Introduction to machine learning optimizers - discussion of common optimizer types and applications

The optimizer is an optimization algorithm used to find parameter values ​​that minimize the error to improve the accuracy of the model. In machine learning, an optimizer finds the best solution to a given problem by minimizing or maximizing a cost function.

In different algorithm models, there are many different types of optimizers, each of which has its unique advantages and disadvantages. The most common optimizers are gradient descent, stochastic gradient descent, stochastic gradient descent with momentum, adaptive gradient descent, and root mean square. Each optimizer has some adjustable parameter settings that can be adjusted to improve performance.

Common optimizer types

Gradient descent (GD)

Gradient descent is a A basic first-order optimization algorithm that relies on the first derivative of the loss function. It searches for the value of the minimum cost function by updating the weights of the learning algorithm and finds the most suitable parameter values ​​corresponding to the global minimum. Through backpropagation, the loss is passed from one layer to another, and the parameters of the model are adjusted according to the loss to minimize the loss function.

This is one of the oldest and most common optimizers used in neural networks and is best suited for situations where the data is arranged in a way that has a convex optimization problem.

The gradient descent algorithm is very simple to implement, but there is a risk of getting stuck in a local minimum, that is, it will not converge to the minimum.

Stochastic Gradient Descent (SGD)

As an extension of the gradient descent algorithm, stochastic gradient descent overcomes some of the shortcomings of the gradient descent algorithm. In stochastic gradient descent, instead of getting the entire dataset every iteration, batches of data are randomly selected, which means that only a small number of samples are taken from the dataset.

Therefore, the stochastic gradient descent algorithm requires more iterations to reach the local minimum. As the number of iterations increases, the overall computation time increases. But even after increasing the number of iterations, the computational cost is still lower than the gradient descent optimizer.

Stochastic Gradient Descent with Momentum

From the above we know that the path taken by stochastic gradient descent will have a greater impact than gradient descent. noise, and the calculation time will be longer. To overcome this problem, we use stochastic gradient descent with momentum algorithm.

The role of momentum is to help the loss function converge faster. However, you should remember when using this algorithm that the learning rate decreases with high momentum.

Adaptive Gradient Descent (Adagrad)

The adaptive gradient descent algorithm is slightly different from other gradient descent algorithms. This is because the algorithm uses a different learning rate for each iteration. The learning rate changes depending on the differences in parameters during training. The greater the parameter change, the smaller the learning rate change.

The advantage of using adaptive gradient descent is that it eliminates the need to manually modify the learning rate, will reach convergence faster, and adaptive gradient descent is better than the gradient descent algorithm and its Variants will be more reliable.

But the adaptive gradient descent optimizer will monotonically reduce the learning rate, causing the learning rate to become very small. Due to the small learning rate, the model cannot obtain more improvements, which ultimately affects the accuracy of the model.

Root Mean Square (RMS Prop) Optimizer

Root Mean Square is one of the popular optimizers among deep learning enthusiasts. Although it has not been officially released, it is still well known in the community. Root mean square is also considered an improvement over adaptive gradient descent optimizers because it reduces the monotonically decreasing learning rate.

The root mean square algorithm mainly focuses on speeding up the optimization process by reducing the number of function evaluations to reach a local minimum. This algorithm keeps a moving average of the squared gradients for each weight and divides the gradients by the square root of the mean square.

Compared to the gradient descent algorithm, this algorithm converges quickly and requires fewer adjustments. The problem with the root mean square optimizer is that the learning rate must be defined manually, and its recommended values ​​do not apply to all applications.

Adam optimizer

The name Adam comes from adaptive moment estimation. This optimization algorithm is a further extension of stochastic gradient descent and is used to update network weights during training. Instead of maintaining a single learning rate through stochastic gradient descent training, the Adam optimizer updates the learning rate of each network weight individually.

The Adam optimizer inherits the characteristics of adaptive gradient descent and root mean square algorithms. The algorithm is easy to implement, has faster runtime, has low memory requirements, and requires fewer adjustments than other optimization algorithms.

Optimizer usage

• Stochastic gradient descent can only be used in shallow networks.
• Except for stochastic gradient descent, other optimizers eventually converge one after another, among which the adam optimizer converges the fastest.
• Adaptive gradient descent can be used with sparse data.
• The Adam optimizer is considered the best algorithm among all the above algorithms.

The above are some of the optimizers that are widely used in machine learning tasks. Each optimizer has its advantages and disadvantages. Therefore, understanding the requirements of the task and the type of data that needs to be processed is crucial to choosing an optimizer and achieving excellent results. Crucial.

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