What does the val_sample_weight option in model.fit do?
I understand that it enables calculating weighted matrices (weighted accuracy for example) for validation set, but does (and if so, how?) it effect the calculation of the loss for the validation set?
Because I get different results for the loss of the validation set when using it vs not using it.
I use class_weight in both scenarios.
In the Keras website under model.fit it doesn't really say.
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I've seen in multiple places that you should disable dropout during validation and testing stages and only keep it during the training phase. Is there a reason why that should happen? I haven't been able to find a good reason for that and was just wondering.
One reason I'm asking is because I trained a model with dropout, and the results turned out well - about 80% accuracy. Then, I went on to validate the model but forgot to set the prob to 1 and the model's accuracy went down to about 70%. Is it supposed to be that drastic? And is it as simple as setting the prob to 1 in each dropout layer?
Thanks in advance!
Dropout is a random process of disabling neurons in a layer with chance p. This will make certain neurons feel they are 'wrong' in each iteration - basically, you are making neurons feel 'wrong' about their output so that they rely less on the outputs of the nodes in the previous layer. This is a method of regularization and reduces overfitting.
However, there are two main reasons you should not use dropout to test data:
Dropout makes neurons output 'wrong' values on purpose
Because you disable neurons randomly, your network will have different outputs every (sequences of) activation. This undermines consistency.
However, you might want to read some more on what validation/testing exactly is:
Training set: a set of examples used for learning: to fit the parameters of the classifier In the MLP case, we would use the training set to find the “optimal” weights with the back-prop rule
Validation set: a set of examples used to tune the parameters of a classifier In the MLP case, we would use the validation set to find the “optimal” number of hidden units or determine a stopping point for the back-propagation algorithm
Test set: a set of examples used only to assess the performance of a fully-trained classifier In the MLP case, we would use the test to estimate the error rate after we have chosen the final model (MLP size and actual weights) After assessing the final model on the test set, YOU MUST NOT tune the model any further!
Why separate test and validation sets? The error rate estimate of the final model on validation data will be biased (smaller than the true error rate) since the validation set is used to select the final model After assessing the final model on the test set, YOU MUST NOT tune the model any further!
source : Introduction to Pattern Analysis,Ricardo Gutierrez-OsunaTexas A&M University, Texas A&M University (answer)
So even for validation, how would you determine which nodes you remove if the nodes have a random probability of being disactivated?
Dropout is a method of making bagging practical for ensembles of very many large neural networks.
Along the same line we may remember that using the following false explanation:
For the new data, we can predict their classes by taking the average of the results from all the learners:
Since N is a constant we can just ignore it and the result remains the same, so we should disable dropout during validation and testing.
The true reason is much more complex. It is because of the weight scaling inference rule:
We can approximate p_{ensemble} by evaluating p(y|x) in one model: the model with all units, but with the weights going out of unit i multiplied by the probability of including unit i. The motivation for this modification is to capture the right expected value of the output from that unit. There is not yet any theoretical argument for the accuracy of this approximate inference rule in deep nonlinear networks, but empirically it performs very well.
When we train the model using dropout(for example for one layer) we zero out some outputs of some neurons and scale the others up by 1/keep_prob to keep the expectation of the layer almost the same as before. In the prediction process, we can use dropout but we can only get different predictions each time because we drop the values out randomly, then we need to run the prediction many times to get the expected output. Such a process is time-consuming so we can remove the dropout and the expectation of the layer remains the same.
Reference:
Difference between Bagging and Boosting?
7.12 of Deep Learning
Simplest reason can be, during prediction(test, validation or after production deployment) you want to use the capability of each and every learned neurons and really don't like to skip some of them randomly.
Thats the only reason we set probability as 1 during testing.
There is a Bayesian technique called Monte Carlo dropout in which the dropout would be not disabled during testing. The model will run several times with the same dropout rate(or in one go as a batch), and the mean(line 6 depicted below) and variance(line 7 depicted below) of the results will be calculated to determine the uncertainty.
Here is Uber's application to quantify uncertainty:
Short answer:
Dropouts to bring down over fitting in the training data. They are used as a regularization parameters. So if you have high variance (i.e. look at the difference between training set and validation set accuracy for this) then use drop out on training data, as it won't be good enough to apply dropout on test and validation data as you haven't been sure about the neurons which are going to shut off hence laying off the importance of random neurons which can be important.
I mostly understand how k-fold cross-validation works and have begun implementing it into my MATLAB scripts, however I have two questions.
When using it to select network features (hidden units, weight decay prior and no. iterations in my case). Should I re-intialise the weights after each 'fold', or should I just feed my next training fold into the already trained network (it has weights that have been optimised for the previous fold) ?
It seems that doing the latter should give lower errors as the previous fold of data will be a good approximation of the next, and so the weights will be closer than those initialised randomly from a gaussian distribution.
Additionally, having validated the network using k-fold validation, and chosen network hyper parameters etc., and I want to start using the network, am I right in thinking that I should stop using k-fold validation and just train once, using all of the available data?
Many thanks for any help.
Yes you should reinitialize the weights after each fold, in order to start with a "blank" network. If you don't do this, then each fold will "leak" into each other, and that's not what K-Fold CV is supposed to do.
