Remove arbitrary float restrictions for constant_ and random_uniform_…

…initializer

constant_initializer clearly makes sense for any type.
random_uniform_initializer also should work for integers now that
random_uniform works for integers.
Change: 129489215

tensorflow/python/kernel_tests/init_ops_test.py

@@ -119,6 +119,16 @@ def testConstantOneInitializer(self):
       x.initializer.run()
       self.assertAllEqual(x.eval(), np.ones(shape))
 
+  def testConstantIntInitializer(self):
+    with self.test_session():
+      shape = [2, 3]
+      x = tf.get_variable(
+          "x", shape=shape, dtype=tf.int32,
+          initializer=tf.constant_initializer(7))
+      x.initializer.run()
+      self.assertEqual(x.dtype.base_dtype, tf.int32)
+      self.assertAllEqual(x.eval(), 7 * np.ones(shape, dtype=np.int32))
+
 
 class RandomNormalInitializationTest(tf.test.TestCase):
 
@@ -178,28 +188,23 @@ class RandomUniformInitializationTest(tf.test.TestCase):
 
   def testInitializerIdentical(self):
     for use_gpu in [False, True]:
-      for dtype in [tf.float32, tf.float64]:
-        init1 = tf.random_uniform_initializer(0.0, 1.0, seed=1, dtype=dtype)
-        init2 = tf.random_uniform_initializer(0.0, 1.0, seed=1, dtype=dtype)
+      for dtype in [tf.float32, tf.float64, tf.int64]:
+        init1 = tf.random_uniform_initializer(0, 7, seed=1, dtype=dtype)
+        init2 = tf.random_uniform_initializer(0, 7, seed=1, dtype=dtype)
         self.assertTrue(identicaltest(self, init1, init2, use_gpu))
 
   def testInitializerDifferent(self):
     for use_gpu in [False, True]:
-      for dtype in [tf.float32, tf.float64]:
-        init1 = tf.random_uniform_initializer(0.0, 1.0, seed=1, dtype=dtype)
-        init2 = tf.random_uniform_initializer(0.0, 1.0, seed=2, dtype=dtype)
+      for dtype in [tf.float32, tf.float64, tf.int32, tf.int64]:
+        init1 = tf.random_uniform_initializer(0, 7, seed=1, dtype=dtype)
+        init2 = tf.random_uniform_initializer(0, 7, seed=2, dtype=dtype)
         self.assertFalse(identicaltest(self, init1, init2, use_gpu))
 
   def testDuplicatedInitializer(self):
     for use_gpu in [False, True]:
       init = tf.random_uniform_initializer(0.0, 1.0)
       self.assertFalse(duplicated_initializer(self, init, use_gpu, 1))
 
-  def testInvalidDataType(self):
-    self.assertRaises(
-        ValueError,
-        tf.random_uniform_initializer, 0.0, 1.0, dtype=tf.string)
-
 
 class UniformUnitScalingInitializationTest(tf.test.TestCase):
 

tensorflow/python/ops/init_ops.py

@@ -55,46 +55,40 @@ def ones_initializer(shape, dtype=dtypes.float32):
   return array_ops.ones(shape, dtype)
 
 
-def constant_initializer(value=0.0, dtype=dtypes.float32):
+def constant_initializer(value=0, dtype=dtypes.float32):
   """Returns an initializer that generates tensors with a single value.
 
   Args:
     value: A Python scalar. All elements of the initialized variable
       will be set to this value.
-    dtype: The data type. Only floating point types are supported.
+    dtype: The data type.
 
   Returns:
     An initializer that generates tensors with a single value.
-
-  Raises:
-    ValueError: if `dtype` is not a floating point type.
   """
-  def _initializer(shape, dtype=_assert_float_dtype(dtype)):
+  def _initializer(shape, dtype=dtype):
     return constant_op.constant(value, dtype=dtype, shape=shape)
   return _initializer
 
 
-def random_uniform_initializer(minval=0.0, maxval=1.0, seed=None,
+def random_uniform_initializer(minval=0, maxval=None, seed=None,
                                dtype=dtypes.float32):
   """Returns an initializer that generates tensors with a uniform distribution.
 
   Args:
-    minval: a python scalar or a scalar tensor. lower bound of the range
-      of random values to generate.
-    maxval: a python scalar or a scalar tensor. upper bound of the range
+    minval: A python scalar or a scalar tensor. Lower bound of the range
       of random values to generate.
+    maxval: A python scalar or a scalar tensor. Upper bound of the range
+      of random values to generate.  Defaults to 1 for float types.
     seed: A Python integer. Used to create random seeds. See
       [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
       for behavior.
-    dtype: The data type. Only floating point types are supported.
+    dtype: The data type.
 
   Returns:
     An initializer that generates tensors with a uniform distribution.
-
-  Raises:
-    ValueError: if `dtype` is not a floating point type.
   """
-  def _initializer(shape, dtype=_assert_float_dtype(dtype)):
+  def _initializer(shape, dtype=dtype):
     return random_ops.random_uniform(shape, minval, maxval, dtype, seed=seed)
   return _initializer