Removed unnecessary Span<T>.Copy method

Author: Sergio0694

NeuralNetwork.NET/Extensions/SpanExtensions.cs

@@ -81,20 +81,6 @@ public static unsafe int GetContentHashCode<T>(this Span<T> span) where T : stru
             }
         }
 
-        /// <summary>
-        /// Returns a deep copy of the input <see cref="Span{T}"/>
-        /// </summary>
-        /// <param name="span">The <see cref="Span{T}"/> to clone</param>
-        /// <remarks>This method avoids the boxing of the <see cref="Array.Clone"/> method, and it is faster thanks to the use of the methods in the <see cref="Buffer"/> class</remarks>
-        [Pure]
-        [CollectionAccess(CollectionAccessType.Read)]
-        public static T[] Copy<T>(this Span<T> span) where T : struct
-        {
-            T[] result = new T[span.Length];
-            span.CopyTo(result);
-            return result;
-        }
-
         /// <summary>
         /// Extracts a single row from a given matrix
         /// </summary>

NeuralNetwork.NET/Networks/Layers/Cpu/BatchNormalizationLayer.cs

@@ -107,6 +107,6 @@ public static INetworkLayer Deserialize([NotNull] Stream stream)
         }
 
         /// <inheritdoc/>
-        public override INetworkLayer Clone() => new BatchNormalizationLayer(InputInfo, NormalizationMode, Weights.AsSpan().Copy(), Biases.AsSpan().Copy(), Iteration, Mu.AsSpan().Copy(), Sigma2.AsSpan().Copy(), ActivationType);
+        public override INetworkLayer Clone() => new BatchNormalizationLayer(InputInfo, NormalizationMode, Weights.AsSpan().ToArray(), Biases.AsSpan().ToArray(), Iteration, Mu.AsSpan().ToArray(), Sigma2.AsSpan().ToArray(), ActivationType);
     }
 }

NeuralNetwork.NET/Networks/Layers/Cpu/ConvolutionalLayer.cs

@@ -129,7 +129,7 @@ public override bool Equals(INetworkLayer other)
         }
 
         /// <inheritdoc/>
-        public override INetworkLayer Clone() => new ConvolutionalLayer(InputInfo, OperationInfo, KernelInfo, OutputInfo, Weights.AsSpan().Copy(), Biases.AsSpan().Copy(), ActivationType);
+        public override INetworkLayer Clone() => new ConvolutionalLayer(InputInfo, OperationInfo, KernelInfo, OutputInfo, Weights.AsSpan().ToArray(), Biases.AsSpan().ToArray(), ActivationType);
 
         /// <inheritdoc/>
         public override void Serialize(Stream stream)

NeuralNetwork.NET/Networks/Layers/Cpu/FullyConnectedLayer.cs

@@ -74,7 +74,7 @@ public override unsafe void Backpropagate(in Tensor x, in Tensor y, in Tensor dy
         #endregion
 
         /// <inheritdoc/>
-        public override INetworkLayer Clone() => new FullyConnectedLayer(InputInfo, OutputInfo.Size, Weights.AsSpan().Copy(), Biases.AsSpan().Copy(), ActivationType);
+        public override INetworkLayer Clone() => new FullyConnectedLayer(InputInfo, OutputInfo.Size, Weights.AsSpan().ToArray(), Biases.AsSpan().ToArray(), ActivationType);
 
         /// <summary>
         /// Tries to deserialize a new <see cref="FullyConnectedLayer"/> from the input <see cref="Stream"/>

NeuralNetwork.NET/Networks/Layers/Cpu/OutputLayer.cs

@@ -31,7 +31,7 @@ public OutputLayer(in TensorInfo input, int outputs, [NotNull] float[] weights,
             : base(input, outputs, weights, biases, activation, cost) { }
 
         /// <inheritdoc/>
-        public override INetworkLayer Clone() => new OutputLayer(InputInfo, OutputInfo.Size, Weights.AsSpan().Copy(), Biases.AsSpan().Copy(), ActivationType, CostFunctionType);
+        public override INetworkLayer Clone() => new OutputLayer(InputInfo, OutputInfo.Size, Weights.AsSpan().ToArray(), Biases.AsSpan().ToArray(), ActivationType, CostFunctionType);
 
         /// <summary>
         /// Tries to deserialize a new <see cref="OutputLayer"/> from the input <see cref="Stream"/>

NeuralNetwork.NET/Networks/Layers/Cpu/SoftmaxLayer.cs

@@ -40,7 +40,7 @@ public override unsafe void Forward(in Tensor x, out Tensor z, out Tensor a)
         }
 
         /// <inheritdoc/>
-        public override INetworkLayer Clone() => new SoftmaxLayer(InputInfo, OutputInfo.Size, Weights.AsSpan().Copy(), Biases.AsSpan().Copy());
+        public override INetworkLayer Clone() => new SoftmaxLayer(InputInfo, OutputInfo.Size, Weights.AsSpan().ToArray(), Biases.AsSpan().ToArray());
 
         /// <summary>
         /// Tries to deserialize a new <see cref="SoftmaxLayer"/> from the input <see cref="Stream"/>

NeuralNetwork.NET/Networks/Layers/Cuda/CuDnnBatchNormalizationLayer.cs

@@ -167,7 +167,7 @@ public static INetworkLayer Deserialize([NotNull] System.IO.Stream stream)
         }
 
         /// <inheritdoc/>
-        public override INetworkLayer Clone() => new CuDnnBatchNormalizationLayer(InputInfo, NormalizationMode, Weights.AsSpan().Copy(), Biases.AsSpan().Copy(), Iteration, Mu.AsSpan().Copy(), Sigma2.AsSpan().Copy(), ActivationType);
+        public override INetworkLayer Clone() => new CuDnnBatchNormalizationLayer(InputInfo, NormalizationMode, Weights.AsSpan().ToArray(), Biases.AsSpan().ToArray(), Iteration, Mu.AsSpan().ToArray(), Sigma2.AsSpan().ToArray(), ActivationType);
 
         #region IDisposable
 

NeuralNetwork.NET/Networks/Layers/Cuda/CuDnnConvolutionalLayer.cs

@@ -166,7 +166,7 @@ public override void Backpropagate(in Tensor x, in Tensor y, in Tensor dy, in Te
         }
 
         /// <inheritdoc/>
-        public override INetworkLayer Clone() => new CuDnnConvolutionalLayer(InputInfo, OperationInfo, KernelInfo, OutputInfo, Weights.AsSpan().Copy(), Biases.AsSpan().Copy(), ActivationType);
+        public override INetworkLayer Clone() => new CuDnnConvolutionalLayer(InputInfo, OperationInfo, KernelInfo, OutputInfo, Weights.AsSpan().ToArray(), Biases.AsSpan().ToArray(), ActivationType);
 
         #endregion
     }

NeuralNetwork.NET/Networks/Layers/Cuda/CuDnnFullyConnectedLayer.cs

@@ -95,6 +95,6 @@ public override void Backpropagate(in Tensor x, in Tensor y, in Tensor dy, in Te
         }
 
         /// <inheritdoc/>
-        public override INetworkLayer Clone() => new CuDnnFullyConnectedLayer(InputInfo, OutputInfo.Size, Weights.AsSpan().Copy(), Biases.AsSpan().Copy(), ActivationType);
+        public override INetworkLayer Clone() => new CuDnnFullyConnectedLayer(InputInfo, OutputInfo.Size, Weights.AsSpan().ToArray(), Biases.AsSpan().ToArray(), ActivationType);
     }
 }

NeuralNetwork.NET/Networks/Layers/Cuda/CuDnnSoftmaxLayer.cs

@@ -112,6 +112,6 @@ public override void Backpropagate(in Tensor x, in Tensor yHat, in Tensor y, in
         }
 
         /// <inheritdoc/>
-        public override INetworkLayer Clone() => new CuDnnSoftmaxLayer(InputInfo, OutputInfo.Size, Weights.AsSpan().Copy(), Biases.AsSpan().Copy());
+        public override INetworkLayer Clone() => new CuDnnSoftmaxLayer(InputInfo, OutputInfo.Size, Weights.AsSpan().ToArray(), Biases.AsSpan().ToArray());
     }
 }