Merge pull request #1014 from Wanglongzhi2001/master

Fix validation_split has no output and add validation_data parameter to model.fit

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

@@ -15,5 +15,5 @@ public interface ICallback
     void on_predict_end();
     void on_test_begin();
     void on_test_batch_begin(long step);
-    void on_test_batch_end(long end_step, IEnumerable<(string, Tensor)> logs);
+    void on_test_batch_end(long end_step, Dictionary<string, float> logs);
 }

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

@@ -22,6 +22,7 @@ public interface IModel : ILayer
             int verbose = 1,
             List<ICallback> callbacks = null,
             float validation_split = 0f,
+            (NDArray val_x, NDArray val_y)? validation_data = null,
             bool shuffle = true,
             int initial_epoch = 0,
             int max_queue_size = 10,
@@ -34,6 +35,7 @@ public interface IModel : ILayer
             int verbose = 1,
             List<ICallback> callbacks = null,
             float validation_split = 0f,
+            (IEnumerable<NDArray> val_x, NDArray val_y)? validation_data = null,
             bool shuffle = true,
             int initial_epoch = 0,
             int max_queue_size = 10,
@@ -65,7 +67,8 @@ public interface IModel : ILayer
             int max_queue_size = 10,
             int workers = 1,
             bool use_multiprocessing = false,
-            bool return_dict = false);
+            bool return_dict = false,
+            bool is_val = false);
 
     Tensors predict(Tensors x,
             int batch_size = -1,

src/TensorFlowNET.Keras/Callbacks/CallbackList.cs

@@ -69,7 +69,7 @@ public class CallbackList
     {
         callbacks.ForEach(x => x.on_test_batch_begin(step));
     }
-    public void on_test_batch_end(long end_step, IEnumerable<(string, Tensor)> logs)
+    public void on_test_batch_end(long end_step, Dictionary<string, float> logs)
     {
         callbacks.ForEach(x => x.on_test_batch_end(end_step, logs));
     }

src/TensorFlowNET.Keras/Callbacks/Earlystopping.cs

@@ -121,7 +121,7 @@ public class EarlyStopping: ICallback
     public void on_predict_end() { }
     public void on_test_begin() { }
     public void on_test_batch_begin(long step) { }
-    public void on_test_batch_end(long end_step, IEnumerable<(string, Tensor)> logs) { }
+    public void on_test_batch_end(long end_step, Dictionary<string, float> logs) { }
 
     float get_monitor_value(Dictionary<string, float> logs)
     {

src/TensorFlowNET.Keras/Callbacks/History.cs

@@ -48,7 +48,7 @@ public class History : ICallback
             {
                 history[log.Key] = new List<float>();
             }
-            history[log.Key].Add((float)log.Value);
+            history[log.Key].Add(log.Value);
         }
     }
 
@@ -78,7 +78,7 @@ public class History : ICallback
 
     }
 
-    public void on_test_batch_end(long end_step, IEnumerable<(string, Tensor)> logs)
+    public void on_test_batch_end(long end_step, Dictionary<string, float> logs)
     {
     }
 }

src/TensorFlowNET.Keras/Callbacks/ProgbarLogger.cs

@@ -105,11 +105,11 @@ namespace Tensorflow.Keras.Callbacks
         {
             _sw.Restart();
         }
-        public void on_test_batch_end(long end_step, IEnumerable<(string, Tensor)> logs)
+        public void on_test_batch_end(long end_step, Dictionary<string, float> logs)
         {
             _sw.Stop();
             var elapse = _sw.ElapsedMilliseconds;
-            var results = string.Join(" - ", logs.Select(x => $"{x.Item1}: {(float)x.Item2.numpy():F6}"));
+            var results = string.Join(" - ", logs.Select(x => $"{x.Key}: {x.Value:F6}"));
 
             Binding.tf_output_redirect.Write($"{end_step + 1:D4}/{_parameters.Steps:D4} - {elapse}ms/step - {results}");
             if (!Console.IsOutputRedirected)

