Xchai8/AML-project-dimensionality-reduction-for-images

Project 2

This is an individual assignment.

Code Implementation & Technical Report

The final deliverables include a 4-page IEEE-format report, code implementation and a detailed GitHub readme file.

Project 2 is due Monday, November 14 @ 11:59 PM. Find the complete rubric in the Canvas assignment.

Custom Handwritten Characters Dataset

The dataset you will be working with is available for download here:

• "data_train.npy"
• "labels_train.npy"
• "data_test.npy"
• "labels_test.npy"

Files contained

1. training.ipynb ---- code used for training the datasets.
2. test.ipynb ---- code used for testing the datasets.
3. Project 2 - Dimentionality Reduction.ipynb ---- description of project 1
4. Model ---- contains all models generated from the training.ipynb.
5. Dataset ---- contains LDA, t-SNE and PCA components I get from question 3 and original dataset.
6. Project_1_report.docx ---- report in docx form
7. Project_1_report.pdf ---- report in pdf form
8. training.pdf ---- pdf form of training.ipynb
9. test.pdf ---- pdf form of test.ipynb
10. README.md ---- this file.

How to use

The models are in the Model file which is in my Google Drive, you need to down load from here.

This file doesn't include 4 original dataset, so you need to load these data before running my code.

Training: Training code do not need any parameters to input. It generates totally 10 models in file Model and 3 numpy arrays in file Dataset. And these models and data will be use in test code.

Test: Test code has already loaded the datasets and resampled dataset, and all models have been loaded too. Except running all codes, you do not need any operation in Jupyter.