Skip to content

Latest commit

 

History

History
101 lines (74 loc) · 3.63 KB

stories.md

File metadata and controls

101 lines (74 loc) · 3.63 KB

Project Planning

Agile stories for the project.


Contents


Project Initiation

Story: Create a Python3 project

  • Estimate: 3d
  • Acceptance Criteria:
    • Language: Python
    • Initialize with .gitignore, GPL LICENSE and README.md
    • Should have .dockerignore, .gitattributes, and .pylintrc
    • Should have Dockerfile
    • Should have Makefile with targets: clean, clean-all, docker, test, test-all
    • Should have setup.cfg, and setup.py
    • Should have a "docs" folder
    • Should have a project folder, e.g. "ml"
    • Should have a library folder, e.g. "ml/utils"
    • Should include necessary tools in tools folder
    • Should have ml/config.py, ml/logging.yaml, ml/utils/logger*.py with unit tests
    • Should run pytest with linter, flake8, and pep8 to generate coverage report
    • Should run all tests in python venv (virtual env)
    • Should have travis ci integrated


Digit-recognizer Trainning Epic

This epic is to create a digit recognizer that can classifiy hand written digits. The project is to use deep learning algorithms to train an model and to predict digits in training set.

Story: Collect and modify data set

  • Estimation: 1d
  • Acceptance criteria:
    • Data should be downloaded from Kaggle digit recognizer competetion to datasets folder
    • Data set should be in the form of a matrix as each collumn being each image, and each row being each pixel. load label set in a 1:m matrix
    • Should create functions: load_data, load_dataset in digit-recognizer/dataSvc.py
    • Should nomalize data sets

Story: Create math extensions

  • Estimation: 2d
  • Acceptance criteria:
    • Should be common/mathEx.py
    • Should include basic activation functions: relu, leaky_relu, and sigmoid, with the backward version of them
    • Should include functions of forward and backward activation: l_model_forward, l_model_backward_with_l2, linear_activation_backward_with_l2, linear_activation_forward, linear_backward_with_l2, linear_forward,
    • Should include function: compute_cost
    • Should include initialiation function with "He initialization": initialize_parameters_deep_he
    • Should include function: change_to_multi_class
    • Should include function for gradient descent: update_parameters

Story: build deep training model

  • Estimation: 1d
  • Acceptance criteria:
    • Should needed load data from files to normalized training set
    • Should define hyper-parameters: Layer dementsions, learning rate alpha, regularization parameter lambda, number of iterations
    • Should be able to print costs
    • Should go through forward and backward propagation in each iteration
    • Should save parameters to numpy file after training


Digit-recognizer Prediction Epic

Story: Predict on certain set

  • Estimation: 1d
  • Acceptance criteria:
    • Should load datas from files to noramlized traning set and test set
    • Should load parameters from numpy files
    • Should go through one iteration of forward propagation
    • Should calculate and print accuracy

Recognizer Epic

Story: Predict on certain hand written image

  • Estimation: 1d
  • Acceptance criteria:
    • Should load jpg images from files to normalized vectors
    • Should tell the recognizer if the image is white based or black based
    • Should go through one iteration of forward iteration
    • Should print loaded image to system
    • Should print predicted answer and actual answer to system