Multi-Layer Perceptron (MLP)-based scoring
Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.
The code for this algorithm in Python
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This algorithm implements a scoring procedure for a multi-layer perceptron (MLP) [Bishop] [Duda], a neural network architecture that has some well-defined characteristics such as a feed-forward structure.
This implementation relies on the Bob library.
The inputs are:
The output scores is the corresponding set of score values.
|Pattern Recognition and Machine Learning, C.M. Bishop, chapter 5
|Pattern Classification, Duda, Hart and Stork, chapter 6
This table shows the number of times this algorithm has been successfully run using the given environment. Note this does not provide sufficient information to evaluate if the algorithm will run when submitted to different conditions.