Progress on serialization

keras_core/losses/__init__.py

@@ -1,11 +1,75 @@
 from keras_core.api_export import keras_core_export
 from keras_core.losses.loss import Loss
+from keras_core.losses.losses import CategoricalHinge
+from keras_core.losses.losses import Hinge
 from keras_core.losses.losses import LossFunctionWrapper
+from keras_core.losses.losses import MeanAbsoluteError
+from keras_core.losses.losses import MeanAbsolutePercentageError
 from keras_core.losses.losses import MeanSquaredError
+from keras_core.losses.losses import MeanSquaredLogarithmicError
+from keras_core.losses.losses import SquaredHinge
+from keras_core.losses.losses import categorical_hinge
+from keras_core.losses.losses import hinge
+from keras_core.losses.losses import mean_absolute_error
+from keras_core.losses.losses import mean_absolute_percentage_error
+from keras_core.losses.losses import mean_squared_error
+from keras_core.losses.losses import mean_squared_logarithmic_error
+from keras_core.losses.losses import squared_hinge
+from keras_core.saving import serialization_lib
+
+ALL_OBJECTS = {
+    Loss,
+    LossFunctionWrapper,
+    MeanSquaredError,
+    MeanAbsoluteError,
+    MeanAbsolutePercentageError,
+    MeanSquaredLogarithmicError,
+    Hinge,
+    SquaredHinge,
+    CategoricalHinge,
+    mean_squared_error,
+    mean_absolute_error,
+    mean_absolute_percentage_error,
+    mean_squared_logarithmic_error,
+    hinge,
+    squared_hinge,
+    categorical_hinge,
+}
+
+ALL_OBJECTS_DICT = {cls.__name__: cls for cls in ALL_OBJECTS}
 
 
-def deserialize(obj):
+@keras_core_export("keras_core.losses.serialize")
-    raise NotImplementedError
+def serialize(loss):
+    """Serializes loss function or `Loss` instance.
+
+    Args:
+        loss: A Keras `Loss` instance or a loss function.
+
+    Returns:
+        Loss configuration dictionary.
+    """
+    return serialization_lib.serialize_keras_object(loss)
+
+
+@keras_core_export("keras_core.losses.deserialize")
+def deserialize(name, custom_objects=None):
+    """Deserializes a serialized loss class/function instance.
+
+    Args:
+        name: Loss configuration.
+        custom_objects: Optional dictionary mapping names (strings) to custom
+          objects (classes and functions) to be considered during
+          deserialization.
+
+    Returns:
+        A Keras `Loss` instance or a loss function.
+    """
+    return serialization_lib.deserialize_keras_object(
+        name,
+        module_objects=ALL_OBJECTS_DICT,
+        custom_objects=custom_objects,
+    )
 
 
 @keras_core_export("keras_core.losses.get")

keras_core/losses/loss.py

@@ -45,7 +45,7 @@ class Loss:
         raise NotImplementedError
 
     def get_config(self):
-        return {"name": self.name}
+        return {"name": self.name, "reduction": self.reduction}
 
     @classmethod
     def from_config(cls, config):

keras_core/losses/losses.py

@@ -3,6 +3,7 @@ from keras_core import operations as ops
 from keras_core.api_export import keras_core_export
 from keras_core.losses.loss import Loss
 from keras_core.losses.loss import squeeze_to_same_rank
+from keras_core.saving import serialization_lib
 
 
 class LossFunctionWrapper(Loss):
@@ -18,11 +19,16 @@ class LossFunctionWrapper(Loss):
         return self.fn(y_true, y_pred, **self._fn_kwargs)
 
     def get_config(self):
-        raise NotImplementedError
+        base_config = super().get_config()
+        config = {"fn": serialization_lib.serialize_keras_object(self.fn)}
+        config.update(serialization_lib.serialize_keras_object(self._fn_kwargs))
+        return {**base_config, **config}
 
     @classmethod
-    def from_config(clf, config):
+    def from_config(cls, config):
-        raise NotImplementedError
+        if "fn" in config:
+            config = serialization_lib.deserialize_keras_object(config)
+        return cls(**config)
 
 
 @keras_core_export("keras_core.losses.MeanSquaredError")
@@ -47,6 +53,9 @@ class MeanSquaredError(LossFunctionWrapper):
     ):
         super().__init__(mean_squared_error, reduction=reduction, name=name)
 
+    def get_config(self):
+        return Loss.get_config(self)
+
 
 @keras_core_export("keras_core.losses.MeanAbsoluteError")
 class MeanAbsoluteError(LossFunctionWrapper):
@@ -70,6 +79,9 @@ class MeanAbsoluteError(LossFunctionWrapper):
     ):
         super().__init__(mean_absolute_error, reduction=reduction, name=name)
 
+    def get_config(self):
+        return Loss.get_config(self)
+
 
 @keras_core_export("keras_core.losses.MeanAbsolutePercentageError")
 class MeanAbsolutePercentageError(LossFunctionWrapper):
@@ -97,6 +109,9 @@ class MeanAbsolutePercentageError(LossFunctionWrapper):
             mean_absolute_percentage_error, reduction=reduction, name=name
         )
 
+    def get_config(self):
+        return Loss.get_config(self)
+
 
 @keras_core_export("keras_core.losses.MeanSquaredLogarithmicError")
 class MeanSquaredLogarithmicError(LossFunctionWrapper):
@@ -124,6 +139,9 @@ class MeanSquaredLogarithmicError(LossFunctionWrapper):
             mean_squared_logarithmic_error, reduction=reduction, name=name
         )
 
+    def get_config(self):
+        return Loss.get_config(self)
+
 
 @keras_core_export("keras_core.losses.Hinge")
 class Hinge(LossFunctionWrapper):
@@ -148,6 +166,9 @@ class Hinge(LossFunctionWrapper):
     def __init__(self, reduction="sum_over_batch_size", name="hinge"):
         super().__init__(hinge, reduction=reduction, name=name)
 
+    def get_config(self):
+        return Loss.get_config(self)
+
 
 @keras_core_export("keras_core.losses.SquaredHinge")
 class SquaredHinge(LossFunctionWrapper):
@@ -172,6 +193,9 @@ class SquaredHinge(LossFunctionWrapper):
     def __init__(self, reduction="sum_over_batch_size", name="squared_hinge"):
         super().__init__(squared_hinge, reduction=reduction, name=name)
 
+    def get_config(self):
+        return Loss.get_config(self)
+
 
 @keras_core_export("keras_core.losses.CategoricalHinge")
 class CategoricalHinge(LossFunctionWrapper):
@@ -198,6 +222,9 @@ class CategoricalHinge(LossFunctionWrapper):
     ):
         super().__init__(categorical_hinge, reduction=reduction, name=name)
 
+    def get_config(self):
+        return Loss.get_config(self)
+
 
 def convert_binary_labels_to_hinge(y_true):
     """Converts binary labels into -1/1 for hinge loss/metric calculation."""

keras_core/testing/test_case.py

@@ -1,4 +1,6 @@
 import json
+import os
+import tempfile
 import unittest
 
 import numpy as np
@@ -8,6 +10,11 @@ from tensorflow import nest
 class TestCase(unittest.TestCase):
     maxDiff = None
 
+    def get_temp_dir(self):
+        temp_dir = tempfile.mkdtemp()
+        self.addCleanup(lambda: os.rmdir(temp_dir))
+        return temp_dir
+
     def assertAllClose(self, x1, x2, atol=1e-7, rtol=1e-7):
         np.testing.assert_allclose(x1, x2, atol=atol, rtol=rtol)