I have a question regarding convolutional neural network (CNN) training.
I have managed to train a network using tensorflow that takes an input image (1600 pixels) and output one of three classes that matches it.
Testing the network with variations of the trained classes is giving good results. However; when I give it a different -fourth- image (does not contain any of the trained 3 image), it always returns a random match to one of the classes.
My question is, how can I train a network to classify that the image does not belong to either of the three trained images? A similar example, if i trained a network against the mnist database and then a gave it the character "A" or "B". Is there a way to discriminate that the input does not belong to either of the classes?
Thank you
Your model will always make predictions like your labels, so for example if you train your model with MNIST data, when you will make predictions, prediction will always be 0-9 just like MNIST labels.
What you can do is train a different model first with 2 classes in which you will predict if an image belongs to data set A or B. E.x. for MNIST data you label all data as 1 and add data from other sources that are different (not 0-9) and label them as 0. Then train a model to find if image belongs to MNIST or not.
Convolutional Neural Network (CNN) predicts the result from the defined classes after training. CNN always return from one of the classes regardless of accuracy. I have faced similar problem, what you can do is to check for accuracy value. If the accuracy is below some threshold value then it's belong to none category. Hope this helps.
You probably have three output nodes, and choose the maximum value (one-hot encoding). That's a bit unfortunate as it's a low number of outputs. Non-recognized inputs tend to cause pretty random outputs.
Now, with 3 outputs, roughly speaking you can get 7 outcomes. You might get a single high value (3 possibilities) but non-recognized input can also cause 2 high outputs (also 3 possibilities) or approximately equal output (also 3 possibilities). So there's a decent chance (~ 3/7) of random inputs producing a pattern on the output nodes which you'd only expect for a recognized input.
Now, if you had 15 classes and thus 15 output nodes, you'd be looking at roughly 32767 possible outcomes for unrecognized inputs, only 15 of which correspond to expected one-hot outcomes.
Underlying this is a lack of training data. If your training set has examples outside the 3 classes, you can just dump this in a 4th "other" category and train with that. This by itself isn't a reliable indication, as usually the theoretical "other" set is huge, but you now have 2 complementary ways of detecting other inputs: either by the "other" output node or by one of the 11 ambiguous outputs.
Another solution would be to check what outcome your CNN usually gives when given something else. I believe the last layer must be softmax and your CNN should return probabilities of the three given classes. If none of these probabilities is close to 1 this might be a sign that this is something else assuming your CNN is well trained (it must be fined for overconfidence when predicting wrong labels).
Related
I have five classes and I want to compare four of them against one and the same class. This isn't a One vs Rest classifier, as for each output I want to score them against one base class.
The four outputs should be: base class vs classA, base class vs classB, etc.
I could do this by having multiple binary classification tasks, but that's wasting computation time if the first layers are BERT preprocessing + pretrained BERT layers, and the only differences between the four classifiers are the last few layers of BERT (finetuned ones) and the Dense layer.
So why not merge the graphs for more performance?
My inputs are four different datasets, each annotated with true/false for each class.
As I understand it, I can re-use most of the pipeline (BERT preprocessing and the first layers of BERT), as those have shared weights. I should then be able to train the last few layers of BERT and the Dense layer on top differently depending on the branch of the classifier (maybe using something like keras.switch?).
I have tried many alternative options including multi-class and multi-label classifiers, with actual and generated (eg, machine-annotated) labels in the case of multiple input labels, different activation and loss functions, but none of the results were acceptable to me (none were as good as the four separate models).
Is there a solution for merging the four different models for more performance, or am I stuck with using 4x binary classifiers?
When you train DNN for specific task it will be (in vast majority of cases) be better than the more general model that can handle several task simultaneously. Saying that, based on my experience the properly trained general model produces very similar results to the original binary ones. Anyways, here couple of suggestions for training strategies (assuming your training datasets for each task are completely different):
Weak supervision approach
Train your binary classifiers, and label your datasets using them (i.e. label with binary classifier trained on dataset 2 datasets [1,3,4]). Then train your joint model as multilabel task using all the newly labeled datasets (don't forget to randomize samples before feeding them to trainer ;) ). Here you will need to experiment if you will use threshold and set a label to 0/1 or use the scores of the binary classifiers.
Create custom loss function that will not penalize if no information provided for certain class. So when your will introduce sample from (say) dataset 2, your loss will be calculated only for the 2nd class.
Of course you can apply both simultaneously. For example, if you know that binary classifier produces scores that are polarized (most results are near 0 or 1), you can use weak labels, and automatically label your data with scores. Now during the second stage penalize loss such that for score x' = 4(x-0.5)^2 (note that you get logits from the model, so you will need to apply sigmoid function). This way you will increase contribution of the samples binary classifier is confident about, and reduce that of less certain ones.
As for releasing last layers of BERT, usually unfreezing upper 3-6 layers is enough. Releasing more layers improves results very little and increases time and memory requirements.
I am training a cnn model with five classes using keras library. Using model.predict function i get prediction percentage of the classes. My problem is for a image which doesn't belong to these classes and completely irrelevant, the predict class still predicts the percentages according to the classes.
How do I prevent it? How do I identify it as irrelevant?
I assume you are using a softmax activation on your last layer to generate the probabilities for each class. By definition, the sum of the outputs from the softmax activation must add up to 1. Therefore, it is impossible for the neural net to say that the image does not belong to any of your classes, with your current setup.
There are two potential ways you could address this:
Add another class that represents "other" or "unknown" objects (so you have 6 classes).
