Update workflows for TensorFlow/Keras 2.14

Signed-off-by: Beat Buesser <[email protected]>

Author: beat-buesser

tests/estimators/classification/test_scikitlearn.py

@@ -300,38 +300,32 @@ def test_class_gradient_none_2(self):
 
     def test_class_gradient_int_1(self):
         grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:1], label=1)
-        grad_expected = [[[-0.56322294, -0.70493763, -0.98874801, -0.67053026]]]
+        grad_expected = [[[-0.56317311, -0.70493763, -0.98908609, -0.67106276]]]
 
         for i_shape in range(4):
-            print(grad_predicted[0, 0, i_shape])
             self.assertAlmostEqual(grad_predicted[0, 0, i_shape], grad_expected[0][0][i_shape], 3)
 
     def test_class_gradient_int_2(self):
         grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:2], label=1)
         grad_expected = [
-            [[-0.56322294, -0.70427608, -0.98874801, -0.67053026]],
-            [[-0.50528532, -0.71700042, -0.82467848, -0.59614766]],
+            [[-0.56317306, -0.70493776, -0.98908573, -0.67106259]],
+            [[-0.50522697, -0.71762568, -0.82497531, -0.5966416]],
         ]
-        print("grad_predicted")
-        print(grad_predicted)
         np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
 
     def test_class_gradient_list_1(self):
         grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:1], label=[1])
-        grad_expected = [[[-0.56322294, -0.70427608, -0.98874801, -0.67053026]]]
+        grad_expected = [[[-0.56317311, -0.70493763, -0.98874801, -0.67053026]]]
 
         for i_shape in range(4):
-            print(grad_predicted[0, 0, i_shape])
             self.assertAlmostEqual(grad_predicted[0, 0, i_shape], grad_expected[0][0][i_shape], 3)
 
     def test_class_gradient_list_2(self):
         grad_predicted = self.classifier.class_gradient(self.x_test_iris[0:2], label=[1, 2])
         grad_expected = [
-            [[-0.56322294, -0.70427608, -0.98874801, -0.67053026]],
-            [[0.70875132, 0.25104877, 1.70929277, 0.88410652]],
+            [[-0.56317306, -0.70493776, -0.98908573, -0.67106259]],
+            [[0.70866591, 0.25158876, 1.70947325, 0.88450021]],
         ]
-        print("grad_predicted")
-        print(grad_predicted)
         np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
 
     def test_class_gradient_label_wrong_type(self):
@@ -345,9 +339,7 @@ def test_class_gradient_label_wrong_type(self):
 
     def test_loss_gradient(self):
         grad_predicted = self.classifier.loss_gradient(self.x_test_iris[0:1], self.y_test_iris[0:1])
-        grad_expected = np.asarray([[-0.21693791, -0.08792436, -0.51507443, -0.26990796]])
-        print("grad_predicted")
-        print(grad_predicted)
+        grad_expected = np.asarray([[-0.21690657, -0.08809226, -0.51512082, -0.27002635]])
         np.testing.assert_array_almost_equal(grad_predicted, grad_expected, decimal=4)
 
     def test_save(self):