Fix stop_gradient inconsistent API

The stop_gradient documentation states that the argument should be a list of
variables. The Theano implementation crashes if the argument is a list of
variables and the CNTK implementation crashes if it is not.

This commit handles both cases as can be expected.

keras/backend/cntk_backend.py

@@ -1886,7 +1886,10 @@ def batch_set_value(tuples):
 
 
 def stop_gradient(variables):
-    return C.stop_gradient(C.combine(variables))
+    if isinstance(variables, (list, tuple)):
+        return map(C.stop_gradient, variables)
+    else:
+        return C.stop_gradient(variables)
 
 
 def switch(condition, then_expression, else_expression):

keras/backend/tensorflow_backend.py

@@ -2301,15 +2301,21 @@ def gradients(loss, variables):
 
 
 def stop_gradient(variables):
-    """Returns `variables` but with zero gradient w.r.t. every other variable.
+    """Returns `variables` but with zero gradient with respect to every other
+    variable.
 
     # Arguments
-        variables: List of variables.
+        variables: tensor or list of tensors to consider constant with respect
+            to any other variable
 
     # Returns
-        The same list of variables.
+        A single tensor or a list of tensors (depending on the passed argument)
+            that has constant gradient with respect to any other variable
     """
-    return tf.stop_gradient(variables)
+    if isinstance(variables, (list, tuple)):
+        return map(tf.stop_gradient, variables)
+    else:
+        return tf.stop_gradient(variables)
 
 
 # CONTROL FLOW

keras/backend/theano_backend.py

@@ -1212,9 +1212,20 @@ def gradients(loss, variables):
 
 def stop_gradient(variables):
     """Returns `variables` but with zero gradient with respect to every other
-    variables.
+    variable.
+
+    # Arguments
+        variables: tensor or list of tensors to consider constant with respect
+            to any other variable
+
+    # Returns
+        A single tensor or a list of tensors (depending on the passed argument)
+            that has constant gradient with respect to any other variable
     """
-    return theano.gradient.disconnected_grad(variables)
+    if isinstance(variables, (list, tuple)):
+        return map(theano.gradient.disconnected_grad, variables)
+    else:
+        return theano.gradient.disconnected_grad(variables)
 
 
 # CONTROL FLOW

tests/keras/backend/backend_test.py

@@ -491,6 +491,17 @@ def test_gradient(self):
             assert_allclose(zero_list[i], z_list[i], atol=1e-05)
             assert_allclose(zero_list[i + 1], zero_list[i + 1], atol=1e-05)
 
+    def test_stop_gradient(self):
+        # This test checks the consistency of the stop_gradient backend API.
+        # It doesn't check the functionality (which is checked at the
+        # test_gradient test).
+        val = np.random.random((4, 2))
+        for k in BACKENDS:
+            a = k.variable(val)
+            b = k.square(a)
+            c, d = k.stop_gradient([a, b])
+            e = k.stop_gradient(b)
+
     # cntk currently not support function in this way, so can't test as this
     def test_function(self):
         test_backend = [KTH, KTF]