///<summary>Выполняет прямой проход через кодирующую нейросеть.</summary>
///<param name="input">Входные данные.</param>
public Tensor Encode(Tensor input)
{
var Temp = Layers.Conv2D(input, this.Data.Conv1_1_Weights, this.Data.Conv1_1_Biases);
if (Step != null)
{
Step(5);
}
Temp = Layers.ReLU(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.MaxPool2D(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.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.MaxPool2D(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.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_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_4_Weights, this.Data.Conv3_4_Biases);
if (Step != null)
{
Step(5);
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
Step(5);
}
Temp = Layers.MaxPool2D(Temp);
if (Step != null)
{
Step(5);
}
Temp = Layers.Conv2D(Temp, this.Data.Conv4_1_Weights, this.Data.Conv4_1_Biases);
Temp = Layers.ReLU(Temp);
if (Step != null)
{
Step(5);
}
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(1f / 12f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 12f * 100f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv1_2_Weights, this.Data.Conv1_2_Biases);
if (this.Step != null)
{
this.Step(1f / 12f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 12f * 100f);
}
Temp = Layers.MaxPool2D(Temp);
if (this.Step != null)
{
this.Step(1f / 12f * 100f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv2_1_Weights, this.Data.Conv2_1_Biases);
if (this.Step != null)
{
this.Step(1f / 12f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 12f * 100f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv2_2_Weights, this.Data.Conv2_2_Biases);
if (this.Step != null)
{
this.Step(1f / 12f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 12f * 100f);
}
Temp = Layers.MaxPool2D(Temp);
if (this.Step != null)
{
this.Step(1f / 12f * 100f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv3_1_Weights, this.Data.Conv3_1_Biases);
if (this.Step != null)
{
this.Step(1f / 12f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 12f * 100f);
}
return(Temp);
}
///<summary>Выполняет прямой проход через кодирующую нейросеть.</summary>
///<param name="input">Входные данные.</param>
public Tuple <Tensor, Tensor> Encode(Tensor input)
{
var Temp = input;
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)
{
Step(100f / 30f);
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
Step(100f / 30f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv1_2_Weights, this.Data.Conv1_2_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.Conv2_1_Weights, this.Data.Conv2_1_Biases);
if (Step != null)
{
Step(100f / 30f);
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
Step(100f / 30f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv2_2_Weights, this.Data.Conv2_2_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.Conv3_1_Weights, this.Data.Conv3_1_Biases);
if (Step != null)
{
Step(100f / 30f);
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
Step(100f / 30f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv3_2_Weights, this.Data.Conv3_2_Biases);
if (Step != null)
{
Step(100f / 30f);
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
Step(100f / 30f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv3_3_Weights, this.Data.Conv3_3_Biases);
if (Step != null)
{
Step(100f / 30f);
}
Temp = Layers.ReLU(Temp);
if (Step != null)
{
Step(100f / 30f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv3_4_Weights, this.Data.Conv3_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.Conv4_1_Weights, this.Data.Conv4_1_Biases);
if (Step != null)
{
Step(100f / 30f);
}
Temp = Layers.ReLU(Temp);
var Conv4_1 = Temp;
if (Step != null)
{
Step(100f / 30f);
}
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);
var Conv5_1 = Temp;
if (Step != null)
{
Step(100f / 30f);
}
return(new Tuple <Tensor, Tensor>(Conv4_1, Conv5_1));
}
///<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);
}