Add warning and list remedies if the input layer is bigger than 100k …

…neurons.

src/main/java/hex/deeplearning/DeepLearningModel.java

@@ -11,7 +11,9 @@
 import water.fvec.Vec;
 import water.util.*;
 
+import java.lang.reflect.Field;
 import java.util.Arrays;
+import java.util.Comparator;
 import java.util.Random;
 
 /**
@@ -326,6 +328,34 @@ public DeepLearningModelInfo(final Job job, final DataInfo dinfo) {
         units[0] = num_input;
       System.arraycopy(get_params().hidden, 0, units, 1, layers);
       units[layers+1] = num_output;
+
+      if ((long)units[0] > 100000L) {
+        final String[][] domains = dinfo._adaptedFrame.domains();
+        int[] levels = new int[domains.length];
+        for (int i=0; i<levels.length; ++i) {
+          levels[i] = domains[i] != null ? domains[i].length : 0;
+        }
+        Arrays.sort(levels);
+        Log.warn("===================================================================================================================================");
+        Log.warn(num_input + " input features" + (dinfo._cats > 0 ? " (after categorical one-hot encoding)" : "") + ". Can be slow and require a lot of memory.");
+        if (levels[levels.length-1] > 0) {
+          int levelcutoff = levels[levels.length-1-Math.min(10, levels.length)];
+          int count = 0;
+          for (int i=0; i<dinfo._adaptedFrame.numCols() - (get_params().autoencoder ? 0 : 1) && count < 10; ++i) {
+            if (dinfo._adaptedFrame.domains()[i] != null && dinfo._adaptedFrame.domains()[i].length >= levelcutoff) {
+              Log.warn("Categorical feature '" + dinfo._adaptedFrame._names[i] + "' has cardinality " + dinfo._adaptedFrame.domains()[i].length + ".");
+            }
+          }
+        }
+        Log.warn("Suggestions:");
+        Log.warn(" *) Limit the size of the first hidden layer");
+        if (dinfo._cats > 0) {
+          Log.warn(" *) Limit the total number of one-hot encoded features with the parameter 'max_categorical_features'");
+          Log.warn(" *) Run h2o.interaction(...,pairwise=F) on high-cardinality categorical columns to limit the factor count, see http://learn.h2o.ai");
+        }
+        Log.warn("===================================================================================================================================");
+      }
+
       // weights (to connect layers)
       dense_row_weights = new Neurons.DenseRowMatrix[layers+1];
       dense_col_weights = new Neurons.DenseColMatrix[layers+1];