#CONDA INSTALL AUTOPEP8 CODE#

For example, reading an import statement in Python, such as import, should clearly indicate the meaning or functionality of the code being imported.

Package and module naming should be intuitive and non-repetitive.

For example, analysis and helper are local packages, whereas predictions.py and convert.py are local modules.

Local code should be abstracted into packages and modules.

Contains code files (i.e., emoRec.py) used during training, testing, and building phase of the machine learning model.

dev folder contains all peripheral code used in developing and testing the machine learning tensorflow graph.

For example, emoRecStream.py is a complete standalone example to analyse video stream.

example folder contains a complete, runnable, well-abstracted Python code to illustrate the entire machine learning model.

For details of how the documents are generated, refer to the code documentation steps.

doc folder contains the documents generated from Python code comments.

Complete dataset is advised to be stored in an external Hadoop cluster.

dataset folder should contain minimal amount of sample test data used in the project for testing and demonstration purposes.

For example, it can contain images used for explanation in Readme.md.

assets folder should contain miscellaneous files.

| в””в”Ђв”Ђ preprocess.py # Pre-processing functions

| в”њв”Ђв”Ђ haarcascade_frontalface_default.xml #. | в””в”Ђв”Ђ emoRec.py # Code used during model development | | в””в”Ђв”Ђ emotion_recognition_structure.json # Machine learning saved structure

| | в”њв”Ђв”Ђ emotion_recognition_weights.h5 # Machine learning saved weights | | в””в”Ђв”Ђ predictions.py # Module - Predictions Emotion-recognition # Repository name / Project root

Conda install autopep8