///<summary>Выполняет прямой проход через декодирующую нейросеть.</summary>
///<param name="input">Входные данные.</param>
public Tensor Decode(Tensor input)
{
var Temp = input;
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv4_1_Weights, this.Data.Conv4_1_Biases);
if (this.Step != null)
{
this.Step(100f / 20f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(100f / 20f);
}
Temp = Layers.Upsample2D(Temp);
if (this.Step != null)
{
this.Step(100f / 20f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv3_4_Weights, this.Data.Conv3_4_Biases);
if (this.Step != null)
{
this.Step(100f / 20f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(100f / 20f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv3_3_Weights, this.Data.Conv3_3_Biases);
if (this.Step != null)
{
this.Step(100f / 20f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(100f / 20f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv3_2_Weights, this.Data.Conv3_2_Biases);
if (this.Step != null)
{
this.Step(100f / 20f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(100f / 20f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv3_1_Weights, this.Data.Conv3_1_Biases);
if (this.Step != null)
{
this.Step(100f / 20f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(100f / 20f);
}
Temp = Layers.Upsample2D(Temp);
if (this.Step != null)
{
this.Step(100f / 20f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv2_2_Weights, this.Data.Conv2_2_Biases);
if (this.Step != null)
{
this.Step(100f / 20f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(100f / 20f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv2_1_Weights, this.Data.Conv2_1_Biases);
if (this.Step != null)
{
this.Step(100f / 20f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(100f / 20f);
}
Temp = Layers.Upsample2D(Temp);
if (this.Step != null)
{
this.Step(100f / 20f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv1_2_Weights, this.Data.Conv1_2_Biases);
if (this.Step != null)
{
this.Step(100f / 20f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(100f / 20f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv1_1_Weights, this.Data.Conv1_1_Biases);
if (this.Step != null)
{
this.Step(100f / 20f);
}
return(Temp);
}
///<summary>Выполняет прямой проход через декодирующую нейросеть.</summary>
///<param name="input">Входные данные.</param>
public Tensor Decode(Tensor input)
{
var Temp = Layers.Conv2D(input, this.Data.Conv4_1_Weights, this.Data.Conv4_1_Biases);
if (Step != null)
{
Step(5);
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
Step(5);
}
Temp = Layers.Upsample2D(Temp);
if (Step != null)
{
Step(5);
}
Temp = Layers.Conv2D(Temp, this.Data.Conv3_4_Weights, this.Data.Conv3_4_Biases);
if (Step != null)
{
Step(5);
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
Step(5);
}
Temp = Layers.Conv2D(Temp, this.Data.Conv3_3_Weights, this.Data.Conv3_3_Biases);
if (Step != null)
{
Step(5);
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
Step(5);
}
Temp = Layers.Conv2D(Temp, this.Data.Conv3_2_Weights, this.Data.Conv3_2_Biases);
if (Step != null)
{
Step(5);
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
Step(5);
}
Temp = Layers.Conv2D(Temp, this.Data.Conv3_1_Weights, this.Data.Conv3_1_Biases);
if (Step != null)
{
Step(5);
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
Step(5);
}
Temp = Layers.Upsample2D(Temp);
if (Step != null)
{
Step(5);
}
Temp = Layers.Conv2D(Temp, this.Data.Conv2_2_Weights, this.Data.Conv2_2_Biases);
if (Step != null)
{
Step(5);
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
Step(5);
}
Temp = Layers.Conv2D(Temp, this.Data.Conv2_1_Weights, this.Data.Conv2_1_Biases);
if (Step != null)
{
Step(5);
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
Step(5);
}
Temp = Layers.Upsample2D(Temp);
if (Step != null)
{
Step(5);
}
Temp = Layers.Conv2D(Temp, this.Data.Conv1_2_Weights, this.Data.Conv1_2_Biases);
if (Step != null)
{
Step(5);
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
Step(5);
}
Temp = Layers.Conv2D(Temp, this.Data.Conv1_1_Weights, this.Data.Conv1_1_Biases);
if (Step != null)
{
Step(5);
}
Temp = Layers.ReLU(Temp);
return(Temp);
}
///<summary>Выполняет прямой проход через декодирующую нейросеть.</summary>
///<param name="input">Входные данные.</param>
public Tensor Decode(Tensor Content, Tensor Style)
