Fail fast on bad dtype.

Change: 128727035

2 files changed, 8 insertions, 2 deletions

diff --git a/tensorflow/contrib/layers/python/layers/initializers.py b/tensorflow/contrib/layers/python/layers/initializers.py
index 03665a4951..1786b71dcf 100644
--- a/tensorflow/contrib/layers/python/layers/initializers.py
+++ b/tensorflow/contrib/layers/python/layers/initializers.py
@@ -102,12 +102,13 @@ def variance_scaling_initializer(factor=2.0, mode='FAN_IN', uniform=False,
     TypeError: if `mode` is not in ['FAN_IN', 'FAN_OUT', 'FAN_AVG'].
   """
   if not dtype.is_floating:
-    raise TypeError('Cannot create initializer for non-floating point '
-                    'type.')
+    raise TypeError('Cannot create initializer for non-floating point type.')
   if mode not in ['FAN_IN', 'FAN_OUT', 'FAN_AVG']:
     raise TypeError('Unknow mode %s [FAN_IN, FAN_OUT, FAN_AVG]', mode)
   def _initializer(shape, dtype=dtype):
     """Initializer function."""
+    if not dtype.is_floating:
+      raise TypeError('Cannot create initializer for non-floating point type.')
     # Estimating fan_in and fan_out is not possible to do perfectly, but we try.
     # This is the right thing for matrix multiply and convolutions.
     fan_in = float(shape[-2])
diff --git a/tensorflow/contrib/layers/python/layers/initializers_test.py b/tensorflow/contrib/layers/python/layers/initializers_test.py
index bacf16c1ad..d619dd8ee0 100644
--- a/tensorflow/contrib/layers/python/layers/initializers_test.py
+++ b/tensorflow/contrib/layers/python/layers/initializers_test.py
@@ -64,6 +64,11 @@ class VarianceScalingInitializerTest(tf.test.TestCase):
         TypeError,
         'Cannot create initializer for non-floating point type.'):
       tf.contrib.layers.variance_scaling_initializer(dtype=tf.int32)
+    initializer = tf.contrib.layers.variance_scaling_initializer()
+    with self.assertRaisesRegexp(
+        TypeError,
+        'Cannot create initializer for non-floating point type.'):
+      initializer([], dtype=tf.int32)
 
   def _test_variance(self, initializer, shape, variance, factor, mode, uniform):
     with tf.Graph().as_default() as g: