Updates

• Patched the train.py script to accept tensors as input
• Crafted a script that generates LFW pairs for validation
• Implemented a validation mechanism using embeddings

bob/learn/tensorflow/test/test_utils.py

+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
+
+import numpy
+from bob.learn.tensorflow.utils import compute_embedding_accuracy
+
+"""
+Some unit tests for the datashuffler
+"""
+
+def test_embedding_accuracy():
+
+    numpy.random.seed(10)
+    samples_per_class = 5
+    
+    class_a = numpy.random.normal(loc=0, scale=0.1, size=(samples_per_class, 2))
+    labels_a = numpy.zeros(samples_per_class)
+    
+    class_b = numpy.random.normal(loc=10, scale=0.1, size=(samples_per_class, 2))
+    labels_b = numpy.ones(samples_per_class)
+
+    data = numpy.vstack((class_a, class_b))
+    labels = numpy.concatenate((labels_a, labels_b))
+
+    assert compute_embedding_accuracy(data, labels) == 1.
+
+    # Adding noise
+    noise = numpy.random.normal(loc=0, scale=0.1, size=(samples_per_class, 2))
+    noise_labels = numpy.ones(samples_per_class)
+    
+    data = numpy.vstack((data, noise))
+    labels = numpy.concatenate((labels, noise_labels))
+    
+    assert compute_embedding_accuracy(data, labels) == 10 / 15.
+