Training the Model

In this chapter, we’ll focus on training the neural network we created in the previous chapter using the wine quality dataset. The goal is to predict wine quality categories based on its features. We'll define the optimizer, loss function, and training loop while monitoring the model's performance over multiple epochs.

Preparing for Training

First, we need to ensure that the model, loss function, and optimizer are properly defined. Let’s go through each step:

1. Loss function: for classification, we use CrossEntropyLoss, which expects raw logits as input and automatically applies softmax.
2. Optimizer: we'll use the Adam optimizer for efficient gradient updates.

Training Loop

The training loop involves the following steps for each epoch:

1. Forward Pass: Pass the input features through the model to generate predictions.
2. Loss Calculation: Compare the predictions with the ground truth using the loss function.
3. Backward Pass: Compute gradients with respect to the model parameters using backpropagation.
4. Parameter Update: Adjust model parameters using the optimizer.
5. Monitoring Progress: Print the loss periodically to observe convergence.

Implementation

Here’s how the training loop is implemented:

Observing Convergence

• Convergence point: look for the point where the training loss stabilizes. If the loss stops decreasing significantly, it indicates that the model has likely converged.
• Adjusting hyperparameters: If the loss doesn’t decrease well, consider:
  • Lowering the learning rate.
  • Increasing the number of epochs.
  • Checking the input data for proper scaling and quality.