The need for having part of the training set used as verification data is straightforward, but I am not really clear on how and at what stage of the training should it be incoperated?
Is it at the end of the training (after reaching a good minimum for the training data)? If so, what should be done if the verification data yeilds a big error?
Is it throughout the training (keep looking for a minimum while errors for both the training and verification data aren't satisfactory)?
No matter what I try it seems that the network is having a trouble to learn both training and verification when the verification set reaches a certain size (I recall reading somewhere that 70% training 30% verification is a common ratio, I get stuck at a much smaller one), while it has no problem to learn the same data when used entirely for training.
The important thing is that your verification set must have no feedback on the training. You can plot the error rate on the verification set, but the training algorithm can only use the error rate on the training set to correct itself.
The validation data set is mostly used for early stopping.
Train network for epoch i on test data. Let test eerror be e(t, i).
Evaluate network on validation set. Let that be e(v, i).
If e(v, i) > e(v, i-1) stop training. Else goto 1.
So it helps you to see, when the network overfits, which means that it models the specifics of the test data too much. The idea is that with an ANN, you want to achieve good generalization from training data to unseen data. The validation set helps you to determine, when the point is reached when it specializes too much on the training data.
means that Over-Training
i advise check a verification set' MSE during training
see Overtraining Caution System of FannTool
http://fanntool.googlecode.com/files/FannTool_Users_Guide.zip
Related
For PyTorch's tutorial on performing transfer learning for computer vision (https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html), we can see that there is a higher validation accuracy than training accuracy. Applying the same steps to my own dataset, I see similar results. Why is this the case? Does it have something to do with ResNet 18's architecture?
Assuming there aren't bugs in your code and the train and validation data are in the same domain, then there are a couple reasons why this may occur.
Training loss/acc is computed as the average across an entire training epoch. The network begins the epoch with one set of weights and ends the epoch with a different (hopefully better!) set of weights. During validation you're evaluating everything using only the most recent weights. This means that the comparison between validation and train accuracy is misleading since training accuracy/loss was computed with samples from potentially much worse states of your model. This is usually most noticeable at the start of training or right after the learning rate is adjusted since the network often starts the epoch in a much worse state than it ends. It's also often noticeable when the training data is relatively small (as is the case in your example).
Another difference is the data augmentations used during training that aren't used during validation. During training you randomly crop and flip the training images. While these random augmentations are useful for increasing the ability of your network to generalize they aren't performed during validation because they would diminish performance.
If you were really motivated and didn't mind spending the extra computational power you could get a more meaningful comparison by running the training data back through your network at the end of each epoch using the same data transforms used for validation.
The short answer is that train and validation data are from different distributions, and it's "easier" for model to predict target in validation data then it is for training.
The likely reason for this particular case, as indicated by this answer, is data augmentation during training. This is a way to regularize your model by increasing variability in the training data.
Other architectures can use Dropout (or its modifications), which are deliberately "hurting" training performance, reducing the potential of overfitting.
Notice, that you're using pretrained model, which already contains some information about how to solve classification problem. If your domain is not that different from the data it was trained on, you can expect good performance off-the-shelf.
Since I'm new to data science, I just want to know that is there any specific data behavior that is responsible for overfitting and/or underfitting? Because if we are dealing with linear regression and we are supposed to get the Best fit line through gradient descent. Now, how can we get overfitting or underfitting? I know what is overfitting and underfitting but the problem is that how is it possible when you already applied gradient descent to get best fit line. I hope my question would be cleared to all, by the way.
Thanks and regards.
Less number of samples in the data can be a major reason for model over-fitting. Even if your model is simple, less variance (or variation) in the data samples can make the model learn to perform well for "only" those samples, and may not generalize well.
We can detect over fitting on a linear model by looking at the no. of features and the training error as well as the testing error.
If the model over fits:
1. Enough data is been provided for training i.e more no. of features used to train.
2. Training error is very less than the testing error.
If the model under fits:
1. Less data is been provided for training i.e less no. of features used to train.
2. Test error is very less than training error.
Using Gradient Descent is a good option.But it may lead to Over fitting and fail on real life data.
Hope this may help.
The title says it all: Should a neural network be able to have a perfect train accuracy? Mine saturates at ~0.9 accuracy and I am wondering if that indicates a problem with my network or the training data.
Training instances: ~4500 sequences with an average length of 10 elements.
Network: Bi-directional vanilla RNN with a softmax layer on top.
