I am learning to write code for custom models using TensorFlow and Keras.
I am a bit confused about when to evaluate metrics (like accuracy, loss…etc) during the training and validation.
Should I have to evaluate metrics after each training iteration or after each epoch?
Do I have to take the average of metrics of all batches, at the end of each epoch?
Please share the preferred approach for training and validation metrics evaluation.
Thanks in advance.