From 3c9d80aaff32f7484b90c0ce0cdef96cfba924d9 Mon Sep 17 00:00:00 2001
From: Pablo Olivares <pablolivares1502@gmail.com>
Date: Mon, 22 Apr 2024 23:46:52 +0200
Subject: [PATCH] Added documentation to `Callback` advances #17

---
 callbacks/callback.py | 60 ++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 59 insertions(+), 1 deletion(-)

diff --git a/callbacks/callback.py b/callbacks/callback.py
index 7f47a3e..f92b9e1 100644
--- a/callbacks/callback.py
+++ b/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