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- yes. Save your model as.h5 When you want to train your model, load it again and do a model.fit as normal. Make sure you do not compile your model after loading it as this will reset your weights.
stackoverflow.com/questions/51854463/is-it-possible-to-retrain-a-previously-saved-keras-model
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How to retrain a saved model?
Can a model be saved after training?
How do I save a training model?
Can I train a model after loading it?
Aug 15, 2018 · After loading the saved model, you can retrain as usual using loaded_model.fit(). model.save('./MyModel_tf',save_format='tf') # loading the saved model loaded_model = tf.keras.models.load_model('./MyModel_tf') # retraining the model loaded_model.fit(x_train, y_train, epochs = 10, validation_data = (x_test,y_test),verbose=1)
Mar 8, 2017 · # saving the model in tensorflow format model.save('./MyModel_tf',save_format='tf') # loading the saved model loaded_model = tf.keras.models.load_model('./MyModel_tf') # retraining the model loaded_model.fit(x_train, y_train, epochs = 10, validation_data = (x_test,y_test),verbose=1)
Apr 30, 2018 · To check if it was a problem of the new points or the way I’m loading the model, I saved a trained model and load it again to retrain over the same set of points. When doing this, the error on the very first epoch increases a lot with respect to the error on the trained model. Is this normal?
Mar 8, 2024 · Method 1: Using load_model() Function. The load_model() function in Keras is designed to easily load complete models that are saved in the HDF5 format, including the model architecture, weights, and training configuration (loss, optimizer).
Feb 20, 2019 · You can load the parameters using model.load_state_dict(): # Initialize model model = MyModel() # Load state_dict model.load_state_dict(torch.load('my_weights.pth')) Have a look at the Transfer Learning Tutorial to see how you can fine-tune your model.
Apr 3, 2024 · Model progress can be saved during and after training. This means a model can resume where it left off and avoid long training times. Saving also means you can share your model and others can recreate your work. When publishing research models and techniques, most machine learning practitioners share: code to create the model, and
Feb 3, 2022 · First things first, double check your data. I know it seems way to obvious, but minor oversights can lead to poor performance when reloading your model from disk. If for instance, you are...
