|| get_params().activation == DeepLearning.Activation.TanhWithDropout)
bodySb.i(3).p("ACTIVATION[i][r] = 1f - 2f / (1f + (float)Math.exp(2*ACTIVATION[i][r]));").nl();
else if (get_params().activation == DeepLearning.Activation.Rectifier
|| get_params().activation == DeepLearning.Activation.RectifierWithDropout)
bodySb.i(3).p("ACTIVATION[i][r] = Math.max(0f, ACTIVATION[i][r]);").nl();
<BUG>else throw new UnsupportedOperationException("Maxout POJO scoring is not yet implemented.");
bodySb.i(2).p("}").nl();</BUG>
bodySb.i(1).p("}").nl();
if (isClassifier()) {
bodySb.i(1).p("else {").nl();

|| get_params().activation == DeepLearning.Activation.TanhWithDropout)
bodySb.i(3).p("ACTIVATION[i][r] = 1f - 2f / (1f + (float)Math.exp(2*ACTIVATION[i][r]));").nl();
else if (get_params().activation == DeepLearning.Activation.Rectifier
|| get_params().activation == DeepLearning.Activation.RectifierWithDropout)
bodySb.i(3).p("ACTIVATION[i][r] = Math.max(0f, ACTIVATION[i][r]);").nl();
else throw new UnsupportedOperationException("Maxout POJO scoring is not yet implemented.");
if (get_params().hidden_dropout_ratios != null)
bodySb.i(3).p("ACTIVATION[i][r] *= HIDDEN_DROPOUT_RATIOS[i-1];").nl();
bodySb.i(2).p("}").nl();
bodySb.i(1).p("}").nl();
if (isClassifier()) {
bodySb.i(1).p("else {").nl();
