Fix summary when optimizer is None

keras_core/utils/summary_utils.py

@@ -308,7 +308,7 @@ def print_layer(layer, nested_level=0):
     non_trainable_count = count_params(model.non_trainable_weights)
     non_trainable_memory_size = weight_memory_size(model.non_trainable_weights)
 
-    if model.compiled and model.optimizer.built:
+    if model.compiled and model.optimizer and model.optimizer.built:
         optimizer_weight_count = count_params(model.optimizer.variables)
         optimizer_memory_size = weight_memory_size(model.optimizer.variables)
     else:

keras_core/utils/summary_utils_test.py

@@ -2,41 +2,42 @@
 
 import numpy as np
 import pytest
+from absl.testing import parameterized
 
 from keras_core import layers
 from keras_core import models
 from keras_core import testing
 from keras_core.utils import summary_utils
 
 
-class SummaryUtilsTest(testing.TestCase):
+class SummaryUtilsTest(testing.TestCase, parameterized.TestCase):
     @pytest.mark.requires_trainable_backend
-    def test_print_model_summary(self):
+    @parameterized.parameters([("adam",), (None,)])
+    def test_print_model_summary(self, optimizer):
         inputs = layers.Input((2,))
         outputs = layers.Dense(3)(inputs)
         model = models.Model(inputs, outputs)
-        model.compile(optimizer="adam", loss="mse", metrics=["mse"])
-        # Trigger the optimizer weights creation
-        model.fit(x=np.zeros([4, 2]), y=np.zeros([4, 3]))
+        model.compile(optimizer=optimizer, loss="mse", metrics=["mse"])
+        if optimizer:
+            # Trigger the optimizer weights creation
+            model.fit(x=np.zeros([4, 2]), y=np.zeros([4, 3]))
 
-        file_name = "model_1.txt"
-        temp_dir = self.get_temp_dir()
-        fpath = os.path.join(temp_dir, file_name)
-        writer = open(fpath, "w")
+        summary_content = []
 
-        def print_to_file(text, line_break=False):
-            print(text, file=writer)
+        def print_to_variable(text, line_break=False):
+            summary_content.append(text)
 
         try:
-            summary_utils.print_summary(model, print_fn=print_to_file)
-            writer.close()
-            self.assertTrue(os.path.exists(fpath))
-            with open(fpath, "r") as reader:
-                summary_content = reader.read()
-            # self.assertEqual(len(lines), 15)
-            self.assertIn("Total params: 29", summary_content)
-            self.assertIn("Trainable params: 9", summary_content)
-            self.assertIn("Non-trainable params: 0", summary_content)
-            self.assertIn("Optimizer params: 20", summary_content)
+            summary_utils.print_summary(model, print_fn=print_to_variable)
+            summary_content = "\n".join(summary_content)
+            if optimizer:
+                self.assertIn("Total params: 29", summary_content)
+                self.assertIn("Trainable params: 9", summary_content)
+                self.assertIn("Non-trainable params: 0", summary_content)
+                self.assertIn("Optimizer params: 20", summary_content)
+            else:
+                self.assertIn("Total params: 9", summary_content)
+                self.assertIn("Trainable params: 9", summary_content)
+                self.assertIn("Non-trainable params: 0", summary_content)
         except ImportError:
             pass