Refactor test cases to improve unit test quality

keras/benchmarks/eager_microbenchmarks_test.py

@@ -135,7 +135,6 @@ def fn():
         self._run(fn, 20)
 
     def benchmark_layers_embeddings_embedding_overhead(self):
-
         layer = tf.keras.layers.Embedding(1, 1)
         x = tf.zeros((1, 1), dtype="int32")
 
@@ -148,7 +147,6 @@ def fn():
 class KerasLayerCallOverheadBenchmarks(
     MicroBenchmarksBase, metaclass=tf.__internal__.test.ParameterizedBenchmark
 ):
-
     # The set of layers for benchmarking. To add benchmarks for new layers,
     # please add the parameter configs to "_benchmark_paramters".
 
@@ -225,7 +223,6 @@ class KerasLayerCallOverheadBenchmarks(
     ]
 
     def benchmark_layer(self, layer, input_shape, kwargs=None):
-
         x = tf.ones(input_shape)
 
         def fn():

keras/tests/add_loss_correctness_test.py

@@ -261,7 +261,7 @@ def call(self, inputs):
         layer = MyLayer()
         outputs = layer(inputs)
         model = Model(inputs, outputs)
-        self.assertEqual(len(model.losses), 1)
+        self.assertLen(model.losses, 1)
         model.compile("sgd", "mse", run_eagerly=test_utils.should_run_eagerly())
         loss = model.train_on_batch(np.ones((2, 3)), np.ones((2, 3)))
         self.assertEqual(loss, 2 * 3)
@@ -373,7 +373,7 @@ def call(self, inputs):
         m = Sequential([shared_layer])
         m2 = Sequential([shared_layer, m])
         m2(tf.constant([1, 2, 3]))
-        self.assertEqual(len(m2.losses), 2)
+        self.assertLen(m2.losses, 2)
         self.assertAllClose(m2.losses, [6, 12])
 
     @test_combinations.run_all_keras_modes
@@ -394,36 +394,36 @@ def call(self, x):
         x1 = tf.ones((1, 1))
         _ = l(x1)
         if not tf.executing_eagerly():
-            self.assertEqual(len(l.get_losses_for(x1)), 2)
-            self.assertEqual(len(l.get_losses_for(None)), 1)
+            self.assertLen(l.get_losses_for(x1), 2)
+            self.assertLen(l.get_losses_for(None), 1)
 
         x2 = tf.ones((1, 1))
         _ = l(x2)
         if not tf.executing_eagerly():
-            self.assertEqual(len(l.get_losses_for(x1)), 2)
-            self.assertEqual(len(l.get_losses_for(x2)), 2)
-            self.assertEqual(len(l.get_losses_for(None)), 1)
+            self.assertLen(l.get_losses_for(x1), 2)
+            self.assertLen(l.get_losses_for(x2), 2)
+            self.assertLen(l.get_losses_for(None), 1)
 
         outputs = l(inputs)
         model = Model(inputs, outputs)
         if not tf.executing_eagerly():
-            self.assertEqual(len(model.losses), 7)
-            self.assertEqual(len(l.get_losses_for(x1)), 2)
-            self.assertEqual(len(l.get_losses_for(x2)), 2)
-            self.assertEqual(len(l.get_losses_for(None)), 1)
+            self.assertLen(model.losses, 7)
+            self.assertLen(l.get_losses_for(x1), 2)
+            self.assertLen(l.get_losses_for(x2), 2)
+            self.assertLen(l.get_losses_for(None), 1)
 
         x3 = tf.ones((1, 1))
         model(x3)
         x4 = tf.ones((1, 1))
         model(x4)
         if tf.executing_eagerly():
             # Eager losses are cleared every `__call__`.
-            self.assertEqual(len(model.losses), 3)
+            self.assertLen(model.losses, 3)
         else:
-            self.assertEqual(len(model.losses), 11)
-            self.assertEqual(len(model.get_losses_for(x3)), 2)
-            self.assertEqual(len(model.get_losses_for(x4)), 2)
-            self.assertEqual(len(model.get_losses_for(None)), 1)
+            self.assertLen(model.losses, 11)
+            self.assertLen(l.get_losses_for(x3), 2)
+            self.assertLen(l.get_losses_for(x4), 2)
+            self.assertLen(l.get_losses_for(None), 1)
 
     @test_combinations.run_all_keras_modes(always_skip_v1=True)
     def test_invalid_constant_input(self):

keras/tests/model_subclassing_test.py

@@ -512,9 +512,9 @@ def call(self, inputs):
         model(x)
 
         if tf.executing_eagerly():
-            self.assertEqual(0, len(model.updates))
+            self.assertLen(model.updates, 0)
         else:
-            self.assertEqual(2, len(model.updates))
+            self.assertLen(model.updates, 2)
 
 
 class GraphSpecificModelSubclassingTests(tf.test.TestCase):
@@ -557,7 +557,6 @@ def test_multi_io_workflow_with_tensors(self):
             _ = model.evaluate(steps=10, verbose=0)
 
     def test_updates_and_losses_for_nested_models_in_subclassed_model(self):
-
         # Case 1: deferred-build sequential nested in subclass.
         class TestModel1(keras.Model):
             def __init__(self):