Add another output to your neural net (or train a completely independent neural net) that does binary classification on whether or not the image is in one of the 5 classes. That way, if your secondary output says that the image is not in the 5 classes, you can ignore the softmax output.
In both cases, you will need to augment your dataset with images that do not fall in your 5 classes.
I'm currently facing a Machine Learning problem and I've reached a point where I need some help to proceed.
I have various time series of positional (x, y, z) data tracked by sensors. I've developed some more features. For example, I rasterized the whole 3D space and calculated a cell_x, cell_y and cell_z for every time step. The time series itself have variable lengths.
My goal is to build a model which classifies every time step with the labels 0 or 1 (binary classification based on past and future values). Therefore I have a lot of training time series where the labels are already set.
One thing which could be very problematic is that there are very few 1's labels in the data (for example only 3 of 800 samples are labeled with 1).
It would be great if someone can help me in the right direction because there are too many possible problems:
Wrong hyperparameters
Incorrect model
Too few 1's labels, but I think that's not a big problem because I only need the model to suggests the right time steps. So I would only use the peaks of the output.
Bad or too less training data
Bad features
I appreciate any help and tips.
Your model seems very strange. Why only use 2 units in lstm layer? Also your problem is a binary classification. In this case you should choose only one neuron in your output layer (try to insert one additional dense layer between and lstm layer and try dropout layers between them).
Binary crossentropy does not make much sense with 2 output neurons, if you don't have a multi label problem. But if you're switching to one output neuron it's the right one. You also need sigmoid then as activation function.
As last advice: Try class weights.
http://scikit-learn.org/stable/modules/generated/sklearn.utils.class_weight.compute_class_weight.html
This can make a huge difference, if you're label are unbalanced.
You can create the model using tensorflow BasicLSTMCell, the shape of your data fits for BasicLSTMCell in TensorFlow you can find Documentation for BasicLSTMCell here and for creating the model this Documentation contain code that will help to build BasicLstmCell model . Hope this will help you, Cheers.
I have a multilayer perceptron with 5 hidden layers and 256 neurons each. When I start training, I get different prediction probabilities for each train sample until epoch 50, but then the number of duplicate predictions increases, on epoch 300 I already have 30% of duplicate predictions which does not make sense since the input data is different for all training samples. Any idea what causes this behavior?
Clarifications:
with "duplicate predictions", I mean items with the exactly same predicted probability to belong to class A (it's a binary classification problem)
I have 4000 training samples with 200 features each and all samples are different, it does not make sense that the number of duplicate predictions increases to 30% while training. So I wonder what can cause this behavior.
One point, you say you are doing a binary prediction, and when you say "duplicate predictions", even with your clarification it's hard to understand your meaning. I am guessing that you have two outputs for your binary classifier, one for class A and one for class B and you are getting roughly the same value for a given sample. If that's the case, then the first thing to do is to use 1 output. A binary classification problem is better modeled with 1 output that ranges between 0 and 1 (sigmoid the output neuron). This way there will be no ambiguity, the network will have to choose one or the other, or when it's confused you'll get ~0.5 and it will be clear.
Second, it is very common for a network to start learning well and then to perform more poorly after overtraining. Especially with small datasets such as what you have. In fact, even with the little knowledge I have of your dataset I would put a small bet on you getting better performance out of an algorithm like XGA Boost than a neural network (I assume you're using a neural net and not literally a perceptron).
But regarding the performance degrading over time. When this happens you want to look into something called "early stopping". At some point the network will start memorizing the input, and may be part of what's happening. Essentially you train until the performance on your held out test data starts to worsen.
To address this you can apply various forms of regularization (L2 regularization, dropout, batch normalization all come to mind). You can also reduce the size of your network. 5 layers of 256 neurons sounds too big for the problem. Try trimming this down and I bet your results will improve. There is a sweet spot for architecture size in neural networks. When your network is too large it can, and often will, over fit. When it's too small it won't be expressive enough for the data. Angrew Ng's coursera class has some helpful practical advice on dealing with this.
I want to train a convolutional neural network with TensorFlow to do multi-output multi-class classification.
For example: If we take the MNIST sample set and always combine two random images two a single one and then want to classify the resulting image. The result of the classification should be the two digits shown in the image.
So the output of the network could have the shape [-1, 2, 10] where the first dimension is the batch, the second represents the output (is it the first or the second digit) and the third is the "usual" classification of the shown digit.
I tried googling for this for a while now, but wasn't able find something useful. Also, I don't know if multi-output multi-class classification is the correct naming for this task. If not, what is the correct naming? Do you have any links/tutorials/documentations/papers explaining what I'd need to do to build the loss function/training operations?
What I tried was to split up the output of the network into the single outputs with tf.split and then use softmax_cross_entropy_with_logits on every single output. The result I averaged over all outputs but it doesn't seem to work. Is this even a reasonable way?
For nomenclature of classification problems, you can have a look at this link:
http://scikit-learn.org/stable/modules/multiclass.html
So your problem is called "Multilabel Classification". In normal TensorFlow multiclass classification (classic MNIST) you will have 10 output units and you will use softmax at the end for computing losses i.e. "tf.nn.softmax_cross_entropy_with_logits".
Ex: If your image has "2", then groundtruth will be [0,0,1,0,0,0,0,0,0,0]
But here, your network output will have 20 units and you will use sigmoid i.e. "tf.nn.sigmoid_cross_entropy_with_logits"
Ex: If your image has "2" & "4", then groundtruth will be [0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0], i.e. first ten bits to represent first digit class and second to represent second digit class.
First you have to provide two labels to an image comprised of two different images. Then change your objective loss function so it maximizes the outputs of the two given labels and train your model. I don't think you need to split the outputs.