77 lines
2.3 KiB
Python
77 lines
2.3 KiB
Python
import numpy as np
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from copy import deepcopy
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class Network:
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def __init__(self, layers):
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self.layers = layers
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self.n_layers = 4
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self.n_inputs = 784
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self.n_outputs = 10
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self.n = [self.n_inputs, 512, 256, self.n_outputs]
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self.x = np.random.rand(self.n_inputs)
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def run(self):
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result = []
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for n in self.layers:
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l = n(self.x, W_file = 'W_1.txt', b_file = 'b_1.txt')
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result = l.run()
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return result
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class Layer:
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def __init__(self, x, W_file=None, b_file=None):
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# define dimensions
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self.n_layers = 4
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self.n_inputs = 784
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self.n_outputs = 10
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self.n = [self.n_inputs, 512, 256, self.n_outputs]
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self.x = x
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# define weights and biases
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# generate random wheights if no file is provided. else read the file
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files = read(W_file, b_file)
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self.W = np.load(files.get('W'))
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self.b = np.load(files.get('b'))
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print('Y dimensjon 1 lag: ', len(self.W_list[0]), len(self.b_list[0]), len(self.x))
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print('Y dimensjon siste lag: ', len(self.W_list[-1]), len(self.b_list[-1]), len(self.x))
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def run(self):
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return layer(self.W_list, self.b_list, self.x)
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def read(W_file, b_file):
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with open(W_file) as f:
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lines = f.readlines()
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W_list = [x.split(' ') for x in lines]
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W_list = [[float(n) for n in x] for x in W_list]
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with open(b_file) as f:
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lines = f.readlines()
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b_list = [x.split(' ') for x in lines]
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b_list = [[float(n) for n in x] for x in W_list]
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return {'W': W_list, 'b': b_list}
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# define activation function
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def sigma(y):
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if y > 0:
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return y
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else:
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return 0
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sigma_vec = np.vectorize(sigma)
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# define layer function for given weight matrix, input and bias
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def layer(W, x, b):
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return sigma_vec(W @ x + b)
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# define neural network with all weights W and all biases b in W_list and b_list
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def f(W_list, b_list, x):
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y = deepcopy(x) # deepcopy so that input is not changed
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for W, b in zip(W_list, b_list):
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y = layer(W, y, b) # call layer multiple times with all weights and biases
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return y
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def main():
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network = Network([Layer, Layer])
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network.run()
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if __name__ == '__main__':
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main() |