CODEWITHSUNDEEP
pythontensorflowkeras
Aziz Siyaev
Aziz Siyaev

Reputation: 599

UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually

After a training procedure, I wanted to check the accuracy by loading the created model.h5 and executing an evaluation procedure. However, I am getting a following warning:

/usr/local/lib/python3.5/dist-packages/keras/engine/saving.py:269: UserWarning: No training configuration found in save file: the model was not compiled. Compile it manually. warnings.warn('No training configuration found in save file:

enter image description here

This dist-packages/keras/engine/saving.py file

so the problem in loading created model -> this line of code

train_model = load_model('model.h5')

Problem indicates that the model was not compiled, however, I did it.

optimizer = Adam(lr=lr, clipnorm=0.001)
train_model.compile(loss=dummy_loss, optimizer=optimizer)

I can't understand what I am doing wrong . . . Please help me! SOS :-(

Upvotes: 55

Views: 68311

Answers (3)

Tomasz Gandor
Tomasz Gandor

Reputation: 8833

Intro

I'd like to add to olejorgenb's answer - for a specific scenario, where you don't want to train the model, just use it (e.g. in production).

"Compile" means "prepare for training", which includes mainly setting up the optimizer. It could also have been saved before, and then you can continue the "same" training after loading the saved model.

The fix

But, what about the scenario - I want to just run the model? Well, use the compile=False argument to load_model like that:

trained_model = load_model('model.h5', compile=False)

You won't be able to .fit() this model without using trained_model.compile(...) first, but most importantly - the warning will go away.

Misc Notes

Btw, in my Keras version, the argument include_optimizer has a default of True. This should work also for trainig callbacks like Checkpoint. This means, when loading a model saved by Keras, you can usually count on the optimizer being included (except for the situation: see Hull Gasper's answer).

But, when you have a model which was not trained by Keras (e.g. when converting a model trained by Darknet), the model is saved un-compiled. This produces the warning, and you can get rid of it in the way described above.

Upvotes: 88

Hüll Gasper
Hüll Gasper

Reputation: 48

As mentioned keras can't save Tensorflow optimizers. Use the keras one:

optimizer = keras.optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)

model.compile(optimizer=optimizer,
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(...)
model.save('...')

This way works for me without manual compiling after calling load.

Upvotes: 3

olejorgenb
olejorgenb

Reputation: 1328

Do you get this warning when saving the model?

WARNING:tensorflow:TensorFlow optimizers do not make it possible to access 
optimizer attributes or optimizer state after instantiation. As a result, we 
cannot save the optimizer as part of the model save file.You will have to 
compile your model again after loading it. Prefer using a Keras optimizer 
instead (see keras.io/optimizers).

Seems tensorflow optimizers can't be preserved by keras :/

Upvotes: 3

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