uke3.py

@@ -3,7 +3,7 @@
 """
 Created on Thu Sep 28 08:23:56 2023
 
-@author: Inna Gumauri, Trygve Børte Nomeland
+@author: innagumauri
 """
 
 #%% Task 1
@@ -47,7 +47,7 @@ def print_imp_dir(path="./"):
     p = Path(path)
     files = list(p.glob('*.py'))
     for f in files:
-        print(f'{Path.cwd()}/{f}: {get_imp_file(f)}')
+        print(f'{Path.cwd()}+{f}: {get_imp_file(f)}')
         
 print_imp_dir()
 # %%

uke5.py

@@ -1,25 +0,0 @@
-import numpy as np
-
-def relu(y):
-    return np.maximum(0, y)
-
-def layer(W, x, b):
-    return relu(W @ x + b)
-
-n = [64,128,128,128,10]
-
-print(f"Dimensions: {n}")
-
-# First layer
-x = np.random.rand(n[0])
-b = np.random.rand(n[1])
-y = np.random.rand(128,64)
-
-y = layer(y, x, b)
-
-for i in range(2, len(n)):
-    W = np.random.rand(n[i], n[i - 1])
-    b = np.random.rand(n[i])
-    y = layer(W, y, b)
-
-print(y)

uke6.py

@@ -1,107 +0,0 @@
-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
-        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)
-
-        # 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))
-    def run(self):
-        print(f(self.W_list, self.b_list, self.x))
-
-# define activation function
-def sigma(y):
-    if y > 0:
-        return y
-    else:
-        return 0
-sigma_vec = np.vectorize(sigma)
-
-# define layer function for given weight matrix, input and bias
-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():
-    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)
-
-if __name__ == '__main__':
-    main()
-    gamle_greier()
-
-
-
-
-
-
-
-
-
-
-