The following argument(s) are not supported with the native Keras format: ['options']

I am building a Keras deep learning Algorithm on Dogs vs cats dataset. I am able to run my code in colab. But in Jupyter lab I am getting this error.

"The following argument(s) are not supported with the native Keras format: ['options']"

Below is the code:

import os
import shutil
import pathlib
original_dir = pathlib.Path("/content/drive/MyDrive/Reva/dogs_vs_cats/train/train")
new_base_dir = pathlib.Path("/content/drive/MyDrive/Reva/dogs_vs_cats/")
def make_subset(subset_name, start_index, end_index):
for category in ("cat", "dog"):
dir = new_base_dir / subset_name / category
# Check if the folder exists and delete it if it does
if os.path.exists(dir):
shutil.rmtree(dir)
# Create the folder again
os.makedirs(dir)
fnames = [f"{category}.{i}.jpg" for i in range(start_index, end_index)]
for fname in fnames:
shutil.copyfile(src=original_dir / fname,
dst=dir / fname)
make_subset("train", start_index=0, end_index=1000)
make_subset("validation", start_index=1000, end_index=1500)
make_subset("test", start_index=1500, end_index=2500)
from tensorflow.keras.utils import image_dataset_from_directory
train_dataset = image_dataset_from_directory(
new_base_dir / "train",
image_size=(180, 180),
batch_size=32)
validation_dataset = image_dataset_from_directory(
new_base_dir / "validation",
image_size=(180, 180),
batch_size=32)
test_dataset = image_dataset_from_directory(
new_base_dir / "test",
image_size=(180, 180),
batch_size=32)
from tensorflow import keras
from tensorflow.keras import layers
inputs = keras.Input(shape=(180, 180, 3))
x = layers.Rescaling(1./255)(inputs)
x = layers.Conv2D(filters=32, kernel_size=3, activation="relu")(x)
x = layers.MaxPooling2D(pool_size=2)(x)
x = layers.Conv2D(filters=64, kernel_size=3, activation="relu")(x)
x = layers.MaxPooling2D(pool_size=2)(x)
x = layers.Conv2D(filters=128, kernel_size=3, activation="relu")(x)
x = layers.MaxPooling2D(pool_size=2)(x)
x = layers.Conv2D(filters=256, kernel_size=3, activation="relu")(x)
x = layers.MaxPooling2D(pool_size=2)(x)
x = layers.Conv2D(filters=256, kernel_size=3, activation="relu")(x)
x = layers.Flatten()(x)
outputs = layers.Dense(1, activation="sigmoid")(x)
model = keras.Model(inputs=inputs, outputs=outputs)
model.compile(loss="binary_crossentropy",
optimizer="rmsprop",
metrics=["accuracy"])
callbacks = [
keras.callbacks.ModelCheckpoint(
filepath="convnet_from_scratch.keras",
save_best_only=True,
monitor="val_loss")
]
history = model.fit(
train_dataset,
epochs=30,
validation_data=validation_dataset,
callbacks=callbacks)

I need to know how to resolve the above code. Any suggestions to improve the time required to run the code is also welcome.

TensorFlow team did not modify the ModelCheckpoint. The error occurs because there should not be an argument called "options" when saving the model, but ModelCheckpoint includes it.

The solution is to remove that part manually. Go to the location where ModelCheckpoint is defined (if you are using VSCode, it can be easily done).

Find the following line using Ctrl+F:
"options=self._options,"

Replace all occurrences with empty spaces and save. This will resolve the issue.

As I mentioned in the comments, there seems to be a weird behaviour related to keras saving and also versioning of TF/Keras. I could replicate your error when running TF/Keras with version 2.13 (newest right now) on colab. Standard install on colab is 2.12, where the error doesn't come up.
So one solution would be to downgrade TF/Keras to 2.12.x, or change

keras.callbacks.ModelCheckpoint(
filepath="convnet_from_scratch.keras",
..)

to

keras.callbacks.ModelCheckpoint(
filepath="convnet_from_scratch.x",
..)

where x stands for whatever you fancy (NOT "keras") to not save in the .keras format.

It is best if you create your own ModelCheckPoint function:

def save_model_checkpoint(epoch, logs):
if logs['val_loss'] < save_model_checkpoint.best_val_loss:
save_model_checkpoint.best_val_loss = logs['val_loss']
model.save_weights(new_base_dir / 'model_checkpoint')
print('Model checkpoint saved.')
save_model_checkpoint.best_val_loss = float('inf')
# Initialize your model here
model.fit(
# Other model.fit arguments...
callbacks=[tf.keras.callbacks.LambdaCallback(on_epoch_end=save_model_checkpoint)]
)

The save_model_checkpoint function is defined to monitor the validation loss and save the weights of the best model. It is called at the end of each epoch.
The best_val_loss attribute of the save_model_checkpoint function is initialized to float('inf') to track the best validation loss seen so far.
Inside the function, if the current validation loss (logs['val_loss']) is lower than the best_val_loss, the best_val_loss is updated, and the model weights are saved using model.save_weights(). You can customize the saving path and filename as needed.
During the model.fit() call, you can pass the save_model_checkpoint function as a callback using tf.keras.callbacks.LambdaCallback(on_epoch_end=save_model_checkpoint). This ensures that the save_model_checkpoint function is called at the end of each epoch.