Added documentation to Callback advances #17

callbacks/callback.py

@@ -1,15 +1,73 @@
 class Callback:
-    def should_continue(self) -> bool:
+    """
+    A base class for defining callbacks in a training process.
+
+    Callbacks are functions that can be executed at various stages during training.
+    They can be used to perform additional actions or modify the behavior of the training process.
+
+    Methods:
+        should_continue(logs=None) -> bool:
+            Determines whether the training process should continue or stop.
+
+        on_epoch_begin(epoch, logs=None) -> None:
+            Executed at the beginning of each epoch.
+
+        on_epoch_end(epoch, logs=None) -> None:
+            Executed at the end of each epoch.
+
+        on_train_begin(logs=None) -> None:
+            Executed at the beginning of the training process.
+
+        on_train_end(logs=None) -> None:
+            Executed at the end of the training process.
+    """
+
+    def should_continue(self, logs=None) -> bool:
+        """
+        Determines whether the training process should continue or stop.
+
+        Args:
+            logs (dict): Optional dictionary containing training logs.
+
+        Returns:
+            bool: True if the training process should continue, False otherwise.
+        """
         return True
 
     def on_epoch_begin(self, epoch, logs=None) -> None:
+        """
+        Executed at the beginning of each epoch.
+
+        Args:
+            epoch (int): The current epoch number.
+            logs (dict): Optional dictionary containing training logs.
+        """
         pass
 
     def on_epoch_end(self, epoch, logs=None) -> None:
+        """
+        Executed at the end of each epoch.
+
+        Args:
+            epoch (int): The current epoch number.
+            logs (dict): Optional dictionary containing training logs.
+        """
         pass
 
     def on_train_begin(self, logs=None) -> None:
+        """
+        Executed at the beginning of the training process.
+
+        Args:
+            logs (dict): Optional dictionary containing training logs.
+        """
         pass
 
     def on_train_end(self, logs=None) -> None:
+        """
+        Executed at the end of the training process.
+
+        Args:
+            logs (dict): Optional dictionary containing training logs.
+        """
         pass