--- title: "BNNSFilterCreateLayerConvolution(_:_:)" framework: accelerate role: symbol role_heading: Function path: "accelerate/bnnsfiltercreatelayerconvolution(_:_:)" --- # BNNSFilterCreateLayerConvolution(_:_:) Returns a new convolution layer. ## Declaration ```swift func BNNSFilterCreateLayerConvolution(_ layer_params: UnsafePointer, _ filter_params: UnsafePointer?) -> BNNSFilter? ``` ## Parameters - `layer_params`: Layer parameters. - `filter_params`: The filter runtime parameters. ## Discussion Discussion Use convolution to convolve an input image over all dimensions. For example, given the following inputs and weights: let channelZero: [Float] = [10, 11, 12, 13] let channelOne: [Float] = [20, 21, 22, 23] let channelTwo: [Float] = [30, 31, 32, 33] let input = channelZero + channelOne + channelTwo var weights = [Float](repeating: 1 / Float(input.count), count: input.count) The following code creates and applies convolution: var bias = [Float(0)] var output = [Float](repeating: 0, count: 1) weights.withUnsafeMutableBufferPointer { weightsPtr in bias.withUnsafeMutableBufferPointer { biasPtr in let inDescriptor = BNNSNDArrayDescriptor(flags: BNNSNDArrayFlags(0), layout: BNNSDataLayoutImageCHW, size: (2, 2, 3, 0, 0, 0, 0, 0), stride: (0, 0, 0, 0, 0, 0, 0, 0), data: nil, data_type: .float, table_data: nil, table_data_type: .float, data_scale: 1, data_bias: 0) let outDescriptor = BNNSNDArrayDescriptor(flags: BNNSNDArrayFlags(0), layout: BNNSDataLayoutImageCHW, size: (1, 1, 1, 0, 0, 0, 0, 0), stride: (0, 0, 0, 0, 0, 0, 0, 0), data: nil, data_type: .float, table_data: nil, table_data_type: .float, data_scale: 1, data_bias: 0) let weightsDescriptor = BNNSNDArrayDescriptor(flags: BNNSNDArrayFlags(0), layout: BNNSDataLayoutConvolutionWeightsOIHW, size: (2, 2, 3, 1, 0, 0, 0, 0), stride: (0, 0, 0, 0, 0, 0, 0, 0), data: weightsPtr.baseAddress, data_type: .float, table_data: nil, table_data_type: .float, data_scale: 1, data_bias: 0) let biasDescriptor = BNNSNDArrayDescriptor(flags: BNNSNDArrayFlags(0), layout: BNNSDataLayoutVector, size: (1, 0, 0, 1, 0, 0, 0, 0), stride: (0, 0, 0, 0, 0, 0, 0, 0), data: biasPtr.baseAddress, data_type: .float, table_data: nil, table_data_type: .float, data_scale: 1, data_bias: 0) var parameters = BNNSLayerParametersConvolution(i_desc: inDescriptor, w_desc: weightsDescriptor, o_desc: outDescriptor, bias: biasDescriptor, activation: .identity, x_stride: 1, y_stride: 1, x_dilation_stride: 0, y_dilation_stride: 0, x_padding: 0, y_padding: 0, groups: 1, pad: (0, 0, 0, 0)) guard let filter = BNNSFilterCreateLayerConvolution(¶meters, nil) else { fatalError("`BNNSFilterCreateLayerConvolution` returns `nil`.") } defer { BNNSFilterDestroy(filter) } BNNSFilterApply(filter, input, &output) } } On return, the output contains [21.5], calculated using the following arithmetic: // 1.0 / 12.0 ≅ 0.0833 ( (10.0 * 0.0833) + (11.0 * 0.0833) + (12.0 * 0.0833) + (13.0 * 0.0833) ) + ( (20.0 * 0.0833) + (21.0 * 0.0833) + (22.0 * 0.0833) + (23.0 * 0.0833) ) + ( (30.0 * 0.0833) + (31.0 * 0.0833) + (32.0 * 0.0833) + (33.0 * 0.0833) ) = 21.5 ## See Also ### Convolution layers - [BNNSConvolutionLayerParameters](accelerate/bnnsconvolutionlayerparameters.md) - [BNNSFilterCreateConvolutionLayer(_:_:_:_:)](accelerate/bnnsfiltercreateconvolutionlayer(_:_:_:_:).md) - [BNNS.ConvolutionLayer](accelerate/bnns/convolutionlayer.md) - [BNNSLayerParametersConvolution](accelerate/bnnslayerparametersconvolution.md) - [BNNSFilterCreateLayerTransposedConvolution(_:_:)](accelerate/bnnsfiltercreatelayertransposedconvolution(_:_:).md)