Optimizer and Random State
I started my first machine learning lecture today, and I found many interesting aspects of it. The explanations about mathematics were easy to understand. However, as I delved deeper, I found myself thinking more, and the learning process took more time than I initially expected.
During the lecture, I had questions about setting the random state to a specific number, like 2021, and the difference between setting the optimizer to SGD and ADAM, so I decided to look into it.
Here is a brief explanation of those topics.
Random State
random_state is primarily a parameter used in machine learning and deep learning models to set a seed for random number generation. Setting a seed ensures that the same random number sequence is generated when the same seed is used, thus allowing for reproducibility of the model’s results.
The value used for random_state, for example, 2021, is essentially an arbitrary choice. 2021 is just a numerical representation of a random seed. However, using a specific number to set the seed ensures that others sharing the same seed, or systems, can achieve the same random results, thus ensuring experiment reproducibility.
Therefore, when setting random_state to 2021 in TensorFlow or other machine learning frameworks, it means that the random elements generated by the model, such as weight initialization or data sampling, will remain consistent. Even when others run the same code, they will obtain the same results. However, it’s worth noting that 2021 is just an arbitrarily chosen number, and you can use other numbers for the same purpose.
Optimizer
Certainly, here’s a table explaining some common optimizers used in TensorFlow for training machine learning and deep learning models:
| Optimizer | Description |
|---|---|
| Gradient Descent (SGD: Stochastic Gradient Descent) | The basic optimizer that uses the gradient of the loss function to update model parameters. It’s simple but can be slow to converge. |
| Adam (Adaptive Moment Estimation) | Combines the ideas of momentum and RMSprop. Automatically adapts the learning rate and maintains moving averages of gradients. Effective in many tasks. |
| RMSprop (Root Mean Square Propagation) | Adjusts the learning rate using a moving average of the squared gradients. It helps stabilize training in some cases. |
| Adagrad (Adaptive Gradient Algorithm) | Adjusts the learning rate individually for each parameter based on the historical gradient information. Useful for sparse data. |
| Nadam (Nesterov-accelerated Adaptive Moment Estimation) | A combination of Nesterov momentum and Adam. Provides faster convergence and better stability in training. |
| Adadelta | Similar to RMSprop but uses a moving average of past updates rather than past gradients’ squared values. Doesn’t require manually tuning a learning rate. |
| FTRL (Follow the Regularized Leader) | An optimizer designed for large-scale linear models. It includes L1 and L2 regularization and can be effective for regression tasks. |
Each of these optimizers has its own strengths and weaknesses, and the choice of optimizer depends on the specific problem and dataset. Experimentation and hyperparameter tuning are often necessary to determine the best optimizer for a given task.