java - DL4J中如何获取CNN网络卷积层的滤波器数据来绘制激活图？

Question

如何从像这样的配置和模型的图层对象中获取过滤器数据？

  ComputationGraphConfiguration config =
        new NeuralNetConfiguration.Builder()
            .seed(seed)
            .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
            .l2(1e-3)
            .updater(new Adam(1e-3))
            .weightInit(WeightInit.XAVIER_UNIFORM)
            .graphBuilder()
            .addInputs("trainFeatures")
            .setInputTypes(InputType.convolutional(60, 200, 3))
            .setOutputs("out1", "out2", "out3", "out4", "out5", "out6")
            .addLayer(
                "cnn1",
                new ConvolutionLayer.Builder(new int[] {5, 5}, new int[] {1, 1}, new int[] {0, 0})
                    .nIn(3)
                    .nOut(48)
                    .activation(Activation.RELU)
                    .build(),
                "trainFeatures")
            .addLayer(
                "maxpool1",
                new SubsamplingLayer.Builder(
                        PoolingType.MAX, new int[] {2, 2}, new int[] {2, 2}, new int[] {0, 0})
                    .build(),
                "cnn1")
            .addLayer(
                "cnn2",
                new ConvolutionLayer.Builder(new int[] {5, 5}, new int[] {1, 1}, new int[] {0, 0})
                    .nOut(64)
                    .activation(Activation.RELU)
                    .build(),
                "maxpool1")
            .addLayer(
                "maxpool2",
                new SubsamplingLayer.Builder(
                        PoolingType.MAX, new int[] {2, 1}, new int[] {2, 1}, new int[] {0, 0})
                    .build(),
                "cnn2")
            .addLayer(
                "cnn3",
                new ConvolutionLayer.Builder(new int[] {3, 3}, new int[] {1, 1}, new int[] {0, 0})
                    .nOut(128)
                    .activation(Activation.RELU)
                    .build(),
                "maxpool2")
            .addLayer(
                "maxpool3",
                new SubsamplingLayer.Builder(
                        PoolingType.MAX, new int[] {2, 2}, new int[] {2, 2}, new int[] {0, 0})
                    .build(),
                "cnn3")
            .addLayer(
                "cnn4",
                new ConvolutionLayer.Builder(new int[] {4, 4}, new int[] {1, 1}, new int[] {0, 0})
                    .nOut(256)
                    .activation(Activation.RELU)
                    .build(),
                "maxpool3")
            .addLayer(
                "maxpool4",
                new SubsamplingLayer.Builder(
                        PoolingType.MAX, new int[] {2, 2}, new int[] {2, 2}, new int[] {0, 0})
                    .build(),
                "cnn4")
            .addLayer("ffn0", new DenseLayer.Builder().nOut(3072).build(), "maxpool4")
            .addLayer("ffn1", new DenseLayer.Builder().nOut(3072).build(), "ffn0")
            .addLayer(
                "out1",
                new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                    //.nOut(36)
                        .nOut(10)
                    .activation(Activation.SOFTMAX)
                    .build(),
                "ffn1")
            .addLayer(
                "out2",
                new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                    //.nOut(36)
                        .nOut(10)
                    .activation(Activation.SOFTMAX)
                    .build(),
                "ffn1")
            .addLayer(
                "out3",
                new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                    //.nOut(36)
                        .nOut(10)
                    .activation(Activation.SOFTMAX)
                    .build(),
                "ffn1")
            .addLayer(
                "out4",
                new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                    //.nOut(36)
                        .nOut(10)
                    .activation(Activation.SOFTMAX)
                    .build(),
                "ffn1")
            .addLayer(
                "out5",
                new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                    //.nOut(36)
                        .nOut(10)
                    .activation(Activation.SOFTMAX)
                    .build(),
                "ffn1").addLayer(
                "out6",
                new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                        //.nOut(36)
                        .nOut(10)
                        .activation(Activation.SOFTMAX)
                        .build(),
                "ffn1")

            //.pretrain(false)
            //.backprop(true)
            .build();

我的意思是模型训练后卷积层激活的 NDArray(或者什么？)，用于绘制这样的激活图:

我不清楚哪种 Layer 的 API 返回用于构建该层的 2D 数据。

Answer 1

如果您使用 DL4J ui 模块，只需添加 ConvolutionalIterationListener 即可获得这些可视化效果。作为模型的另一个监听器。

如果您不想使用监听器，您至少可以查看其代码以了解如何自行创建这些可视化效果。