Other than mean square error, are there other quantities that we can use to detect anomalies using autoencoder in keras?
Generally, the idea is to measure the reconstruction and classify anomalies as those datapoints that cause a significant deviation from the input. Thus, one can other other norms such as mae. However, the results will probably be very similar.
I would suggest different flavors of the auto encoder. First of all, if your are not already using it, the variational autoencoder is better than a standard auto encoder in all aspects.
Second, the performance of a variational autoencoder can be significantly improved by using the reconstruction probability. The idea is to output the parameters for probability distributions not only for the latent space but also for the feature space. This means that the decoder would output a mean and a variance to parameterize a normal distribution when used with continuous data. Then the reconstruction probability is basically the negative log likehood of the normal distribution N(x; decoder_mu, decoder_var). Using the 2-sigma rule, the variance can be interpreted as confidence intervall and thus even small errors can lead to an high error.
Other than that, there are other flavors like vae-gan, which combines a vae and gan uses a combined anomaly score with the reconstruction error and the discriminator prediction. Also depending on your problem type, you can also go into the route of a vae-sl that adds an additional classifier in the bottleneck. The model is then trained on mixed data which can be fully or sparsed labelled. Then the classifier can be used for anomaly detection.
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I am trying to classify around 400K data with 13 attributes. I have used python sklearn's SVM package, but it didn't work, and then I learned that SVM's are not suitable for large dataset classification. Then I used the (sklearn) ANN using the following MLPClassifier:
MLPClassifier(solver='adam', alpha=1e-5, random_state=1,activation='relu', max_iter=500)
and trained the system using 200K samples, and tested the model on the remaining ones. The classification worked well. However, my concern is that the system is over trained or overfit. Can you please guide me on the number of hidden layers and node sizes to make sure that there is no overfit? (I have learned that the default implementation has 100 hidden neurons. Is it ok to use the default implementation as is?)
To know if your are overfitting you have to compute:
Training set accuracy
Test set accuracy
Once you have calculated this scores, compare it. If training set score is much better than your test set score, then you are overfitting. This means that your model is "memorizing" your data, instead of learning from it to make future predictions.
If you are overfitting with Neuronal Networks you probably have to reduce the number of layers and reduce the number of neurons per layer. There isn't any strict rule that says the number of layer or neurons you need depending on you dataset size. Every dataset can behaves completely different with the same dataset size.
So, to conclude, if you are overfitting, you would have to evaluate your model accuracy using different parameters of layers and number of neurons, and, then, observe with which values you obtain the best results. There are some methods you can use to find the best parameters, is like gridsearchCV.
I applied batch normalization technique to increase the accuracy of my cnn model.The accuracy of model without batch Normalization was only 46 % but after applying batch normalization it crossed 83% but a here arisen a bif overfitting problem that the model was giving validation Accuracy only 15%. Also please tell me how to decide no of filters strides in convolution layer and no of units in dence layer
Batch normalization has been shown to help in many cases but is not always optimal. I found that it depends where it resides in your model architecture and what you are trying to achieve. I have done a lot with different GAN CNNs and found that often BN is not needed and can even degrade performance. It's purpose is to help the model generalize faster but sometimes it increases training times. If I am trying to replicate images, I skip BN entirely. I don't understand what you mean with regards to the accuracy. Do you mean it achieved 83% accuracy with the training data but dropped to 15% accuracy on the validation data? What was the validation accuracy without the BN? In general, the validation accuracy is the more important metric. If you have a high training accuracy and a low validation accuracy, you are indeed overfitting. If you have several convolution layers, you may want to apply BN after each. If you still over-fit, try increasing your strides and kernel size. If that doesn't work you might need to look at the data again and make sure you have enough and that it is somewhat diverse. Assuming you are working with image data, are you creating samples where you rotate your images, crop them, etc. Consider synthetic data to augment your real data to help combat overfiiting.
I have trained a model and it took me quite a while to find the correct hyperparameters.
The model has now been trained for 15h and it seems to to its job quite well.
When I observed the training and validation loss though, the training loss is somewhat higher than the validation loss. (red curve: training, green: validation)
I use dropout to regularize my model and as far as I have understood, droput is is only applied during training which might be the reason.
Now Iam wondering if I have trained a valid model?
It doesn't seem like the model is heavily underfitted?
Thanks in advance for any advice,
cheers,
M
First, check whether you have good data set, i.e., if it is a classification, then get equal number of images for all classes and get it from same source not from different sources. And regularization, dropout are used for overfitting/High variance so don't worry about these.
Then, I think your model is doing good when you trained your model the initial error between them are different but as you increased the epochs then they both got into some steady path. So it is good. And may be reason for this is as I mentioned above or you should try shuffle them then using train_test_split for getting better distribution of training and validation sets.
