Refactor: Change Model evaluate

IModel.Dictionary<string, float> evaluate(NDArray, NDArray, ...) is now IModel.Dictionary<string, float> evaluate(Tensor, Tensor, ...)
Merge Model.Evaluate.test_step_multi_inputs_function(...) and Model.Evaluate.test_function(...)

Note: An internal function need to add an explicit cast in Tensor

src/TensorFlowNET.Core/Keras/Engine/IModel.cs

@@ -60,7 +60,7 @@ public interface IModel : ILayer
         bool skip_mismatch = false, 
         object options = null);
 
-    Dictionary<string, float> evaluate(NDArray x, NDArray y,
+    Dictionary<string, float> evaluate(Tensor x, Tensor y,
             int batch_size = -1,
             int verbose = 1,
             int steps = -1,

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

@@ -27,7 +27,7 @@ namespace Tensorflow.Keras.Engine
         /// <param name="use_multiprocessing"></param>
         /// <param name="return_dict"></param>
         /// <param name="is_val"></param>
-        public Dictionary<string, float> evaluate(NDArray x, NDArray y,
+        public Dictionary<string, float> evaluate(Tensor x, Tensor y,
             int batch_size = -1,
             int verbose = 1,
             int steps = -1,
@@ -91,7 +91,7 @@ namespace Tensorflow.Keras.Engine
             return results;
         }
 
-        public Dictionary<string, float> evaluate(IEnumerable<Tensor> x, NDArray y, int verbose = 1, bool is_val = false)
+        public Dictionary<string, float> evaluate(IEnumerable<Tensor> x, Tensor y, int verbose = 1, bool is_val = false)
         {
             var data_handler = new DataHandler(new DataHandlerArgs
             {
@@ -119,7 +119,7 @@ namespace Tensorflow.Keras.Engine
                 foreach (var step in data_handler.steps())
                 {
                     callbacks.on_test_batch_begin(step);
-                    logs = test_step_multi_inputs_function(data_handler, iterator);
+                    logs = test_function(data_handler, iterator);
                     var end_step = step + data_handler.StepIncrement;
                     if (is_val == false)
                         callbacks.on_test_batch_end(end_step, logs);
@@ -178,20 +178,14 @@ namespace Tensorflow.Keras.Engine
         }
 
         Dictionary<string, float> test_function(DataHandler data_handler, OwnedIterator iterator)
-        {
-            var data = iterator.next();
-            var outputs = test_step(data_handler, data[0], data[1]);
-            tf_with(ops.control_dependencies(new object[0]), ctl => _test_counter.assign_add(1));
-            return outputs;
-        }
-        Dictionary<string, float> test_step_multi_inputs_function(DataHandler data_handler, OwnedIterator iterator)
         {
             var data = iterator.next();
             var x_size = data_handler.DataAdapter.GetDataset().FirstInputTensorCount;
             var outputs = train_step(data_handler, new Tensors(data.Take(x_size)), new Tensors(data.Skip(x_size)));
-            tf_with(ops.control_dependencies(new object[0]), ctl => _train_counter.assign_add(1));
+            tf_with(ops.control_dependencies(new object[0]), ctl => _test_counter.assign_add(1));
             return outputs;
         }
+
         Dictionary<string, float> test_step(DataHandler data_handler, Tensor x, Tensor y)
         {
             (x, y) = data_handler.DataAdapter.Expand1d(x, y);

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

@@ -266,7 +266,7 @@ namespace Tensorflow.Keras.Engine
                 {
                     // 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_logs = evaluate(validation_data.Value.Item1, validation_data.Value.Item2, is_val:true);
+                    var val_logs = evaluate((Tensor)validation_data.Value.Item1, validation_data.Value.Item2, is_val:true);
                     foreach (var log in val_logs)
                     {
                         logs["val_" + log.Key] = log.Value;