Shared Weights Embedding Layer (Stack / Unstack)

thomas · July 2, 2021, 7:10pm

Hi,
i want to get a model nearly like your test case:

eclipse/deeplearning4j/blob/6335b5e933f13d2bdb5c1f7e5896c45eb96839d7/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/graph/graphnodes/TestGraphNodes.java#L295-L304


      
          ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().graphBuilder().addInputs("in1", "in2")
                          .addVertex("stack", new org.deeplearning4j.nn.conf.graph.StackVertex(), "in1", "in2")
                          .addLayer("1", new EmbeddingLayer.Builder().nIn(5).nOut(5).build(), "stack")
                          .addVertex("unstack1", new org.deeplearning4j.nn.conf.graph.UnstackVertex(0, 2), "1")
                          .addVertex("unstack2", new org.deeplearning4j.nn.conf.graph.UnstackVertex(0, 2), "1")
                          .addLayer("out1", new OutputLayer.Builder().activation(Activation.TANH)
                                          .lossFunction(LossFunctions.LossFunction.L2).nIn(5).nOut(5).build(), "unstack1")
                          .addLayer("out2", new OutputLayer.Builder().activation(Activation.TANH)
                                          .lossFunction(LossFunctions.LossFunction.L2).nIn(5).nOut(5).build(), "unstack2")
                          .setOutputs("out1", "out2").build();

my question: ist every unstack vertex like a pop operation to the given stack. i would like to merge every result from the embedded layer to one big output, which goes to one single target layer.
like

addVertex("unstack1", new UnstackVertex(0, 2),"embedded")
addVertex("unstack2", new UnstackVertex(0, 2),"embedded")
addVertex("merge", new MergeVertex(), "unstack1","unstack2")

Best regards

Thomas

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Shared Weights Embedding Layer (Stack / Unstack)

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