Minor code changes

Sergio0694 · Sergio0694 · commit 7af1c6f5942a · 2017-12-30T13:40:46.000+01:00
diff --git a/NeuralNetwork.NET/APIs/Structs/Tensor.cs b/NeuralNetwork.NET/APIs/Structs/Tensor.cs
@@ -44,13 +44,18 @@ public int Size
         /// <summary>
         /// Gets whether or not the current instance is linked to an allocated memory area
         /// </summary>
-        public bool Null
+        public bool IsNull
         {
             [Pure]
             [MethodImpl(MethodImplOptions.AggressiveInlining)]
             get => Ptr == IntPtr.Zero;
         }
 
+        /// <summary>
+        /// Gets a null instance
+        /// </summary>
+        public static readonly Tensor Null = new Tensor(IntPtr.Zero, 0, 0);
+
         #region Initialization
 
         // Private constructor
diff --git a/NeuralNetwork.NET/Networks/Implementations/NeuralNetwork.cs b/NeuralNetwork.NET/Networks/Implementations/NeuralNetwork.cs
@@ -252,7 +252,7 @@ internal unsafe void Backpropagate(in TrainingBatch batch, float dropout, [NotNu
                      * Multiply the previous delta with the transposed weights of the following layer
                      * Compute d(l), the Hadamard product of z'(l) and delta(l + 1) * W(l + 1)T */
                     _Layers[l + 1].Backpropagate(*deltas[l + 1], zList[l], _Layers[l].ActivationFunctions.ActivationPrime);
-                    if (!dropoutMasks[l].Null) zList[l].InPlaceHadamardProduct(dropoutMasks[l]);
+                    if (!dropoutMasks[l].IsNull) zList[l].InPlaceHadamardProduct(dropoutMasks[l]);
                     deltas[l] = zList + l;
                 }
 
diff --git a/NeuralNetwork.NET/Networks/Layers/Abstract/NetworkLayerBase.cs b/NeuralNetwork.NET/Networks/Layers/Abstract/NetworkLayerBase.cs
@@ -75,10 +75,10 @@ protected NetworkLayerBase(in TensorInfo input, in TensorInfo output, Activation
         /// <summary>
         /// Backpropagates the error to compute the delta for the inputs of the layer
         /// </summary>
-        /// <param name="delta_1">The output error delta</param>
+        /// <param name="dy">The output error delta</param>
         /// <param name="z">The activity on the inputs of the layer. It will be modified to become the computed delta</param>
         /// <param name="activationPrime">The activation prime function performed by the previous layer</param>
-        public abstract void Backpropagate(in Tensor delta_1, in Tensor z, ActivationFunction activationPrime);
+        public abstract void Backpropagate(in Tensor dy, in Tensor z, ActivationFunction activationPrime);
 
         #region Equality check
 
diff --git a/NeuralNetwork.NET/Networks/Layers/Cpu/ConvolutionalLayer.cs b/NeuralNetwork.NET/Networks/Layers/Cpu/ConvolutionalLayer.cs
@@ -90,13 +90,13 @@ public override unsafe void Forward(in Tensor x, out Tensor z, out Tensor a)
         }
 
         /// <inheritdoc/>
-        public override unsafe void Backpropagate(in Tensor delta_1, in Tensor z, ActivationFunction activationPrime)
+        public override unsafe void Backpropagate(in Tensor dy, in Tensor z, ActivationFunction activationPrime)
         {
             fixed (float* pw = Weights)
             {
                 Tensor.Reshape(pw, OutputInfo.Channels, KernelInfo.Size, out Tensor wTensor);
                 wTensor.Rotate180(KernelInfo.Channels, out Tensor w180);
-                delta_1.ConvoluteBackwards(OutputInfo, w180, KernelInfo, out Tensor delta);
+                dy.ConvoluteBackwards(OutputInfo, w180, KernelInfo, out Tensor delta);
                 w180.Free();
                 z.InPlaceActivationAndHadamardProduct(delta, activationPrime);
                 delta.Free();
diff --git a/NeuralNetwork.NET/Networks/Layers/Cpu/FullyConnectedLayer.cs b/NeuralNetwork.NET/Networks/Layers/Cpu/FullyConnectedLayer.cs
@@ -44,13 +44,13 @@ public override unsafe void Forward(in Tensor x, out Tensor z, out Tensor a)
         }
 
