Is Adam sensitive to learning rate?
Adam is not the only optimizer with adaptive learning rates. As the Adam paper states itself, it’s highly related to Adagrad and Rmsprop, which are also extremely insensitive to hyperparameters.
Does Adam optimizer need learning rate decay?
Yes, absolutely. From my own experience, it’s very useful to Adam with learning rate decay. Without decay, you have to set a very small learning rate so the loss won’t begin to diverge after decrease to a point.
Is 0.1 a good learning rate?
The range of values to consider for the learning rate is less than 1.0 and greater than 10^-6. A traditional default value for the learning rate is 0.1 or 0.01, and this may represent a good starting point on your problem.
What happens when learning rate is too high?
A learning rate that is too large can cause the model to converge too quickly to a suboptimal solution, whereas a learning rate that is too small can cause the process to get stuck. If you have time to tune only one hyperparameter, tune the learning rate.
What’s the accuracy of Adam as an optimizer?
When using Adam as optimizer, and learning rate at 0.001, the accuracy will only get me around 85% for 5 epocs, topping at max 90% with over 100 epocs tested. But when loading again at maybe 85%, and doing 0.0001 learning rate, the accuracy will over 3 epocs goto 95%, and 10 more epocs it’s around 98-99%.
Which is the best learning rate for Adam method?
Then, I think your presented curve is ok. Concerning the learning rate, Tensorflow, Pytorch and others recommend a learning rate equal to 0.001. But in Natural Language Processing, the best results were achieved with learning rate between 0.002 and 0.003.
When to reduce step size in Adam optimizer?
Adam uses the initial learning rate, or step size according to the original paper’s terminology, while adaptively computing updates. Step size also gives an approximate bound for updates. In this regard, I think it is a good idea to reduce step size towards the end of training.
Is there a learning rate that works for all optimizers?
There is no learning rate that works for all optimizers. Learning rate can affect training time by an order of magnitude. Summarizing the above, it’s crucial you choose the correct learning rate as otherwise your network will either fail to train, or take much longer to converge.