How do you find the optimal learning rate of gradient descent?

How to Choose an Optimal Learning Rate for Gradient Descent

1. Choose a Fixed Learning Rate. The standard gradient descent procedure uses a fixed learning rate (e.g. 0.01) that is determined by trial and error.
2. Use Learning Rate Annealing.
3. Use Cyclical Learning Rates.
4. Use an Adaptive Learning Rate.
5. References.

What is learning rate in gradient descent?

Deep learning neural networks are trained using the stochastic gradient descent optimization algorithm. The learning rate is a hyperparameter that controls how much to change the model in response to the estimated error each time the model weights are updated.

What happens if learning rate of gradient descent is very large?

As such, gradient descent is taking successive steps in the direction of the minimum. If the step size η is too large, it can (plausibly) “jump over” the minima we are trying to reach, ie. we overshoot. This can lead to osculations around the minimum or in some cases to outright divergence.

What is the significance of gradient descent and learning rate in neural network?

One of the key hyperparameters to set in order to train a neural network is the learning rate for gradient descent. As a reminder, this parameter scales the magnitude of our weight updates in order to minimize the network’s loss function.

How do you find the best learning rate?

There are multiple ways to select a good starting point for the learning rate. A naive approach is to try a few different values and see which one gives you the best loss without sacrificing speed of training. We might start with a large value like 0.1, then try exponentially lower values: 0.01, 0.001, etc.

How can we choose an appropriate learning rate?

Is Tanh computationally expensive?

This simplicity makes it more difficult than the Sigmoid activation function and the Tangens hyperbolicus (Tanh) activation function, which use more difficult formulas and are computationally more expensive.

What does a high learning rate mean?

In the adaptive control literature, the learning rate is commonly referred to as gain. A too high learning rate will make the learning jump over minima but a too low learning rate will either take too long to converge or get stuck in an undesirable local minimum.

Is a higher learning rate better?

Generally, a large learning rate allows the model to learn faster, at the cost of arriving on a sub-optimal final set of weights. A smaller learning rate may allow the model to learn a more optimal or even globally optimal set of weights but may take significantly longer to train.