How to understand loss acc val_loss val_acc in Keras model fitting

Question

I\'m new on Keras and have some questions on how to understanding my model results. Here is my result:(for your convenience, I only paste the loss acc val_loss val_acc after eac

Answer 1

1. What are loss and val_loss?

In deep learning, the loss is the value that a neural network is trying to minimize. That is how a neural network learns—by adjusting weights and biases in a manner that reduces the loss.

For instance, in regression tasks, you have a continuous target, e.g., height. What you want to minimize is the difference between your predictions, and the actual height. You can use mean_absolute_error as loss so the neural network knows this what it needs to minimize.

In classification, it's a little more complicated, but very similar. Predicted classes are based on probability. The loss is therefore also based on probability. In classification, the neural network minimizes the likelihood to assign a low probability to the actual class. The loss is typically categorical_crossentropy.

loss and val_loss differ because the former is applied to the train set, and the latter the test set. As such, the latter is a good indication of how the model performs on unseen data. You can get a validation set by using validation_data=[x_test, y_test] or validation_split=0.5.

It's best to rely on the val_loss to prevent overfitting. Overfitting is when the model fits the training data too closely, and the loss keeps decreasing while the val_loss is stale, or increases.

In Keras, you can use EarlyStopping to stop training when the val_loss stops decreasing. Read here.

Read more about deep learning losses here: Loss and Loss Functions for Training Deep Learning Neural Networks.

2. What are acc and val_acc?

Accuracy is a metric only for classification. It gives the percentage of instances that are correctly classified.

Once again, acc is on the training data, and val_acc is on the validation data. It's best to rely on val_acc for a fair representation of model performance because a good neural network will end up fitting the training data at 100%, but would perform poorly on unseen data.