         /// <inheritdoc/>
-        public override unsafe void Backpropagate(in Tensor delta_1, in Tensor z, ActivationFunction activationPrime)
+        public override unsafe void Backpropagate(in Tensor dy, in Tensor z, ActivationFunction activationPrime)
         {
             fixed (float* pw = Weights)
             {
                 Tensor.Reshape(pw, InputInfo.Size, OutputInfo.Size, out Tensor wTensor);
                 wTensor.Transpose(out Tensor wt);
-                z.InPlaceMultiplyAndHadamardProductWithActivationPrime(delta_1, wt, activationPrime);
+                z.InPlaceMultiplyAndHadamardProductWithActivationPrime(dy, wt, activationPrime);
                 wt.Free();
             }
         }
diff --git a/NeuralNetwork.NET/Networks/Layers/Cpu/PoolingLayer.cs b/NeuralNetwork.NET/Networks/Layers/Cpu/PoolingLayer.cs
@@ -45,7 +45,7 @@ public override void Forward(in Tensor x, out Tensor z, out Tensor a)
         }
 
         /// <inheritdoc/>
-        public override void Backpropagate(in Tensor delta_1, in Tensor z, ActivationFunction activationPrime) => z.UpscalePool2x2(delta_1, InputInfo.Channels);
+        public override void Backpropagate(in Tensor dy, in Tensor z, ActivationFunction activationPrime) => z.UpscalePool2x2(dy, InputInfo.Channels);
 
         /// <inheritdoc/>
         public override INetworkLayer Clone() => new PoolingLayer(InputInfo, OperationInfo, ActivationFunctionType);
diff --git a/NeuralNetwork.NET/Networks/Layers/Cuda/CuDnnConvolutionalLayer.cs b/NeuralNetwork.NET/Networks/Layers/Cuda/CuDnnConvolutionalLayer.cs
@@ -108,15 +108,15 @@ public override void Forward(in Tensor x, out Tensor z, out Tensor a)
         }
 
         /// <inheritdoc/>
-        public override void Backpropagate(in Tensor delta_1, in Tensor z, ActivationFunction activationPrime)
+        public override void Backpropagate(in Tensor dy, in Tensor z, ActivationFunction activationPrime)
         {
             using (DeviceMemory<float> delta_gpu = DnnInstance.Gpu.AllocateDevice<float>(z.Size))
             {
                 // Convolution
                 DnnInstance.GetConvolutionBackwardDataAlgorithm(FilterDescription, OutputDescription, ConvolutionDescription, InputDescription, ConvolutionBwdDataPreference.PREFER_FASTEST, IntPtr.Zero, out ConvolutionBwdDataAlgo algorithm);
                 DnnInstance.GetConvolutionBackwardDataWorkspaceSize(FilterDescription, OutputDescription, ConvolutionDescription, InputDescription, algorithm, out IntPtr size);
                 using (DeviceMemory<float>
-                    delta_1_gpu = DnnInstance.Gpu.AllocateDevice(delta_1),
+                    delta_1_gpu = DnnInstance.Gpu.AllocateDevice(dy),
                     w_gpu = DnnInstance.Gpu.AllocateDevice(Weights))
                 using (DeviceMemory<byte> workspace_gpu = DnnInstance.Gpu.AllocateDevice<byte>(size))
                 {
diff --git a/NeuralNetwork.NET/Networks/Layers/Cuda/CuDnnFullyConnectedLayer.cs b/NeuralNetwork.NET/Networks/Layers/Cuda/CuDnnFullyConnectedLayer.cs
@@ -45,10 +45,10 @@ public override void Forward(in Tensor x, out Tensor z, out Tensor a)
         }
 
         /// <inheritdoc/>
-        public override void Backpropagate(in Tensor delta_1, in Tensor z, ActivationFunction activationPrime)
+        public override void Backpropagate(in Tensor dy, in Tensor z, ActivationFunction activationPrime)
         {
             using (DeviceMemory<float>
-                delta_1_gpu = DnnInstance.Gpu.AllocateDevice(delta_1),
+                delta_1_gpu = DnnInstance.Gpu.AllocateDevice(dy),
                 w_gpu = DnnInstance.Gpu.AllocateDevice(Weights),
                 z_gpu = DnnInstance.Gpu.AllocateDevice(z))
             {
diff --git a/NeuralNetwork.NET/Networks/Layers/Cuda/CuDnnInceptionLayer.cs b/NeuralNetwork.NET/Networks/Layers/Cuda/CuDnnInceptionLayer.cs
@@ -443,7 +443,7 @@ public override void Forward(in Tensor x, out Tensor z, out Tensor a)
         }
 
