mean_absolute_percentage_error

src/TensorFlowNET.Core/Variables/ResourceVariable.Index.cs

@@ -19,6 +19,7 @@ using System;
 using System.Collections.Generic;
 using System.Text;
 using static Tensorflow.Binding;
+using System.Linq;
 
 namespace Tensorflow
 {
@@ -62,5 +63,8 @@ namespace Tensorflow
                 });
             }
         }
+
+        public Tensor this[params string[] slices]
+            => this[slices.Select(x => new Slice(x)).ToArray()];
     }
 }

src/TensorFlowNET.Keras/Engine/MetricsContainer.cs

@@ -77,9 +77,9 @@ namespace Tensorflow.Keras.Engine
                     metric_obj = keras.metrics.categorical_accuracy;
             }
             else if(metric == "mean_absolute_error" || metric == "mae")
-            {
                 metric_obj = keras.metrics.mean_absolute_error;
-            }
+            else if (metric == "mean_absolute_percentage_error" || metric == "mape")
+                metric_obj = keras.metrics.mean_absolute_percentage_error;
             else
                 throw new NotImplementedException("");
 

src/TensorFlowNET.Keras/Engine/Model.Compile.cs

@@ -42,9 +42,10 @@ namespace Tensorflow.Keras.Engine
                 _ => throw new NotImplementedException("")
             };
 
-            var _loss = loss switch
+            ILossFunc _loss = loss switch
             {
                 "mse" => new MeanSquaredError(),
+                "mae" => new MeanAbsoluteError(),
                 _ => throw new NotImplementedException("")
             };
 

src/TensorFlowNET.Keras/Metrics/MetricsApi.cs

@@ -46,5 +46,12 @@ namespace Tensorflow.Keras.Metrics
             y_true = math_ops.cast(y_true, y_pred.dtype);
             return keras.backend.mean(math_ops.abs(y_pred - y_true), axis: -1);
         }
+
+        public Tensor mean_absolute_percentage_error(Tensor y_true, Tensor y_pred)
+        {
+            y_true = math_ops.cast(y_true, y_pred.dtype);
+            var diff = (y_true - y_pred) / math_ops.maximum(math_ops.abs(y_true), keras.backend.epsilon());
+            return 100f * keras.backend.mean(math_ops.abs(diff), axis: -1);
+        }
     }
 }