From 50012de6c2fd3eae0aa04b7a2b4e497dc0bae27a Mon Sep 17 00:00:00 2001
From: hanemile <emile.hansmaennel@gmail.com>
Date: Tue, 16 Jan 2018 15:59:01 +0100
Subject: updated the source files

---
 src/python/nn.py | 60 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 60 insertions(+)
 create mode 100755 src/python/nn.py

(limited to 'src/python/nn.py')

diff --git a/src/python/nn.py b/src/python/nn.py
new file mode 100755
index 0000000..b36b103
--- /dev/null
+++ b/src/python/nn.py
@@ -0,0 +1,60 @@
+#!/usr/bin/env python
+
+# Import numpy for using matrix operations
+import numpy as np
+
+# define an actovation function
+def sigmoid(x,deriv=False):
+    if(deriv==True):
+        return x*(1-x)
+    return 1/(1+np.exp(-x))
+
+# 0 0 1 0
+# 0 1 1 0
+# 1 0 1 1
+# 1 1 1 1
+
+# define an input matrix
+X = np.array([ [0,0,1], [0,1,1], [1,0,1], [1,1,1] ])
+
+# define an output matrix
+y = np.array([[0,0,1,1]]).T
+
+# seed numpy
+np.random.seed(1)
+
+# generate some weights
+syn0 = 2*np.random.random((3,4)) - 1
+
+# define how often the calculations should be run
+
+n = 100000
+
+# loop
+for i in range(n):
+    # define the first layer
+    l0 = X
+
+    # define the second layer using the first layer and the weights
+    l1 = sigmoid(np.dot(l0,syn0))
+
+    # calculate an error
+    l1_error = y - l1
+
+    # calculate how fatal the error is
+    l1_delta = l1_error * sigmoid(l1,True)
+
+    # adjust the weights
+    syn0 += np.dot(l0.T,l1_delta)
+
+    # print some information
+    if (i % (n / 10000) == 0):
+        print("l1: " + str(l1))
+
+print("")
+print("Output After Training:")
+print(l1)
+
+l0 = np.array([0, 1, 0])
+l1 = sigmoid(np.dot(l0, syn0))
+print(l1)
-- 
cgit 1.4.1