BEAT - tutorial/mlp

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

Endpoint Groups 2

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

import bob
import numpy
import scipy.spatial

def 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 mlp

class 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 True

xxxxxxxxxx
 
import bob
import numpy
import scipy.spatial
​
​
def 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 mlp
​
​
class 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 True
​

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

Attestation:

Privacy:

Status:

			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.

algorithms tutorial mlp_scoring 3

Endpoint Groups 2

Group: probes

Unnamed group

Experiments

algorithms

tutorial

mlp_scoring

3