{
var Temp = Layers.StyleSwap(Content, Style);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ConvTranspose2D(Temp, this.Data.TConv3_1_Weights, this.Data.TConv3_1_Biases, 1);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.InstanceNorm2D(Temp, this.Data.TConv3_1_Shift, this.Data.TConv3_1_Scale);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ConvTranspose2D(Temp, this.Data.TConv2_4_Weights, this.Data.TConv2_4_Biases, 1);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.InstanceNorm2D(Temp, this.Data.TConv2_4_Shift, this.Data.TConv2_4_Scale);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.Upsample2D(Temp);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ConvTranspose2D(Temp, this.Data.TConv2_3_Weights, this.Data.TConv2_3_Biases, 1);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.InstanceNorm2D(Temp, this.Data.TConv2_3_Shift, this.Data.TConv2_3_Scale);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ConvTranspose2D(Temp, this.Data.TConv2_2_Weights, this.Data.TConv2_2_Biases, 1);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.InstanceNorm2D(Temp, this.Data.TConv2_2_Shift, this.Data.TConv2_2_Scale);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ConvTranspose2D(Temp, this.Data.TConv2_1_Weights, this.Data.TConv2_1_Biases, 1);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.InstanceNorm2D(Temp, this.Data.TConv2_1_Shift, this.Data.TConv2_1_Scale);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ConvTranspose2D(Temp, this.Data.TConv1_4_Weights, this.Data.TConv1_4_Biases, 1);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.InstanceNorm2D(Temp, this.Data.TConv1_4_Shift, this.Data.TConv1_4_Scale);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.Upsample2D(Temp);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ConvTranspose2D(Temp, this.Data.TConv1_3_Weights, this.Data.TConv1_3_Biases, 1);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.InstanceNorm2D(Temp, this.Data.TConv1_3_Shift, this.Data.TConv1_3_Scale);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ConvTranspose2D(Temp, this.Data.TConv1_2_Weights, this.Data.TConv1_2_Biases, 1);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.InstanceNorm2D(Temp, this.Data.TConv1_2_Shift, this.Data.TConv1_2_Scale);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ConvTranspose2D(Temp, this.Data.TConv1_1_Weights, this.Data.TConv1_1_Biases, 1);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.Sigmoid(Temp);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
return(Temp);
}
///<summary>Выполняет прямой проход через кодирующую нейросеть.</summary>
///<param name="input">Входные данные.</param>
public Tensor Encode(Tensor input)
{
var Temp = input;
if ((this.Depth == EncoderType.Conv1) || (this.Depth == EncoderType.Conv2) || (this.Depth == EncoderType.Conv3) || (this.Depth == EncoderType.Conv4) || (this.Depth == EncoderType.Conv5))
{
Temp = Layers.Conv2D1x1(input, this.Data.Conv0_Weights, this.Data.Conv0_Biases);
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv1_1_Weights, this.Data.Conv1_1_Biases);
if (Step != null)
{
switch (this.Depth)
{
case EncoderType.Conv1:
{
Step(100f / 2f);
break;
}
case EncoderType.Conv2:
{
Step(100f / 7f);
break;
}
case EncoderType.Conv3:
{
Step(100f / 12f);
break;
}
case EncoderType.Conv4:
{
Step(100f / 21f);
break;
}
case EncoderType.Conv5:
{
Step(100f / 30f);
break;
}
}
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
switch (this.Depth)
{
case EncoderType.Conv1:
{
Step(100f / 2f);
break;
}
case EncoderType.Conv2:
{
Step(100f / 7f);
break;
}
case EncoderType.Conv3:
{
Step(100f / 12f);
break;
}
case EncoderType.Conv4:
{
Step(100f / 21f);
break;
}
case EncoderType.Conv5:
{
Step(100f / 30f);
break;
}
}
}
}
if ((this.Depth == EncoderType.Conv2) || (this.Depth == EncoderType.Conv3) || (this.Depth == EncoderType.Conv4) || (this.Depth == EncoderType.Conv5))
{
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv1_2_Weights, this.Data.Conv1_2_Biases);
if (Step != null)
{
switch (this.Depth)
{
case EncoderType.Conv2:
{
Step(100f / 7f);
break;
}
case EncoderType.Conv3:
{
Step(100f / 12f);
break;
}
case EncoderType.Conv4:
{
Step(100f / 21f);
break;
}
case EncoderType.Conv5:
{
Step(100f / 30f);
break;
}
}
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