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

@@ -26,6 +26,7 @@ namespace Tensorflow.Keras.Engine
         /// <param name="workers"></param>
         /// <param name="use_multiprocessing"></param>
         /// <param name="return_dict"></param>
+        /// <param name="is_val"></param>
         public Dictionary<string, float> evaluate(NDArray x, NDArray y,
             int batch_size = -1,
             int verbose = 1,
@@ -33,7 +34,9 @@ namespace Tensorflow.Keras.Engine
             int max_queue_size = 10,
             int workers = 1,
             bool use_multiprocessing = false,
-            bool return_dict = false)
+            bool return_dict = false,
+            bool is_val = false
+            )
         {
             if (x.dims[0] != y.dims[0])
             {
@@ -63,11 +66,11 @@ namespace Tensorflow.Keras.Engine
             });
             callbacks.on_test_begin();
 
-            IEnumerable<(string, Tensor)> logs = null;
+            //Dictionary<string, float>? logs = null;
+            var logs = new Dictionary<string, float>();
             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())
@@ -75,19 +78,64 @@ namespace Tensorflow.Keras.Engine
                     callbacks.on_test_batch_begin(step);
                     logs = test_function(data_handler, iterator);
                     var end_step = step + data_handler.StepIncrement;
-                    callbacks.on_test_batch_end(end_step, logs);
+                    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.Item1] = (float)log.Item2;
+                results[log.Key] = log.Value;
             }
             return results;
         }
 
-        public Dictionary<string, float> evaluate(IDatasetV2 x, int verbose = 1)
+        public Dictionary<string, float> evaluate(IEnumerable<Tensor> x, NDArray y, int verbose = 1, bool is_val = false)
+        {
+            var data_handler = new DataHandler(new DataHandlerArgs
+            {
+                X = new Tensors(x),
+                Y = y,
+                Model = this,
+                StepsPerExecution = _steps_per_execution
+            });
+
+            var callbacks = new CallbackList(new CallbackParams
+            {
+                Model = this,
+                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_step_multi_inputs_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;
+        }
+
+
+        public Dictionary<string, float> evaluate(IDatasetV2 x, int verbose = 1, bool is_val = false)
         {
             var data_handler = new DataHandler(new DataHandlerArgs
             {
@@ -104,7 +152,7 @@ namespace Tensorflow.Keras.Engine
             });
             callbacks.on_test_begin();
 
-            IEnumerable<(string, Tensor)> logs = null;
+            Dictionary<string, float> logs = null;
             foreach (var (epoch, iterator) in data_handler.enumerate_epochs())
             {
                 reset_metrics();
@@ -113,28 +161,38 @@ namespace Tensorflow.Keras.Engine
 
                 foreach (var step in data_handler.steps())
                 {
-                    // callbacks.on_train_batch_begin(step)
+                    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.Item1] = (float)log.Item2;
+                results[log.Key] = log.Value;
             }
             return results;
         }
 
-        IEnumerable<(string, Tensor)> test_function(DataHandler data_handler, OwnedIterator iterator)
+        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;
         }
-
-        List<(string, Tensor)> test_step(DataHandler data_handler, Tensor x, Tensor y)
+        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));
+            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);
@@ -142,7 +200,7 @@ namespace Tensorflow.Keras.Engine
 
             compiled_metrics.update_state(y, y_pred);
 
-            return metrics.Select(x => (x.Name, x.result())).ToList();
+            return metrics.Select(x => (x.Name, x.result())).ToDictionary(x=>x.Item1, x=>(float)x.Item2);
         }
     }
 }

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

@@ -22,6 +22,7 @@ namespace Tensorflow.Keras.Engine
         /// <param name="callbacks"></param>
         /// <param name="verbose"></param>
         /// <param name="validation_split"></param>
+        /// <param name="validation_data"></param>
         /// <param name="shuffle"></param>
         public ICallback fit(NDArray x, NDArray y,
             int batch_size = -1,
@@ -29,6 +30,7 @@ namespace Tensorflow.Keras.Engine
             int verbose = 1,
             List<ICallback> callbacks = null,
             float validation_split = 0f,
+            (NDArray val_x, NDArray val_y)? validation_data = null,
             bool shuffle = true,
             int initial_epoch = 0,
             int max_queue_size = 10,
@@ -40,11 +42,17 @@ namespace Tensorflow.Keras.Engine
                 throw new InvalidArgumentError(
                     $"The array x and y should have same value at dim 0, but got {x.dims[0]} and {y.dims[0]}");
             }
-            int train_count = Convert.ToInt32(x.dims[0] * (1 - validation_split));
-            var train_x = x[new Slice(0, train_count)];
-            var train_y = y[new Slice(0, train_count)];
-            var val_x = x[new Slice(train_count)];
-            var val_y = y[new Slice(train_count)];
+
+            var train_x = x;
+            var train_y = y;
+
+            if (validation_split != 0f && validation_data == null)
+            {
+                int train_count = Convert.ToInt32(x.dims[0] * (1 - validation_split));
+                train_x = x[new Slice(0, train_count)];
+                train_y = y[new Slice(0, train_count)];
+                validation_data = (val_x: x[new Slice(train_count)], val_y: y[new Slice(train_count)]);
+            }
 
