@OptionMetadata( displayName = "pooling type", description = "The type of pooling to use (default = MAX; options: MAX, AVG, SUM, NONE).", commandLineParamName = "poolingType", commandLineParamSynopsis = "-poolingType <string>", displayOrder = 10 ) public PoolingType getPoolingType() { return PoolingType.fromBackend(backend.getPoolingType()); }
map.put("Stride", Arrays.toString(layer1.getStride())); map.put("Padding", Arrays.toString(layer1.getPadding())); map.put("Pooling Type", layer1.getPoolingType().toString()); } else if (layer instanceof BaseOutputLayer) { BaseOutputLayer ol = (BaseOutputLayer) layer;
map.put("Stride", Arrays.toString(layer1.getStride())); map.put("Padding", Arrays.toString(layer1.getPadding())); map.put("Pooling Type", layer1.getPoolingType().toString()); } else if (layer instanceof BaseOutputLayer) { BaseOutputLayer ol = (BaseOutputLayer) layer;
map.put("Stride", Arrays.toString(layer1.getStride())); map.put("Padding", Arrays.toString(layer1.getPadding())); map.put("Pooling Type", layer1.getPoolingType().toString()); } else if (layer instanceof BaseOutputLayer) { BaseOutputLayer ol = (BaseOutputLayer) layer;
fullLine.append("Stride: ").append(Arrays.toString(layer1.getStride())).append("<br/>"); fullLine.append("Padding: ").append(Arrays.toString(layer1.getPadding())).append("<br/>"); fullLine.append("Pooling type: ").append(layer1.getPoolingType().toString()).append("<br/>"); } else if (layer.conf().getLayer() instanceof FeedForwardLayer) { org.deeplearning4j.nn.conf.layers.FeedForwardLayer layer1 =
fullLine.append("Stride: ").append(Arrays.toString(layer1.getStride())).append("<br/>"); fullLine.append("Padding: ").append(Arrays.toString(layer1.getPadding())).append("<br/>"); fullLine.append("Pooling type: ").append(layer1.getPoolingType().toString()).append("<br/>"); } else if (layer.conf().getLayer() instanceof FeedForwardLayer) { org.deeplearning4j.nn.conf.layers.FeedForwardLayer layer1 =
layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerSubsamplingPoolingType"), ssl.getPoolingType().toString()});
layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerSubsamplingPoolingType"), ssl.getPoolingType().toString()});
layerInfoRows.add(new String[] { i18N.getMessage("train.model.layerinfotable.layerSubsamplingPoolingType"), ssl.getPoolingType().toString()});
INDArray ret = helper.activate(input, training, kernel, strides, pad, layerConf().getPoolingType(), convolutionMode); if (ret != null) { switch (layerConf().getPoolingType()) { case AVG: return input; default: throw new IllegalStateException("Unknown/not supported pooling type: " + layerConf().getPoolingType() + " " + layerId());
layerConf().getPoolingType(), convolutionMode); if (ret != null) { return ret; switch (layerConf().getPoolingType()) { case MAX: return new Pair<>(retGradient, epsilon); default: throw new IllegalStateException("Unknown or unsupported pooling type: " + layerConf().getPoolingType() + " " + layerId()); Convolution.col2im(col6dPermuted, outEpsilon, strides[0], strides[1], pad[0], pad[1], inputHeight, inputWidth); if (layerConf().getPoolingType() == PoolingType.AVG) outEpsilon.divi(ArrayUtil.prod(layerConf().getKernelSize())); return new Pair<>(retGradient, outEpsilon);