switch (this.Depth)
{
case EncoderType.Conv2:
{
Step(100f / 7f);
break;
}
case EncoderType.Conv3:
{
Step(100f / 12f);
break;
}
case EncoderType.Conv4:
{
Step(100f / 21f);
break;
}
case EncoderType.Conv5:
{
Step(100f / 30f);
break;
}
}
}
Temp = Layers.MaxPool2D(Temp);
if (Step != null)
{
switch (this.Depth)
{
case EncoderType.Conv2:
{
Step(100f / 7f);
break;
}
case EncoderType.Conv3:
{
Step(100f / 12f);
break;
}
case EncoderType.Conv4:
{
Step(100f / 21f);
break;
}
case EncoderType.Conv5:
{
Step(100f / 30f);
break;
}
}
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv2_1_Weights, this.Data.Conv2_1_Biases);
if (Step != null)
{
switch (this.Depth)
{
case EncoderType.Conv2:
{
Step(100f / 7f);
break;
}
case EncoderType.Conv3:
{
Step(100f / 12f);
break;
}
case EncoderType.Conv4:
{
Step(100f / 21f);
break;
}
case EncoderType.Conv5:
{
Step(100f / 30f);
break;
}
}
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
switch (this.Depth)
{
case EncoderType.Conv2:
{
Step(100f / 7f);
break;
}
case EncoderType.Conv3:
{
Step(100f / 12f);
break;
}
case EncoderType.Conv4:
{
Step(100f / 21f);
break;
}
case EncoderType.Conv5:
{
Step(100f / 30f);
break;
}
}
}
}
if ((this.Depth == EncoderType.Conv3) || (this.Depth == EncoderType.Conv4) || (this.Depth == EncoderType.Conv5))
{
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv2_2_Weights, this.Data.Conv2_2_Biases);
if (Step != null)
{
switch (this.Depth)
{
case EncoderType.Conv3:
{
Step(100f / 12f);
break;
}
case EncoderType.Conv4:
{
Step(100f / 21f);
break;
}
case EncoderType.Conv5:
{
Step(100f / 30f);
break;
}
}
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
switch (this.Depth)
{
case EncoderType.Conv3:
{
Step(100f / 12f);
break;
}
case EncoderType.Conv4:
{
Step(100f / 21f);
break;
}
case EncoderType.Conv5:
{
Step(100f / 30f);
break;
}
}
}
Temp = Layers.MaxPool2D(Temp);
if (Step != null)
{
switch (this.Depth)
{
case EncoderType.Conv3:
{
Step(100f / 12f);
break;
}
case EncoderType.Conv4:
{
Step(100f / 21f);
break;
}
case EncoderType.Conv5:
{
Step(100f / 30f);
break;
}
}
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv3_1_Weights, this.Data.Conv3_1_Biases);
if (Step != null)
{
switch (this.Depth)
{
case EncoderType.Conv3:
{
Step(100f / 12f);
break;
}
case EncoderType.Conv4:
{
Step(100f / 21f);
break;
}
case EncoderType.Conv5:
{
Step(100f / 30f);
break;
}
}
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
switch (this.Depth)
{
case EncoderType.Conv3:
{
Step(100f / 12f);
break;
}
case EncoderType.Conv4:
{
Step(100f / 21f);
break;
}
case EncoderType.Conv5:
{
Step(100f / 30f);
break;
}
}
}
}
if ((this.Depth == EncoderType.Conv4) || (this.Depth == EncoderType.Conv5))
{
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv3_2_Weights, this.Data.Conv3_2_Biases);
if (Step != null)
{
switch (this.Depth)
{
case EncoderType.Conv4:
{
Step(100f / 21f);
break;
}
case EncoderType.Conv5:
{
Step(100f / 30f);
break;
}
}
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
switch (this.Depth)
{
case EncoderType.Conv4:
{
Step(100f / 21f);
break;
}
case EncoderType.Conv5:
{
Step(100f / 30f);
break;
}
}
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv3_3_Weights, this.Data.Conv3_3_Biases);
if (Step != null)
{
switch (this.Depth)
{
case EncoderType.Conv4:
{
Step(100f / 21f);
break;
}
case EncoderType.Conv5:
{
Step(100f / 30f);
break;
}
}
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
switch (this.Depth)
{
case EncoderType.Conv4:
{
Step(100f / 21f);
break;
}
case EncoderType.Conv5:
{
Step(100f / 30f);
break;
}
}
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv3_4_Weights, this.Data.Conv3_4_Biases);
if (Step != null)
{
switch (this.Depth)
{
case EncoderType.Conv4:
{
Step(100f / 21f);
break;
}
case EncoderType.Conv5:
{
Step(100f / 30f);
break;
}
}
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
switch (this.Depth)
{
case EncoderType.Conv4:
{
Step(100f / 21f);
break;
}
case EncoderType.Conv5:
{
Step(100f / 30f);
break;
}
}
}
Temp = Layers.MaxPool2D(Temp);
if (Step != null)
{
switch (this.Depth)
{
case EncoderType.Conv4:
{
Step(100f / 21f);
break;
}
case EncoderType.Conv5:
{
Step(100f / 30f);
break;
}
}
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv4_1_Weights, this.Data.Conv4_1_Biases);
if (Step != null)
{
switch (this.Depth)
