Vet ikke hva jeg gjør

.~lock.2021-09-14_party distribution_1_st_2021.csv#

@@ -0,0 +1 @@
+,trygve,trygves-laptop,19.09.2023 21:12,file:///home/trygve/.config/libreoffice/4;

uke1/3:sq_table.py

@@ -15,4 +15,4 @@ def main():
     print(table(n_list, sq_list, cube_list))
 
 if __name__ == "__main__":
-    main()
+    main() 

uke3.py

@@ -49,5 +49,4 @@ def print_imp_dir(path="./"):
     for f in files:
         print(f'{Path.cwd()}/{f}: {get_imp_file(f)}')
 
-print_imp_dir()
-# %%
+print_imp_dir()

uke6.py

@@ -2,46 +2,53 @@ import numpy as np
 from copy import deepcopy
 
 class Network:
-    def __init__(self):
-        self.layers = []
-
-class Layer:
-    def __init__(self, w_file=None, b_file=None):
-        # define dimensions
+    def __init__(self, layers):
+        self.layers = layers
         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 = 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]
+        self.x = x
+
         # define weights and biases
         # generate random wheights if no file is provided. else read the file
-        if w_file == None:
-            self.W_list = []
-            for (self.n_cur, self.n_next) in zip(self.n[:-1], self.n[1:]):
-                self.W_list.append(np.random.rand(self.n_next, self.n_cur))
-        else:
-            with open(w_file) as f:
-                lines = f.readlines()
-                self.W_list = [x.split('	') for x in lines]
-                self.W_list = [[float(n) for n in x] for x in self.W_list]
-                
-                f.close()
-        if b_file == None:
-            self.b_list = []
-            for (self.n_cur, self.n_next) in zip(self.n[:-1], self.n[1:]):
-                self.b_list.append(np.random.rand(self.n_next))
-        else: 
-            with open(b_file) as f:
-                lines = f.readlines()
-                self.b_list = [x.split('	') for x in lines]
-                self.b_list = [[float(n) for n in x] for x in self.W_list]
-                
-                f.close()
-        print(len(self.W_list[0]), len(self.b_list[0]), len(self.x))
-        print(len(self.W_list[-1]), len(self.b_list[-1]), len(self.x))
+        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))
     def run(self):
-        print(f(self.W_list, self.b_list, self.x))
+        
+        return layer(self.W_list, self.b_list, self.x)
+
+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}
 
 # define activation function
 def sigma(y):
@@ -63,45 +70,8 @@ def f(W_list, b_list, x):
     return y
 
 def main():
-    l = Layer()
-    l.run()
-    l2 = Layer(w_file = 'W_1.txt', b_file = 'b_1.txt')
-    l2.run()
-
-def gamle_greier():
-    # define dimensions
-    n_layers = 4
-    n_inputs = 64
-    n_outputs = 10
-    n = [n_inputs, 128, 128, n_outputs]
-
-    # define weights and biases
-    W_list = []
-    b_list = []
-    for (n_cur, n_next) in zip(n[:-1], n[1:]):
-        W_list.append(np.random.rand(n_next, n_cur))
-        b_list.append(np.random.rand(n_next))
-
-    # generate random input (this would usually be pixels of an image)
-    x = np.random.rand(n_inputs)
-
-    # call the network
-    print(f(W_list, b_list, x))
-
-    for W in W_list:
-        print(W.shape)
+    network = Network([Layer, Layer])
+    network.run()
 
 if __name__ == '__main__':
-    main()
-    gamle_greier()
-
-
-
-
-
-
-
-
-
-
-
+    main()