227
edits
Changes
Jump to navigation
Jump to search
https'''Current condition/issue''' of the model://keras* loss: 0.9109, accuracy: 0.9428* The model runs with no problem, however, it does not make classification.io/preprocessing/image/All predictions on the test set are the same
→CNN Model
'''''NOTE: '''''[https://keras.io/ Keras] package (with TensorFlow backend) is used for setting up the model
Some '''factors/problems''' to consider for '''future implementation''' on the model:
* Class label is highly imbalanced: o (not cohort) is way more than 1 (cohort) class
**may cause our model favoring the larger class, then the accuracy metric is not reliable
**several suggestions to fix this: A. ) under-sampling the larger class B.)over-sampling the smaller class
* Convert image data into same format: [https://www.oreilly.com/library/view/linux-multimedia-hacks/0596100760/ch01s04.html Make image thumbnail]
**we can modify image target size in our CNN, but we cannot know for sure how Keras library crop or re-scale image with given target size
*I chose to group images into cohort folder or not_cohort folder to let our CNN model detect the class label of an image. There are certainly other ways to detect class label and one may want to modify the Screenshot Tool and <code>cnn.py</code> to assist with other approaches
Useful rescource:
*Image generator in Keras: https://keras.io/preprocessing/image/
*Keras tutorial for builindg a CNN: https://adventuresinmachinelearning.com/keras-tutorial-cnn-11-lines/
===Workflow===
