Why is my validation loss lower than my training loss?

Why is my validation loss lower than my training loss?

Regularization methods often sacrifice training accuracy to improve validation/testing accuracy — in some cases that can lead to your validation loss being lower than your training loss. Secondly, keep in mind that regularization methods such as dropout are not applied at validation/testing time.

How does validation loss and accuracy remain constant?

As far as I know, I have followed the architecture mentioned in the paper. However, the validation loss and accuracy just remain flat throughout. The accuracy seems to be fixed at ~57.5%. Any help on where I might be going wrong would be greatly appreciated.

How is the validation loss at each epoch computed?

The training loss at each epoch is usually computed on the entire training set. The validation loss at each epoch is usually computed on one minibatch of the validation set, so it is normal for it to be more noisey. Solution: You can report the Exponential Moving Average of the validation loss across different epochs to have less fluctuations.

Is it normal for validation loss to oscillate?

The validation loss at each epoch is usually computed on one minibatch of the validation set, so it is normal for it to be more noisey. Solution: You can report the Exponential Moving Average of the validation loss across different epochs to have less fluctuations.

Why does the training loss increase with time?

However a couple of epochs later I notice that the training loss increases and that my accuracy drops. This seems weird to me as I would expect that on the training set the performance should improve with time not deteriorate. I am using cross entropy loss and my learning rate is 0.0002. Update: It turned out that the learning rate was too high.

What happens to head loss when flow rate doubles?

Since the head loss is roughly proportional to the square of the flow rate, then if the flow rate is doubled, the head loss increases by a factor of four. The head loss is reduced by half (for laminar flow) when the viscosity of the fluid is reduced by half.

Why does loss increase with higher learning rates?

With higher learning rates you are moving too much in the direction opposite to the gradient and may move away from the local minima which can increase the loss. Learning rate scheduling and gradient clipping can help.

Is the validation accuracy less than the training accuracy?

It is not overfitting since your validation accuracy is not less than the training accuracy. In fact, it sounds like your model is underfitting since your validation accuracy > training accuracy.

Which is more stable validation loss or prediction loss?

Generally, your model is not better than flipping a coin. The reason the validation loss is more stable is that it is a continuous function: It can distinguish that prediction 0.9 for a positive sample is more correct than a prediction 0.51.

Why is there a gap in validation accuracy?

The gap between accuracy on training data and test data shows you have over fitted on training. Maybe regularization can help. There are few ways to try in your situation. Firstly try to increase the batch size, which helps the mini-batch SGD less wandering wildly.

Why is validation accuracy so important in machine learning?

As @JanKukacka pointed out, arriving at the area “too close to” a minima might cause overfitting, so if α is too small it would get sensitive to “high-frequency” noise in your data. α should be somewhere in between.

When do neural networks use a validation set?

Neural networks (and other model types) typically use a validation set on every epoch, because training too long can cause over-fitting, and models don’t recover from that, they just get worse form that point on.

How is loss calculated in a neural network?

The loss is calculated on training and validation and its interperation is how well the model is doing for these two sets. Unlike accuracy, loss is not a percentage. It is a summation of the errors made for each example in training or validation sets.

How is the accuracy of a neural network determined?

Ideally, one would expect the reduction of loss after each, or several, iteration(s). The accuracy of a model is usually determined after the model parameters are learned and fixed and no learning is taking place.

How is validation error averaged over whole epoch?

Training error is averaged over whole epoch, rather all at once at the end of the epoch, but validation error is only at end of epoch. As we sample our training data to compute gradients, we might as well compute the loss over them as well.

When can validation accuracy be greater than training accuracy?

If you are using data augmentation to “noisify” your training data, then it can make sense that you are getting better accuracy on the validation set, because it will be an easier dataset. If this is the case, then you don’t really have a problem. As a rule, your validation set should be as close as possible to your test set or real-life use case.

Which is better validation data or training data?

Even though the data is under fitted, the validation data may perform well under circumstances that the validation data fits better in your model than does training data. Increase more convolution layers and loosen up on Dropout layers, lesser dropout layers or lower percentage of units dropped out.

How to prevent model errors in machine learning?

Since the consequences are often dire, I’m going to discuss how to prevent mistakes in model validation and the necessary components of a correct validation. To kick off the discussion, let’s get grounded in some of the basic concepts of validating machine learning models: predictive modeling, training error, test error and cross validation

How to validate a model with training data?

Table 2: A table with training data. We created a predictive model and applied it to the same data. This leads to a prediction for each row, stored in column p. Now we can easily compare how often our predictions are wrong.

Is the validation loss increasing or decreasing in Python?

My training loss decreases well but the validation loss increases, so my model is definitely overfitting. I used two hidden layers with size 125, 50. I used learning rate of 0.075 ran the model with 600 iterations. I also tried using regularization with lambda = 0.01 or 0.03, but still didn’t help. Any solutions to this problem?