Adding test for associative property of pairwise squared loss.

Change: 149955752

tensorflow/contrib/losses/python/losses/loss_ops_test.py

@@ -30,6 +30,7 @@ from tensorflow.python.framework import random_seed
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
@@ -265,7 +266,8 @@ class SoftmaxCrossEntropyLossTest(test.TestCase):
                                    [1, 0, 0],
                                    [0, 1, 0]])
     weights = [[2.3], [2.4], [2.5]]
-    weights_placeholder = array_ops.placeholder(dtypes.float32, shape=[None, None])
+    weights_placeholder = array_ops.placeholder(
+        dtypes.float32, shape=[None, None])
     loss = loss_ops.softmax_cross_entropy(logits, labels, weights_placeholder)
     with self.test_session() as sess:
       loss = sess.run(loss, {weights_placeholder: weights})
@@ -479,8 +481,10 @@ class SparseSoftmaxCrossEntropyLossTest(test.TestCase):
                                    [0.0, 0.0, 10.0]])
     labels = constant_op.constant([2, 0, 1])
     weights = [2.3, 2.4, 2.5]
-    weights_placeholder = array_ops.placeholder(dtypes.float32, shape=[None])
-    loss = loss_ops.sparse_softmax_cross_entropy(logits, labels, weights_placeholder)
+    weights_placeholder = array_ops.placeholder(
+        dtypes.float32, shape=[None])
+    loss = loss_ops.sparse_softmax_cross_entropy(
+        logits, labels, weights_placeholder)
     with self.test_session() as sess:
       loss = sess.run(loss, {weights_placeholder: weights})
       self.assertAlmostEqual(np.average(weights) * 10.0, loss, 3)
@@ -491,8 +495,10 @@ class SparseSoftmaxCrossEntropyLossTest(test.TestCase):
                                    [0.0, 0.0, 10.0]])
     labels = constant_op.constant([2, 0, 1])
     weights = [[2.3], [2.4], [2.5]]
-    weights_placeholder = array_ops.placeholder(dtypes.float32, shape=[None, None])
-    loss = loss_ops.sparse_softmax_cross_entropy(logits, labels, weights_placeholder)
+    weights_placeholder = array_ops.placeholder(
+        dtypes.float32, shape=[None, None])
+    loss = loss_ops.sparse_softmax_cross_entropy(
+        logits, labels, weights_placeholder)
     with self.test_session() as sess:
       loss = sess.run(loss, {weights_placeholder: weights})
       self.assertAlmostEqual(np.average(weights) * 10.0, loss, 3)
@@ -1054,6 +1060,41 @@ class MeanPairwiseSquaresErrorTest(test.TestCase):
     with self.test_session():
       self.assertAlmostEqual(0.0, loss.eval(), 3)
 
+  def testLossIsAssociativeAcrossBatchElements(self):
+    with ops.Graph().as_default():
+      random_seed.set_random_seed(0)
+
+      height = 3
+      width = 4
+      shape = (1, height, width, 1)
+
+      labels0 = random_ops.random_uniform(
+          shape, minval=0, maxval=1, dtype=dtypes.float32)
+      predictions0 = random_ops.random_uniform(
+          shape, minval=0, maxval=1, dtype=dtypes.float32)
+
+      labels1 = random_ops.random_uniform(
+          shape, minval=0, maxval=1, dtype=dtypes.float32)
+      predictions1 = random_ops.random_uniform(
+          shape, minval=0, maxval=1, dtype=dtypes.float32)
+
+      loss0 = loss_ops.mean_pairwise_squared_error(
+          predictions=predictions0,
+          labels=labels0)
+      loss1 = loss_ops.mean_pairwise_squared_error(
+          predictions=predictions1,
+          labels=labels1)
+      loss0_1 = loss_ops.mean_pairwise_squared_error(
+          predictions=array_ops.concat([predictions0, predictions1], 0),
+          labels=array_ops.concat([labels0, labels1], 0))
+
+      with self.test_session() as session:
+        loss0, loss1, loss0_1 = session.run([loss0, loss1, loss0_1])
+
+        self.assertTrue(loss0 > 0)
+        self.assertTrue(loss1 > 0)
+        self.assertAlmostEqual(loss0 + loss1, loss0_1, 5)
+
 
 class CosineDistanceLossTest(test.TestCase):
 

tensorflow/python/kernel_tests/losses_test.py

@@ -28,6 +28,7 @@ from tensorflow.python.framework import random_seed
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import variable_scope
 from tensorflow.python.ops import variables
 from tensorflow.python.ops.losses import losses
@@ -1015,6 +1016,41 @@ class MeanPairwiseSquaredErrorTest(test.TestCase):
         self._labels, self._predictions, expected_loss=0.0,
         weights=np.zeros((2, 1)))
 
+  def testLossIsAssociativeAcrossBatchElements(self):
+    with ops.Graph().as_default():
+      random_seed.set_random_seed(0)
+
+      height = 3
+      width = 4
+      shape = (1, height, width, 1)
+
+      labels0 = random_ops.random_uniform(
+          shape, minval=0, maxval=1, dtype=dtypes.float32)
+      predictions0 = random_ops.random_uniform(
+          shape, minval=0, maxval=1, dtype=dtypes.float32)
+
+      labels1 = random_ops.random_uniform(
+          shape, minval=0, maxval=1, dtype=dtypes.float32)
+      predictions1 = random_ops.random_uniform(
+          shape, minval=0, maxval=1, dtype=dtypes.float32)
+
+      loss0 = losses.mean_pairwise_squared_error(
+          labels=labels0,
+          predictions=predictions0)
+      loss1 = losses.mean_pairwise_squared_error(
+          labels=labels1,
+          predictions=predictions1)
+      loss0_1 = losses.mean_pairwise_squared_error(
+          labels=array_ops.concat([labels0, labels1], 0),
+          predictions=array_ops.concat([predictions0, predictions1], 0))
+
+      with self.test_session() as session:
+        loss0, loss1, loss0_1 = session.run([loss0, loss1, loss0_1])
+
+        self.assertTrue(loss0 > 0)
+        self.assertTrue(loss1 > 0)
+        self.assertAlmostEqual(loss0 + loss1, loss0_1, 5)
+
 
 class CosineDistanceLossTest(test.TestCase):