Refactor: Model.Evaluate.cs

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

@@ -14,6 +14,38 @@ namespace Tensorflow.Keras.Engine
 {
     public partial class Model
     {
+        protected Dictionary<string, float> evaluate(CallbackList callbacks, DataHandler data_handler, bool is_val)
+        {
+            callbacks.on_test_begin();
+
+            //Dictionary<string, float>? logs = null;
+            var logs = new Dictionary<string, float>();
+            int x_size = data_handler.DataAdapter.GetDataset().FirstInputTensorCount;
+            foreach (var (epoch, iterator) in data_handler.enumerate_epochs())
+            {
+                reset_metrics();
+                callbacks.on_epoch_begin(epoch);
+                // data_handler.catch_stop_iteration();
+
+                foreach (var step in data_handler.steps())
+                {
+                    callbacks.on_test_batch_begin(step);
+
+                    var data = iterator.next();
+
+                    logs = train_step(data_handler, new Tensors(data.Take(x_size)), new Tensors(data.Skip(x_size)));
+                    tf_with(ops.control_dependencies(Array.Empty<object>()), ctl => _test_counter.assign_add(1));
+
+                    var end_step = step + data_handler.StepIncrement;
+
+                    if (!is_val)
+                        callbacks.on_test_batch_end(end_step, logs);
+                }
+            }
+
+            return logs;
+        }
+
         /// <summary>
         /// Returns the loss value & metrics values for the model in test mode.
         /// </summary>
@@ -64,31 +96,8 @@ namespace Tensorflow.Keras.Engine
                 Verbose = verbose,
                 Steps = data_handler.Inferredsteps
             });
-            callbacks.on_test_begin();
-
-            //Dictionary<string, float>? logs = null;
-            var logs = new Dictionary<string, float>();
-            foreach (var (epoch, iterator) in data_handler.enumerate_epochs())
-            {
-                reset_metrics();
-                // data_handler.catch_stop_iteration();
 
-                foreach (var step in data_handler.steps())
-                {
-                    callbacks.on_test_batch_begin(step);
-                    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);
-                }
-            }
-
-            var results = new Dictionary<string, float>();
-            foreach (var log in logs)
-            {
-                results[log.Key] = log.Value;
-            }
-            return results;
+            return evaluate(callbacks, data_handler, is_val);
         }
 
         public Dictionary<string, float> evaluate(IEnumerable<Tensor> x, Tensor y, int verbose = 1, bool is_val = false)
@@ -107,31 +116,8 @@ namespace Tensorflow.Keras.Engine
                 Verbose = verbose,
                 Steps = data_handler.Inferredsteps
             });
-            callbacks.on_test_begin();
 
-            Dictionary<string, float> logs = null;
-            foreach (var (epoch, iterator) in data_handler.enumerate_epochs())
-            {
-                reset_metrics();
-                callbacks.on_epoch_begin(epoch);
-                // data_handler.catch_stop_iteration();
-
-                foreach (var step in data_handler.steps())
-                {
-                    callbacks.on_test_batch_begin(step);
-                    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);
-                }
-            }
-
-            var results = new Dictionary<string, float>();
-            foreach (var log in logs)
-            {
-                results[log.Key] = log.Value;
-            }
-            return results;
+            return evaluate(callbacks, data_handler, is_val);
         }
 
 
@@ -150,51 +136,8 @@ namespace Tensorflow.Keras.Engine
                 Verbose = verbose,
                 Steps = data_handler.Inferredsteps
             });
-            callbacks.on_test_begin();
-
-            Dictionary<string, float> logs = null;
-            foreach (var (epoch, iterator) in data_handler.enumerate_epochs())
-            {
-                reset_metrics();
-                callbacks.on_epoch_begin(epoch);
-                // data_handler.catch_stop_iteration();
-
-                foreach (var step in data_handler.steps())
-                {
-                    callbacks.on_test_batch_begin(step);
-                    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);
-                }
-            }
-
-            var results = new Dictionary<string, float>();
-            foreach (var log in logs)
-            {
-                results[log.Key] = log.Value;
-            }
-            return results;
-        }
-
-        Dictionary<string, float> test_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 => _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);
-            var y_pred = Apply(x, training: false);
-            var loss = compiled_loss.Call(y, y_pred);
-
-            compiled_metrics.update_state(y, y_pred);
 
-            return metrics.Select(x => (x.Name, x.result())).ToDictionary(x=>x.Item1, x=>(float)x.Item2);
+            return evaluate(callbacks, data_handler, is_val);
         }
     }
-}
+}