for (int i = 0; i < l.activationTensors.Count; i++)
{
mpLayer.SetActivationTensorForEpoch(l.activationTensors[i], i);
}
return(inst);
}
case LayerType.FC:
{
GameObject go = GetFCLayerPrefab();
GameObject inst = Instantiate(go);
FCLayer fcLayer = inst.GetComponent <FCLayer>();
fcLayer.input = input;
fcLayer.filterSpacing = 0.025f;
fcLayer.reducedDepth = l.weightShape[1];
fcLayer.fullDepth = l.weightShape[1];
//TODO: here loading is reducing non output fc layers automatically by 4
if (!l.name.Contains("out"))
{
fcLayer.reducedDepth = l.weightShape[1];
}
else
{
fcLayer.lineCircleGrid = 0;
}
if (l.name.Contains("0"))
{
fcLayer.collapseInput = 1f;
}
//fcLayer.edgeBundle = 1.0f;
fcLayer.SetTensorShape(l.weightShape);
for (int i = 0; i < l.weightTensors.Count; i++)
{
fcLayer.SetTensorForEpoch(l.weightTensors[i], i);
}
fcLayer.SetActivationTensorShape(l.activationShape);
for (int i = 0; i < l.activationTensors.Count; i++)
{
fcLayer.SetActivationTensorForEpoch(l.activationTensors[i], i);
}
return(inst);
}
default:
break;
}
return(null);
}
public GAN()
{
#region 128x128
/*discriminator = InputLayer.Create(StartSide, 3);
* discriminator_back = ActivationLayer.Create<Sigmoid>();
* discriminator.Append(
* ConvLayer.Create(3, 8).Append( //o = 126
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8).Append( //o = 124
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8).Append( //o = 122
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8).Append( //o = 120
* ActivationLayer.Create<LeakyReLU>().Append(
* pooling_1.Append( //o = 60
* ConvLayer.Create(3, 8).Append( //o = 58
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8).Append( //o = 56
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8).Append( //o = 54
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8).Append( //o = 52
* ActivationLayer.Create<LeakyReLU>().Append(
* pooling_2.Append( //o = 26
* ConvLayer.Create(3, 8).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8).Append( //o = 22
* ActivationLayer.Create<LeakyReLU>().Append(
* pooling_3.Append( //o = 11
* ConvLayer.Create(3, 8).Append( //o = 22
* ActivationLayer.Create<LeakyReLU>().Append(
* FCLayer.Create(1, 1).Append(
* discriminator_back
* )))))))))))))))))))))))))));
*
* generator = InputLayer.Create(32, 8);
* generator_back = ActivationLayer.Create<Tanh>();
* generator.Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 3, 2).Append( //o = 26
* generator_back
* ))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))));*/
#endregion
/*
* discriminator = InputLayer.Create(StartSide, 3);
* discriminator_back = ActivationLayer.Create<Sigmoid>();
* discriminator.Append(
* ConvLayer.Create(3, 8).Append( //o = 30
* ActivationLayer.Create<LeakyReLU>().Append(
* DropoutLayer.Create(0.2f).Append(
* FCLayer.Create(1, 512).Append( //o = 28
* ActivationLayer.Create<LeakyReLU>().Append(
* DropoutLayer.Create(0.2f).Append(
* FCLayer.Create(1, 256).Append( //o = 26
* ActivationLayer.Create<LeakyReLU>().Append(
* DropoutLayer.Create(0.2f).Append(
* FCLayer.Create(1, 256).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* DropoutLayer.Create(0.2f).Append(
* FCLayer.Create(1, 128).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* DropoutLayer.Create(0.2f).Append(
* FCLayer.Create(1, 64).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* DropoutLayer.Create(0.2f).Append(
* FCLayer.Create(1, 32).Append( //o = 24
* ActivationLayer.Create<LeakyReLU>().Append(
* DropoutLayer.Create(0.2f).Append(
* FCLayer.Create(1, 1).Append(
* discriminator_back
* )))))))))))))))))))))));
*
* generator = InputLayer.Create(1, LatentSize);
* generator_back = ActivationLayer.Create<Tanh>();
* generator.Append(
* FCLayer.Create(1, 32).Append( //o = 18
