add README and author/licence

README.md

@@ -0,0 +1,17 @@
+# Multi-Layer Perceptron
+
+This is a Python 3 implementation of a multi-layer perceptron using Numpy and optionally Matplotlib for cost function visualization.
+
+It is provided under GPLv3 licence.
+
+It implements forward-propagation and backward-propagation (gradient descent).
+
+It allows to learn examples and trying to minimize cost function.
+
+It provides also Momentum optimization, RMSProp optimization, Adam optimization and Forbenius norm regularization.
+
+Note: I developed this only to implement myself the algorithms for fun and not for any production purpose. I already coded MLPs years ago in C and in C++.
+
+I tested it with images of characters (in PGM format) extracted from a scanned image with an other program I developed many years ago in C++ but very dirty so I don't provide it.
+
+I provide the jupyter notebook I used for some tests.

letters.py

@@ -1,3 +1,6 @@
+__author__ = "Adel Daouzli"
+__licence__ = "GPLv3"
+
 import os
 from pnmimage import PnmImage
 

mlp.py

@@ -1,5 +1,8 @@
 #!/usr/bin/env python3
 
+__author__ = "Adel Daouzli"
+__licence__ = "GPLv3"
+
 import struct
 import numpy as np
 try:

pnmimage.py

@@ -1,3 +1,6 @@
+__author__ = "Adel Daouzli"
+__licence__ = "GPLv3"
+
 import struct
 
 class PnmImage(object):