This is a new architecture for the project. Currently, we only have the option to customize Feed-Forward architectures. It works well for some cases, and we achieved 85% accuracy on the MNIST dataset. However, we do not utilize the positional information of pixels in a given image. The CNN (Convolutional Neural Network) will address this. The main reasons for introducing this new architecture are:
To leverage the spatial information in a given image.
To work with large images without needing to resize them to smaller sizes and losing information.
To handle 3D data, without the need to convert RGB images to grayscale.
The key points for introducing this new module are:
Create the Max-pooling feature.
Implement the Convolutional Layer.
Enable it to work with resource fields.
This new module should be provided by the API. The way this architecture is used should be similar to the current one. We may need to refactor the Neural class to offer a cleaner way to select which architecture the front-end will use and to improve code organization
This is a new architecture for the project. Currently, we only have the option to customize Feed-Forward architectures. It works well for some cases, and we achieved 85% accuracy on the MNIST dataset. However, we do not utilize the positional information of pixels in a given image. The CNN (Convolutional Neural Network) will address this. The main reasons for introducing this new architecture are:
The key points for introducing this new module are:
This new module should be provided by the API. The way this architecture is used should be similar to the current one. We may need to refactor the Neural class to offer a cleaner way to select which architecture the front-end will use and to improve code organization