include/api/callback/callback.h

@@ -31,6 +31,7 @@ class CallbackImpl;
 
 using GraphPoint = std::pair<int, float>;
 
+/// The TrainCallBackData class defines a set of parameters for training callbacks. 
 struct TrainCallBackData {
   TrainCallBackData(bool train_mode, int epoch, int step, Model *model): train_mode_(train_mode), epoch_(epoch),
                     step_(step), model_(model) {}
@@ -41,6 +42,7 @@ struct TrainCallBackData {
   Model *model_;  /**< pointer to the Model object */
 };
 
+/// The CallbackRetValue class represents whether to continue looping in training. 
 enum CallbackRetValue : uint32_t {
   kContinue = 0,
   kStopTraining = 1,
@@ -48,6 +50,8 @@ enum CallbackRetValue : uint32_t {
   kUnknownRetValue = 0xFFFFFFFF
 };
 
+/// TrainCallBack is the training callback class in MindSpore Lite. 
+/// In MindSpore, the callback function is actually not a function but a class.  
 class TrainCallBack {
  public:
   virtual ~TrainCallBack() = default;
@@ -88,6 +92,8 @@ class TrainCallBack {
   virtual void StepEnd(const TrainCallBackData &cb_data) {}
 
  protected:
+  /// Due to TrainCallBack being used for implementing callbacks during the training process, the Model class and ModelImpl class need to access its private methods.
+  /// This is achieved through the use of friend classes. 
   friend class Model;
   friend class ModelImpl;
   CallbackImpl* callback_impl_ = nullptr;

include/api/metrics/metrics.h

@@ -13,6 +13,7 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
+/// Prevent multiple inclusion of header files 
 #ifndef MINDSPORE_INCLUDE_API_METRICS_METRICS_H
 #define MINDSPORE_INCLUDE_API_METRICS_METRICS_H
 #include <vector>
@@ -24,11 +25,19 @@ class MetricsImpl;
 class ModelImpl;
 class MSTensor;
 
+/// Metrics are indicators used to evaluate the performance of a model. 
 class Metrics {
  public:
-  virtual ~Metrics() = default;
+  /// Virtual destructor
+  virtual ~Metrics() = default;   
   virtual void Clear() {}
+  
+  /// Eval() is a method for evaluating the model. 
+  /// This method returns a floating-point number that represents the evaluation result of the model using the specified metrics. 
+  /// Default return 0.0 .
   virtual float Eval() { return 0.0; }
+  
+  
   virtual void Update(std::vector<MSTensor *> inputs, std::vector<MSTensor *> outputs) {}
  protected:
   friend class Model;