As part of my masters thesis I have been tasked with predicting a label integer (0-255) which is a binned representation of an angle. The feature columns are also integers, in the range (0-255).
So far I have used the custom Tensorflow layers estimator, implementing a 256 output classifier which performs well. However, my issue with the classification approach I am using is the following:
My classification model thinks that predicting a 3 instead of a 28 is as good/bad as predicting a 27 as a 28
The numerical interval / ordinal nature of my data (not sure which) leads me to believe that if I used regression I would achieve results with less drastically incorrect predictions or outliers.
My goal:
to reduce the number of drastically incorrect predicted outliers
My questions:
Is regression the better approach, or can I improve my
classification to include an ordinal/interval relationship between
my labels?
If I choose regression, is there a way to bound my predicted output between 0-255 (I know I will have to round float values predicted).
Thanks in advance. Any other comments, suggestions or ideas to help me to best tackle the problem are also very helpful.
If I made any incorrect assumptions or mistake in my interpretation of the problem feel free to correct me.
Question 1: Regression is the simpler approach, however, you can also use classification and manipulate the loss function to have a lower loss for misclassifications that are "close" to the original class.
Question 2: The tensorflow command for bounding your prediction is tf.clip_by_value. Are you mapping all 360 degrees to [0,255]? In that case you will want to consider the boundary cases, i.e. your estimator yields -4 and the true value is 251, but they are the actually representing the same value so loss should be 0.
Related
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'm currently using keras (tensorflow) to create a feed-forward neural network to predict a sales value. When I look at the test set comparing predicted vs real sales values a fit line ends up having a good R squared value but a low slope value. So it's predicting the variability in the data correctly, but not predicting the magnitude of the data. When I look at the data in smaller subsets, the underprediction is consistent. Has anybody had experience with this or have an idea what could cause this? I have a feeling it may be a data bias/normalization issue.
I'm trying to predict sequences of 2D coordinates. But I don't want only the most probable future path but all the most probable paths to visualize it in a grid map.
For this I have traning data consisting of 40000 sequences. Each sequence consists of 10 2D coordinate pairs as input and 6 2D coordinate pairs as labels.
All the coordinates are in a fixed value range.
What would be my first step to predict all the probable paths? To get all probable paths I have to apply a softmax in the end, where each cell in the grid is one class right? But how to process the data to reflect this grid like structure? Any ideas?
A softmax activation won't do the trick I'm afraid; if you have an infinite number of combinations, or even a finite number of combinations that do not already appear in your data, there is no way to turn this into a multi-class classification problem (or if you do, you'll have loss of generality).
The only way forward I can think of is a recurrent model employing variational encoding. To begin with, you have a lot of annotated data, which is good news; a recurrent network fed with a sequence X (10,2,) will definitely be able to predict a sequence Y (6,2,). But since you want not just one but rather all probable sequences, this won't suffice. Your implicit assumption here is that there is some probability space hidden behind your sequences, which affects how they play out over time; so to model the sequences properly, you need to model that latent probability space. A Variational Auto-Encoder (VAE) does just that; it learns the latent space, so that during inference the output prediction depends on sampling over that latent space. Multiple predictions over the same input can then result in different outputs, meaning that you can finally sample your predictions to empirically approximate the distribution of potential outputs.
Unfortunately, VAEs can't really be explained within a single paragraph over stackoverflow, and even if they could I wouldn't be the most qualified person to attempt it. Try searching the web for LSTM-VAE and arm yourself with patience; you'll probably need to do some studying but it's definitely worth it. It might also be a good idea to look into Pyro or Edward, which are probabilistic network libraries for python, better suited to the task at hand than Keras.
I'm using TensorFlow for a multi-target regression problem. Specifically, in a convolutional network with pixel-wise labeling with the input being an image and the label being a "heat-map" where each pixel has a float value. More specifically, the ground truth labeling for each pixel is lower bounded by zero, and, while technically having no upper bound, usually gets no larger than 1e-2.
