CODEWITHSUNDEEP
tensorflowkeras
Mejdi Dallel
Mejdi Dallel

Reputation: 642

Fine-Tuning Keras model

I'm working on facial expression recognition using CNN. I'm using Keras and Tensorflow as backend. My model is saved to h5 format.

I want to retrain my network, and fine-tune my model with the VGG model.

How can I do that with keras ?

Upvotes: 3

Views: 718

Answers (2)

blackHoleDetector
blackHoleDetector

Reputation: 3033

You can use the Keras model.save(filepath) function.

Details for the various Keras saving and loading techniques are discussed with examples in this YouTube video: Save and load a Keras model

model.save(filepath)saves:

  • The architecture of the model, allowing to re-create the model.
  • The weights of the model.
  • The training configuration (loss, optimizer).
  • The state of the optimizer, allowing to resume training exactly where you left off.

To load this saved model, you would use the following:

from keras.models import load_model
new_model = load_model(filepath)

If you used model.to_json(), you would only be saving the architecture of the model. Additionally, if you used model.save_weights(), you would only be saving the weights of the model. With both of these alternative saving techniques, you would not be saving the training configuration (loss, optimizer), nor would you be saving the state of the optimizer.

Upvotes: 1

Harsha Pokkalla
Harsha Pokkalla

Reputation: 1802

Save your models architecture and weights: 

json_string = model.to_json()
model.save_weights('model_weights.h5')

Load model architecture and weights:

from keras.models import model_from_json
model = model_from_json(json_string)
model.load_weights('model_weights.h5')

Start training again from here for finetuning. I hope this helps.

Upvotes: 3

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