.build(), "input1") .addLayer("stem-batch1", new BatchNormalization.Builder(false).nIn(64).nOut(64).build(), "stem-cnn1") .addLayer("stem-activation1", new ActivationLayer.Builder().activation(Activation.RELU).build(), .cudnnAlgoMode(ConvolutionLayer.AlgoMode.NO_WORKSPACE).build(), "stem-lrn1") .addLayer("inception-2-batch1", new BatchNormalization.Builder(false).nIn(64).nOut(64).build(), "inception-2-cnn1") .addLayer("inception-2-activation1", "inception-2-activation1") .addLayer("inception-2-batch2", new BatchNormalization.Builder(false).nIn(192).nOut(192).build(), "inception-2-cnn2") .addLayer("inception-2-activation2",
input) .addLayer("stem-batch1", new BatchNormalization.Builder(false).decay(0.995).eps(0.001).nIn(32).nOut(32) .build(), "stem-cnn1") "stem-batch1") .addLayer("stem-batch2", new BatchNormalization.Builder(false).decay(0.995).eps(0.001).nIn(32).nOut(32) .build(), "stem-cnn2") .cudnnAlgoMode(ConvolutionLayer.AlgoMode.NO_WORKSPACE).build(), "stem-batch2") .addLayer("stem-batch3", new BatchNormalization.Builder(false).decay(0.995).eps(0.001).nIn(64) .nOut(64).build(), "stem-cnn3") "stem-pool4") .addLayer("stem-batch5", new BatchNormalization.Builder(false).decay(0.995).eps(0.001).nIn(80).nOut(80) .build(), "stem-cnn5") "stem-batch5") .addLayer("stem-batch6", new BatchNormalization.Builder(false).decay(0.995).eps(0.001).nIn(128).nOut(128) .build(), "stem-cnn6")
previousBlock) .addLayer(nameLayer(blockName, "batch1", i), new BatchNormalization.Builder(false).decay(0.995).eps(0.001).nIn(32) .nOut(32).build(), nameLayer(blockName, "cnn1", i)) previousBlock) .addLayer(nameLayer(blockName, "batch2", i), new BatchNormalization.Builder(false).decay(0.995).eps(0.001).nIn(32) .nOut(32).build(), nameLayer(blockName, "cnn2", i)) nameLayer(blockName, "batch2", i)) .addLayer(nameLayer(blockName, "batch3", i), new BatchNormalization.Builder(false).decay(0.995).eps(0.001).nIn(32) .nOut(32).build(), nameLayer(blockName, "cnn3", i)) previousBlock) .addLayer(nameLayer(blockName, "batch4", i), new BatchNormalization.Builder(false).decay(0.995).eps(0.001).nIn(32) .nOut(32).build(), nameLayer(blockName, "cnn4", i)) nameLayer(blockName, "batch4", i)) .addLayer(nameLayer(blockName, "batch5", i), new BatchNormalization.Builder(false).decay(0.995).eps(0.001).nIn(32) .nOut(32).build(), nameLayer(blockName, "cnn5", i))
previousBlock) .addLayer(nameLayer(blockName, "batch1", i), new BatchNormalization.Builder(false).decay(0.995).eps(0.001).nIn(128) .nOut(128).build(), nameLayer(blockName, "cnn1", i)) previousBlock) .addLayer(nameLayer(blockName, "batch2", i), new BatchNormalization.Builder(false).decay(0.995).eps(0.001).nIn(128) .nOut(128).build(), nameLayer(blockName, "cnn2", i)) nameLayer(blockName, "batch2", i)) .addLayer(nameLayer(blockName, "batch3", i), new BatchNormalization.Builder(false).decay(0.995).eps(0.001).nIn(128) .nOut(128).build(), nameLayer(blockName, "cnn3", i)) nameLayer(blockName, "batch3", i)) .addLayer(nameLayer(blockName, "batch4", i), new BatchNormalization.Builder(false).decay(0.995).eps(0.001).nIn(128) .nOut(128).build(), nameLayer(blockName, "cnn4", i)) nameLayer(blockName, "merge1", i)) .addLayer(nameLayer(blockName, "batch5", i), new BatchNormalization.Builder(false).decay(0.995).eps(0.001).nIn(576) .nOut(576).build(), nameLayer(blockName, "cnn5", i))
previousBlock) .addLayer(nameLayer(blockName, "batch1", i), new BatchNormalization.Builder(false).decay(0.995).eps(0.001).nIn(192) .nOut(192).build(), nameLayer(blockName, "cnn1", i)) previousBlock) .addLayer(nameLayer(blockName, "batch2", i), new BatchNormalization.Builder(false).decay(0.995).eps(0.001).nIn(192) .nOut(192).build(), nameLayer(blockName, "cnn2", i)) nameLayer(blockName, "batch2", i)) .addLayer(nameLayer(blockName, "batch3", i), new BatchNormalization.Builder(false).decay(0.995).eps(0.001).nIn(192) .nOut(192).build(), nameLayer(blockName, "cnn3", i)) .addLayer(nameLayer(blockName, "batch4", i), new BatchNormalization.Builder(false).decay(0.995).eps(0.001) .activation(Activation.TANH).nIn(192).nOut(192).build(), nameLayer(blockName, "cnn4", i)) .addLayer(nameLayer(blockName, "batch5", i), new BatchNormalization.Builder(false).decay(0.995).eps(0.001) .activation(Activation.TANH).nIn(1344).nOut(1344).build(), nameLayer(blockName, "cnn5", i)) .addVertex(nameLayer(blockName, "scaling", i), new ScaleVertex(activationScale),
public static BatchNormalization batchNorm(int in, int out) { return new BatchNormalization.Builder(false).nIn(in).nOut(out).build(); }