A plot of learning curves shows a good fit if:
The plot of training loss decreases to a point of stability.
The plot of validation loss decreases to a point of stability and has a small gap with the training loss.
In your case these conditions are satisfied.
Still if you want to deal with High Bias/underfitting then here are few methods:
Train bigger models
Train longer. Use better optimization techniques
Try different Neural Network Architecture and also hyper parameters
And also you can use cross-validation or GridSearchCV for finding better optimizer or hyper parameters but it may take really long because you have to train it on different parameters each time considering your time which is 15 hours then it might be very long but you will find better parameters and then train on it.
Above all I think your model is doing okay.
If your model underfits, its performance will be lower, similar as in the case of overfitting, because actually it can not learn effectively to get the optimal result, i.e the proper function to fit the given distribution. So you have to use less regularization technique e.g. less dropout to get the optimal result.
Furthermore the sampling can also be crucial, because there can be training-validation subsets where your model performs well on validation set and less effective on training set and vice-versa. This is one of the reason why we use crossvalidation and different sampling methods e.g. stratified k-fold.
I recently have implemented the RESUNET for a parasite segmentation on blood sample images. The model is described in this papaer, https://arxiv.org/pdf/1711.10684.pdf and here is the code https://github.com/DuFanXin/deep_residual_unet/blob/master/res_unet.py. The segmentation output is a binary image. I trained the model with the weighted Binary cross-entropy Loss, given more weight to the parasite class since there is an imbalance of classes in my images. The last ouput layer has a sigmoid activation.
I calculate precision, recall, and Dice Coefficient value to verify how good is the segmentation on trainning. On training and validation I got good numerical results:
Training
dice_coeff: .6895, f2: 0.8611, precision: 0.6320, recall: 0.9563
Validation
val_dice_coeff: .6433, val_f2: 0.7752, val_precision: 0.6052, val_recall: 0.8499
However, when I try to visually see the segmentations of the validation set my algorithm outputs all black. After analyzing the predictions returned by the model, almost all values are close to zero, so it cannot correctly differenciate between background and foreground. The problems is: Why my metrics shows good numerical values but the segmentation ouput not?
I mean, the metrics are not giving me good information? Why the recall value is higher even if the output is all black?
I trained for about 50 epochs, and my training curves shows constantly learning. Is this because the vanishing gradient problem?
No, you do not have a vanishing gradient issue.
I am almost 100% sure that the problem is related to the way in which you test.
The numbers in your training/validation do not lie.
Ensure that you use the exact same preprocessing on your test dataset, exactly the same preprocessing that is applied during the training.
E.g. : If you use "rescale = 1/255.0" parameter in Keras ImageDataGenerator(), ensure that when you load the test image, divide it by 255.0 before predicting on it.
Note that the aforementioned is a pure example; your inconsistency in train/test preprocessing may stem from other reasons.
I have a machine learning model built that tries to predict weather data, and in this case I am doing a prediction on whether or not it will rain tomorrow (a binary prediction of Yes/No).
In the dataset there is about 50 input variables, and I have 65,000 entries in the dataset.
I am currently running a RNN with a single hidden layer, with 35 nodes in the hidden layer. I am using PyTorch's NLLLoss as my loss function, and Adaboost for the optimization function. I've tried many different learning rates, and 0.01 seems to be working fairly well.
After running for 150 epochs, I notice that I start to converge around .80 accuracy for my test data. However, I would wish for this to be even higher. However, it seems like the model is stuck oscillating around some sort of saddle or local minimum. (A graph of this is below)
What are the most effective ways to get out of this "valley" that the model seems to be stuck in?
Not sure why exactly you are using only one hidden layer and what is the shape of your history data but here are the things you can try:
Try more than one hidden layer
Experiment with LSTM and GRU layer and combination of these layers together with RNN.
Shape of your data i.e. the history you look at to predict the weather.
Make sure your features are scaled properly since you have about 50 input variables.
Your question is little ambiguous as you mentioned RNN with a single hidden layer. Also without knowing the entire neural network architecture, it is tough to say how can you bring in improvements. So, I would like to add a few points.
You mentioned that you are using "Adaboost" as the optimization function but PyTorch doesn't have any such optimizer. Did you try using SGD or Adam optimizers which are very useful?
Do you have any regularization term in the loss function? Are you familiar with dropout? Did you check the training performance? Does your model overfit?
Do you have a baseline model/algorithm so that you can compare whether 80% accuracy is good or not?
150 epochs just for a binary classification task looks too much. Why don't you start from an off-the-shelf classifier model? You can find several examples of regression, classification in this tutorial.