Perfect accuracy on training data is usually a sign of a phenomenon called overfitting (https://en.wikipedia.org/wiki/Overfitting) and the model may generalize poorly to unseen data. So, no, probably this alone is not an indication that there is something wrong (you could still be overfitting but it is not possible to tell from the information in your question).
You should check the accuracy of the NN on the validation set (data your network has not seen during training) and judge its generalizability. usually it's an iterative process where you train many networks with different configurations in parallel and see which one performs best on the validation set. Also see cross validation (https://en.wikipedia.org/wiki/Cross-validation_(statistics))
If you have low measurement noise, a model may still not get zero training error. This could be for many reasons including that the model is not flexible enough to capture the true underlying function (which can be a complicated, high-dimensional, non-linear function). You can try increasing the number of hidden layers and nodes but you have to be careful about the same things like overfitting and only judge based on evaluation through cross validation.
You can definitely get a 100% accuracy on training datasets by increasing model complexity but I would be wary of that.
You cannot expect your model to be better on your test set than on your training set. This means if your training accuracy is lower than the desired accuracy, you have to change something. Most likely you have to increase the number of parameters of your model.
The reason why you might be ok with not having a perfect training accuracy is (1) the problem of overfitting (2) training time. The more complex your model is, the more likely is overfitting.
You might want to have a look at Structural Risc Minimization:
(source: svms.org)
In an on-line implementation of a Backpropagation ANN, how would you determine the stopping criteria?
The way that I have been doing it(which I am sure is incorrect) is to average the error of each output node and then average this error over each epoch.
Is this an incorrect method? Is there a standard way of stopping an on-line implementation?
You should always consider the error (e.g. Root Mean Squared Error) on a validation set which is disjunct from your training set. If you train too long, your neural network will begin to overfit. This means, that the error on your training set will become minimal or even 0, but the error on general data will become worse.
To end up with the model parameters which yielded the best generalization performance, you should copy&save your model parameters whenever the error on your validation set is a new minimum. If performance is a problem, you can do this check only every N steps.
In an on-line learning setup, you will train with single training samples or mini-batches of a small number of training samples. You can consider the succsessive training of all samples/mini-batches that cover your total data as one training epoch.
There are several possibilities to define a so called Early Stopping Criterion. E.g. you could consider the best-so-far RMS Error on your validation set after each full epoch. You would stop as soon as there has not been a new optimum for M epochs. Depending on the complexity of your problem you must choose M high enough. You can also start with a rather small M and whenever you get a new optimum, you set M to the number of epochs you needed to reach it. It depends on whether it is more important to quickly converge or to be as thorough as possible.
You will always have situations where both your validation and/or training error will get bigger temporarily, because the learning algorithm is hill-climbing. This means it traverses regions on the error surface which render bad performance, but must be passed to reach a new, better optimum. If you simply stop as soon your validation or training error gets worse between two subsequent steps, you will end up in suboptimal solutions prematurely.
I understand the intuitive meaning of overfitting and underfitting. Now, given a particular machine learning model that is trained upon the training data, how can you tell if the training overfitted or underfitted the data? Is there a quantitative way to measure these factors?
Can we look at the error and say if it has overfit or underfit?
I believe the easiest approach is to have two sets of data. Training data and validation data. You train the model on the training data as long as the fitness of the model on the training data is close to the fitness of the model on the validation data. When the models fitness is increasing on the training data but not on the validation data then you're overfitting.
The usual way, I think, is known as cross-validation. The idea is to split the training set into several pieces, known as folds, then pick one at a time for evaluation and train on the remaining ones.
It does not, of course, measure the actual overfitting or underfitting, but if you can vary the complexity of the model, e.g. by changing the regularization term, you can find the optimal point. This is as far as one can go with just training and testing, I think.
You don't look at the error on the training data, but on the validation data only.
A common way of testing is to try different model complexities, and see how the error changes with model complexity. Usually these have a typical curve. In the beginning, the errors quickly improve. Then there is saturation (where the model is good), then they start decreasing again, but not because of being a better model, but because of overfitting. You want to be on the low complexity end of the plateau, the simplest model that provides a reasonable generalization.
The existing answers are not strictly speaking wrong, but they are not complete. Yes, you do need a validation set, but an important issue here is that you do not simply look at the model error on the validation set and try to minimize it. It will lead to overfitting all the same, because you will effectively be fitting on a validation set that way. The right approach is not minimizing the error on your sets, but making an error independent from which training and validation sets you use. If error on validation set is significantly different (doesn't matter if it is worse, or better), then the model is overfit. Also, certainly, this should be done in a cross-validation way when you train on some random set and then validate on another random set.