         /// <inheritdoc/>
-        public override void Backpropagate(in Tensor delta_1, in Tensor z, ActivationFunction activationPrime)
+        public override void Backpropagate(in Tensor dy, in Tensor z, ActivationFunction activationPrime)
         {
             using (DeviceMemory<float> 
                 dx_gpu = DnnInstance.Gpu.AllocateDevice<float>(z.Size),
@@ -452,7 +452,7 @@ public override void Backpropagate(in Tensor delta_1, in Tensor z, ActivationFun
                 // First 1x1 convolution
                 DnnInstance.GetConvolutionBackwardDataAlgorithm(_1x1FilterDescription, _1x1OutputDescription, _1x1ConvolutionDescription, InputDescription, ConvolutionBwdDataPreference.PREFER_FASTEST, IntPtr.Zero, out ConvolutionBwdDataAlgo algorithm);
                 DnnInstance.GetConvolutionBackwardDataWorkspaceSize(_1x1FilterDescription, _1x1OutputDescription, _1x1ConvolutionDescription, InputDescription, algorithm, out IntPtr size);
-                using (DeviceMemory<float> dy_gpu = DnnInstance.Gpu.AllocateDevice(delta_1, 0, InputInfo.SliceSize * OperationInfo.Primary1x1ConvolutionKernels))
+                using (DeviceMemory<float> dy_gpu = DnnInstance.Gpu.AllocateDevice(dy, 0, InputInfo.SliceSize * OperationInfo.Primary1x1ConvolutionKernels))
                 using (DeviceMemory<byte> workspace_gpu = DnnInstance.Gpu.AllocateDevice<byte>(size))
                 {
                     DnnInstance.ConvolutionBackwardData(1, _1x1FilterDescription, w_gpu.Ptr, _1x1OutputDescription, dy_gpu.Ptr, _1x1ConvolutionDescription, algorithm, workspace_gpu.Ptr, size, 0, InputDescription, dx_gpu.Ptr);
@@ -465,7 +465,7 @@ public override void Backpropagate(in Tensor delta_1, in Tensor z, ActivationFun
                     DnnInstance.GetConvolutionBackwardDataAlgorithm(_3x3FilterDescription, _3x3OutputDescription, _3x3ConvolutionDescription, _3x3Reduce1x1OutputDescription, ConvolutionBwdDataPreference.PREFER_FASTEST, IntPtr.Zero, out algorithm);
                     DnnInstance.GetConvolutionBackwardDataWorkspaceSize(_3x3FilterDescription, _3x3OutputDescription, _3x3ConvolutionDescription, _3x3Reduce1x1OutputDescription, algorithm, out size);
                     using (DeviceMemory<float> 
-                        dy_gpu = DnnInstance.Gpu.AllocateDevice(delta_1, InputInfo.SliceSize * OperationInfo.Primary1x1ConvolutionKernels, InputInfo.SliceSize * OperationInfo.Secondary3x3ConvolutionKernels),
+                        dy_gpu = DnnInstance.Gpu.AllocateDevice(dy, InputInfo.SliceSize * OperationInfo.Primary1x1ConvolutionKernels, InputInfo.SliceSize * OperationInfo.Secondary3x3ConvolutionKernels),
                         _3x3Reduce1x1dx_gpu = DnnInstance.Gpu.AllocateDevice<float>(_3x3Reduce1x1Z.Size))
                     using (DeviceMemory<byte> workspace_gpu = DnnInstance.Gpu.AllocateDevice<byte>(size))
                     {
@@ -493,7 +493,7 @@ public override void Backpropagate(in Tensor delta_1, in Tensor z, ActivationFun
