What is the difference between Loss, accuracy, validation loss, Validation accuracy?

Question

At the end of each epoch, I am getting for example the following output:

Epoch 1/25
2018-08-06 14:54:12.555511: 
2/2 [==============================] - 86s 43s/s         


        

Answer 1

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:

1. val_loss starts increasing, val_acc starts decreasing(means model is cramming values not learning)

2. 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)

3. 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