AIT736 – PA3


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Artificial Neural Networks

In this programming assignment, you will have two tasks. The first task is to manually calculate the updated weights in a given neural network using backpropagation. The second task is to implement artificial neural networks with given datasets.

Please follow the instructions to complete the below two tasks:

Task 1(20 points)

Follow A Step by Step Backpropagation Example to calculate the updated weights in a given neural network. In the neural network as shown in the example, given the initial parameters as follows:

Initial weights:

w1 = 0.5, w2 = 0.45, w3 = 0.4, w4 = 0.35,

w5 = 0.3, w6 = 0.25, w7 = 0.20, w8 = 0.15.


b1 = 0.3

b2 = 0.1.

training inputs:

i1 = 0.1, i2 = 0.05.

learning rate:

r = 0.1.

training outputs:

o1 = 0.99, o2 = 0.01.

Calculate the updated w1 using the backpropagation algorithm. The activation function is the logistic function. You can also write the code to help calculate them. Write into a document, AIT736-DL#_Team#_PA#.docx/pdf, and submit.

Task 1(20 points)

Follow the ANNs code example to implement it on Google Colab (A Step by Step Guide). Then use different sklearn built-in datasets, load_digits and load_breast_cancer, to implement them. Code & HTML files are required to be AIT736-DL#_Team#_PA#.ipynb/HTML.

Hints: classifications for two datasets are different. The former has multiple classes but the latter has only 1 or 0 and is binary classification.

Explanation & Answer

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