Implements multi-layer perceptron (MLP) training
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 |
|---|---|---|
| class_id | system/uint64/1 | Input |
| image | system/array_2d_floats/1 | Input |
| model | tutorial/mlp/1 | Output |
Parameters allow users to change the configuration of an algorithm when scheduling an experiment
| Name | Description | Type | Default | Range/Choices |
|---|---|---|---|---|
| number-of-iterations | uint32 | 50 | ||
| seed | uint32 | 0 | ||
| number-of-hidden-units | uint32 | 10 |
xxxxxxxxxximport bobimport numpydef mlp_from_data(data): """Unmangles a dict of bob.machine.MLP from a BEAT Data object""" n_inputs = data.input_subtract.shape[0] n_outputs = data.biases[-1].value.shape[0] n_hidden = len(data.biases) - 1 shape = [n_inputs] for i in range(n_hidden): shape.append(data.biases[i].value.shape[0]) shape.append(n_outputs) mlp = bob.machine.MLP(shape) mlp.input_subtract = data.input_subtract mlp.input_divide = data.input_divide weights = [] for v in data.weights: weights.append(v.value) biases = [] for v in data.biases: biases.append(v.value) mlp.weights = weights mlp.biases = biases return mlpclass Algorithm: def __init__(self): self.model = None def process(self, inputs, outputs): # retrieve the model if self.model is None: inputs['model'].next() self.model = mlp_from_data(inputs['model'].data) # probe image image = inputs['image'].data.value.flatten() scores = {} scores = self.model(image) # Get the class_id with maximum score max_id = scores.argmax() outputs['scores'].write({ 'real_identity': inputs['class_id'].data.value, 'estimated_identity': numpy.uint64(max_id), 'score': scores[max_id] }) return TrueThe 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.
| [Bishop] | Pattern Recognition and Machine Learning, C.M. Bishop, chapter 5 |
| [Duda] | Pattern Classification, Duda, Hart and Stork, chapter 6 |