{
case EncoderType.Conv4:
{
Step(100f / 21f);
break;
}
case EncoderType.Conv5:
{
Step(100f / 30f);
break;
}
}
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
switch (this.Depth)
{
case EncoderType.Conv4:
{
Step(100f / 21f);
break;
}
case EncoderType.Conv5:
{
Step(100f / 30f);
break;
}
}
}
}
if (this.Depth == EncoderType.Conv5)
{
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv4_2_Weights, this.Data.Conv4_2_Biases);
if (Step != null)
{
Step(100f / 30f);
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
Step(100f / 30f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv4_3_Weights, this.Data.Conv4_3_Biases);
if (Step != null)
{
Step(100f / 30f);
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
Step(100f / 30f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv4_4_Weights, this.Data.Conv4_4_Biases);
if (Step != null)
{
Step(100f / 30f);
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
Step(100f / 30f);
}
Temp = Layers.MaxPool2D(Temp);
if (Step != null)
{
Step(100f / 30f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv5_1_Weights, this.Data.Conv5_1_Biases);
if (Step != null)
{
Step(100f / 30f);
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
Step(100f / 30f);
}
}
return(Temp);
}
///<summary>Выполняет прямой проход через кодирующую нейросеть.</summary>
///<param name="input">Входные данные.</param>
public Tensor Encode(Tensor input)
{
var Temp = input;
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv1_1_Weights, this.Data.Conv1_1_Biases);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv1_2_Weights, this.Data.Conv1_2_Biases);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.AvgPool2D(Temp);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv2_1_Weights, this.Data.Conv2_1_Biases);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv2_2_Weights, this.Data.Conv2_2_Biases);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.AvgPool2D(Temp);
if (this.Step != null)
{
//this.Step(5f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv3_1_Weights, this.Data.Conv3_1_Biases);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv3_2_Weights, this.Data.Conv3_2_Biases);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv3_3_Weights, this.Data.Conv3_3_Biases);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv3_4_Weights, this.Data.Conv3_4_Biases);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.AvgPool2D(Temp);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv4_1_Weights, this.Data.Conv4_1_Biases);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(5f);
}
return(Temp);
}
///<summary>Выполняет прямой проход через кодирующую нейросеть.</summary>
///<param name="input">Входные данные.</param>
public Tensor[] EncodeStyle(Tensor input)
{
var Temp = input;
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv1_1_Weights, this.Data.Conv1_1_Biases);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.ReLU(Temp);
var Conv1 = Temp;
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv1_2_Weights, this.Data.Conv1_2_Biases);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.AvgPool2D(Temp);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv2_1_Weights, this.Data.Conv2_1_Biases);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.ReLU(Temp);
var Conv2 = Temp;
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv2_2_Weights, this.Data.Conv2_2_Biases);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.AvgPool2D(Temp);
if (this.Step != null)
{
//this.Step(5f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv3_1_Weights, this.Data.Conv3_1_Biases);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.ReLU(Temp);
var Conv3 = Temp;
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv3_2_Weights, this.Data.Conv3_2_Biases);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv3_3_Weights, this.Data.Conv3_3_Biases);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv3_4_Weights, this.Data.Conv3_4_Biases);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.AvgPool2D(Temp);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv4_1_Weights, this.Data.Conv4_1_Biases);
if (this.Step != null)
{
this.Step(5f);
}
Temp = Layers.ReLU(Temp);
var Conv4 = Temp;
if (this.Step != null)
{
this.Step(5f);
}
return(new Tensor[4] {
Conv1, Conv2, Conv3, Conv4
});
}
