Revise backend tests for dtype (#12544)

* Revise backend tests for dtype

* Revise `check_dtype` for CNTK

* Revise `check_dtype` for CNTK

* Revise `check_dtype` for CNTK

* Improve `check_dtype`

tests/keras/backend/backend_test.py

@@ -5,7 +5,6 @@
 import warnings
 
 from keras import backend as K
-from keras.backend import floatx, set_floatx, variable
 from keras.utils.conv_utils import convert_kernel
 from keras.backend import numpy_backend as KNP
 
@@ -44,10 +43,10 @@
 
 
 def check_dtype(var, dtype):
-    if K.backend() == 'theano':
-        assert var.dtype == dtype
+    if K.backend() == 'tensorflow':
+        assert dtype in str(var.dtype.name)
     else:
-        assert var.dtype.name == '%s_ref' % dtype
+        assert dtype in str(var.dtype)
 
 
 def cntk_func_tensors(function_name, shapes_or_vals, **kwargs):
@@ -2020,61 +2019,37 @@ def test_in_test_phase(self, training):
         check_two_tensor_operation('in_test_phase', (2, 3), (2, 3), WITH_NP,
                                    training=training)
 
-    def test_setfloatx_incorrect_values(self):
+    @pytest.mark.parametrize('dtype', ['', 'beerfloat', 123])
+    def test_setfloatx_incorrect_values(self, dtype):
         # Keep track of the old value
-        old_floatx = floatx()
-        # Try some incorrect values
-        initial = floatx()
-        for value in ['', 'beerfloat', 123]:
-            with pytest.raises(ValueError):
-                set_floatx(value)
-        assert floatx() == initial
-        # Restore old value
-        set_floatx(old_floatx)
+        old_floatx = K.floatx()
+        with pytest.raises(ValueError):
+            K.set_floatx(dtype)
+        assert K.floatx() == old_floatx
 
-    def test_setfloatx_correct_values(self):
+    @pytest.mark.parametrize('dtype', ['float16', 'float32', 'float64'])
+    def test_setfloatx_correct_values(self, dtype):
         # Keep track of the old value
-        old_floatx = floatx()
+        old_floatx = K.floatx()
         # Check correct values
-        for value in ['float16', 'float32', 'float64']:
-            set_floatx(value)
-            assert floatx() == value
+        K.set_floatx(dtype)
+        assert K.floatx() == dtype
+        # Make sure that changes to the global floatx are effectively
+        # taken into account by the backend.
+        check_dtype(K.variable([10]), dtype)
         # Restore old value
-        set_floatx(old_floatx)
-
-    @pytest.mark.skipif((K.backend() == 'cntk'),
-                        reason='cntk does not support float16')
-    def test_set_floatx(self):
-        """
-        Make sure that changes to the global floatx are effectively
-        taken into account by the backend.
-        """
-        # Keep track of the old value
-        old_floatx = floatx()
-
-        set_floatx('float16')
-        var = variable([10])
-        check_dtype(var, 'float16')
+        K.set_floatx(old_floatx)
 
-        set_floatx('float64')
-        var = variable([10])
-        check_dtype(var, 'float64')
-
-        # Restore old value
-        set_floatx(old_floatx)
-
-    def test_dtype(self):
-        assert K.dtype(K.variable(1, dtype='float64')) == 'float64'
-        assert K.dtype(K.variable(1, dtype='float32')) == 'float32'
-        assert K.dtype(K.variable(1, dtype='float16')) == 'float16'
+    @pytest.mark.parametrize('dtype', ['float16', 'float32', 'float64'])
+    def test_dtype(self, dtype):
+        assert K.dtype(K.variable(1, dtype=dtype)) == dtype
 
+    @pytest.mark.skipif(K.backend() == 'cntk', reason='Not supported')
     def test_variable_support_bool_dtype(self):
-        # Github issue: 7819
-        if K.backend() == 'tensorflow':
-            assert K.dtype(K.variable(1, dtype='int16')) == 'int16'
-            assert K.dtype(K.variable(False, dtype='bool')) == 'bool'
-            with pytest.raises(TypeError):
-                K.variable('', dtype='unsupported')
+        assert K.dtype(K.variable(1, dtype='int16')) == 'int16'
+        assert K.dtype(K.variable(False, dtype='bool')) == 'bool'
+        with pytest.raises(TypeError):
+            K.variable('', dtype='unsupported')
 
     @pytest.mark.parametrize('shape', [(4, 2), (2, 3)])
     def test_clip_supports_tensor_arguments(self, shape):