Minor fixes

keras_core/saving/serialization_lib.py

@@ -13,6 +13,7 @@ from keras_core.api_export import keras_core_export
 from keras_core.backend.common import global_state
 from keras_core.saving import object_registration
 from keras_core.utils import python_utils
+from keras_core.utils.module_utils import tensorflow as tf
 
 PLAIN_TYPES = (str, int, float, bool)
 
@@ -127,11 +128,6 @@ def serialize_keras_object(obj):
         A python dict that represents the object. The python dict can be
         deserialized via `deserialize_keras_object()`.
     """
-    if backend.backend() == "tensorflow":
-        import tensorflow as tf
-    else:
-        tf = None
-
     if obj is None:
         return obj
 
@@ -163,7 +159,7 @@ def serialize_keras_object(obj):
                 "keras_history": history,
             },
         }
-    if tf is not None and isinstance(obj, tf.TensorShape):
+    if tf.available and isinstance(obj, tf.TensorShape):
         return obj.as_list() if obj._dims is not None else None
     if backend.is_tensor(obj):
         return {
@@ -185,7 +181,7 @@ def serialize_keras_object(obj):
         else:
             # Treat numpy floats / etc as plain types.
             return obj.item()
-    if tf is not None and isinstance(obj, tf.DType):
+    if tf.available and isinstance(obj, tf.DType):
         return obj.name
     if isinstance(obj, types.FunctionType) and obj.__name__ == "<lambda>":
         warnings.warn(
@@ -203,7 +199,7 @@ def serialize_keras_object(obj):
                 "value": python_utils.func_dump(obj),
             },
         }
-    if tf is not None and isinstance(obj, tf.TypeSpec):
+    if tf.available and isinstance(obj, tf.TypeSpec):
         ts_config = obj._serialize()
         # TensorShape and tf.DType conversion
         ts_config = list(
@@ -472,11 +468,6 @@ def deserialize_keras_object(
     Returns:
         The object described by the `config` dictionary.
     """
-    if backend.backend() == "tensorflow":
-        import tensorflow as tf
-    else:
-        tf = None
-
     safe_scope_arg = in_safe_mode()  # Enforces SafeModeScope
     safe_mode = safe_scope_arg if safe_scope_arg is not None else safe_mode
 

keras_core/trainers/epoch_iterator.py

@@ -46,6 +46,7 @@ from keras_core.trainers.data_adapters import generator_data_adapter
 from keras_core.trainers.data_adapters import py_dataset_adapter
 from keras_core.trainers.data_adapters import tf_dataset_adapter
 from keras_core.trainers.data_adapters import torch_data_adapter
+from keras_core.utils.module_utils import tensorflow as tf
 
 
 class EpochIterator:
@@ -60,11 +61,6 @@ class EpochIterator:
         class_weight=None,
         steps_per_execution=1,
     ):
-        try:
-            import tensorflow as tf
-        except ImportError:
-            tf = None
-
         self.steps_per_epoch = steps_per_epoch
         self.steps_per_execution = steps_per_execution
         if steps_per_epoch:
@@ -80,7 +76,7 @@ class EpochIterator:
                 batch_size=batch_size,
                 steps=steps_per_epoch,
             )
-        elif tf is not None and isinstance(x, tf.data.Dataset):
+        elif tf.available and isinstance(x, tf.data.Dataset):
             self.data_adapter = tf_dataset_adapter.TFDatasetAdapter(
                 x, class_weight=class_weight
             )
@@ -164,11 +160,6 @@ class EpochIterator:
         return iterator
 
     def enumerate_epoch(self, return_type="np"):
-        try:
-            import tensorflow as tf
-        except ImportError:
-            tf = None
-
         buffer = []
         if self.steps_per_epoch:
             if not self._current_iterator:
@@ -179,10 +170,10 @@ class EpochIterator:
                 if self._insufficient_data:
                     break
 
-                if tf is None:
-                    errors = (StopIteration,)
-                else:
+                if tf.available:
                     errors = (StopIteration, tf.errors.OutOfRangeError)
+                else:
+                    errors = (StopIteration,)
 
                 try:
                     data = next(self._current_iterator)

keras_core/trainers/trainer.py

@@ -771,7 +771,7 @@ def resolve_auto_jit_compile(model):
 def model_supports_jit(model):
     if platform.system() == "Darwin" and "arm" in platform.processor().lower():
         if backend.backend() == "tensorflow":
-            import tensorflow as tf
+            from keras_core.utils.module_utils import tensorflow as tf
 
             if tf.config.list_physical_devices("GPU"):
                 return False