#7352 MultiLayerNetwork.output(DataSetIterator) validation

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/multilayer/MultiLayerNetwork.java

@@ -2355,13 +2355,19 @@ public INDArray output(INDArray input) {
     /**
      * Generate the output for all examples/batches in the input iterator, and concatenate them into a single array.
      * See {@link #output(INDArray)}<br>
-     * NOTE: The output array can require a considerable amount of memory for iterators with a large number of examples
+     * NOTE 1: The output array can require a considerable amount of memory for iterators with a large number of examples<br>
+     * NOTE 2: This method cannot be used for variable length time series outputs, as this would require padding arrays
+     * for some outputs, or returning a mask array (which cannot be done with this method). For variable length time
+     * series applications, use one of the other output methods. This method also cannot be used with fully convolutional
+     * networks with different output sizes (for example, segmentation on different input image sizes).
+     *
      *
      * @param iterator Data to pass through the network
      * @return output for all examples in the iterator, concatenated into a
      */
     public INDArray output(DataSetIterator iterator, boolean train) {
         List<INDArray> outList = new ArrayList<>();
+        long[] firstOutputShape = null;
         while (iterator.hasNext()) {
             DataSet next = iterator.next();
             INDArray features = next.getFeatures();
@@ -2371,7 +2377,23 @@ public INDArray output(DataSetIterator iterator, boolean train) {
 
             INDArray fMask = next.getFeaturesMaskArray();
             INDArray lMask = next.getLabelsMaskArray();
-            outList.add(this.output(features, train, fMask, lMask));
+            INDArray output = this.output(features, train, fMask, lMask);
+            outList.add(output);
+            if(firstOutputShape == null){
+                firstOutputShape = output.shape();
+            } else {
+                //Validate that shapes are the same (may not be, for some RNN variable length time series applications)
+                long[] currShape = output.shape();
+                Preconditions.checkState(firstOutputShape.length == currShape.length, "Error during forward pass:" +
+                        "different minibatches have different output array ranks - first minibatch shape %s, last minibatch shape %s", firstOutputShape, currShape);
+                for( int i=1; i<currShape.length; i++ ){    //Skip checking minibatch dimension, fine if this varies
+                    Preconditions.checkState(firstOutputShape[i] == currShape[i], "Current output shape does not match first" +
+                            " output array shape at position %s: all dimensions must match other than the first dimension.\n" +
+                            " For variable length output size/length use cases such as for RNNs with multiple sequence lengths," +
+                            " use one of the other (non iterator) output methods. First batch output shape: %s, current batch output shape: %s",
+                            i, firstOutputShape, currShape);
+                }
+            }
         }
         return Nd4j.concat(0, outList.toArray(new INDArray[outList.size()]));
     }