Add EarlyStopping callback

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

@@ -4,6 +4,7 @@ public interface ICallback
 {
     Dictionary<string, List<float>> history { get; set; }
     void on_train_begin();
+    void on_train_end();
     void on_epoch_begin(int epoch);
     void on_train_batch_begin(long step);
     void on_train_batch_end(long end_step, Dictionary<string, float> logs);

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

@@ -17,6 +17,7 @@ public interface IModel : ILayer
             int batch_size = -1,
             int epochs = 1,
             int verbose = 1,
+            List<ICallback> callbacks = null,
             float validation_split = 0f,
             bool shuffle = true,
             int initial_epoch = 0,
@@ -28,6 +29,7 @@ public interface IModel : ILayer
             int batch_size = -1,
             int epochs = 1,
             int verbose = 1,
+            List<ICallback> callbacks = null,
             float validation_split = 0f,
             bool shuffle = true,
             int initial_epoch = 0,
@@ -73,4 +75,6 @@ public interface IModel : ILayer
     void summary(int line_length = -1, float[] positions = null);
 
     IKerasConfig get_config();
+
+    void set_stopTraining_true();
 }

src/TensorFlowNET.Keras/Callbacks/CallbackList.cs

@@ -7,7 +7,8 @@ namespace Tensorflow.Keras.Callbacks;
 
 public class CallbackList
 {
-    List<ICallback> callbacks = new List<ICallback>();
+    // 改成public使得新定义的callback可以加入到callbacks里
+    public List<ICallback> callbacks = new List<ICallback>();
     public History History => callbacks[0] as History;
 
     public CallbackList(CallbackParams parameters)
@@ -66,7 +67,7 @@ public class CallbackList
 
     public void on_test_batch_begin(long step)
     {
-        callbacks.ForEach(x => x.on_train_batch_begin(step));
+        callbacks.ForEach(x => x.on_test_batch_begin(step));
     }
     public void on_test_batch_end(long end_step, IEnumerable<(string, Tensor)> logs)
     {

