@OptionMetadata( displayName = "number of rows in padding", description = "The number of rows in the padding (default = 0).", commandLineParamName = "paddingRows", commandLineParamSynopsis = "-paddingRows <int>", displayOrder = 8 ) public int getPaddingRows() { return backend.getPadding()[0]; }
@ProgrammaticProperty public int[] getPadding() { return backend.getPadding(); }
@OptionMetadata( displayName = "number of columns in padding", description = "The number of columns in the padding (default = 0).", commandLineParamName = "paddingColumns", commandLineParamSynopsis = "-paddingColumns <int>", displayOrder = 9 ) public int getPaddingColumns() { return backend.getPadding()[1]; }
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) { SubsamplingLayer layer1 = (SubsamplingLayer) 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) { SubsamplingLayer layer1 = (SubsamplingLayer) 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) { SubsamplingLayer layer1 = (SubsamplingLayer) layer;
(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/>"); fullLine.append("Outputs number: ").append(layer1.getNOut()).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/>"); fullLine.append("Outputs number: ").append(layer1.getNOut()).append("<br/>");
kernel = cl.getKernelSize(); stride = cl.getStride(); padding = cl.getPadding(); } else { SubsamplingLayer ssl = (SubsamplingLayer) layer;
kernel = cl.getKernelSize(); stride = cl.getStride(); padding = cl.getPadding(); } else { SubsamplingLayer ssl = (SubsamplingLayer) layer;
kernel = cl.getKernelSize(); stride = cl.getStride(); padding = cl.getPadding(); } else { SubsamplingLayer ssl = (SubsamplingLayer) layer;
pad = layerConf().getPadding();
strides); } else { pad = layerConf().getPadding();