What is the difference between Loss, accuracy, validation loss, Validation accuracy?
When we mention validation_split as fit parameter while fitting DL model, it splits data into two parts for every epoch i.e. training data and validation data. It trains the model on training data and validate the model on validation data by checking its loss and accuracy.
Usually with every epoch increasing, loss goes lower and accuracy goes higher. But with val_loss and val_acc, many cases can be possible:
val_loss starts increasing, val_acc starts decreasing(means model is cramming values not learning)
val_loss starts increasing, val_acc also increases.(could be case of overfitting or diverse probability values in cases softmax is used in output layer)
val_loss starts decreasing, val_acc starts increasing(Correct, means model build is learning and working fine)
This is a link to refer as well in which there is more description given. Thanks. How to interpret "loss" and "accuracy" for a machine learning model
I have tried to explain at https://www.javacodemonk.com/difference-between-loss-accuracy-validation-loss-validation-accuracy-when-training-deep-learning-model-with-keras-ff358faa
In your model.compile function you have defined a loss function and a metrics function.
Your "loss" is the value of your loss function (unknown as you do not show your code) Your "acc" is the value of your metrics (in this case accuracy) The val_* simply means that the value corresponds to your validation data.
Only the loss function is used to update your model's parameters, the accuracy is only used for you to see how well your model is doing.
You should seek to minimize your loss and maximize your accuracy. Ideally the difference between your validation data results and your training data results should be similar (allthough some difference are expected)