Add Tests for Modelling

[Rishit-dagli]

Description

This pull request introduces comprehensive JUnit style tests to validate the functionality and correctness of Neural Net creation and training in the project. The aim is to enhance code reliability and ensure the Neural Net module performs as expected throughout its lifecycle.

Changes Made

• Implemented a set of JUnit tests that cover various scenarios of Neural Net creation, including different architectures and configurations.
• Added test cases to verify the proper initialization of weights, biases, and activation functions.
• Incorporated tests to validate the forward propagation process and the output of the Neural Net.
• Introduced tests for backpropagation and gradient calculation, ensuring the correctness of the weight updates during training.
• Addressed corner cases and edge scenarios to improve test coverage and robustness.

Motivation for the Changes

With the increasing complexity of the Neural Net module, it becomes crucial to have a robust and automated testing suite to verify its behavior under diverse circumstances. By adding JUnit style tests, we can systematically ensure that our Neural Net implementation adheres to expected functionality and produces accurate results during training and inference.

Testing Environment

• JUnit 5.0+ has been utilized for writing and executing the tests.
• The tests have been executed on different platforms and environments to ensure cross-compatibility and reliability.

[Rishit-dagli]

/cc Shivesh777

[Rishit-dagli]

/cc Ford-Robert

[Rishit-dagli]

/area modelling

[Rishit-dagli]

/hold until #50 is merged

[Rishit-dagli]

/remove hold

[Rishit-dagli]

/cc Shivesh777