                     DnnInstance.GetConvolutionBackwardDataAlgorithm(_5x5FilterDescription, _5x5OutputDescription, _5x5ConvolutionDescription, _5x5Reduce1x1OutputDescription, ConvolutionBwdDataPreference.PREFER_FASTEST, IntPtr.Zero, out algorithm);
                     DnnInstance.GetConvolutionBackwardDataWorkspaceSize(_5x5FilterDescription, _5x5OutputDescription, _5x5ConvolutionDescription, _5x5Reduce1x1OutputDescription, algorithm, out size);
                     using (DeviceMemory<float> 
-                        dy_gpu = DnnInstance.Gpu.AllocateDevice(delta_1, InputInfo.SliceSize * (OperationInfo.Primary1x1ConvolutionKernels + OperationInfo.Secondary3x3ConvolutionKernels), InputInfo.SliceSize * OperationInfo.Secondary5x5ConvolutionKernels),
+                        dy_gpu = DnnInstance.Gpu.AllocateDevice(dy, InputInfo.SliceSize * (OperationInfo.Primary1x1ConvolutionKernels + OperationInfo.Secondary3x3ConvolutionKernels), InputInfo.SliceSize * OperationInfo.Secondary5x5ConvolutionKernels),
                         _5x5Reduce1x1dx_gpu = DnnInstance.Gpu.AllocateDevice<float>(_5x5Reduce1x1Z.Size))
                     using (DeviceMemory<byte> workspace_gpu = DnnInstance.Gpu.AllocateDevice<byte>(size))
                     {
@@ -521,7 +521,7 @@ public override void Backpropagate(in Tensor delta_1, in Tensor z, ActivationFun
                     DnnInstance.GetConvolutionBackwardDataAlgorithm(Secondary1x1FilterDescription, Secondary1x1OutputDescription, _1x1ConvolutionDescription, PoolingOutputDescription, ConvolutionBwdDataPreference.PREFER_FASTEST, IntPtr.Zero, out algorithm);
                     DnnInstance.GetConvolutionBackwardDataWorkspaceSize(Secondary1x1FilterDescription, Secondary1x1OutputDescription, _1x1ConvolutionDescription, PoolingOutputDescription, algorithm, out size);
                     using (DeviceMemory<float> 
-                        dy_gpu = DnnInstance.Gpu.AllocateDevice(delta_1, InputInfo.SliceSize * (OperationInfo.Primary1x1ConvolutionKernels + OperationInfo.Secondary3x3ConvolutionKernels + OperationInfo.Secondary5x5ConvolutionKernels), InputInfo.SliceSize * OperationInfo.Secondary1x1AfterPoolingConvolutionKernels),
+                        dy_gpu = DnnInstance.Gpu.AllocateDevice(dy, InputInfo.SliceSize * (OperationInfo.Primary1x1ConvolutionKernels + OperationInfo.Secondary3x3ConvolutionKernels + OperationInfo.Secondary5x5ConvolutionKernels), InputInfo.SliceSize * OperationInfo.Secondary1x1AfterPoolingConvolutionKernels),
                         poolDx_gpu = DnnInstance.Gpu.AllocateDevice<float>(_PoolingZ.Size))
                     using (DeviceMemory<byte> workspace_gpu = DnnInstance.Gpu.AllocateDevice<byte>(size))
                     {
diff --git a/NeuralNetwork.NET/Networks/Layers/Cuda/CuDnnPoolingLayer.cs b/NeuralNetwork.NET/Networks/Layers/Cuda/CuDnnPoolingLayer.cs
@@ -80,14 +80,14 @@ public override void Forward(in Tensor x, out Tensor z, out Tensor a)
         }
 
