///<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); }