Entrenamiento, funciones de pérdida y optimizadores

How to select a loss function and optimizer in PyTorch?

Selecting the right loss function and optimizer is crucial to effectively train a linear regression model. PyTorch facilitates this process by offering a variety of options within its nn and torch.optim modules. The PyTorch documentation is an excellent source of information for exploring these alternatives, along with similar frameworks such as NumPy, which also offer a number of loss functions.

What is the L1Loss loss function and why is it recommended?

The loss function is essential for measuring how far the model predictions are from the actual values. For this linear regression example, we use the L1Loss function, also known as mean absolute error (MAE). This function is popular for tabular data with continuous variables, as it measures the average magnitude of the errors in a set of predictions without considering their direction.

To implement L1Loss in PyTorch, it is used as follows:

import torch.nn as nn
 # Define loss functionfn_loss = nn.L1Loss()

How to set up an optimizer like SGD?

The optimization process is key to improve the parameters of our model through methods such as Stochastic Gradient Descent (SGD). To set it up in PyTorch, it is important to define the learning rate, a hyperparameter that determines the magnitude of the parameter updates.

A recommended practice when setting the learning rate is to start with a value such as 0.01, experimenting later to find the optimal value. The optimizer is configured as follows:

import torch.optim as optim
 # Define optimizeroptimizer optimizer = optim.SGD(model.parameters(),  lr=0.01)

How to train a linear regression model?

Training a model involves multiple steps, starting with the initialization of important hyperparameters such as the number of epochs. These determine how many times the training data will be checked.

What are the steps in the training loop?

1. Train mode: We set up the model in training mode, allowing us to improve parameters and calculate gradients.
2. Prediction and loss calculation: perform a prediction with the training data and calculate the resulting loss:

   y_pred = model(x_training)loss = fn_loss(y_pred, y_training).

3. Gradient reset: It is crucial to reset the gradients to get new clean values at each iteration.

   optimizer.zero_grad()

   .
4. Backpropagation: We calculate the gradients of the loss function with respect to the parameters.

   loss.backward()

5. Parameter update: We apply the learning rate to adjust the parameters.

   optimizer.step()

How to evaluate the model after training?

The evaluation mode, unlike the training mode, does not modify the model parameters. It serves to validate how the model predicts the data when it is not trained:

 with torch.no_grad(): # Disables gradient calculation model.eval() y_pred_test = model(x_test) lost_test = fn_lost(y_pred_test, y_test.float()).

Correctly choosing and adapting these elements based on the characteristics of the data is vital to maximize model performance. As you move into more complex applications, continued experimentation and tweaking of these hyperparameters will lead to more accurate results. Go ahead, keep exploring and optimizing your models!