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.
| Endpoint Name | Data Format | Nature |
|---|---|---|
| image | system/array_2d_uint8/1 | Input |
| mean | system/array_2d_floats/1 | Input |
| normalized_image | system/array_2d_floats/1 | Output |
xxxxxxxxxximport bobimport numpyclass Algorithm: def __init__(self): self.mean = None def setup(self, parameters): return True def process(self, inputs, outputs): if self.mean is None: self.mean = inputs['mean'].data.value / 255. normalized_image = inputs["image"].data.value.astype('float64') / 255. - self.mean outputs["normalized_image"].write({'value': normalized_image}) return TrueThe code for this algorithm in Python
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| 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.