src/TensorFlowNET.Keras/Callbacks/Earlystopping.cs

@@ -0,0 +1,155 @@
+using Tensorflow.Keras.Engine;
+namespace Tensorflow.Keras.Callbacks;
+
+
+/// <summary>
+/// Stop training when a monitored metric has stopped improving. 
+/// </summary>
+/// <param name="parameters"></param>
+/// <param name="monitor"></param>
+
+public class EarlyStopping: ICallback
+{
+    int _paitence;
+    int _min_delta;
+    int _verbose;
+    int _stopped_epoch;
+    int _wait;
+    int _best_epoch;
+    int _start_from_epoch;
+    float _best;
+    float _baseline;
+    string _monitor;
+    string _mode;
+    bool _restore_best_weights;
+    List<IVariableV1>? _best_weights;
+    CallbackParams _parameters;
+    public Dictionary<string, List<float>>? history { get; set; }
+    // user need to pass a CallbackParams to EarlyStopping, CallbackParams at least need the model
+    public EarlyStopping(CallbackParams parameters,string monitor = "val_loss", int min_delta = 0, int patience = 0,
+        int verbose = 1, string mode = "auto", float baseline = 0f, bool restore_best_weights = false,
+        int start_from_epoch = 0)
+    {
+        _parameters = parameters;
+        _stopped_epoch = 0;
+        _wait = 0;
+        _monitor = monitor;
+        _paitence = patience;
+        _verbose = verbose;
+        _baseline = baseline;
+        _start_from_epoch = start_from_epoch;
+        _min_delta = Math.Abs(min_delta);
+        _restore_best_weights = restore_best_weights;
+        _mode = mode;
+        if (mode != "auto" && mode != "min" && mode != "max")
+        {
+            Console.WriteLine("EarlyStopping mode %s is unknown, fallback to auto mode.", mode);
+        }
+    }
+    public void on_train_begin()
+    {
+        _wait = 0;
+        _stopped_epoch = 0;
+        _best_epoch = 0;
+        _best = (float)np.Inf;
+    }
+
+    public void on_epoch_begin(int epoch)
+    {
+
+    }
+
+    public void on_train_batch_begin(long step)
+    {
+
+    }
+
+    public void on_train_batch_end(long end_step, Dictionary<string, float> logs)
+    {
+    }
+
+    public void on_epoch_end(int epoch, Dictionary<string, float> epoch_logs)
+    {
+        var current = get_monitor_value(epoch_logs);
+        // If no monitor value exists or still in initial warm-up stage.
+        if (current == 0f || epoch < _start_from_epoch)
+            return;
+        // Restore the weights after first epoch if no progress is ever made.
+        if (_restore_best_weights && _best_weights == null)
+        {
+            _best_weights = _parameters.Model.TrainableWeights;
+        }
+        _wait += 1;
+
+        if (_is_improvement(current, _best))
+        {
+            _best = current;
+            _best_epoch = epoch;
+            if (_restore_best_weights)
+                _best_weights = _parameters.Model.TrainableWeights;
+            // Only restart wait if we beat both the baseline and our previous best.
+            if (_baseline == 0f || _is_improvement(current, _baseline))
+                _wait = 0;
+        }
+        // Only check after the first epoch.
+        if (_wait >= _paitence && epoch > 0)
+        {
+            _stopped_epoch = epoch;
+            _parameters.Model.set_stopTraining_true();
+            if (_restore_best_weights && _best_weights != null)
+            {
+                if (_verbose > 0)
+                {
+                    Console.WriteLine($"Restoring model weights from the end of the best epoch: {_best_epoch + 1}");
+                }
+            }
+            // Because loading the weight variable into the model has not yet been implemented, so Earlystopping can't load best_weight yet.
+            // TODO(Wanglongzhi2001): implement it.
+            // _parameters.Model.load_weights(best_weights);
+        }
+    }
+    public void on_train_end()
+    {
+        if (_stopped_epoch > 0 && _verbose > 0)
+        {
+            Console.WriteLine($"Epoch {_stopped_epoch + 1}: early stopping");
+        }
+    }
+    public void on_predict_begin() { }
+    public void on_predict_batch_begin(long step) { }
+    public void on_predict_batch_end(long end_step, Dictionary<string, Tensors> logs) { }
+    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) { }
+
+    float get_monitor_value(Dictionary<string, float> logs)
+    {
+        logs = logs ?? new Dictionary<string, float>();
+        float monitor_value = logs[_monitor];
+        if (monitor_value == 0f)
+        {
+            Console.WriteLine($"Early stopping conditioned on metric {_monitor} " +
+                $"which is not available. Available metrics are: {string.Join(", ", logs.Keys)}");
+        }
+        return monitor_value;
+    }
+    public bool _is_improvement(float monitor_value, float reference_value)
+    {
+        bool less_op = (monitor_value - _min_delta) < reference_value;
+        bool greater_op = (monitor_value - _min_delta) >= reference_value;
+        if (_mode == "min")
+            return less_op;
+        else if (_mode == "max")
+            return greater_op;
+        else
+        {
+            if (_monitor.EndsWith("acc") || _monitor.EndsWith("accuracy") || _monitor.EndsWith("auc"))
+            {
+                return greater_op;
+            }
+            else
+                return less_op;
+        }
+    }
+}

src/TensorFlowNET.Keras/Callbacks/History.cs

@@ -23,6 +23,7 @@ public class History : ICallback
         epochs = new List<int>();
         history = new Dictionary<string, List<float>>();
     }
+    public void on_train_end() { }
     public void on_epoch_begin(int epoch)
     {
 

src/TensorFlowNET.Keras/Callbacks/ProgbarLogger.cs

@@ -22,6 +22,7 @@ namespace Tensorflow.Keras.Callbacks
             _called_in_fit = true;
             _sw = new Stopwatch();
         }
+        public void on_train_end() { }
         public void on_test_begin()
         {
             _sw = new Stopwatch();

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

@@ -19,6 +19,7 @@ namespace Tensorflow.Keras.Engine
         /// <param name="y"></param>
         /// <param name="batch_size"></param>
         /// <param name="epochs"></param>
+        /// <param name="callbacks"></param>
         /// <param name="verbose"></param>
         /// <param name="validation_split"></param>
         /// <param name="shuffle"></param>
@@ -26,6 +27,7 @@ namespace Tensorflow.Keras.Engine
             int batch_size = -1,
             int epochs = 1,
             int verbose = 1,
+            List<ICallback> callbacks = null,
             float validation_split = 0f,
             bool shuffle = true,
             int initial_epoch = 0,
@@ -59,7 +61,7 @@ namespace Tensorflow.Keras.Engine
                 StepsPerExecution = _steps_per_execution
             });
 
-            return FitInternal(data_handler, epochs, verbose, validation_data: null,
+            return FitInternal(data_handler, epochs, verbose, callbackList: callbacks, validation_data: null,
                     train_step_func: train_step_function);
         }
 
