Comments (2)
This will work for Model object (keras.models.Model or tf.keras.models.Model) as long as the optimizer function used when compiling the model has a "learning_rate" parameter.
Actually, I just checked the code for this repo and it uses "lr", which is technically the same in the Keras source code, but only supported for the sake of backwards compatibility. The recommended usage "learning_rate", and I will refactor that and make a pull request after I'm done with this comment. 😄
So you could even a multi-input model with a CNN, an RNN, and a Feed-Forward network within one model and use this - the model doesn't matter. I would recommend learning a bit more about how this works and the corresponding login.
Here are some resources you could look over:
"How to Use the Learning Rate Finder in TensorFlow" by Ashwath Salimath
Learning Rate Finder Documentation from Fast.ai
Also, I'm not sure how, but this issue should be closed.
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No, it should work for different kinds of neural networks, including CNN. This approach was tried on fully connected networks, CNN and LSTM.
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Related Issues (20)
- Update the pip with the new 'generator' feature
- ValueError: You are trying to load a weight file containing 0 layers into a model with 2 layers.
- Automatic Select Best LR HOT 1
- Incorrect lr_mult value when using find_generator HOT 2
- Add Exponential Smoothing Plot HOT 2
- AttributeError: 'Adam' object has no attribute 'learning_rate' HOT 3
- How to use get_best_lr method? HOT 1
- Unsafe save_weights/load_weights method HOT 4
- Why does this lr_finder use training loss instead of validation loss? HOT 7
- Problem with the model or LRFinder? HOT 2
- Attribute error occur in Lamdacallback HOT 3
- get_best_lr - argmax HOT 2
- May I use this with sequential(keras.utils.Sequence) data?
- What is kw_fit?
- Does this lr finder support generators?
- I had an error 'Model' object has no attribute 'optimizer' HOT 1
- LRFinder doesn't work with Multi-Input data HOT 1
- Does this work with learning rate scheduler?
- Fixed Issue when using python
- Fixed Issue when using python 2.7 HOT 2
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