Table of Contents
- 1 How does learning rate affect gradient descent?
- 2 How it is decided in gradient descent whether weights have to be increased or decreased?
- 3 What is the role of learning rate α in gradient descent explain the impact of high values of α and low values of α?
- 4 What is the most common learning rate for gradient descent?
- 5 How does momentum affect the global learning rate?
How does learning rate affect gradient descent?
Learning Rate and Gradient Descent Deep learning neural networks are trained using the stochastic gradient descent algorithm. 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.
When the training slope tend to grow exponentially instead of decaying it’s referred to as?
When the slope is too small, the problem is known as a “Vanishing Gradient.” When the slope tends to grow exponentially instead of decaying, it’s referred to as an “Exploding Gradient.” Gradient problems lead to long training times, poor performance, and low accuracy.
How it is decided in gradient descent whether weights have to be increased or decreased?
Optimization algorithms like gradient descent use derivates to decide whether to increase or decrease the weights to increase or decrease any objective function. If we are able to compute the derivative of a function, we know in which direction to proceed to minimize it.
Does gradient descent always converge in logistic regression?
Gradient Descent need not always converge at global minimum. It all depends on following conditions; If the line segment between any two points on the graph of the function lies above or on the graph then it is convex function.
What is the role of learning rate α in gradient descent explain the impact of high values of α and low values of α?
The learning rate determines how big the step would be on each iteration. If α is very small, it would take long time to converge and become computationally expensive. If α is large, it may fail to converge and overshoot the minimum.
Why does the gradient descent work?
Why does it work? The key intuition from gradient descent is that it takes the fastest route towards the minimum point from each step to converge fast. Since the function J(.) is convex, the initial guess’s derivative will be negative (the slope at the initial point will be negative).
What is the most common learning rate for gradient descent?
Figure 2: Gradient descent with different learning rates. Source The most commonly used rates are : 0.001, 0.003, 0.01, 0.03, 0.1, 0.3. 3. Make sure to scale the data if it’s on a very different scales.
What happens when the gradient keeps pointing in the same direction?
When the gradient keeps pointing in the same direction, this will increase the size of the steps taken towards the minimum. It is otherefore often necessary to reduce the global learning rate µ when using a lot of momentum (m close to 1).
How does momentum affect the global learning rate?
It is otherefore often necessary to reduce the global learning rate µ when using a lot of momentum (m close to 1). If you combine a high learning rate with a lot of momentum, you will rush past the minimum with huge steps! When the gradient keeps changing direction, momentum will smooth out the variations.
What are the disadvantages of vanishing gradient in machine learning?
This results in less or no convergence of the neural network. Due to Vanishing Gradient, your slope becomes too small and decreases gradually to a very small value (sometimes negative). This leads to poor performance of the model and the accuracy is very low.