I am looking for examples of how to build a multivariate time-series RNN using Tensorflow. Is this possible with an LSTM cell or similar?
e.g. the data might look something like this:
Time,A,B,C,...
0,3.5,4.5,7.7,...
1,2.1,6.4,8.2,...
...
Any help much appreciated. Thanks, John
It depends on exactly what you mean, but yes, it should be possible. If you write more specifically how exactly your input and target data looks like, somebody may be able to help. You can generally have sequential continuous or categorical input data and sequential continuous or categorical output data or a mix of those. I would suggest you look at the tutorials and try out a few things, then ask again here.
Thanks. I have figured it out now. I misunderstood the docs 'inputs: A length T list of inputs, each a vector with shape [batch_size].'
The following link was useful:
https://m.reddit.com/r/MachineLearning/comments/3sok8k/tensorflow_basic_rnn_example_with_variable_length/
Related
I think the same purpose among sklearn.OneHotEncoder, pandas.get_dummies, and keras.to_categorical. But I don't know the difference.
Apart from the difference of the output/input type there is no difference, they all achieve the same result.
There's some technical difference:
Keras is very simple, you give him the target vector and he one -hot encodes it, use keras if you need to encode the labels vector.
Pandas is the most complex, it creates a new column for every class of the data, the good part is that works on dataframes where you want to one-hot only one of the columns (so you could say this is more of a multi purpose method, but not the preferable option if you need to train a NN)
Sklearn lets you one-hot encode multiple features in the same variable, is a bit more flexible that the use keras offers, if the method from keras is too simple try with sklearn, if keras is enough stick with it.
ml beginner here.
I have a dataset containing the GPA, GRE, TOEFL, SOP&LOR Ranking(out of 5)etc. (all numerical) , and a final column that states whether or not they were admitted to a university(0 or 1), which is what we'll use as y_train.
I'm supposed to not just classify the predicted labels, but also calculate the probability of each person getting admitted.
edit: so from the from the first comment, I built a Logistic Regression model, and with some googling I found 'predict_proba' from sklearn and tried implementing it. There werent any syntactical errors but the code values given by predict_proba were horribly wrong.
Link: https://github.com/tarunn2799/gre-pred/blob/master/GRE%20Admission%20Probability-%20Extraaedge.ipynb
please help me finding where I've gone wrong, and also tips to reduce the loss
thank you!
I read your notebook, but I'm confused why you think the predict_proba are horribly wrong..
Is the predict accuracy not good, or the format of predict_proba not as you expected?
You could use sklearn.metrics.accuracy_score(), sklearn.metrics.confusion_matrix() to check your predict label, or use sklearn.metrics.roc_auc_score() to check the result of predict_proba. Check both train & text parts are better.
I think the format of predict_proba is correct, or maybe you could try the predict_log_proba() to calculate the log probability?
Hope this could help you.
I am a beginner with DNN and pytorch.
I am dealing with a multi-classification problem where my label are encoded into a one-hotted vector, say of dimension D.
To this end, I am using the CrossEntropyLoss. However now I want to modify or change such criterion in order to penalize value distant from the actual one, say classify 4 instead of 5 is better than 2 instead of 5.
Is there a function already built-in in Pytorch that implement this behavior? Otherwise how can I modify the CrossEntropyLoss to achieve it?
This could help you. It is a PyTorch implementation ordinal regression:
https://www.ethanrosenthal.com/2018/12/06/spacecutter-ordinal-regression/
I'm using the ebalance Stata package to calculate post-stratification weights, and I'd like to convert the weights output (_webal, which is generated as a double with format %10.0g) to a matrix.
I'd like to normalize all weights in the "control" group, but I can't seem to convert the variable to a matrix in order to manipulate the weights individually (I'm a novice to Stata, so I was just going to do this using a loop––I'd normally just export and do this in R, but I have to calculate results within a bootstrap). I can, however, view the individual-level weights produced by the output, and I can use them to calculate sample statistics.
Any ideas, anyone? Thanks so much!
This is not an answer, but it doesn't fit within a comment box.
As a self-described novice in Stata, you are asking the wrong question.
Your problem is that you have a variable that you want to do some calculations on, and since you can't just use R and you don't know how to do those (unspecified) calculations directly in Stata, you have decided that the first step is to create a matrix from the variable.
Your question would be better phrased as a simple description of the relevant portions of your data and the calculation you need to do using that data (ebalance is an obscure distraction that probably lost you a few readers) and where you are stuck.
See also https://stackoverflow.com/help/mcve for a discussion of completing a minimal complete example with a description of the results you expect for that example.
how do you train a neural network to map from a vector representation, to one hot vectors? The example I'm interested in is where the vector representation is the output of a word2vec embedding, and I'd like to map onto the the individual words which were in the language used to train the embedding, so I guess this is vec2word?
In a bit more detail; if I understand correctly, a cluster of points in embedded space represents similar words. Thus if you sample from points in that cluster, and use it as the input to vec2word, the output should be a mapping to similar individual words?
I guess I could do something similar to an encoder-decoder, but does it have to be that complicated/use so many parameters?
There's this TensorFlow tutorial, how to train word2vec, but I can't find any help to do the reverse? I'm happy to do it using any deeplearning library, and it's OK to do it using sampling/probabilistic.
Thanks a lot for your help, Ajay.
One easiest thing that you can do is to use the nearest neighbor word. Given a query feature of an unknown word fq, and a reference feature set of known words R={fr}, then you can find out what is the nearest fr* for fq, and use the corresponding fr* word as fq's word.