I am suddenly facing the problem wherein i get the message ' Line search fails in two-class probability estimates' when using libsvm for binary classification' when classifying the test images . The training database is of size 2000 and test database is of size 2000. The feature vector size is 200. However, for another feature of size 256, the problem does not arise. Even for other large size features the problem didnot occur.Suddenly the problem has occured. I am using LIBSVM and it is binary classification. What can be the possible reason? Please help asap.Thanks in advance.
I have tried the solution suggested in earlier similar question,but of no use.
Related
I´m trying to prepare my dataset ideally for binary document classification with an SVM algorithm in R.
The dataset is a combination of 150171 labelled variables and 2099 observations stored in a dataframe. The variables are a combination uni- and bigrams which were retrieved from a text dataset.
When I´m trying to calculate the Information gain as a feature selection method, the Error "cannot allocate vector of size X Gb" occurs although I already extended my memory and I´m running on a 64-bit operating system. I tried the following package:
install.packages("FSelector")
library(FSelector)
value <- information.gain(Usefulness ~., dat_SentimentAnalysis)
Does anybody know a solution/any trick for this problem?
Thank you very much in advance!
I am training the Tacotron2 model using TensorflowTTS for a new language.
I managed to train the model (performed pre-processing, normalization, and decoded the few generated output files)
The files in the output directory are .npy files. Which makes sense as they are mel-spectograms.
I am trying to find a way to convert said files to a .wav file in order to check if my work has been fruitfull.
I used this :
melspectrogram = librosa.feature.melspectrogram(
"/content/prediction/tacotron2-0/paol_wavpaol_8-norm-feats.npy", sr=22050,
window=scipy.signal.hanning, n_fft=1024, hop_length=256)
print('melspectrogram.shape', melspectrogram.shape)
print(melspectrogram)
audio_signal = librosa.feature.inverse.mel_to_audio(
melspectrogram, sr22050, n_fft=1024, hop_length=256, window=scipy.signal.hanning)
print(audio_signal, audio_signal.shape)
sf.write('test.wav', audio_signal, sample_rate)
But it is given me this error : Audio data must be of type numpy.ndarray.
Although I am already giving it a numpy.ndarray file.
Does anyone know where the issue might be, and if anyone knows a better way to do it?
I'm not sure what your error is, but the output of a Tacotron 2 system are log Mel spectral features and you can't just apply the inverse Fourier transform to get a waveform because you are missing the phase information and because the features are not invertible. You can learn about why this is at places like Speech.Zone (https://speech.zone/courses/)
Instead of using librosa like you are doing, you need to use a vocoder like HiFiGan (https://github.com/jik876/hifi-gan) that is trained to reconstruct a waveform from log Mel spectral features. You can use a pre-trained model, and most off-the-shelf vocoders, but make sure that the sample rate, Mel range, FFT, hop size and window size are all the same between your Tacotron2 feature prediction network and whatever vocoder you choose otherwise you'll just get noise!
I am trying to use spacy's 'pre-train' feature for a NER task, so here is what I tried doing(I am still trying to use it),
Step 1: I started by initializing the model with 'en_core_web_lg' next I saved this model to disk and tested its NER capability on few lines to see if it recognizes the tags in those test lines. (Made a note of ignored tags)
Step 2: Next I created a .jsonl file with new data to train on (about 20 new lines, I wanted to see the model's capability given new data around an entity(ignored tags found earlier) will it be able to correctly identify tags after doing transfer learning). So using this .jsonl and the model I saved earlier file I used 'spacy pre-train' command to train, this created a token2vec .bin file for me (model999.bin).
Step 3: Next I created a function that takes the location of an earlier saved model(model saved in step 1) and location of token2vec (model999.bin file obtained in step 2). Inside the function it loads the model>creates/gets pipe>disables rest of the files>uses (pipe_name).model.tok2vec.from_bytes(file_.read()) to read from model999.bin and broadcast the learned vectors to base model.
But when I run this function, I get this error:
ValueError: could not broadcast input array from shape (96,3,384) into shape (96,3,480)
(I have uploaded the entire notebook here: [https://github.com/pratikdk/ner_test/blob/master/base_model_contextual_TF.ipynb ]).
In order to pre-train I used this function
python -m spacy pre-train ub.jsonl model_saves w2s
Here are the 20 lines I tried training on top of the base model
[ https://github.com/pratikdk/ner_test/blob/master/ub.jsonl ]
What am I doing wrong here exactly? Please can you also point the fix, I am sure many would need insight on this.
Environment
Operating System: CentOS
Python Version Used: 3.7.3
spaCy Version Used: 2.1.3
Environment Information: Anaconda Jupyter Lab
So I was able to fix this, the developer(on github) answered my question.
