How could we estimate optimal learning rate for machine learning model, if we realized from a gap occurs between validation and trainining graph. When epoch number increased, validation accuracy remained similar although model accuracy increased slightly (we concluded a local maximum exists)? We used a little learning rate, such as 0.000001, almost to skip the local maksimum. Could it be result of using number of layers in 3D-CNN model even we applied dropout. Do you have any idea or suggestion? All ideas are welcome and tanks to all.
Best regards,
If you observe a gap between the validation and training accuracy curves as the epoch number increases, it may indicate that the model is overfitting to the training data and is not generalizing well to the validation data. This can be caused by several factors, such as having a large number of parameters relative to the size of the training set, or using a learning rate that is too high.
To address this issue, you may want to try using regularization techniques, such as adding a weight decay term to the loss function or using dropout, to reduce the risk of overfitting. You may also want to consider increasing the size of the training set, if possible, as this can help the model learn more robust patterns in the data.
In terms of selecting an optimal learning rate, one approach you can try is to use a learning rate scheduling method, such as the adaptive gradient algorithm (AdaGrad) or the Adam optimization algorithm. These methods can automatically adjust the learning rate during training based on the gradient of the loss function, which can help the model converge more quickly and avoid getting stuck in local minima or maxima.
You may also want to try using a learning rate that is slightly larger than the one you are currently using, as this can help the model escape from local minima or maxima and continue to improve. However, it is important to be careful not to use a learning rate that is too high, as this can cause the model to diverge or oscillate.
It is also possible that the gap between the validation and training accuracy curves could be due to the number of layers in your 3D-CNN model, or to other factors such as the choice of activation functions or the initialization of the model parameters. It may be useful to experiment with different model architectures and hyperparameter settings to see which ones work best for your particular dataset and task.
Thank you Mr. Jose leaving time to answer my question in a such deep fashion. I am appreciate. I find it very useful including more detail inside other than answer to the my question.
Best Regards.
Respected professor,
you don't need to find ideal value for learning rate. I prefer to use greater learning rate and methods called ReduceLROnPlateau and EarlyStopping.
First reduce learning rate during fitting process and second stops when there are no progress. Monitor should be set on validation metrics.
I will give you example for keras model in python:
early_stopping = keras.callbacks.EarlyStopping(monitor="val_cosine_similarity",mode='max', patience=2,verbose=1,min_delta=0.0001)
reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor="val_cosine_similarity",mode='max', patience=1,verbose=1)
You should add it to fit method like this:
history = model.fit(
X_train,
y_train,
batch_size=1,
epochs=30,
validation_data=(X_valid, y_valid),
callbacks=[reduce_lr,early_stopping],
shuffle=True,
)
If you want to go even further you could implement methods for finding minimum of functions where your function is ML model that accept value of learning rate and for example dropout value, and return loss value (i.e. mse) from validation or test dataset.
I'm using Particle swarm optimization form pyswarms library.
Best regards,
Dušan
Thank you for dealing giving an answer dear Mr. Dušan. I am impressed with your suggestion.
Best Regards,
Metin Turan
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