Added tests for the new losses

bob/learn/tensorflow/loss/utils.py

+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+# @author: Amir Mohammadi <amir.mohammadi@idiap.ch>
+
 import tensorflow as tf
 
 
-def balanced_softmax_cross_entropy_loss_weights(labels, dtype):
+def balanced_softmax_cross_entropy_loss_weights(labels, dtype="float32"):
     """Computes weights that normalizes your loss per class.
 
     Labels must be a batch of one-hot encoded labels. The function takes labels and
@@ -82,7 +86,7 @@ def balanced_softmax_cross_entropy_loss_weights(labels, dtype):
     return weights
 
 
-def balanced_sigmoid_cross_entropy_loss_weights(labels, dtype):
+def balanced_sigmoid_cross_entropy_loss_weights(labels, dtype="float32"):
     """Computes weights that normalizes your loss per class.
 
     Labels must be a batch of binary labels. The function takes labels and

bob/learn/tensorflow/test/test_loss.py

+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
+
+import tensorflow as tf
+import numpy
+from bob.learn.tensorflow.loss import balanced_softmax_cross_entropy_loss_weights,\
+                                      balanced_sigmoid_cross_entropy_loss_weights
+
+
+def test_balanced_softmax_cross_entropy_loss_weights():
+    labels = numpy.array([[1, 0, 0],
+                          [1, 0, 0],
+                          [0, 0, 1],
+                          [0, 1, 0],
+                          [0, 0, 1],
+                          [1, 0, 0],
+                          [1, 0, 0],
+                          [0, 0, 1],
+                          [1, 0, 0],
+                          [1, 0, 0],
+                          [1, 0, 0],
+                          [1, 0, 0],
+                          [1, 0, 0],
+                          [1, 0, 0],
+                          [0, 1, 0],
+                          [1, 0, 0],
+                          [0, 1, 0],
+                          [1, 0, 0],
+                          [0, 0, 1],
+                          [0, 0, 1],
+                          [1, 0, 0],
+                          [0, 0, 1],
+                          [1, 0, 0],
+                          [1, 0, 0],
+                          [0, 1, 0],
+                          [1, 0, 0],
+                          [1, 0, 0],
+                          [1, 0, 0],
+                          [0, 1, 0],
+                          [1, 0, 0],
+                          [0, 0, 1],
+                          [1, 0, 0]], dtype="int32")
+
+    with tf.Session() as session:
+        weights = session.run(balanced_softmax_cross_entropy_loss_weights(labels))
+ 
+    expected_weights = numpy.array([0.53333336, 0.53333336, 1.5238096 , 2.1333334,\
+                                    1.5238096 , 0.53333336, 0.53333336, 1.5238096,\
+                                    0.53333336, 0.53333336, 0.53333336, 0.53333336,\
+                                    0.53333336, 0.53333336, 2.1333334 , 0.53333336,\
+                                    2.1333334 , 0.53333336, 1.5238096 , 1.5238096 ,\
+                                    0.53333336, 1.5238096 , 0.53333336, 0.53333336,\
+                                    2.1333334 , 0.53333336, 0.53333336, 0.53333336,\
+                                    2.1333334 , 0.53333336, 1.5238096 , 0.53333336],\
+                                    dtype="float32")
+
+    assert numpy.allclose(weights, expected_weights)
+
+
+def test_balanced_sigmoid_cross_entropy_loss_weights():
+    labels = numpy.array([1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0,
+                          1, 1, 0, 1, 1, 1, 0, 1, 0, 1], dtype="int32")
+    
+    with tf.Session() as session:
+        weights = session.run(balanced_sigmoid_cross_entropy_loss_weights(labels, dtype='float32'))
+        
+    expected_weights = numpy.array([0.8, 0.8, 1.3333334, 1.3333334, 1.3333334, 0.8,
+                                    0.8, 1.3333334, 0.8, 0.8, 0.8, 0.8,
+                                    0.8, 0.8, 1.3333334, 0.8, 1.3333334, 0.8,
+                                    1.3333334, 1.3333334, 0.8, 1.3333334, 0.8, 0.8,
+                                    1.3333334, 0.8, 0.8, 0.8, 1.3333334, 0.8,
+                                    1.3333334, 0.8], dtype="float32")
+
+    assert numpy.allclose(weights, expected_weights)
+