After finding the best hyperparameters, yes, you can train it with all the available data. Just remember to keep some hold-out testing data for final testing.
I'm trying to get started using neural networks for a classification problem. I chose to use the Encog 3.x library as I'm working on the JVM (in Scala). Please let me know if this problem is better handled by another library.
I've been using resilient backpropagation. I have 1 hidden layer, and e.g. 3 output neurons, one for each of the 3 target categories. So ideal outputs are either 1/0/0, 0/1/0 or 0/0/1. Now, the problem is that the training tries to minimize the error, e.g. turn 0.6/0.2/0.2 into 0.8/0.1/0.1 if the ideal output is 1/0/0. But since I'm picking the highest value as the predicted category, this doesn't matter for me, and I'd want the training to spend more effort in actually reducing the number of wrong predictions.
So I learnt that I should use a softmax function as the output (although it is unclear to me if this becomes a 4th layer or I should just replace the activation function of the 3rd layer with softmax), and then have the training reduce the cross entropy. Now I think that this cross entropy needs to be calculated either over the entire network or over the entire output layer, but the ErrorFunction that one can customize calculates the error on a neuron-by-neuron basis (reads array of ideal inputs and actual inputs, writes array of error values). So how does one actually do cross entropy minimization using Encog (or which other JVM-based library should I choose)?
I'm also working with Encog, but in Java, though I don't think it makes a real difference. I have similar problem and as far as I know you have to write your own function that minimizes cross entropy.
And as I understand it, softmax should just replace your 3rd layer.
I am new to image classification, currently working on SVM(support Vector Machine) method for classifying four groups of images by multisvm function, my algorithm every time the training and testing data are randomly selected and the performance is varies at every time. Some one suggested to do cross validation i did not understand why we need cross validation and what is the main purpose of this? . My actual data set consist training matrix size 28×40000 and testing matrix size 17×40000. how to do cross validation by this data set help me. thanks in advance .
Cross validation is used to select your model. The out-of-sample error can be estimated from your validation error. As a result, you would like to select the model with the least validation error. Here the model refers to the features you want to use, and of more importance, the gamma and C in your SVM. After cross validation, you will use the selected gamma and C with the least average validation error to train the whole training data.
You may also need to estimate the performance of your features and parameters to avoid both high-bias and high-variance. Whether your model suffers underfitting or overfitting can be observed from both in-sample-error and validation error.
Ideally 10-fold is often used for cross validation.
I'm not familiar with multiSVM but you may want to check out libSVM, it is a popular, free SVM library with support for a number of different programming languages.
Here they describe cross validation briefly. It is a way to avoid over-fitting the model by breaking up the training data into sub groups. In this way you can find a model (defined by a set of parameters) which fits both sub groups optimally.
For example, in the following picture they plot the validation accuracy contours for parameterized gamma and C values which are used to define the model. From this contour plot you can tell that the heuristically optimal values (from those tested) are those that give an accuracy closer to 84 instead of 81.
Refer to this link for more detailed information on cross-validation.
You always need to cross-validate your experiments in order to guarantee a correct scientific approach. For instance, if you don't cross-validate, the results you read (such as accuracy) might be highly biased by your test set. In an extreme case, your training step might have been very weak (in terms of fitting data) and your test step might have been very good. This applies to ALL machine learning and optimization experiments, not only SVMs.
To avoid such problems just divide your initial dataset in two (for instance), then train in the first set and test in the second, and repeat the process invesely, training in the second and testing in the first. This will guarantee that any biases to the data are visible to you. As someone suggested, you can perform this with even further division: 10-fold cross-validation, means dividing your data set in 10 parts, then training in 9 and testing in 1, then repeating the process until you have tested in all parts.
I'm implementing a neural network for a supervised classification task in MATLAB.
I have a training set and a test set to evaluate the results.
The problem is that every time I train the network for the same training set I get very different results (sometimes I get a 95% classification accuracy and sometimes like 60%) for the same test set.
Now I know this is because I get different initial weights and I know that I can use 'seed' to set the same initial weights but the question is what does this say about my data and what is the right way to look at this? How do I define the accuracy I'm getting using my designed ANN? Is there a protocol for this (like running the ANN 50 times and get an average accuracy or something)?
Thanks
Make sure your test set is large enough compared to the training set (e.g. 10% of the overall data) and check it regarding diversity. If your test set only covers very specific cases, this could be a reason. Also make sure you always use the same test set. Alternatively you should google the term cross-validation.
Furthermore, observing good training set accuracy while observing bad test set accuracy is a sign for overfitting. Try to apply regularization like a simple L2 weight decay (simply multiply your weight matrices with e.g. 0.999 after each weight update). Depending on your data, Dropout or L1 regularization could also help (especially if you have a lot of redundancies in your input data). Also try to choose a smaller network topology (fewer layers and/or fewer neurons per layer).
To speed up training, you could also try alternative learning algorithms like RPROP+, RPROP- or RMSProp instead of plain backpropagation.
Looks like your ANN is not converging to the optimal set of weights. Without further details of the ANN model, I cannot pinpoint the problem, but I would try increasing the number of iterations.