Simplify eager/graph Layer.losses conditionals

Fixes an issue where losses created while executing eagerly were returned as unevaluated lambdas in a defun.

Lazily evaluates Layer losses by default when possible. Even when graph building this is generally a better thing to do (e.g. losses called in a while_loop).

Allows calls to Layer.add_loss when executing eagerly, but only for losses which are not conditional on inputs (no activity regularizers).

PiperOrigin-RevId: 214947108

1 files changed, 4 insertions, 2 deletions

diff --git a/tensorflow/python/layers/core_test.py b/tensorflow/python/layers/core_test.py
index 46009a30ac..d26f3f4789 100644
--- a/tensorflow/python/layers/core_test.py
+++ b/tensorflow/python/layers/core_test.py
@@ -197,7 +197,8 @@ class DenseTest(test.TestCase):
     _ = dense(inputs)
     loss_keys = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)
     self.assertEqual(len(loss_keys), 1)
-    self.assertListEqual(dense.losses, loss_keys)
+    self.evaluate([v.initializer for v in dense.variables])
+    self.assertAllEqual(self.evaluate(dense.losses), self.evaluate(loss_keys))
 
   def testKernelRegularizerWithReuse(self):
     regularizer = lambda x: math_ops.reduce_sum(x) * 1e-3
@@ -218,7 +219,8 @@ class DenseTest(test.TestCase):
     _ = dense(inputs)
     loss_keys = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)
     self.assertEqual(len(loss_keys), 1)
-    self.assertListEqual(dense.losses, loss_keys)
+    self.evaluate([v.initializer for v in dense.variables])
+    self.assertAllEqual(self.evaluate(dense.losses), self.evaluate(loss_keys))
 
   def testFunctionalDense(self):
     with self.cached_session():