@@ -589,23 +589,17 @@ public static string to_numpy_string(Tensor tensor)
             {
                 return "<unprintable>";
             }
+            else if (dtype == TF_DataType.TF_RESOURCE)
+            {
+                return "<unprintable>";
+            }
 
             var nd = tensor.numpy();
 
             if (nd.size == 0)
                 return "[]";
 
-            switch (dtype)
-            {
-                case TF_DataType.TF_STRING:
-                    return string.Join(string.Empty, nd.ToArray<byte>()
-                        .Select(x => x < 32 || x > 127 ? "\\x" + x.ToString("x") : Convert.ToChar(x).ToString()));
-                case TF_DataType.TF_VARIANT:
-                case TF_DataType.TF_RESOURCE:
-                    return "<unprintable>";
-                default:
-                    return nd.ToString();
-            }
+            return nd.ToString();
         }
 
         public static ParsedSliceArgs ParseSlices(Slice[] slices)
 
@@ -1,6 +1,7 @@
 using System;
 using System.Collections.Generic;
 using System.Linq;
+using Tensorflow.Keras.Losses;
 using Tensorflow.Keras.Metrics;
 using static Tensorflow.KerasApi;
 
@@ -74,11 +75,15 @@ Metric _get_metric_object(string metric, Tensor y_t, Tensor y_p)
                     metric_obj = keras.metrics.sparse_categorical_accuracy;
                 else
                     metric_obj = keras.metrics.categorical_accuracy;
-
-                return new MeanMetricWrapper(metric_obj, metric);
             }
+            else if(metric == "mean_absolute_error" || metric == "mae")
+            {
+                metric_obj = keras.metrics.mean_absolute_error;
+            }
+            else
+                throw new NotImplementedException("");
 
-            throw new NotImplementedException("");
+            return new MeanMetricWrapper(metric_obj, metric);
         }
 
         public IEnumerable<Metric> metrics
 
@@ -40,5 +40,11 @@ public Tensor sparse_categorical_accuracy(Tensor y_true, Tensor y_pred)
 
             return math_ops.cast(math_ops.equal(y_true, y_pred), TF_DataType.TF_FLOAT);
         }
+
+        public Tensor mean_absolute_error(Tensor y_true, Tensor y_pred)
+        {
+            y_true = math_ops.cast(y_true, y_pred.dtype);
+            return keras.backend.mean(math_ops.abs(y_pred - y_true), axis: -1);
+        }
     }
 }
Original file line number	Diff line number	Diff line change
@@ -40,5 +40,11 @@ public Tensor sparse_categorical_accuracy(Tensor y_true, Tensor y_pred)
40	40
41	41	return math_ops.cast(math_ops.equal(y_true, y_pred), TF_DataType.TF_FLOAT);
42	42	}
	43	+
	44	+ public Tensor mean_absolute_error(Tensor y_true, Tensor y_pred)
	45	+ {
	46	+ y_true = math_ops.cast(y_true, y_pred.dtype);
	47	+ return keras.backend.mean(math_ops.abs(y_pred - y_true), axis: -1);
	48	+ }
43	49	}
44	50	}