diff --git a/uke6.py b/uke6.py
index c418368..8454c7e 100644
--- a/uke6.py
+++ b/uke6.py
@@ -2,53 +2,35 @@ import numpy as np
 from copy import deepcopy
 
 class Network:
-    def __init__(self, layers):
+    def __init__(self, layers, W_file_list, b_file_list):
         self.layers = layers
+        self.W_file_list = W_file_list
+        self.b_file_list = b_file_list
         self.n_layers = 4
         self.n_inputs = 784
         self.n_outputs = 10
         self.n = [self.n_inputs, 512, 256, self.n_outputs]
         self.x = np.random.rand(self.n_inputs)
 
-
     def run(self):
-        
-        result = []
-        for n in self.layers:
-            l = n(self.x, W_file = 'W_1.txt', b_file = 'b_1.txt')
+        result = self.x
+        for n, W_file, b_file in zip(self.layers, self.W_file_list, self.b_file_list):
+            y = deepcopy(result)
+            l = n(y, W_file = W_file, b_file = b_file)
             result = l.run()
         return result
 class Layer:
-    def __init__(self, x, W_file=None, b_file=None):
-        # define dimensions
-        self.n_layers = 4
-        self.n_inputs = 784
-        self.n_outputs = 10
-        self.n = [self.n_inputs, 512, 256, self.n_outputs]
+    def __init__(self, x, W_file, b_file):
         self.x = x
-
-        # define weights and biases
-        # generate random wheights if no file is provided. else read the file
         files = read(W_file, b_file)
-        self.W = np.load(files.get('W'))
-        self.b = np.load(files.get('b'))
-        print('Y dimensjon 1 lag:     ', len(self.W_list[0]), len(self.b_list[0]), len(self.x))
-        print('Y dimensjon siste lag: ', len(self.W_list[-1]), len(self.b_list[-1]), len(self.x))
+        self.W = files.get('W')
+        self.b = files.get('b')
+
     def run(self):
-        
-        return layer(self.W_list, self.b_list, self.x)
+        return layer(self.W,  self.x, self.b)
 
 def read(W_file, b_file):
-    with open(W_file) as f:
-        lines = f.readlines()
-        W_list = [x.split('	') for x in lines]
-        W_list = [[float(n) for n in x] for x in W_list]
-    with open(b_file) as f:
-        lines = f.readlines()
-        b_list = [x.split('	') for x in lines]
-        b_list = [[float(n) for n in x] for x in W_list]   
-
-    return {'W': W_list, 'b': b_list}
+    return {'W': np.loadtxt(W_file), 'b': np.loadtxt(b_file)}
 
 # define activation function
 def sigma(y):
@@ -62,16 +44,9 @@ sigma_vec = np.vectorize(sigma)
 def layer(W, x, b):
     return sigma_vec(W @ x + b)
 
-# define neural network with all weights W and all biases b in W_list and b_list
-def f(W_list, b_list, x):
-    y = deepcopy(x) # deepcopy so that input is not changed
-    for W, b in zip(W_list, b_list):
-        y = layer(W, y, b) # call layer multiple times with all weights and biases
-    return y
-
 def main():
-    network = Network([Layer, Layer])
-    network.run()
+    network = Network([Layer, Layer, Layer], ['W_1.txt', 'W_2.txt', 'W_3.txt'], ['b_1.txt', 'b_2.txt', 'b_3.txt'])
+    print(network.run())
 
 if __name__ == '__main__':
     main()
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