Keras 2.14 optimizer format changed causing simple models to not import

Hey everybody.

I am running into an issue when loading a simple python keras model.

Python keras model:

`model = keras.Sequential([ keras.layers.Dense(32, activation='relu', input_shape=(132,)), keras.layers.Dropout(.2), keras.layers.Dense(16, activation='relu'), keras.layers.Dense(41, activation='softmax')
]) optimizer = SGD(learning_rate=0.1) model.compile(optimizer=optimizer, loss=keras.losses.sparse_categorical_crossentropy, metrics=['accuracy'])

model.fit(train_x, train_y, epochs=10, batch_size=32, validation_split=0.1)

test_loss, test_accuracy = model.evaluate(test_x, test_y)
print(f"Test accuracy: {test_accuracy}")

model.save("test_model.h5")`

Java code that gives me the error:

MultiLayerNetwork model = KerasModelImport.importKerasSequentialModelAndWeights("path/to/model.h5");

The error itself: org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException

The error message: Optimizer with name Custom>SGDcan not bematched to a DL4J optimizer. Note that custom TFOptimizers are not supported by model import.

Version Information

Windows 10 x64 JDK 21

My dependencies look like this: https://gist.github.com/YusifCodes/1c275a810b5c966c50fb4303ae3143a7

Note: I used dl4j 1.0.0-M2.1, 1.0.0-beta6, 1.0.0-beta7 previously got the same error. I also replaced Adam with SGD, still no luck.

I relly wish we can resolve this issue, thanks!