Question: 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.

Answer: 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.