Theano fixes

keras/engine/training.py

@@ -680,7 +680,8 @@ def _prepare_total_loss(self, masks=None):
                         y_true, y_pred, sample_weight=sample_weight)
 
                 if len(self.outputs) > 1:
-                    self._output_loss_metrics[i](output_loss)
+                    update_ops = self._output_loss_metrics[i].update_state(output_loss)
+                    self._metric_updates += update_ops
 
                 if total_loss is None:
                     total_loss = loss_weight * output_loss
@@ -698,7 +699,7 @@ def _prepare_total_loss(self, masks=None):
             for loss_tensor in self.losses:
                 total_loss += loss_tensor
 
-        return total_loss
+        return K.mean(total_loss)
 
     def _get_training_eval_metrics(self):
         """Returns all the metrics that are to be reported.

keras/engine/training_utils.py

@@ -1030,10 +1030,12 @@ def call_metric_function(metric_fn,
             weights *= mask
 
     if y_pred is not None:
-        update_op = metric_fn.update_state(y_true, y_pred, sample_weight=weights)
-        result = metric_fn.result()
+        update_ops = metric_fn.update_state(y_true, y_pred, sample_weight=weights)
+        with K.control_dependencies(update_ops):
+            result = metric_fn.result()
     else:
         # `Mean` metric only takes a single value.
-        update_op = metric_fn.update_state(y_true, sample_weight=weights)
-        result = metric_fn.result()
-    return result, update_op
+        update_ops = metric_fn.update_state(y_true, sample_weight=weights)
+        with K.control_dependencies(update_ops):
+            result = metric_fn.result()
+    return result, update_ops

setup.py

@@ -24,13 +24,13 @@
 '''
 
 setup(name='Keras',
-      version='2.2.4',
+      version='2.3.0',
       description='Deep Learning for humans',
       long_description=long_description,
       author='Francois Chollet',
       author_email='francois.chollet@gmail.com',
       url='https://github.com/keras-team/keras',
-      download_url='https://github.com/keras-team/keras/tarball/2.2.4',
+      download_url='https://github.com/keras-team/keras/tarball/2.3.0',
       license='MIT',
       install_requires=['numpy>=1.9.1',
                         'scipy>=0.14',

tests/keras/losses_test.py

@@ -159,6 +159,11 @@ def test_loss_wrapper(self):
         np.allclose(K.eval(loss), 16, atol=1e-2)
 
 
+skipif_not_tf = pytest.mark.skipif(
+    K.backend() != 'tensorflow',
+    reason='Need TensorFlow to __call__ a loss')
+
+
 class TestLossClasses(object):
 
     @pytest.mark.parametrize('cls', all_classes)
@@ -180,6 +185,7 @@ def test_objective_shapes_2d(self, cls):
         assert K.eval(objective_output).shape == ()
 
 
+@skipif_not_tf
 class TestMeanSquaredError:
 
     def test_config(self):
@@ -256,6 +262,7 @@ def test_sum_reduction(self):
         assert np.isclose(K.eval(loss), 227.69998, rtol=1e-3)
 
 
+@skipif_not_tf
 class TestMeanAbsoluteError(object):
 
     def test_config(self):
@@ -332,6 +339,7 @@ def test_sum_reduction(self):
         assert np.isclose(K.eval(loss), 25.29999, rtol=1e-3)
 
 
+@skipif_not_tf
 class TestMeanAbsolutePercentageError(object):
 
     def test_config(self):
@@ -392,6 +400,7 @@ def test_no_reduction(self):
         assert np.allclose(K.eval(loss), [621.8518, 352.6666], rtol=1e-3)
 
 
+@skipif_not_tf
 class TestMeanSquaredLogarithmicError(object):
 
     def test_config(self):
@@ -438,6 +447,7 @@ def test_zero_weighted(self):
         assert np.allclose(K.eval(loss), 0.0, rtol=1e-3)
 
 
+@skipif_not_tf
 class TestBinaryCrossentropy(object):
 
     def test_config(self):
@@ -598,6 +608,7 @@ def test_label_smoothing(self):
         assert np.isclose(K.eval(loss), expected_value, atol=1e-3)
 
 
+@skipif_not_tf
 class TestCategoricalCrossentropy(object):
 
     def test_config(self):
@@ -693,6 +704,7 @@ def test_label_smoothing(self):
         assert np.isclose(K.eval(loss), expected_value, atol=1e-3)
 
 
+@skipif_not_tf
 class TestSparseCategoricalCrossentropy(object):
 
     def test_config(self):

tests/keras/metrics_test.py

@@ -6,6 +6,11 @@
 from keras import backend as K
 
 
+if K.backend() != 'tensorflow':
+    # Need TensorFlow to use metric.__call__
+    pytestmark = pytest.mark.skip
+
+
 class TestSum(object):
 
     def test_sum(self):