* ActivationLayer.Create<LeakyReLU>().Append(
* DropoutLayer.Create(0.3f).Append(
* FCLayer.Create(1, 64).Append( //o = 18
* ActivationLayer.Create<LeakyReLU>().Append(
* DropoutLayer.Create(0.3f).Append(
* FCLayer.Create(1, 128).Append( //o = 18
* ActivationLayer.Create<LeakyReLU>().Append(
* DropoutLayer.Create(0.3f).Append(
* FCLayer.Create(1, 256).Append( //o = 18
* ActivationLayer.Create<LeakyReLU>().Append(
* DropoutLayer.Create(0.3f).Append(
* FCLayer.Create(1, 256).Append( //o = 18
* ActivationLayer.Create<LeakyReLU>().Append(
* DropoutLayer.Create(0.3f).Append(
* FCLayer.Create(1, 512).Append( //o = 18
* ActivationLayer.Create<LeakyReLU>().Append(
* DropoutLayer.Create(0.3f).Append(
* FCLayer.Create(16, 2).Append( //o = 18
* ActivationLayer.Create<LeakyReLU>().Append(
* ConvLayer.Create(3, 8, 2).Append( //o = 18
* ActivationLayer.Create<LeakyReLU>().Append(
* DropoutLayer.Create(0.3f).Append(
* FCLayer.Create(EndSide, 3).Append( //o = 18
* ActivationLayer.Create<LeakyReLU>().Append(
* generator_back
* ))))))))))))))))))))))))));*/
discriminator = InputLayer.Create(StartSide, 3);
discriminator_back = ActivationLayer.Create <Sigmoid>();
discriminator.Append(
FCLayer.Create(1, 1024).Append(
ActivationLayer.Create <LeakyReLU>().Append(
DropoutLayer.Create(0.3f).Append(
FCLayer.Create(1, 512).Append(
ActivationLayer.Create <LeakyReLU>().Append(
DropoutLayer.Create(0.3f).Append(
FCLayer.Create(1, 256).Append(
ActivationLayer.Create <LeakyReLU>().Append(
DropoutLayer.Create(0.3f).Append(
FCLayer.Create(1, 256).Append(
ActivationLayer.Create <LeakyReLU>().Append(
DropoutLayer.Create(0.3f).Append(
FCLayer.Create(1, 64).Append(
ActivationLayer.Create <LeakyReLU>().Append(
DropoutLayer.Create(0.3f).Append(
FCLayer.Create(1, 64).Append(
ActivationLayer.Create <LeakyReLU>().Append(
DropoutLayer.Create(0.3f).Append(
FCLayer.Create(1, 1).Append(
discriminator_back
))))))))))))))))))));
generator = InputLayer.Create(1, LatentSize);
generator_back = ActivationLayer.Create <Tanh>();
generator.Append(
FCLayer.Create(1, 128).Append(
ActivationLayer.Create <ReLU>().Append(
FCLayer.Create(1, 256).Append(
ActivationLayer.Create <ReLU>().Append(
FCLayer.Create(1, 256).Append(
ActivationLayer.Create <ReLU>().Append(
DropoutLayer.Create(0.5f).Append(
FCLayer.Create(1, 256).Append(
ActivationLayer.Create <ReLU>().Append(
FCLayer.Create(1, 512).Append(
ActivationLayer.Create <ReLU>().Append(
FCLayer.Create(1, 1024).Append(
ActivationLayer.Create <ReLU>().Append(
FCLayer.Create(32, 3).Append(
generator_back
)))))))))))))));
encoder = InputLayer.Create(32, 3);
encoder_back = ActivationLayer.Create <LeakyReLU>();
encoder.Append(
FCLayer.Create(1, 1024).Append(
ActivationLayer.Create <ReLU>().Append(
FCLayer.Create(1, 512).Append(
ActivationLayer.Create <ReLU>().Append(
FCLayer.Create(1, 256).Append(
ActivationLayer.Create <ReLU>().Append(
DropoutLayer.Create(0.5f).Append(
FCLayer.Create(1, 256).Append(
ActivationLayer.Create <ReLU>().Append(
FCLayer.Create(1, 256).Append(
ActivationLayer.Create <ReLU>().Append(
FCLayer.Create(1, 128).Append(
ActivationLayer.Create <ReLU>().Append(
FCLayer.Create(1, LatentSize).Append(
encoder_back
)))))))))))))));
//Initialize Weights
discriminator.SetupInternalState();
discriminator.InitializeWeights(new UniformWeightInitializer(3, 0));
generator.SetupInternalState();
generator.InitializeWeights(new UniformWeightInitializer(1, 0));
encoder.SetupInternalState();
encoder.InitializeWeights(new UniformWeightInitializer(2, 0));
}
public void Check()
{
front = InputLayer.Create(3, 1);
back = ActivationLayer.Create <Sigmoid>();
conv = ConvLayer.Create(2, 1);
fc = FCLayer.Create(1, 1);
front.Append(
conv.Append(
