Ferdig med oppgave 6: MNIST modell

MNIST/b_3.txt

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+-0.3120380938053131	0.057060789316892624	0.5383008718490601	-0.4334142804145813	0.20545503497123718	0.8229865431785583	0.26446419954299927	1.3929160833358765	0.40466466546058655	-0.06923668831586838

MNIST/main.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Sep 28 08:23:56 2023
+
+@author: Mohamad Mohannad al Kawadri (mohamad.mohannad.al.kawadri@nmbu.no), Trygve Børte Nomeland (trygve.borte.nomeland@nmbu.no)
+"""
+from abc import ABC, abstractmethod
+import numpy as np
+from copy import deepcopy
+from torchvision import datasets, transforms
+
+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.x = input
+
+    def run(self, x):
+        result = 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
+
+    def evaluate(self, x, expected_value):
+        result = list(self.run(x))
+        max_value_index = result.index(max(result))
+        return int(max_value_index) == expected_value
+
+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')
+        
+    @abstractmethod
+    def run(self):
+        pass
+
+class SigmaLayer(Layer):
+    def run(self):
+        return layer(self.W,  self.x, self.b)
+
+class ReluLayer(Layer):
+    def run(self):
+        return relu_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)
+
+def relu_scalar(x):
+    if x > 0:
+        return x
+    else:
+        return 0
+relu = np.vectorize(relu_scalar)
+
+
+# define layer function for given weight matrix, input and bias
+def layer(W, x, b):
+    return sigma_vec(W @ x + b)
+
+def relu_layer(W, x, b):
+    return sigma_vec(W @ x + b)
+
+# Function from example file "read.py"
+def get_mnist():
+    return datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
+
+# Function from example file "read.py"
+def return_image(image_index, mnist_dataset):
+    image, label = mnist_dataset[image_index]
+    image_matrix = image[0].detach().numpy()  # Grayscale image, so we select the first channel (index 0)
+    return image_matrix.reshape(image_matrix.size), image_matrix, label
+
+def evalualte_on_mnist(image_index, expected_value):
+    mnist_dataset = get_mnist()
+    x, image, label = return_image(image_index, mnist_dataset)
+    network = Network([ReluLayer, ReluLayer, ReluLayer], ['W_1.txt', 'W_2.txt', 'W_3.txt'], ['b_1.txt', 'b_2.txt', 'b_3.txt'])
+    return network.evaluate(x, expected_value)
+
+def run_on_mnist(image_index):
+    mnist_dataset = get_mnist()
+    x, image, label = return_image(image_index, mnist_dataset)
+    network = Network([ReluLayer, ReluLayer, ReluLayer], ['W_1.txt', 'W_2.txt', 'W_3.txt'], ['b_1.txt', 'b_2.txt', 'b_3.txt'])
+    return network.run(x)
+
+def main():
+    print(f'Check if network works on image 19961 (number 4): {evalualte_on_mnist(19961, 4)}')
+    print(f'Check if network works on image 10003 (number 9): {evalualte_on_mnist(10003, 9)}')
+    print(f'Check if network works on image 117 (number 2): {evalualte_on_mnist(117, 2)}')
+    print(f'Check if network works on image 1145 (number 3): {evalualte_on_mnist(1145, 3)}')
+    print(f'Values image 19961 (number 4): {run_on_mnist(19961)}')
+
+if __name__ == '__main__':
+    main()