From 17be224d25e0cf84d9021f794378d7642aa19739 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lo=C3=AFc=20GUEZO?= <loicguezo@gmail.com>
Date: Sun, 18 Jan 2026 18:23:14 +0100
Subject: [PATCH] refactor(network.py): annotacte activation functions

---
 network.py     | 9 ++++-----
 nnetwork.ipynb | 6 +++---
 2 files changed, 7 insertions(+), 8 deletions(-)

diff --git a/network.py b/network.py
index 42159a9..70a7d9a 100644
--- a/network.py
+++ b/network.py
@@ -1,13 +1,12 @@
 import math
 import random
 
-# transform all numbers between 0 and 1
-def sigmoid(x):
+def sigmoid(x: float) -> float:
     return 1 / (1 + math.exp(-x))
 
-# sigmoid's derivation
-def sigmoid_deriv(x): 
-    y = sigmoid(x)
+
+def sigmoid_deriv(x: float) -> float:
+    y: float = sigmoid(x)
     return y * (1 - y)
 
 # neuron class
diff --git a/nnetwork.ipynb b/nnetwork.ipynb
index 59bb766..0912c57 100644
--- a/nnetwork.ipynb
+++ b/nnetwork.ipynb
@@ -224,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "f6de25ea",
    "metadata": {},
    "outputs": [],
@@ -284,7 +284,7 @@
     "        # update bias: bias -= learning_rate * dC/dy * dy/dz * dz/db\n",
     "        self.bias -= learning_rate * dcost_dy * dy_dz * dz_db\n",
     "\n",
-    "        # return gradient vector len(input) dimension\n",
+    "        # return gradient vector len(weight) dimension\n",
     "        return [dcost_dy * dy_dz * w for w in self.weight]"
    ]
   },
@@ -353,7 +353,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.4"
+   "version": "3.13.5"
   }
  },
  "nbformat": 4,