Should validation loss be higher than training loss?

Should validation loss be higher than training loss?

Typically the validation loss is greater than training one, but only because you minimize the loss function on training data. I recommend to use something like the early-stopping method to prevent the overfitting. The results of the network during training are always better than during verification.

Why is the training loss much higher than the testing loss?

In general, if you’re seeing much higher validation loss than training loss, then it’s a sign that your model is overfitting – it learns “superstitions” i.e. patterns that accidentally happened to be true in your training data but don’t have a basis in reality, and thus aren’t true in your validation data.

What if test accuracy is higher than training?

Test accuracy should not be higher than train since the model is optimized for the latter. Ways in which this behavior might happen: you did not use the same source dataset for test. You should do a proper train/test split in which both of them have the same underlying distribution.

What is the relationship between training Loss and Validation loss?

The training loss indicates how well the model is fitting the training data, while the validation loss indicates how well the model fits new data.

How can training loss be reduced in deep learning?

An iterative approach is one widely used method for reducing loss, and is as easy and efficient as walking down a hill. Discover how to train a model using an iterative approach. Understand full gradient descent and some variants, including: mini-batch gradient descent.

What is a good training and validation loss?

A good fit is identified by a training and validation loss that decreases to a point of stability with a minimal gap between the two final loss values. The loss of the model will almost always be lower on the training dataset than the validation dataset.

Does low loss mean high accuracy?

Having a low accuracy but a high loss would mean that the model makes big errors in most of the data. But, if both loss and accuracy are low, it means the model makes small errors in most of the data. However, if they’re both high, it makes big errors in some of the data.

How are validation loss and training loss measured?

Training loss is measured during each epoch While validation loss is measured after each epoch Your training loss is continually reported over the course of an entire epoch; however, validation metrics are computed over the validation set only once the current training epoch is completed.

Which is the best framework to use for PyTorch?

Alternatively you can use a framework that provides basic looping and validation facilities so you don’t have to implement everything by yourself all the time. tnt is torchnet for pytorch, supplying you with different metrics (such as accuracy) and abstraction of the train loop.

What to do about validation loss in deep learning?

If validation loss > training loss you can call it some overfitting. If validation loss < training loss you can call it some underfitting. If validation loss << training loss you can call it underfitting. Your aim is to make the validation loss as low as possible. Some overfitting is nearly always a good thing.

How is training loss calculated in Keras framework?

During training, frameworks like Keras will output the current training loss to the console. The loss is calculated as a moving average over all processed batches, meaning that in the early training stage when loss drops quickly the first batch of an epoch will have a much higher loss than the last.