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What problems might occur if the learning rate is fixed during training?
Fixing your learning rate is resource inefficient Too small fixed learning rates may result in the same, but then because your steps are so small that it (theoretically) takes infinitely long to find the minimum. Hence, there is a range in between learning rates result in quick and approximate convergence.
What happens when the learning rate is large vs small?
The learning rate hyperparameter controls the rate or speed at which the model learns. A learning rate that is too small may never converge or may get stuck on a suboptimal solution. When the learning rate is too large, gradient descent can inadvertently increase rather than decrease the training error.
What happens when the 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. The challenge of training deep learning neural networks involves carefully selecting the learning rate.
Why does the training loss increase with time?
However a couple of epochs later I notice that the training loss increases and that my accuracy drops. This seems weird to me as I would expect that on the training set the performance should improve with time not deteriorate. I am using cross entropy loss and my learning rate is 0.0002. Update: It turned out that the learning rate was too high.
How to reduce oscillating loss in neural network?
Oscillating loss can be attributed to either of the following: Learning rate: Reduce the learning rate so that the gradient descent doesn’t overshoot the minima. Optimizer: Choose ADAM optimizer over the others like SGD. It works well. Thanks for contributing an answer to Data Science Stack Exchange!
When does machine learning loss increase with time?
Have you significantly increased the number of iterations and checked if this behavior comes much later with the new low learning rate? With higher learning rates you are moving too much in the direction opposite to the gradient and may move away from the local minima which can increase the loss.
Why does loss increase with higher learning rates?
With higher learning rates you are moving too much in the direction opposite to the gradient and may move away from the local minima which can increase the loss. Learning rate scheduling and gradient clipping can help.