         /// <inheritdoc/>
-        public override void Backpropagate(in Tensor delta_1, in Tensor z, ActivationFunction activationPrime)
+        public override void Backpropagate(in Tensor dy, in Tensor z, ActivationFunction activationPrime)
         {
             using (DeviceMemory<float> dx_gpu = DnnInstance.Gpu.AllocateDevice<float>(z.Size))
             {
                 using (DeviceMemory<float>
                     x_gpu = DnnInstance.Gpu.AllocateDevice(_X),
                     y_gpu = DnnInstance.Gpu.AllocateDevice(_Z),
-                    dy_gpu = DnnInstance.Gpu.AllocateDevice(delta_1))
+                    dy_gpu = DnnInstance.Gpu.AllocateDevice(dy))
                 {
                     DnnInstance.PoolingBackward(PoolingDescription, 1, OutputDescription, y_gpu.Ptr, OutputDescription, dy_gpu.Ptr, InputDescription, x_gpu.Ptr, 0, InputDescription, dx_gpu.Ptr);
                 }

Original file line number	Diff line number	Diff line change
`@@ -252,7 +252,7 @@ internal unsafe void Backpropagate(in TrainingBatch batch, float dropout, [NotNu`
`252`	`252`	`* Multiply the previous delta with the transposed weights of the following layer`
`253`	`253`	`* Compute d(l), the Hadamard product of z'(l) and delta(l + 1) * W(l + 1)T */`
`254`	`254`	`_Layers[l + 1].Backpropagate(*deltas[l + 1], zList[l], _Layers[l].ActivationFunctions.ActivationPrime);`
`255`		`- if (!dropoutMasks[l].Null) zList[l].InPlaceHadamardProduct(dropoutMasks[l]);`
	`255`	`+ if (!dropoutMasks[l].IsNull) zList[l].InPlaceHadamardProduct(dropoutMasks[l]);`
`256`	`256`	`deltas[l] = zList + l;`
`257`	`257`	`}`
`258`	`258`
Original file line number	Diff line number	Diff line change
`@@ -90,13 +90,13 @@ public override unsafe void Forward(in Tensor x, out Tensor z, out Tensor a)`
`90`	`90`	`}`
`91`	`91`
`92`	`92`	`/// <inheritdoc/>`
`93`		`- public override unsafe void Backpropagate(in Tensor delta_1, in Tensor z, ActivationFunction activationPrime)`
	`93`	`+ public override unsafe void Backpropagate(in Tensor dy, in Tensor z, ActivationFunction activationPrime)`
`94`	`94`	`{`
`95`	`95`	`fixed (float* pw = Weights)`
`96`	`96`	`{`
`97`	`97`	`Tensor.Reshape(pw, OutputInfo.Channels, KernelInfo.Size, out Tensor wTensor);`
`98`	`98`	`wTensor.Rotate180(KernelInfo.Channels, out Tensor w180);`
`99`		`- delta_1.ConvoluteBackwards(OutputInfo, w180, KernelInfo, out Tensor delta);`
	`99`	`+ dy.ConvoluteBackwards(OutputInfo, w180, KernelInfo, out Tensor delta);`
`100`	`100`	`w180.Free();`
`101`	`101`	`z.InPlaceActivationAndHadamardProduct(delta, activationPrime);`
`102`	`102`	`delta.Free();`
Original file line number	Diff line number	Diff line change
`@@ -44,13 +44,13 @@ public override unsafe void Forward(in Tensor x, out Tensor z, out Tensor a)`
`44`	`44`	`}`
`45`	`45`
`46`	`46`	`/// <inheritdoc/>`
`47`		`- public override unsafe void Backpropagate(in Tensor delta_1, in Tensor z, ActivationFunction activationPrime)`
	`47`	`+ public override unsafe void Backpropagate(in Tensor dy, in Tensor z, ActivationFunction activationPrime)`
`48`	`48`	`{`
`49`	`49`	`fixed (float* pw = Weights)`
`50`	`50`	`{`
`51`	`51`	`Tensor.Reshape(pw, InputInfo.Size, OutputInfo.Size, out Tensor wTensor);`
`52`	`52`	`wTensor.Transpose(out Tensor wt);`
`53`		`- z.InPlaceMultiplyAndHadamardProductWithActivationPrime(delta_1, wt, activationPrime);`
	`53`	`+ z.InPlaceMultiplyAndHadamardProductWithActivationPrime(dy, wt, activationPrime);`
`54`	`54`	`wt.Free();`
`55`	`55`	`}`
`56`	`56`	`}`
Original file line number	Diff line number	Diff line change
`@@ -45,7 +45,7 @@ public override void Forward(in Tensor x, out Tensor z, out Tensor a)`
`45`	`45`	`}`
