Multi-Layer Perceptron (MLP)-based scoring

This algorithm is a legacy one. The API has changed since its implementation. New versions and forks will need to be updated.

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.

Group: probes

Endpoint Name Data Format Nature
class_id system/uint64/1 Input
image system/array_2d_floats/1 Input
scores tutorial/multiclass_probe_scores/1 Output

Unnamed group

Endpoint Name Data Format Nature
model tutorial/mlp/1 Input

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:

  • image: a two-dimensional array of floats (64 bits), which is flattened for the training procedure
  • class_id: an identifier for the class of the image, such that supervised training is possible

The output scores is the corresponding set of score values.

[Bishop]Pattern Recognition and Machine Learning, C.M. Bishop, chapter 5
[Duda]Pattern Classification, Duda, Hart and Stork, chapter 6

Experiments

Updated Name Databases/Protocols Analyzers
smarcel/tutorial/digit/2/mnist-mlp-nhu10-niter100-seed2001 mnist/1@idiap tutorial/multiclass_postperf/2

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.

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