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ValueError: The filepath provided must end in .keras (Keras model format). Received: filepath=Models/02_captcha_to_text/202403291006/model.h5 #48
Description
Activity
my code:
Define callbacks
earlystopper = EarlyStopping(monitor="val_CER", patience=50, verbose=1, mode="min")
checkpoint = ModelCheckpoint(f"{configs.model_path}/model.h5", monitor="val_CER", verbose=1, save_best_only=True, mode="min")
trainLogger = TrainLogger(configs.model_path)
tb_callback = TensorBoard(f"{configs.model_path}/logs", update_freq=1)
reduceLROnPlat = ReduceLROnPlateau(monitor="val_CER", factor=0.9, min_delta=1e-10, patience=20, verbose=1, mode="min")
model2onnx = Model2onnx(f"{configs.model_path}/model.h5")Train the model
model.fit(
train_data_provider,
validation_data=val_data_provider,
epochs=configs.train_epochs,
callbacks=[earlystopper, checkpoint, trainLogger, reduceLROnPlat, tb_callback, model2onnx],
workers=configs.train_workers
)Save training and validation datasets as csv files
train_data_provider.to_csv(os.path.join(configs.model_path, "train.csv"))
val_data_provider.to_csv(os.path.join(configs.model_path, "val.csv"))did you find any solutions on that?
I'am still working on thatReacted by maxima120It seems you try to use newest tensorflow version (different from tutorial). Recommend to downgrade tensorflow for compatibility. Otherwise you need to do changes manualy. For example change model.h5 to model.keras in all places. But you may face more serious incompatibilities further
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, so 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".
https://keras.io/guides/migrating_to_keras_3/#saving-a-model-in-the-tf-savedmodel-formatHere's changes for your code:
earlystopper = EarlyStopping(monitor="val_CER", patience=50, verbose=1, mode="min") checkpoint = ModelCheckpoint(f"{configs.model_path}/model.h5", monitor="val_CER", verbose=1, save_best_only=True,, save_weights_only=True, mode="min") trainLogger = TrainLogger(configs.model_path) tb_callback = TensorBoard(f"{configs.model_path}/logs", update_freq=1) reduceLROnPlat = ReduceLROnPlateau(monitor="val_CER", factor=0.9, min_delta=1e-10, patience=20, verbose=1, mode="min") model2onnx = Model2onnx(f"{configs.model_path}/model.h5") model.fit( train_data_provider, validation_data=val_data_provider, epochs=configs.train_epochs, callbacks=[earlystopper, checkpoint, trainLogger, reduceLROnPlat, tb_callback, model2onnx] ) train_data_provider.to_csv(os.path.join(configs.model_path, "train.csv")) val_data_provider.to_csv(os.path.join(configs.model_path, "val.csv"))I have the same problem. I want to save in "tensorflow" format - which is set of files in a directory. I do not want to use either .keras nor .h5
I want to save everything not just weights. And I want to use it for both - future training and inference.
If i save it in .keras format - when I load it for further training or inference, sometimes it throws - optimiser variables mismatch, and doesnt use the saved adam vars.
Which I want to avoid (tell me this is wrong idea).
Versions:
TF Version: 2.18.0
Python Version: 3.11.2 (main, Nov 30 2024, 21:22:50) [GCC 12.2.0]I use
tf.keraseverywhere notkerasdirectly (if it makes any difference).What happened to the "tensorflow" checkpoints? have you removed this feature completely? I dont see anything in the docs indicating it and the samples that supposed to use tensorflow format are throwing the same error (want
.kerassuffix - in either - checkpoint callback and directmodel.save)
Hello,
Can you help me to fix this?
ValueError: The filepath provided must end in
.keras(Keras model format). Received: filepath=Models/02_captcha_to_text/202403291006/model.h5