@Override public ScaleVertex clone() { return new ScaleVertex(scaleFactor); }
@Override public MemoryReport getMemoryReport(InputType... inputTypes) { //Do one dup on the forward pass (output activations). Accounted for in output activations. InputType outputType = getOutputType(-1, inputTypes); return new LayerMemoryReport.Builder(null, ScaleVertex.class, inputTypes[0], outputType).standardMemory(0, 0) //No params .workingMemory(0, 0, 0, 0).cacheMemory(0, 0) //No caching .build(); } }
.activation(Activation.TANH).nIn(1344).nOut(1344).build(), nameLayer(blockName, "cnn5", i)) .addVertex(nameLayer(blockName, "scaling", i), new ScaleVertex(activationScale), nameLayer(blockName, "batch5", i))
.nOut(576).build(), nameLayer(blockName, "cnn5", i)) .addVertex(nameLayer(blockName, "scaling", i), new ScaleVertex(activationScale), nameLayer(blockName, "batch5", i))
.nOut(192).build(), nameLayer(blockName, "cnn7", i)) .addVertex(nameLayer(blockName, "scaling", i), new ScaleVertex(activationScale), nameLayer(blockName, "batch7", i))