Merge pull request #1212 from Wanglongzhi2001/fix_validation_data_pack

fix: fix the validation_pack when multiple input

src/TensorFlowNET.Core/Util/Data.cs

@@ -1,4 +1,5 @@
-using Tensorflow.NumPy;
+using OneOf;
+using Tensorflow.NumPy;
 
 namespace Tensorflow.Util
 {
@@ -8,10 +9,10 @@ namespace Tensorflow.Util
     /// </summary>
     public class ValidationDataPack
     {
-        public NDArray val_x;
-        public NDArray val_y;
-        public NDArray val_sample_weight = null;
-
+        internal OneOf<NDArray, NDArray[]> val_x;
+        internal NDArray val_y;
+        internal NDArray val_sample_weight = null;
+        public bool val_x_is_array = false;
         public ValidationDataPack((NDArray, NDArray) validation_data)
         {
             this.val_x = validation_data.Item1;
@@ -27,15 +28,17 @@ namespace Tensorflow.Util
 
         public ValidationDataPack((IEnumerable<NDArray>, NDArray) validation_data)
         {
-            this.val_x = validation_data.Item1.ToArray()[0];
+            this.val_x = validation_data.Item1.ToArray();
             this.val_y = validation_data.Item2;
+            val_x_is_array = true;
         }
 
         public ValidationDataPack((IEnumerable<NDArray>, NDArray, NDArray) validation_data)
         {
-            this.val_x = validation_data.Item1.ToArray()[0];
+            this.val_x = validation_data.Item1.ToArray();
             this.val_y = validation_data.Item2;
             this.val_sample_weight = validation_data.Item3;
+            val_x_is_array = true;
         }
 
         public static implicit operator ValidationDataPack((NDArray, NDArray) validation_data)
@@ -52,15 +55,24 @@ namespace Tensorflow.Util
 
         public void Deconstruct(out NDArray val_x, out NDArray val_y)
         {
-            val_x = this.val_x;
+            val_x = this.val_x.AsT0;
             val_y = this.val_y;
         }
 
         public void Deconstruct(out NDArray val_x, out NDArray val_y, out NDArray val_sample_weight)
         {
-            val_x = this.val_x;
+            val_x = this.val_x.AsT0;
+            val_y = this.val_y;
+            val_sample_weight = this.val_sample_weight;
+        }
+
+        // add a unuse parameter to make it different from Deconstruct(out NDArray val_x, out NDArray val_y, out NDArray val_sample_weight)
+        public void Deconstruct(out NDArray[] val_x_array, out NDArray val_y, out NDArray val_sample_weight, out NDArray unuse)
+        {
+            val_x_array = this.val_x.AsT1;
             val_y = this.val_y;
             val_sample_weight = this.val_sample_weight;
+            unuse = null;
         }
     }
 }

src/TensorFlowNET.Keras/Engine/DataAdapters/DataAdapter.cs

@@ -92,9 +92,17 @@ namespace Tensorflow.Keras.Engine.DataAdapters
             var train_y = y[new Slice(0, train_count)];
             var val_x = x.Select(x => x[new Slice(train_count)] as NDArray);
             var val_y = y[new Slice(train_count)];
-            NDArray tmp_sample_weight = sample_weight;
-            sample_weight = sample_weight[new Slice(0, train_count)];
-            ValidationDataPack validation_data = (val_x, val_y, tmp_sample_weight[new Slice(train_count)]);
+
+            ValidationDataPack validation_data;
+            if (sample_weight != null)
+            {
+                validation_data = (val_x, val_y, sample_weight[new Slice(train_count)]);
+                sample_weight = sample_weight[new Slice(0, train_count)];
+            }
+            else
+            {
+                validation_data = (val_x, val_y);
+            }
             return ((train_x, train_y, sample_weight), validation_data);
         }
     }

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

@@ -70,13 +70,19 @@ namespace Tensorflow.Keras.Engine
             return evaluate(data_handler, callbacks, is_val, test_function);
         }
 
-        public Dictionary<string, float> evaluate(IEnumerable<Tensor> x, Tensor y, int verbose = 1, bool is_val = false)
+        public Dictionary<string, float> evaluate(
+            IEnumerable<Tensor> x, 
+            Tensor y, 
+            int verbose = 1,
+            NDArray sample_weight = null,
+            bool is_val = false)
         {
             var data_handler = new DataHandler(new DataHandlerArgs
             {
                 X = new Tensors(x.ToArray()),
                 Y = y,
                 Model = this,
+                SampleWeight = sample_weight,
                 StepsPerExecution = _steps_per_execution
             });
 

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

@@ -7,6 +7,7 @@ using Tensorflow.Keras.Engine.DataAdapters;
 using System.Diagnostics;
 using Tensorflow.Keras.Callbacks;
 using Tensorflow.Util;
+using OneOf;
 
 namespace Tensorflow.Keras.Engine
 {
@@ -287,10 +288,24 @@ namespace Tensorflow.Keras.Engine
 
                 if (validation_data != null)
                 {
-                    // Because evaluate calls call_test_batch_end, this interferes with our output on the screen
-                    // so we need to pass a is_val parameter to stop on_test_batch_end
-                    var (val_x, val_y, val_sample_weight) = validation_data;
-                    var val_logs = evaluate(val_x, val_y, sample_weight:val_sample_weight, is_val:true);
+                    NDArray val_x;
+                    NDArray[] val_x_array;
+                    NDArray val_y;
+                    NDArray val_sample_weight;
+                    Dictionary<string, float> val_logs;
+                    if (!validation_data.val_x_is_array)
+                    {
+                        (val_x, val_y, val_sample_weight) = validation_data;
+                        // Because evaluate calls call_test_batch_end, this interferes with our output on the screen
+                        // so we need to pass a is_val parameter to stop on_test_batch_end
+                        val_logs = evaluate(val_x, val_y, sample_weight: val_sample_weight, is_val: true);
+
+                    }
+                    else
+                    {
+                        (val_x_array, val_y, val_sample_weight, _) = validation_data;
+                         val_logs = evaluate(val_x_array, val_y, sample_weight: val_sample_weight, is_val: true);
+                    }
                     foreach (var log in val_logs)
                     {
                         logs["val_" + log.Key] = log.Value;