Oct 9, 2024 · Oscillating loss curve. What three things could you do to try improve the loss curve shown in Figure 21. Check your data against a data schema to detect bad examples, and then remove the...

When I trained my neural network with Theano or Tensorflow, they will report a variable called "loss" per epoch. How should I interpret this variable? Higher loss is better or worse, or what does it mean for the final performance (accuracy) of my neural network?

Feb 2, 2018 · Noisy training loss but good accuracy can be due to this reason: Local minima: The function can have local minimas, So everytime your gradient descent converges towards the local minimum, the lost/cost decreases.

Feb 9, 2021 · Learning curve of an overfit model has a high validation loss at the beginning which gradually decreases upon adding training examples and doesn’t flatten, indicating addition of more training examples can improve the model performance on unseen data.

Here is the complete loss curve of the initial training. See here: https://imgur.com/a/JpdYa5O Since the data is generated, training loss = validation loss, so the results after this loss were almost perfect.

Mar 18, 2024 · In this tutorial, we’ll focus on loss and accuracy. Both of them are essential values to take into account when training our models. 2. Loss is a value that represents the summation of errors in our model. It measures how well (or bad) our model is doing.

Jun 9, 2023 · The loss curve, or training loss curve, gives us insights into how the model's performance improves over time by measuring the error (or dissimilarity) between its predicted output and the true output. The loss represents how far off the model's predictions are from the actual values. 

In this tutorial, you will discover how you can diagnose the fit of your LSTM model on your sequence prediction problem. After completing this tutorial, you will know: How to gather and plot training history of LSTM models. How to diagnose an underfit, good fit, and overfit model.

Mar 18, 2024 · In this article, we explained loss vs. epoch graphs. We use them in machine learning to detect problems during training. Our model is overfitting if the validation loss grows while the training loss decreases and underfitting if both losses are large and there’s a significant difference between them.

Jul 14, 2022 · Learning curve analysis provides a way to visualize problems with ML models and identify them early on. In this blog, we will review what a learning curve is and how the structure...