Is it possible to programmatically stop sound classification process? I don't find any method or function to do it.
Ex:
const classifier = ml5.soundClassifier('path/to/model.json', options, modelReadyCallback);
// start classification
classifier.classify(gotResult);
// but how to stop?
classifier.stop(); // no such method
Reloading the page can solve the problem, but it is not the required solution.
Any idea?
Thanks a lot!
This is what I am doing
classifier.model.model.stopListening()
For the start
The model includes a "listen" method, however, I have yet to discover the appropriate values for it.
Recreating the classifier seems to be a solution.
Related
I am trying to use Mlflow to manage my Bayesian Optimization Model, which has several methods other than the predict (run_optimization() for example). My doubt is that when I log my model to the tracking server the model and retrieve it, it only contains the predict() as it is wrapped as a PyFunctModel; that's a problem because I need the model also to run prescriptions (suggestion of a possible new optimum), does anyone ever tried it? Thanks
I am using XGBClassifier for my image classification. As i am new to machine learning and xgboost. But recently i got to know that the model i am saving by using pickle library after certain iteration is the last iteration not the best iteration. Can anyone tell me how can i save the model from best iteration? Obviously i am using early stop.
I kindly apologize if i make any mistake in asking questions. Please i need the solution as soon as possible because i need it for my thesis.
And those who are suggesting me older questions for best iteration please my question is different i want to save the best iteration in pickle format so that i can use it in future not just use it in predict later in the same code.
Thank you.
use joblib dump/load to save/load the model, and get the booster of the model, to get the best iteration
I am using the keras and fit_generator to train my model. The current model I am doing is an auto-encoder - which does not render desired results. I would therefore like to create a callback that illustrates the training image and groundtruth image in every 500 batch or so. I therefore want to use on_batch_begin I am however unsure of how I can access the current batch to create a tf.Summary.Image.
Can anybody direct me to some information about this or knows how to get the current batch. Or would it be done in the generator? I just do not see how to attach a callback to that.
I have not been able to find an elegant solution. I have added an array to the callback with the files I want to analyse. Then I randomly choose one image just for illustration, and use the current model and illustrate it on tensorboard. It works but I had hoped it would have been more elegant :)
As asked in the title, i would like to know if it is possible to make a model early-stop the epochs during training when the error is reduced enough, so i can avoid overfitting and guessing the right number of epochs at each call.
This is the only thing i have found in the official documentation but it is tobe used in brainscript, and i don't know a single thing about it. I'm using Python 3.6 with CNTK 2.6.
Also, is there a way to perform cross validation in a CNTK CNN?? How could this be done?
Thanks in advance.
The CrossValidationConfig class tells CNTK to periodically evaluate the model on a validation data set, and then call a user-specified callback function, which then can be used to update the learning rate or to return False to indicate early stopping.
For examples on how to implement early stopping:
test_session_cv_callback_early_exit function here
Source code for cntk.train.training_session here.
There isn't any native implementation for early stopping in cntk. For cross validation you can look up CrossValidationConfig
I have a task to compare two images and check whether they are of the same class (using Siamese CNN). Because I have a really small data set, I want to use keras imageDataGenerate.
I have read through the documentation and have understood the basic idea. However, I am not quite sure how to apply it to my use case, i.e. how to generate two images and a label that they are in the same class or not.
Any help would be greatly appreciated?
P.S. I can think of a much more convoluted process using sklearn's extract_patches_2d but I feel there is an elegant solution to this.
Edit: It looks like creating my own data generator may be the way to go. I will try this approach.