@@ -67,6 +69,7 @@ namespace Tensorflow.Keras.Engine
             int batch_size = -1,
             int epochs = 1,
             int verbose = 1,
+            List<ICallback> callbacks = null,
             float validation_split = 0f,
             bool shuffle = true,
             int initial_epoch = 0,
@@ -107,12 +110,12 @@ namespace Tensorflow.Keras.Engine
             if (data_handler.DataAdapter.GetDataset().structure.Length > 2 ||
                 data_handler.DataAdapter.GetDataset().FirstInputTensorCount > 1)
             {
-                return FitInternal(data_handler, epochs, verbose, validation_data: null,
+                return FitInternal(data_handler, epochs, verbose, callbackList: callbacks, validation_data: null,
                     train_step_func: train_step_multi_inputs_function);
             }
             else
             {
-                return FitInternal(data_handler, epochs, verbose, validation_data: null,
+                return FitInternal(data_handler, epochs, verbose, callbackList: callbacks, validation_data: null,
                     train_step_func: train_step_function);
             }
         }
@@ -122,6 +125,7 @@ namespace Tensorflow.Keras.Engine
             int batch_size = -1,
             int epochs = 1,
             int verbose = 1,
+            List<ICallback> callbacks = null,
             float validation_split = 0f,
             bool shuffle = true,
             int initial_epoch = 0,
@@ -143,11 +147,11 @@ namespace Tensorflow.Keras.Engine
                 StepsPerExecution = _steps_per_execution
             });
 
-            return FitInternal(data_handler, epochs, verbose, validation_data: validation_data,
+            return FitInternal(data_handler, epochs, verbose, callbacks, validation_data: validation_data,
                     train_step_func: train_step_function);
         }
 
-        History FitInternal(DataHandler data_handler, int epochs, int verbose, IDatasetV2 validation_data, 
+        History FitInternal(DataHandler data_handler, int epochs, int verbose, List<ICallback> callbackList, IDatasetV2 validation_data, 
             Func<DataHandler, OwnedIterator, Dictionary<string, float>> train_step_func)
         {
             stop_training = false;
@@ -159,6 +163,13 @@ namespace Tensorflow.Keras.Engine
                 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())

src/TensorFlowNET.Keras/Engine/Model.cs

@@ -144,5 +144,11 @@ namespace Tensorflow.Keras.Engine
             var children = base._trackable_children(save_type, cache);
             return children;
         }
+
+
+        void IModel.set_stopTraining_true()
+        {
+            stop_training = true;
+        }
     }
 }

test/TensorFlowNET.Keras.UnitTest/Callbacks/EarlystoppingTest.cs

@@ -0,0 +1,65 @@
+using Microsoft.VisualStudio.TestTools.UnitTesting;
+using Tensorflow.Keras.UnitTest.Helpers;
+using static Tensorflow.Binding;
+using Tensorflow;
+using Tensorflow.Keras.Optimizers;
+using Tensorflow.Keras.Callbacks;
+using Tensorflow.Keras.Engine;
+using System.Collections.Generic;
+using static Tensorflow.KerasApi;
+using Tensorflow.Keras;
+
+
+namespace TensorFlowNET.Keras.UnitTest
+{
+    [TestClass]
+    public class EarltstoppingTest
+    {
+        [TestMethod]
+        // Because loading the weight variable into the model has not yet been implemented,
+        // so you'd better not set patience too large, because the weights will equal to the last epoch's weights.
+        public void Earltstopping()
+        {
+            var layers = keras.layers;
+            var model = keras.Sequential(new List<ILayer>
+            {
+                layers.Rescaling(1.0f / 255, input_shape: (32, 32, 3)),
+                layers.Conv2D(32, 3, padding: "same", activation: keras.activations.Relu),
+                layers.MaxPooling2D(),
+                layers.Flatten(),
+                layers.Dense(128, activation: keras.activations.Relu),
+                layers.Dense(10)
+            });
+
+            
+            model.summary();
+
+            model.compile(optimizer: keras.optimizers.RMSprop(1e-3f),
+            loss: keras.losses.SparseCategoricalCrossentropy(from_logits: true),
+            metrics: new[] { "acc" });
+
+            var num_epochs = 3;
+            var batch_size = 8;
+
+            var ((x_train, y_train), (x_test, y_test)) = keras.datasets.cifar10.load_data();
+            x_train = x_train / 255.0f;
+            // define a CallbackParams first, the parameters you pass al least contain Model and Epochs.
+            CallbackParams callback_parameters = new CallbackParams
+            {
+                Model = model,
+                Epochs = num_epochs,
+            };
+            // define your earlystop
+            ICallback earlystop = new EarlyStopping(callback_parameters, "accuracy");
+            // define a callbcaklist, then add the earlystopping to it.
+            var callbacks = new List<ICallback>();
+            callbacks.add(earlystop);
+
+            model.fit(x_train[new Slice(0, 2000)], y_train[new Slice(0, 2000)], batch_size, num_epochs,callbacks:callbacks);
+        }
+
+    }
+
+
+}
+