Without batch normalization, the network is able to give a reasonable heat-map prediction. With batch normalization, the network takes much long to get to reasonable loss value, and the best it does is making every pixel the average value. This is using the tf.contrib.layers conv2d and batch_norm methods, with the batch_norm being passed to the conv2d's normalization_fn (or not in the case of no batch normalization). I had briefly tried batch normalization on another (single value) regression network, and had trouble then as well (though, I hadn't tested that as extensively). Is there a problem using batch normalization on regression problems in general? Is there a common solution?
If not, what could be some causes batch normalization failing on such an application? I've attempted a variety of initializations, learning rates, etc. I would expect the final layer (which of course does not use batch normalization) could use weights to scale the output of the penultimate layer to the appropriate regression values. Failing that, I removed batch norm from that layer, but with no improvement. I've attempted a small classification problem using batch normalization and saw no problem there, so it seems reasonable that it could be due somehow to the nature of the regression problem, but I don't know how that could cause such a drastic difference. Is batch normalization known to have trouble on regression problems?
I believe your issue is in the labels. Batch norm will scale all input values between 0 and 1. If the labels are not scaled to a similar range the task will be more difficult. This is because it requires the NN to learn values of a different scale.
By removing the batch norm from the penultimate layer, the task may be improved slightly, but you are still requiring an NN layer to learn to downscale values of its input while subsequently normalizing back to the range 0 - 1 (opposite to your objective).
To solve this problem, apply a 0 - 1 scaler to the labels such that your upper bound is no longer 1e-2. During inference, transform the predictions back with the same function to get the actual prediction.
I want to perform a multilabel image classification task for n classes.
I've got sparse label vectors for each image and each dimension of each label vector is currently encoded in this way:
1.0 ->Label true / Image belongs to this class
-1.0 ->Label false / Image does not contain to this class.
0.0 ->missing value/label
E.g.: V= {1.0,-1.0,1.0, 0.0}
For this example V the model should learn, that the corresponding image should be classified in the first and third class.
My problem is currently how to handle the missing values/labels. I've searched through the issues and found this issue:
tensorflow/skflow#113 found here
So could do multilable image classification with:
tf.nn.sigmoid_cross_entropy_with_logits(logits, targets, name=None)
but TensorFlow has this error function for sparse softmax, which is used for exclusive classification:
tf.nn.sparse_softmax_cross_entropy_with_logits(logits, labels, name=None)
So is there something like sparse sigmoid cross entropy? (Couldn't find something) or any suggestions how can I handle my multilabel classification problem with sparse labels.
I used weighted_cross_entropy_with_logits as the loss function with positive weights for 1s.
In my case, all the labels are equally important. But 0 was ten times more likely to be appeared as the value of any label than 1.
So I weighed all the 1s by calling the pos_weight parameter of the aforementioned loss function. I used a pos_weight (= weight on positive values) of 10. By the way, I do not recommend any strategy to calculate the pos_weight. I think it will depend explicitly on the data in hand.
if real label = 1,
weighted_cross_entropy = pos_weight * sigmoid_cross_entropy
Weighted cross entropy with logits is same as the Sigmoid cross entropy with logits, except for the extra weight value multiplied to all the targets with a positive real value i.e.; 1.
Theoretically, it should do the job. I am still tuning other parameters to optimize the performance. Will update with performance statistics later.
First I would like to know what you mean by missing data? What is the difference between miss and false in your case?
Next, I think it is wrong that you represent your data like this. You have unrelated information that you try to represent on the same dimension. (If it was false or true it would work)
What seems to me better is to represent for each of your class a probability if it is good, or is missing or is false.
In your case V = [(1,0,0),(0,0,1),(1,0,0),(0,1,0)]
Ok!
So your problem is more about how to handle the missing data I think.
So I think you should definitely use tf.sigmoid_cross_entropy_with_logits()
Just change the target for the missing data to 0.5. (0 for false and 1 for true).
I never tried this approach but it should let your network learn without biasing it too much.