From 30bd9c2c61c5b7728a8d88c712f5b1b184f20363 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 20 Jun 2023 14:37:16 +0000
Subject: [PATCH] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 .../activation_functions/mish_activation.py      | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/neural_network/activation_functions/mish_activation.py b/neural_network/activation_functions/mish_activation.py
index 59a395163..c292f76f1 100644
--- a/neural_network/activation_functions/mish_activation.py
+++ b/neural_network/activation_functions/mish_activation.py
@@ -17,17 +17,17 @@ from maths.tanh import tangent_hyperbolic as tanh
 
 def mish_activation(vector: np.ndarray) -> np.ndarray:
     """
-        Implements the Mish function
+    Implements the Mish function
 
-        Parameters:
-            vector: np.array
+    Parameters:
+        vector: np.array
 
-        Returns:
-            Mish (np.array): The input numpy array after applying tanh.
+    Returns:
+        Mish (np.array): The input numpy array after applying tanh.
 
-        mathematically, mish = x * tanh(softplus(x)  where
-        softplus = ln(1+e^(x)) and tanh = (e^x - e^(-x))/(e^x + e^(-x))
-        so, mish can be written as x * (2/(1+e^(-2 * softplus))-1
+    mathematically, mish = x * tanh(softplus(x)  where
+    softplus = ln(1+e^(x)) and tanh = (e^x - e^(-x))/(e^x + e^(-x))
+    so, mish can be written as x * (2/(1+e^(-2 * softplus))-1
 
     """
     soft_plus = np.log(np.exp(vector) + 1)