Hybrid Routing Algorithm

Published: 15 February 2023| Version 2 | DOI: 10.17632/z5y9y7cbmk.2
jennifer kittur


The new technique presented is effectively a hybrid of EM and dynamic routing algorithms, with the coupling coefficients changed using both the EM update rule and the dynamic routing update rule. The code and explanations are enclosed


Steps to reproduce

The hybrid routing algorithm is a machine learning technique that combines elements of the Expectation-Maximization (EM) routing and dynamic routing algorithms. The algorithm is implemented in Python and can be executed using a Jupyter Notebook or a Python script. In order to execute the algorithm, you will need to have the following packages installed: NumPy Once you have the required packages installed, you can simply run the algorithm by executing the Python script that contains the implementation. The algorithm takes in three inputs: inputs: a matrix of shape (batch_size, num_inputs) that represents the input to the network num_classes: an integer representing the number of classes to predict num_routing_iterations: the number of iterations to use for the dynamic routing procedure (default is 3) The algorithm returns the output of the network, which is a matrix of shape (batch_size, num_classes). It is important to note that the implementation provided is a simple example and may need to be modified or extended in order to work with real-world data. If you wish to redistribute the hybrid routing algorithm, you must comply with the terms of the open-source license under which it is released (if applicable). You may also need to attribute the original authors of the code and any contributors.


University of Embu


Computer Engineering, Code Metrics, Image Classification, Deep Learning, Computer Vision Algorithms