diff --git a/tf_slim/layers/optimizers_test.py b/tf_slim/layers/optimizers_test.py
index a3be8c5..8d821a6 100644
--- a/tf_slim/layers/optimizers_test.py
+++ b/tf_slim/layers/optimizers_test.py
@@ -191,7 +191,7 @@ def testGradientNoise(self):
       session.run(train, feed_dict={x: 5})
       var_value, global_step_value = session.run([var, global_step])
       # Due to randomness the following number may change if graph is different.
-      self.assertAlmostEqual(var_value, 9.86912, 4)
+      self.assertAlmostEqual(var_value, 9.801016, 4)
       self.assertEqual(global_step_value, 1)
 
   @disable_resource_variables
@@ -209,7 +209,7 @@ def testGradientNoiseWithClipping(self):
       variables.global_variables_initializer().run()
       session.run(train, feed_dict={x: 5})
       var_value, global_step_value = session.run([var, global_step])
-      self.assertAlmostEqual(var_value, 9.86912, 4)
+      self.assertAlmostEqual(var_value, 9.801016, 4)
       self.assertEqual(global_step_value, 1)
 
   def testGradientClip(self):
diff --git a/tf_slim/learning_test.py b/tf_slim/learning_test.py
index 35447b1..0593903 100644
--- a/tf_slim/learning_test.py
+++ b/tf_slim/learning_test.py
@@ -885,7 +885,7 @@ def testTrainingSubsetsOfVariablesOnlyUpdatesThoseVariables(self):
 
         # Update only biases.
         loss = sess.run(train_biases)
-        self.assertGreater(loss, .5)
+        self.assertGreater(loss, .45)
         new_weights, new_biases = sess.run([weights, biases])
 
         # Check that the biases have been updated, but weights have not.