ConvLayer.Create(3, 3, 2).Append(
ActivationLayer.Create <LeakyReLU>().Append(
fc.Append(
FCLayer.Create(1, 1).Append(
back
))))));
front.SetupInternalState();
front.InitializeWeights(new UniformWeightInitializer(0, 0)); //ConstantWeightInitializer());
var lossFunc = new Quadratic();
var optimizer = new SGD(0.7f);
Matrix x0 = new Matrix(9, 1, MemoryFlags.ReadWrite, false);
x0.Write(new float[] { 0, 0, 0, 0, 0, 0, 0, 0, 0 });
Matrix x1 = new Matrix(9, 1, MemoryFlags.ReadWrite, false);
x1.Write(new float[] { 0.1f, 0.2f, 0.3f, 0.4f, 0.5f, 0.6f, 0.7f, 0.8f, 0.9f });
Matrix x2 = new Matrix(9, 1, MemoryFlags.ReadWrite, false);
x2.Write(new float[] { 0, 0, 0, 1, 1, 1, 1, 1, 1 });
Matrix x3 = new Matrix(9, 1, MemoryFlags.ReadWrite, false);
x3.Write(new float[] { 1, 1, 1, 1, 1, 1, 1, 1, 1 });
var loss_vec = new Matrix(1, 1, MemoryFlags.ReadWrite, true);
var x = new Matrix[] { x0, x1, x2, x3 };
var y = new Matrix[]
{
new Matrix(1, 1, MemoryFlags.ReadWrite, true),
new Matrix(1, 1, MemoryFlags.ReadWrite, true),
new Matrix(1, 1, MemoryFlags.ReadWrite, true),
new Matrix(1, 1, MemoryFlags.ReadWrite, true),
};
y[0].Write(new float[] { 0.53f });
y[1].Write(new float[] { 0.77f });
y[2].Write(new float[] { 0.88f });
y[3].Write(new float[] { 1.1f });
float delta = 1e-1f;
float orig_loss_deriv = 0;
float norm_conv = 0.0f, norm_conv_net = 0.0f;
float norm_fc = 0.0f, norm_fc_net = 0.0f;
for (int epoch = 0; epoch < 1; epoch++)
{
for (int idx = 1; idx < x.Length - 2; idx++)
{
{
var output = front.ForwardPropagate(x[idx]);
//Compute loss deriv
loss_vec.Clear();
lossFunc.LossDeriv(output[0], y[idx], loss_vec, 0);
orig_loss_deriv = loss_vec.Memory[0];
back.ComputeGradients(loss_vec);
back.ComputeLayerErrors(loss_vec);
}
{
//Save weights and apply deltas
var conv_l = conv.CurrentLayer as ConvLayer;
for (int f_i = 0; f_i < conv_l.FilterCnt; f_i++)
{
for (int i_i = 0; i_i < conv_l.InputDepth; i_i++)
{
for (int f_y = 0; f_y < conv_l.FilterSz; f_y++)
{
for (int f_x = 0; f_x < conv_l.FilterSz; f_x++)
{
var w_delta = conv_l.WeightErrors[f_i][i_i].Memory[f_y * conv_l.FilterSz + f_x];
conv_l.Weights[f_i][i_i].Memory[f_y * conv_l.FilterSz + f_x] += delta;
var output = front.ForwardPropagate(x[idx]);
loss_vec.Clear();
lossFunc.Loss(output[0], y[idx], loss_vec, 0);
var y1 = loss_vec.Memory[0];
conv_l.Weights[f_i][i_i].Memory[f_y * conv_l.FilterSz + f_x] -= 2 * delta;
output = front.ForwardPropagate(x[idx]);
loss_vec.Clear();
lossFunc.Loss(output[0], y[idx], loss_vec, 0);
var y0 = loss_vec.Memory[0];
conv_l.Weights[f_i][i_i].Memory[f_y * conv_l.FilterSz + f_x] += delta;
var deriv = (y1 - y0) / (2 * delta);
var norm = ((w_delta - deriv) * (w_delta - deriv)) / ((w_delta + deriv) * (w_delta + deriv));
norm_conv += norm;
norm_conv_net++;
}
}
}
}
var fc_l = fc.CurrentLayer as FCLayer;
for (int i = 0; i < fc_l.Weights.Rows * fc_l.Weights.Columns; i++)
{
var w_delta = fc_l.WeightDelta.Memory[i];
fc_l.Weights.Memory[i] += delta;
var output = front.ForwardPropagate(x[idx]);
loss_vec.Clear();
lossFunc.Loss(output[0], y[idx], loss_vec, 0);
var y1 = loss_vec.Memory[0];
fc_l.Weights.Memory[i] -= 2 * delta;
output = front.ForwardPropagate(x[idx]);
loss_vec.Clear();
lossFunc.Loss(output[0], y[idx], loss_vec, 0);
var y0 = loss_vec.Memory[0];
fc_l.Weights.Memory[i] += delta;
var deriv = (y1 - y0) / (2 * delta);
var norm = ((w_delta - deriv) * (w_delta - deriv)) / ((w_delta + deriv) * (w_delta + deriv));
norm_fc += norm;
norm_fc_net++;
}
}
{
back.ResetLayerErrors();
back.ComputeGradients(loss_vec);
back.ComputeLayerErrors(loss_vec);
back.UpdateLayers(optimizer);
}
}
}
Console.WriteLine($"Conv Norm {norm_conv / norm_conv_net}");
Console.WriteLine($"FC Norm {norm_fc / norm_fc_net}");
Console.ReadLine();
}