             var data_handler = new DataHandler(new DataHandlerArgs
             {
@@ -61,7 +69,7 @@ namespace Tensorflow.Keras.Engine
                 StepsPerExecution = _steps_per_execution
             });
 
-            return FitInternal(data_handler, epochs, verbose, callbackList: callbacks, validation_data: null,
+            return FitInternal(data_handler, epochs, verbose, callbackList: callbacks, validation_data: validation_data,
                     train_step_func: train_step_function);
         }
 
@@ -71,6 +79,7 @@ namespace Tensorflow.Keras.Engine
             int verbose = 1,
             List<ICallback> callbacks = null,
             float validation_split = 0f,
+            (IEnumerable<NDArray> val_x, NDArray val_y)? validation_data = null,
             bool shuffle = true,
             int initial_epoch = 0,
             int max_queue_size = 10,
@@ -85,12 +94,19 @@ namespace Tensorflow.Keras.Engine
                         $"The array x and y should have same value at dim 0, but got {tx.dims[0]} and {y.dims[0]}");
                 }
             }
-            int train_count = Convert.ToInt32(y.dims[0] * (1 - validation_split));
-            
-            var train_x = x.Select(x => x[new Slice(0, train_count)] as Tensor);
-            var train_y = y[new Slice(0, train_count)];
-            var val_x = x.Select(x => x[new Slice(train_count)] as Tensor);
-            var val_y = y[new Slice(train_count)];
+
+            var train_x = x;
+            var train_y = y;
+            if (validation_split != 0f && validation_data == null)
+            {
+                int train_count = Convert.ToInt32(y.dims[0] * (1 - validation_split));
+                train_x = x.Select(x => x[new Slice(0, train_count)] as NDArray);
+                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)];
+                validation_data = (val_x, val_y);
+            }
+
 
             var data_handler = new DataHandler(new DataHandlerArgs
             {
@@ -110,29 +126,29 @@ namespace Tensorflow.Keras.Engine
             if (data_handler.DataAdapter.GetDataset().structure.Length > 2 ||
                 data_handler.DataAdapter.GetDataset().FirstInputTensorCount > 1)
             {
-                return FitInternal(data_handler, epochs, verbose, callbackList: callbacks, validation_data: null,
+                return FitInternal(data_handler, epochs, verbose, callbackList: callbacks, validation_data: validation_data,
                     train_step_func: train_step_multi_inputs_function);
             }
             else
             {
-                return FitInternal(data_handler, epochs, verbose, callbackList: callbacks, validation_data: null,
+                return FitInternal(data_handler, epochs, verbose, callbackList: callbacks, validation_data: validation_data,
                     train_step_func: train_step_function);
             }
         }
 
         public History fit(IDatasetV2 dataset, 
-            IDatasetV2 validation_data = null,
             int batch_size = -1,
             int epochs = 1,
             int verbose = 1,
             List<ICallback> callbacks = null,
-            float validation_split = 0f,
+            IDatasetV2 validation_data = null,
             bool shuffle = true,
             int initial_epoch = 0,
             int max_queue_size = 10,
             int workers = 1,
             bool use_multiprocessing = false)
         {
+            
             var data_handler = new DataHandler(new DataHandlerArgs
             {
                 Dataset = dataset,
@@ -147,6 +163,7 @@ namespace Tensorflow.Keras.Engine
                 StepsPerExecution = _steps_per_execution
             });
 
+
             return FitInternal(data_handler, epochs, verbose, callbacks, validation_data: validation_data,
                     train_step_func: train_step_function);
         }
@@ -178,11 +195,13 @@ namespace Tensorflow.Keras.Engine
                 callbacks.on_epoch_begin(epoch);
                 // data_handler.catch_stop_iteration();
                 var logs = new Dictionary<string, float>();
+                long End_step = 0;
                 foreach (var step in data_handler.steps())
                 {
                     callbacks.on_train_batch_begin(step);
                     logs = train_step_func(data_handler, iterator);
                     var end_step = step + data_handler.StepIncrement;
+                    End_step = end_step;
                     callbacks.on_train_batch_end(end_step, logs);
                 }
 
