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Assignment Task

(Un)Supervised Learning

Using the provided dataset, implement a multi-layer perceptron for classification via supervised learning, as well as the unsupervised k-means and AGNES clustering algorithms. For this assignment, you will use python3 and (optionally) the numpy library for vector/matrix-based scientific computation in python. The assignment file you will submit is assignment4.py. Please complete the meth-ods within the class definitions provided. You are free to add additional helper methods and classes. All the code will be run using the run_assignment4.py file. You can modify this file for your own testing, but you can only upload the assignment4.py file to Brightspace, so make sure all your code is in that file. You can expect that any variable in the “run” file could be changed during evaluation though its data type will remain the same. For example, the shape of the data could change (i.e. there may be a different number of points or features). If you don’t hardcode values, you should be fine. During evaluation, we will let the “run” file run for up to 1 minute, but you should really aim for under 10 seconds total (assuming an average modern laptop). Using numpy arrays is not required, but it is recommended. Numpy provides many vector operations that you can run over data (often replacing costly for-loops with parallelized one-line operations). These can help keep your code clean and simple while massively improving performance. 

Algorithms Multi-Layer Perceptron (MLP)-  Much of the MLP is already implemented for you. Please look through the code and try to understand it. What happens if you comment out the line that shuffles the dataset before training? The MLP class calls a fully connected layer (“FCLayer”) and a Sigmoid layer. You need to implement the functions implementing the forward and backward passes for each. The forward pass is for prediction and the backward pass is for doing gradient descent. The backward-pass function takes the previous gradient as input, updates the layer weights (for FCLayer) and returns the gradients for the next layer. Please follow the example from the slides. MLP will be trained with the learning rate and 

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• Uploaded By : Roman

• Posted on : January 03rd, 2020

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