Is it possible that the cost function goes up with each iteration of gradient descent in a neural network?

In Neural Networks, Gradient Descent looks over the entire training set in order to calculate gradient. The cost function decreases over iterations. If cost function increases, it is usually because of errors or inappropriate learning rate.

What is cost function and how it is optimized?

A Cost function is used to gauge the performance of the Machine Learning model. A Cost function basically compares the predicted values with the actual values. Appropriate choice of the Cost function contributes to the credibility and reliability of the model.

How do you reduce cost function?

Well, a cost function is something we want to minimize. For example, our cost function might be the sum of squared errors over the training set. Gradient descent is a method for finding the minimum of a function of multiple variables. So we can use gradient descent as a tool to minimize our cost function.

Why do we minimize cost function?

We need to put a penalty on making bad decisions or errors. A good model will make as few errors as possible, a bad one will make lots of errors. The cost for making those errors is measured by, well, a cost function.

What is momentum SGD optimizer?

Momentum [1] or SGD with momentum is method which helps accelerate gradients vectors in the right directions, thus leading to faster converging. It is one of the most popular optimization algorithms and many state-of-the-art models are trained using it.

How do you optimize a function?

Example: Optimizing a Function. Use the maximize and minimize functions, plus a guess value, to find the point at which the input function is at its maximum or minimum. The guess value tells the solver function to converge on a local maximum or minimum instead of other possible maxima or minima points.

What is the difference between cost function and loss function?

Yes , cost function and loss function are synonymous and used interchangeably but they are “different”. A loss function/error function is for a single training example/input. A cost function, on the other hand, is the average loss over the entire training dataset.

Why is SGD used instead of batch Gradient Descent?

Batch Gradient Descent converges directly to minima. SGD converges faster for larger datasets. But, since in SGD we use only one example at a time, we cannot implement the vectorized implementation on it. This can slow down the computations.

How does SGD stochastic gradient descent differ from normal Gradient Descent?

The only difference comes while iterating. In Gradient Descent, we consider all the points in calculating loss and derivative, while in Stochastic gradient descent, we use single point in loss function and its derivative randomly.