fixes #469

CHANGELOG.md

@@ -6,6 +6,18 @@ Most recent releases are shown at the top. Each release shows:
 - **Changed**: Additional parameters, changes to inputs or outputs, etc
 - **Fixed**: Bug fixes that don't change documented behaviour
 
+## 0.32.1 (2022-12-11)
+
+### new:
+- N/A
+
+### changed
+- N/A
+
+### fixed:
+- In TensorFlow 2.11, the `tf.optimizers.Optimizer` base class points the new keras optimizer that seems to have problems.  Users should use legacy optimizers in `tf.keras.optimizers.legacy` with **ktrain** (which evidently will never be deleted). This means that, in TF 2.11, supplying a string representation of an optimizer like `"adam"` to `model.compile` uses the new optimizer instead of the legacy optimizers. In these cases, **ktrain** will issue a warning and automatically recompile the model with the default `tf.keras.optimizers.legacy.Adam` optimizer. 
+
+
 ## 0.32.0 (2022-12-08)
 
 ### new:

ktrain/__init__.py

@@ -118,7 +118,7 @@ def get_learner(
             learner = BERTTextClassLearner
         else:  # vanilla text classifiers use standard ArrayLearners
             learner = ArrayLearner
-    return learner(
+    l = learner(
         model,
         train_data=train_data,
         val_data=val_data,
@@ -127,6 +127,19 @@ def get_learner(
         workers=workers,
         use_multiprocessing=use_multiprocessing,
     )
+    import tensorflow as tf
+    from tensorflow.keras.optimizers import Optimizer
+    import warnings
+    from packaging import version
+
+    if (version.parse(tf.__version__) >= version.parse("2.11")) and (
+        isinstance(l.model.optimizer, Optimizer)
+    ):
+        warnings.warn(
+            "ktrain currently only supports legacy optimizers in tensorflow>=2.11 - recompiling your model to use legacy Adam"
+        )
+        l._recompile(wd=0)
+    return l
 
 
 # keys

ktrain/version.py

@@ -1,2 +1,2 @@
 __all__ = ["__version__"]
-__version__ = "0.32.0"
+__version__ = "0.32.1"

ktrain/vision/models.py

@@ -141,7 +141,7 @@ def image_classifier(
                             If None, then all layers except new Dense layers
                             will be frozen/untrainable.
         metrics (list):  metrics to use
-        optimizer_name(str): name of Keras optimizer (e.g., 'adam', 'sgd')
+        optimizer_name(str|obj): name of Keras optimizer (e.g., 'adam', 'sgd') or instance of keras Optimizer
         multilabel(bool):  If True, model will be build to support
                            multilabel classificaiton (labels are not mutually exclusive).
                            If False, binary/multiclassification model will be returned.
@@ -259,7 +259,7 @@ def image_model(
                             If None, then all layers except new Dense layers
                             will be frozen/untrainable.
         metrics (list):  metrics to use
-        optimizer_name(str): name of Keras optimizer (e.g., 'adam', 'sgd')
+        optimizer_name(str|obj): name of Keras optimizer (e.g., 'adam', 'sgd') or instance of Keras optimizer
         multilabel(bool):  If True, model will be build to support
                            multilabel classificaiton (labels are not mutually exclusive).
                            If False, binary/multiclassification model will be returned.