@@ -193,6 +212,123 @@ namespace Tensorflow.Keras.Engine
                     {
                         logs["val_" + log.Key] = log.Value;
                     }
+                    callbacks.on_train_batch_end(End_step, logs);
+                }
+
+
+                callbacks.on_epoch_end(epoch, logs);
+
+                GC.Collect();
+                GC.WaitForPendingFinalizers();
+            }
+
+            return callbacks.History;
+        }
+
+        History FitInternal(DataHandler data_handler, int epochs, int verbose, List<ICallback> callbackList, (NDArray, NDArray)? validation_data,
+            Func<DataHandler, OwnedIterator, Dictionary<string, float>> train_step_func)
+        {
+            stop_training = false;
+            _train_counter.assign(0);
+            var callbacks = new CallbackList(new CallbackParams
+            {
+                Model = this,
+                Verbose = verbose,
+                Epochs = epochs,
+                Steps = data_handler.Inferredsteps
+            });
+
+            if (callbackList != null)
+            {
+                foreach (var callback in callbackList)
+                    callbacks.callbacks.add(callback);
+            }
+
+            callbacks.on_train_begin();
+
+            foreach (var (epoch, iterator) in data_handler.enumerate_epochs())
+            {
+                reset_metrics();
+                callbacks.on_epoch_begin(epoch);
+                // data_handler.catch_stop_iteration();
+                var logs = new Dictionary<string, float>();
+                long End_step = 0;
+                foreach (var step in data_handler.steps())
+                {
+                    callbacks.on_train_batch_begin(step);
+                    logs = train_step_func(data_handler, iterator);
+                    var end_step = step + data_handler.StepIncrement;
+                    End_step = end_step;
+                    callbacks.on_train_batch_end(end_step, logs);
+                }
+
+                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_logs = evaluate(validation_data.Value.Item1, validation_data.Value.Item2, is_val:true);
+                    foreach (var log in val_logs)
+                    {
+                        logs["val_" + log.Key] = log.Value;
+                    }
+                    // because after evaluate, logs add some new log which we need to print
+                    callbacks.on_train_batch_end(End_step, logs);
+                }
+
+                callbacks.on_epoch_end(epoch, logs);
+
+                GC.Collect();
+                GC.WaitForPendingFinalizers();
+            }
+
+            return callbacks.History;
+        }
+
+        History FitInternal(DataHandler data_handler, int epochs, int verbose, List<ICallback> callbackList, (IEnumerable<Tensor>, NDArray)? validation_data,
+    Func<DataHandler, OwnedIterator, Dictionary<string, float>> train_step_func)
+        {
+            stop_training = false;
+            _train_counter.assign(0);
+            var callbacks = new CallbackList(new CallbackParams
+            {
+                Model = this,
+                Verbose = verbose,
+                Epochs = epochs,
+                Steps = data_handler.Inferredsteps
+            });
+
+            if (callbackList != null)
+            {
+                foreach (var callback in callbackList)
+                    callbacks.callbacks.add(callback);
+            }
+
+            callbacks.on_train_begin();
+
+            foreach (var (epoch, iterator) in data_handler.enumerate_epochs())
+            {
+                reset_metrics();
+                callbacks.on_epoch_begin(epoch);
+                // data_handler.catch_stop_iteration();
+                var logs = new Dictionary<string, float>();
+                long End_step = 0;
+                foreach (var step in data_handler.steps())
+                {
+                    callbacks.on_train_batch_begin(step);
+                    logs = train_step_func(data_handler, iterator);
+                    var end_step = step + data_handler.StepIncrement;
+                    End_step = end_step;
+                    callbacks.on_train_batch_end(end_step, logs);
+                }
+
+                if (validation_data != null)
+                {
+                    var val_logs = evaluate(validation_data.Value.Item1, validation_data.Value.Item2);
+                    foreach (var log in val_logs)
+                    {
+                        logs["val_" + log.Key] = log.Value;
+                        callbacks.on_train_batch_end(End_step, logs);
+                    }
                 }
 
                 callbacks.on_epoch_end(epoch, logs);