INF201/uke6.py

import numpy as np
from copy import deepcopy

class Network:
    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 = 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, b_file):
        self.x = x
        files = read(W_file, b_file)
        self.W = files.get('W')
        self.b = files.get('b')

    def run(self):
        return layer(self.W,  self.x, self.b)

def read(W_file, b_file):
    return {'W': np.loadtxt(W_file), 'b': np.loadtxt(b_file)}

# 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)

def main():
    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()