`46`	`46`
`47`	`47`	`/// <inheritdoc/>`
`48`		`- public override void Backpropagate(in Tensor delta_1, in Tensor z, ActivationFunction activationPrime) => z.UpscalePool2x2(delta_1, InputInfo.Channels);`
	`48`	`+ public override void Backpropagate(in Tensor dy, in Tensor z, ActivationFunction activationPrime) => z.UpscalePool2x2(dy, InputInfo.Channels);`
`49`	`49`
`50`	`50`	`/// <inheritdoc/>`
`51`	`51`	`public override INetworkLayer Clone() => new PoolingLayer(InputInfo, OperationInfo, ActivationFunctionType);`
Original file line number	Diff line number	Diff line change
`@@ -108,15 +108,15 @@ public override void Forward(in Tensor x, out Tensor z, out Tensor a)`
`108`	`108`	`}`
`109`	`109`
`110`	`110`	`/// <inheritdoc/>`
`111`		`- public override void Backpropagate(in Tensor delta_1, in Tensor z, ActivationFunction activationPrime)`
	`111`	`+ public override void Backpropagate(in Tensor dy, in Tensor z, ActivationFunction activationPrime)`
`112`	`112`	`{`
`113`	`113`	`using (DeviceMemory<float> delta_gpu = DnnInstance.Gpu.AllocateDevice<float>(z.Size))`
`114`	`114`	`{`
`115`	`115`	`// Convolution`
`116`	`116`	`DnnInstance.GetConvolutionBackwardDataAlgorithm(FilterDescription, OutputDescription, ConvolutionDescription, InputDescription, ConvolutionBwdDataPreference.PREFER_FASTEST, IntPtr.Zero, out ConvolutionBwdDataAlgo algorithm);`
`117`	`117`	`DnnInstance.GetConvolutionBackwardDataWorkspaceSize(FilterDescription, OutputDescription, ConvolutionDescription, InputDescription, algorithm, out IntPtr size);`
`118`	`118`	`using (DeviceMemory<float>`
`119`		`- delta_1_gpu = DnnInstance.Gpu.AllocateDevice(delta_1),`
	`119`	`+ delta_1_gpu = DnnInstance.Gpu.AllocateDevice(dy),`
`120`	`120`	`w_gpu = DnnInstance.Gpu.AllocateDevice(Weights))`
`121`	`121`	`using (DeviceMemory<byte> workspace_gpu = DnnInstance.Gpu.AllocateDevice<byte>(size))`
`122`	`122`	`{`
Original file line number	Diff line number	Diff line change
`@@ -45,10 +45,10 @@ public override void Forward(in Tensor x, out Tensor z, out Tensor a)`
`45`	`45`	`}`
`46`	`46`
`47`	`47`	`/// <inheritdoc/>`
`48`		`- public override void Backpropagate(in Tensor delta_1, in Tensor z, ActivationFunction activationPrime)`
	`48`	`+ public override void Backpropagate(in Tensor dy, in Tensor z, ActivationFunction activationPrime)`
`49`	`49`	`{`
`50`	`50`	`using (DeviceMemory<float>`
`51`		`- delta_1_gpu = DnnInstance.Gpu.AllocateDevice(delta_1),`
	`51`	`+ delta_1_gpu = DnnInstance.Gpu.AllocateDevice(dy),`
`52`	`52`	`w_gpu = DnnInstance.Gpu.AllocateDevice(Weights),`
`53`	`53`	`z_gpu = DnnInstance.Gpu.AllocateDevice(z))`
`54`	`54`	`{`
Original file line number	Diff line number	Diff line change
`@@ -80,14 +80,14 @@ public override void Forward(in Tensor x, out Tensor z, out Tensor a)`
`80`	`80`	`}`
`81`	`81`
`82`	`82`	`/// <inheritdoc/>`
`83`		`- public override void Backpropagate(in Tensor delta_1, in Tensor z, ActivationFunction activationPrime)`
	`83`	`+ public override void Backpropagate(in Tensor dy, in Tensor z, ActivationFunction activationPrime)`
`84`	`84`	`{`
`85`	`85`	`using (DeviceMemory<float> dx_gpu = DnnInstance.Gpu.AllocateDevice<float>(z.Size))`
`86`	`86`	`{`
`87`	`87`	`using (DeviceMemory<float>`
`88`	`88`	`x_gpu = DnnInstance.Gpu.AllocateDevice(_X),`
`89`	`89`	`y_gpu = DnnInstance.Gpu.AllocateDevice(_Z),`
`90`		`- dy_gpu = DnnInstance.Gpu.AllocateDevice(delta_1))`
	`90`	`+ dy_gpu = DnnInstance.Gpu.AllocateDevice(dy))`
`91`	`91`	`{`
`92`	`92`	`DnnInstance.PoolingBackward(PoolingDescription, 1, OutputDescription, y_gpu.Ptr, OutputDescription, dy_gpu.Ptr, InputDescription, x_gpu.Ptr, 0, InputDescription, dx_gpu.Ptr);`
`93`	`93`	`}`