@OptionMetadata( displayName = "number of rows in stride", description = "The stride along the rows (default = 1).", commandLineParamName = "strideRows", commandLineParamSynopsis = "-strideRows <int>", displayOrder = 6 ) public int getStrideRows() { return backend.getStride()[0]; }
@OptionMetadata( displayName = "number of columns in stride", description = "The stride along the columns (default = 1).", commandLineParamName = "strideColumns", commandLineParamSynopsis = "-strideColumns <int>", displayOrder = 7 ) public int getStrideColumns() { return backend.getStride()[1]; }
@ProgrammaticProperty public int[] getStride() { return backend.getStride(); }
(org.deeplearning4j.nn.conf.layers.ConvolutionLayer) layer; map.put("Kernel size", Arrays.toString(layer1.getKernelSize())); map.put("Stride", Arrays.toString(layer1.getStride())); map.put("Padding", Arrays.toString(layer1.getPadding())); } else if (layer instanceof SubsamplingLayer) {
(org.deeplearning4j.nn.conf.layers.ConvolutionLayer) layer; map.put("Kernel size", Arrays.toString(layer1.getKernelSize())); map.put("Stride", Arrays.toString(layer1.getStride())); map.put("Padding", Arrays.toString(layer1.getPadding())); } else if (layer instanceof SubsamplingLayer) {
(org.deeplearning4j.nn.conf.layers.ConvolutionLayer) layer; map.put("Kernel size", Arrays.toString(layer1.getKernelSize())); map.put("Stride", Arrays.toString(layer1.getStride())); map.put("Padding", Arrays.toString(layer1.getPadding())); } else if (layer instanceof SubsamplingLayer) {
(org.deeplearning4j.nn.conf.layers.ConvolutionLayer) layer.conf().getLayer(); mainLine.append("K: " + Arrays.toString(layer1.getKernelSize()) + " S: " + Arrays.toString(layer1.getStride()) + " P: " + Arrays.toString(layer1.getPadding())); subLine.append("nIn/nOut: [" + layer1.getNIn() + "/" + layer1.getNOut() + "]"); fullLine.append("Kernel size: ").append(Arrays.toString(layer1.getKernelSize())).append("<br/>"); fullLine.append("Stride: ").append(Arrays.toString(layer1.getStride())).append("<br/>"); fullLine.append("Padding: ").append(Arrays.toString(layer1.getPadding())).append("<br/>"); fullLine.append("Inputs number: ").append(layer1.getNIn()).append("<br/>");
(org.deeplearning4j.nn.conf.layers.ConvolutionLayer) layer.conf().getLayer(); mainLine.append("K: " + Arrays.toString(layer1.getKernelSize()) + " S: " + Arrays.toString(layer1.getStride()) + " P: " + Arrays.toString(layer1.getPadding())); subLine.append("nIn/nOut: [" + layer1.getNIn() + "/" + layer1.getNOut() + "]"); fullLine.append("Kernel size: ").append(Arrays.toString(layer1.getKernelSize())).append("<br/>"); fullLine.append("Stride: ").append(Arrays.toString(layer1.getStride())).append("<br/>"); fullLine.append("Padding: ").append(Arrays.toString(layer1.getPadding())).append("<br/>"); fullLine.append("Inputs number: ").append(layer1.getNIn()).append("<br/>");
ConvolutionLayer cl = (ConvolutionLayer) layer; kernel = cl.getKernelSize(); stride = cl.getStride(); padding = cl.getPadding(); } else {
ConvolutionLayer cl = (ConvolutionLayer) layer; kernel = cl.getKernelSize(); stride = cl.getStride(); padding = cl.getPadding(); } else {
ConvolutionLayer cl = (ConvolutionLayer) layer; kernel = cl.getKernelSize(); stride = cl.getStride(); padding = cl.getPadding(); } else {
Distribution dist = Distributions.createDistribution(layerConf.getDist()); int[] kernel = layerConf.getKernelSize(); int[] stride = layerConf.getStride();
int[] strides = layerConf().getStride(); int[] pad; int[] outSize;
int[] strides = layerConf().getStride();