Why is my validation loss less than training loss?

Why is my validation loss less than training loss?

Reason #2: Training loss is measured during each epoch while validation loss is measured after each epoch. The second reason you may see validation loss lower than training loss is due to how the loss value are measured and reported: Training loss is measured during each epoch.

What does validation loss tell you?

It is the sum of errors made for each example in training or validation sets. Loss value implies how poorly or well a model behaves after each iteration of optimization. An accuracy metric is used to measure the algorithm’s performance in an interpretable way.

Is loss function a Hyperparameter?

Loss function characterizes how well the model performs over the training dataset, regularization term is used to prevent overfitting [7], and λ balances between the two. Conventionally, λ is called hyperparameter. Different ML algorithms use different loss functions and/or regularization terms.

How can validation loss be controlled?

5 Answers

1. Data Preprocessing: Standardizing and Normalizing the data.
2. Model compelxity: Check if the model is too complex. Add dropout, reduce number of layers or number of neurons in each layer.
3. Learning Rate and Decay Rate: Reduce the learning rate, a good starting value is usually between 0.0005 to 0.001.

What does it mean when validation loss is higher than training 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.

Which is better training data or validation data?

As I understand, a model usually fits better training data than validation data but I am getting the opposite results. Loss is lower and accuracy is higher for validation set. As I have read in other answers, this can be because of dropout not being applied during validation.

What is the validation loss for epoch 20 / 20-14?

Epoch 20/20 – 14s – loss: 0.0510 – val_loss: 188.9537 I assume I must be doing something obvious wrong, but can’t realize it since I’m a newbie. I am hoping to either get some useful validation loss achieved (compared to training), or know that my data observations are simply not large enough for useful LSTM modeling.

Is it a good thing to overfit in deep learning?

Some overfitting is nearly always a good thing. All that matters in the end is: is the validation loss as low as you can get it. This often occurs when the training loss is quite a bit lower. Also check how to prevent overfitting.