Vet ikke hva jeg gjør

This commit is contained in:
Trygve 2023-10-26 09:04:31 +02:00
parent d5c072ba3f
commit ea9bf70eaa
6 changed files with 557 additions and 74 deletions

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

512
W_1.txt Normal file

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1
b_1.txt Normal file

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@ -50,4 +50,3 @@ def print_imp_dir(path="./"):
print(f'{Path.cwd()}/{f}: {get_imp_file(f)}')
print_imp_dir()
# %%

104
uke6.py
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@ -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]
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):
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:
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()
self.b_list = [x.split(' ') for x in lines]
self.b_list = [[float(n) for n in x] for x in self.W_list]
b_list = [x.split(' ') for x in lines]
b_list = [[float(n) for n in x] for x in 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))
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()