Here is the answer:
https://github.com/explosion/spaCy/issues/3616
I am using this code to train a word2vec model. I am trying to train it incrementally, with using saver.restore(). I am using new data after restoring the model. Since vocabulary size for the old data and new data are not the same, I got an exception like this:
InvalidArgumentError (see above for traceback): Assign requires shapes of both tensors to match. lhs shape= [28908,200] rhs shape= [71291,200]
Here 71291 is vocabulary size for the old data and 28908 is for new data.
It gets the vocabulary words from the train_data file here, and constructs the network model using size of the vocabulary. I thought that if I could set vocabulary size the same for my old data and new data, I can solve this problem.
So, my question is: Can I do that in this code? As far as I understand, I cannot reach skipgram_word2vec() function.
Or, is there any other way of solving this issue in this code beside what I thought? If it is not possible using this code, I will try other ways for my purpose.
Any help is appreciated.
Having taken a look at the source of word2vec_optimized.py I'd say you will need to change the code there. It operates by opening a text file right up front as "training data". For your purposes, you have to change the build_graph method and allow it to get an option to set all that data ( words, counts, words_per_epoch, current_epoch, total_words_processed, examples, labels, opts.vocab_words, opts.vocab_counts, opts.words_per_epoch ) when initializing, and not from a text file.
Then you need to merge the two text files, and load them once, to produce the vocabulary. Then save all the data above, and use that to restore the network at each subsequent run.
If you use more than 2 texts, you need to include all the text you plan to use in the first data to produce the vocabulary, however.
I am working on an image classification problem with tensorflow. I have 2 different CNNs trained separately (in fact 3 in total but I will deal with the third later), for different tasks and on a AWS (Amazon) machine. One tells if there is text in the image and the other one tells if the image is safe for work or not. Now I want to use them in a single script on my computer, so that I can put an image as input and get the results of both networks as output.
I load the two graphs in a single tensorflow Session, using the import_meta_graph API and the import_scope argument and putting each subgraph in a separate scope. Then I just use the restore method of the created saver, giving it the common Session as argument.
Then, in order to run inference, I retrieve the placeholders and final output with graph=tf.get_default_graph() and my_var=graph.get_operation_by_name('name').outputs[0] before using it in sess.run (I think I could just have put 'name' in sess.run instead of fetching the output tensor and putting it in a variable, but this is not my problem).
My problem is the text CNN works perfectly fine, but the nsfw detector always gives me the same output, no matter the input (even with np.zeros()). I have tried both separately and same story: text works but not nsfw. So I don't think the problem comes from using two networks simultaneaously.
I also tried on the original AWS machine I trained it on, and this time the nsfw CNN worked perfectly.
Both networks are very similar. I checked on Tensorboard if everything was fine and I think it is ok. The differences are in the number of hidden units and the fact that I use batch normalization in the nsfw model and not in the text one. Now why this title ? I observed that I had a warning when running the nsfw model that I didn't have when using only the text model:
W tensorflow/core/framework/op_def_util.cc:332] Op Inv is deprecated. It will cease to work in GraphDef version 17. Use Reciprocal.
So I thougt maybe this was the reason, everything else being equal. I checked my GraphDef version, which seems to be 11, so Inv should still work in theory. By the way the AWS machine use tensroflow version 0.10 and I use version 0.12.
I noticed that the text network only had one Inv operation (via a filtering on the names of the operations given by graph.get_operations()), and that the nsfw model had the same operation plus multiple Inv operations due to the batch normalization layers. As precised in the release notes, tf.inv has simply been renamed to tf.reciprocal, so I tried to change the names of the operations to Reciprocal with tf.group(), as proposed here, but it didn't work. I have seen that using tf.identity() and changing the name could also work, but from what I understand, tensorflow graphs are an append-only structure, so we can't really modify its operations (which seems to be immutable anyway).
The thing is:
as I said, the Inv operation should still work in my GraphDef version;
this is only a warning;
the Inv operations only appear under name scopes that begin with 'gradients' so, from my understanding, this shouldn't be used for inference;
the text model also have an Inv operation.
For these reasons, I have a big doubt on my diagnosis. So my final questions are:
do you have another diagnosis?
if mine is correct, is it possible to replace Inv operations with Reciprocal operations, or do you have any other solution?
After a thorough examination of the output of relevant nodes, with the help of Tensorboard, I am now pretty certain that the renaming of Inv to Reciprocal has nothing to do with my problem.
It appears that the last batch normalization layer eliminates almost any variance of its output when the inputs varies. I will ask why elsewhere.