ValueError: The filepath provided must end in .keras (Keras model format). Received: filepath=Model_ckpt.h5

[kishan042p]

Issue Type

Support

Source

source

Keras Version

3.2.1

Custom Code

Yes

OS Platform and Distribution

Linux Ubuntu 22.04.4

Python version

3.11.5

GPU model and memory

16 gm

Current Behavior?

please solve this error

Standalone code to reproduce the issue or tutorial link

CKPT_path = "Model_ckpt.h5"
checkpointing_cb = tf.keras.callbacks.ModelCheckpoint(CKPT_path, save_best_only=True)

# Orginal train

EPOCHS = 30
VALIDATION_SET = (X_valid, y_valid)

history = model_clf.fit(X_train, y_train, epochs=EPOCHS,
                    validation_data=VALIDATION_SET, batch_size=32, callbacks=[tb_cb, early_stopping_cb,checkpointing_cb] )

Relevant log output

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[37], line 2
      1 CKPT_path = "Model_ckpt.h5"
----> 2 checkpointing_cb = tf.keras.callbacks.ModelCheckpoint(CKPT_path, save_best_only=True)

File ~/anaconda3/lib/python3.11/site-packages/keras/src/callbacks/model_checkpoint.py:191, in ModelCheckpoint.__init__(self, filepath, monitor, verbose, save_best_only, save_weights_only, mode, save_freq, initial_value_threshold)
    189 else:
    190     if not self.filepath.endswith(".keras"):
--> 191         raise ValueError(
    192             "The filepath provided must end in `.keras` "
    193             "(Keras model format). Received: "
    194             f"filepath={self.filepath}"
    195         )

ValueError: The filepath provided must end in `.keras` (Keras model format). Received: filepath=Model_ckpt.h5

[AssiaBou]

I have the same problem in Kaggle notebook while executing the following code:
history=model1.fit(train_img_datagen, steps_per_epoch=steps_per_epoch, epochs=100, verbose=1, callbacks = [ keras.callbacks.ModelCheckpoint('UNet_100.h5', save_weights_only=False, save_best_only=True, monitor='val_loss', mode='min'), keras.callbacks.ReduceLROnPlateau(), ], validation_data=val_img_datagen, validation_steps=val_steps_per_epoch, )

[AssiaBou]

@kishan042p I have just solved the problem by installing an older versio of keras:

!pip install keras==2.15.0
import keras
print(keras.version)

But you have to restart your kernel before using it,

Good luck

[kishan042p]

@kishan042p I have just solved the problem by installing an older versio of keras:

!pip install keras==2.15.0 import keras print(keras.version)

But you have to restart your kernel before using it,

Good luck

But i Want to save model in .h5 file not in .keras so please provide solution regarding it.

[AssiaBou]

Yes I use my old code, where I save my model in .h5 file not .keras
You don't have to change anything, you just have to iinstall an older version of keras and everything will be OK

[sachinprasadhs]

In Keras 3, for checkpoint filepath, you need to provide in .keras format only, if you're saving only weights file name should end with .weights.h5.

You can save and load the model in .h5 using below method
https://keras.io/guides/migrating_to_keras_3/#saving-a-model-in-the-tf-savedmodel-format

[russell919]

In Keras 3, if you want to save your model in .h5 file in any case, set "save_weights_only=True", and change your flie name to "xxx.weights.h5".

for your code:
CKPT_path = "Model_ckpt.weights.h5"
checkpointing_cb = tf.keras.callbacks.ModelCheckpoint(CKPT_path, save_best_only=True, save_weights_only=True)

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[helderdaniel]

https://keras.io/guides/migrating_to_keras_3/#saving-a-model-in-the-tf-savedmodel-format

"The following snippet of code will reproduce the above error:

sequential_model = keras.Sequential([
    keras.layers.Dense(2)
])
sequential_model.save("saved_model")

How to fix it: use model.export(filepath) instead of model.save(filepath)"

[diponkor-bala]

In Keras 3, if you want to save your model in .h5 file in any case, set "save_weights_only=True", and change your flie name to "xxx.weights.h5".

for your code:
CKPT_path = "Model_ckpt.weights.h5"
checkpointing_cb = tf.keras.callbacks.ModelCheckpoint(CKPT_path, save_best_only=True, save_weights_only=True)