Validation Loss
The term val_loss stands for "Validation Loss" during the training of a Recurrent Neural Network (RNN) or any other machine learning model. It is a metric that gives you an insight into how well your model is performing on a subset of data that the model has not seen during training. This subset is called the validation set.
Here's a breakdown of what val_loss means:
Validation Loss (Val_loss)
Definition: This is the error score calculated using the validation dataset during the training of a model. In regression scenarios involving Recurrent Neural Networks (RNNs), this often equates to the Mean Squared Error (MSE) reflecting the difference between the predicted outputs and the actual targets within the validation data.
Significance: Keeping an eye on the val_loss is essential for assessing the model’s ability to perform on data it hasn’t encountered before. A low error on the training set but a high error on the validation set typically points to overfitting. If the model doesn’t do well on either, it’s probably underfitting.
Application: While the model learns by minimizing training loss, your ultimate goal is to ensure it’s also effective on fresh data. That’s why val_loss is a key factor in choosing the right model configuration. Often, the model iteration with the smallest val_loss is preserved as it’s expected to have the best generalization performance.
Training Loss Versus Validation Loss: Comparing val_loss with the training loss (often just called ‘loss’) helps identify model training issues. For instance, if val_loss begins to rise while training loss keeps dropping, your model is most likely memorizing the training data rather than learning from it.
In summary, understanding and monitoring val_loss is vital for refining the model design and its learning parameters, to achieve a model that is truly effective on new, unseen data.