diff options
Diffstat (limited to 'tensorflow/contrib/losses/python/losses/loss_ops_test.py')
| -rw-r--r-- | tensorflow/contrib/losses/python/losses/loss_ops_test.py | 52 |
1 files changed, 52 insertions, 0 deletions
diff --git a/tensorflow/contrib/losses/python/losses/loss_ops_test.py b/tensorflow/contrib/losses/python/losses/loss_ops_test.py index 94b8dfca57..81a4aaba2b 100644 --- a/tensorflow/contrib/losses/python/losses/loss_ops_test.py +++ b/tensorflow/contrib/losses/python/losses/loss_ops_test.py @@ -243,6 +243,34 @@ class SoftmaxCrossEntropyLossTest(test.TestCase): expected_value = 400.0 * label_smoothing / 3.0 self.assertAlmostEqual(loss.eval(), expected_value, 3) + def testLossWithDynamicallyShapedWeights1D(self): + logits = constant_op.constant([[10.0, 0.0, 0.0], + [0.0, 10.0, 0.0], + [0.0, 0.0, 10.0]]) + labels = constant_op.constant([[0, 0, 1], + [1, 0, 0], + [0, 1, 0]]) + weights = [2.3, 2.4, 2.5] + weights_placeholder = array_ops.placeholder(dtypes.float32, shape=[None]) + loss = loss_ops.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) + + def testLossWithDynamicallyShapedWeights2D(self): + logits = constant_op.constant([[10.0, 0.0, 0.0], + [0.0, 10.0, 0.0], + [0.0, 0.0, 10.0]]) + labels = constant_op.constant([[0, 0, 1], + [1, 0, 0], + [0, 1, 0]]) + weights = [[2.3], [2.4], [2.5]] + 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}) + self.assertAlmostEqual(np.average(weights) * 10.0, loss, 3) + class SparseSoftmaxCrossEntropyLossTest(test.TestCase): @@ -445,6 +473,30 @@ class SparseSoftmaxCrossEntropyLossTest(test.TestCase): loss_ops.sparse_softmax_cross_entropy( logits, labels, weights=weights).eval() + def testLossWithDynamicallyShapedWeights1D(self): + logits = constant_op.constant([[10.0, 0.0, 0.0], + [0.0, 10.0, 0.0], + [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) + with self.test_session() as sess: + loss = sess.run(loss, {weights_placeholder: weights}) + self.assertAlmostEqual(np.average(weights) * 10.0, loss, 3) + + def testLossWithDynamicallyShapedWeights2D(self): + logits = constant_op.constant([[10.0, 0.0, 0.0], + [0.0, 10.0, 0.0], + [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) + with self.test_session() as sess: + loss = sess.run(loss, {weights_placeholder: weights}) + self.assertAlmostEqual(np.average(weights) * 10.0, loss, 3) + class SigmoidCrossEntropyLossTest(test.TestCase): |