Better error message when there is no gradient defined (keras-team#9170)

* better error message

* better error messag

* pep8

* changes according to review

keras/optimizers.py

@@ -76,6 +76,10 @@ def get_updates(self, loss, params):
 
     def get_gradients(self, loss, params):
         grads = K.gradients(loss, params)
+        if None in grads:
+            raise ValueError('An operation has `None` for gradient.',
+                             'Please be sure that all of your ops have a gradient defined \"i.e. are differentiable\".',
+                             'Common ops without gradient : K.argmax, K.round, K.cast, K.eval.')
         if hasattr(self, 'clipnorm') and self.clipnorm > 0:
             norm = K.sqrt(sum([K.sum(K.square(g)) for g in grads]))
             grads = [clip_norm(g, self.clipnorm, norm) for g in grads]

tests/keras/optimizers_test.py

@@ -4,9 +4,9 @@
 from numpy.testing import assert_allclose
 
 from keras.utils import test_utils
-from keras import optimizers
-from keras.models import Sequential
-from keras.layers.core import Dense, Activation
+from keras import optimizers, Input
+from keras.models import Sequential, Model
+from keras.layers.core import Dense, Activation, Lambda
 from keras.utils.test_utils import keras_test
 from keras.utils.np_utils import to_categorical
 from keras import backend as K
@@ -64,10 +64,22 @@ def _test_optimizer(optimizer, target=0.75):
     assert_allclose(bias, 2.)
 
 
+@keras_test
+def _test_no_grad(optimizer):
+    inp = Input([3])
+    x = Dense(10)(inp)
+    x = Lambda(lambda l: 1.0 * K.reshape(K.cast(K.argmax(l), 'float32'), [-1, 1]))(x)
+    mod = Model(inp, x)
+    mod.compile(optimizer, 'mse')
+    with pytest.raises(ValueError):
+        mod.fit(np.zeros([10, 3]), np.zeros([10, 1], np.float32), batch_size=10, epochs=10)
+
+
 @keras_test
 def test_sgd():
     sgd = optimizers.SGD(lr=0.01, momentum=0.9, nesterov=True)
     _test_optimizer(sgd)
+    _test_no_grad(sgd)
 
 
 @keras_test