The CoreML instrument tool report shows that in each Prediction, there is 5 step.
Input validation
Neural Engin copy
Neural Engine request
Neural Engine Prediction
Neural Engin copy
The total prediction time is about 10ms. 5ms for input validation and only 3ms for prediction.
Is it possible to disable the "Input Validation"?
Related
I am working on a Machine Learning Project. I have set up a ML pipeline for various stages of project. The Pipeline goes like -
Data Extraction -> Data Validation -> Preprocessing -> Training -> Model Evaluation
Model Evaluation, takes place after training is completed to determine if a model is approved or rejected.
Now what I want is model evaluation to take place during training itself at any point.
Say at about when 60% of the training is complete, the training is stopped and model is evaluated, based on which if the model is approved, it resumes the training.
How can the above scenario be implemented?
No you should only evaluate during the testing time if you tries to evaluate during train time like this you cant get the perfect accuracy of your model. As 60% training is done only the model is not trained on full dataset it might gave you high accuracy but your model can be overfitted model.
Is there a way to load a pre-trained model.
I have tried the load("model.joblib") and save("model.joblib", model) functions but the loaded model only ha about 10% accuracy on the validation data, and successfully generates about 10% successful adversarial examples.
The model accuracy was about 99.3% before saving, and about 87% of adversarial examples generated were successful.
If I train the loaded the loaded model for the same number of epochs as the original then I get the expected accuracy and adversarial example generation rate.
Is there a way to so save the model so that it can be loaded without needing to be retrained?
After calling load(filename) you need to call model.get_logits(x) and CrossEntropy again to update the values used for calculating loss and accuracy.
I am attempting to give classifications to various bodies of text using Azure ML Studio and I have my successful output all the way until I deploy and test a web service. Once I deploy my web service and attempt to test it I get the following error:
Error 0035: Features for The vocabulary is empty. Please check the Minimum n-gram document frequency. required but not provided., Error code: ModuleExecutionError, Http status code: 400
The vocabularies for the extract n-gram modules are not empty. The only aspect that changes from the working model to the Web service error is the web service input.
Training Model
Predictive Model
You need to create 2 N-GRAMS modules in your "Training experiment" as shown on the screenshot of the link below
Follow the steps from the link:
COPY your NGRAMS module,
SET one in "CREATE" (for training) and the other one in "READ" (for testing)
CONNECT the vocabulary output from the training NGRAMS to the input vocabulary of the testing NGRAMS
SAVE the output vocabulary from testing part to a "Convert to Dataset" block.
You can then use this to feed your deployments NGRAMS steps in "READ" mode.
See steps here =>
https://learn.microsoft.com/fr-fr/azure/machine-learning/algorithm-module-reference/extract-n-gram-features-from-text
The MS documentation is misleading here.
What Azure ML Studio is looking for is a second input for the "extract n-grams" pill in the predictive experiment.
The desired second input is a dataset. The one you want is produced by the "extract n-grams" pill in the training experiment. To get and use this dataset, go to your training experiment and add a "Convert to CSV" pill on the second output node of the "Extract N-Grams" pill. Then save this as a dataset.
Now you use it as a second input in your predictive model's "n-grams" pill. You should be good to go!
From the Azure GitHub: https://github.com/MicrosoftDocs/azure-docs/blob/master/articles/machine-learning/algorithm-module-reference/extract-n-gram-features-from-text.md
Score or publish a model that uses n-grams
Copy the Extract N-Gram Features from Text module from the training dataflow to the scoring dataflow.
Connect the Result Vocabulary output from the training dataflow to
Input Vocabulary on the scoring dataflow.
In the scoring workflow, modify the Extract N-Gram Features from
Text module and set the Vocabulary mode parameter to ReadOnly. Leave
all else the same.
To publish the pipeline, save Result Vocabulary as a dataset.
Connect the saved dataset to the Extract N-Gram Features from Text
module in your scoring graph.
I am building an machine learning text classification model in R. I want to classify the sentence into more than one label if it falls into multiple categories.
e.g.: "The phone screen resolution is awesome and the battery life as well" - currently I am able to classify the sentence into either Battery or Phone feature category but I want it to be classified into both.
The output can be like:
It will be great if anyone can help me with ideas or methods to get the above result.
I would suggest training a binary classifier for each label.
With some algorithms - like logistic regression - all you can do is train every binary classifier independently.
There are also so-called multilabel algorithms - they train all binary classifiers at the same time, and extract the same features from data for every classifier. An example is a neural network with a sigmoid last layer. See "support multilabel" section in http://scikit-learn.org/stable/modules/multiclass.html for a list of multilabel algorithms.
Of course, a multilabel algorithm will not necessarily outperform logistic regression, you have to try and see what works best for your problem.
I am trying to run a regression model for a data set containing over 2000000 rows. I tried using linear regression and boosted decision tree regression without tuning model hyperparameter, I didn't get the expected accuracy. so I tried to use Tune model hyperparameter for the boosted decision tree, the model runs over 20 min. the decision forest also takes to0 long (even without tuning model hyperparameter). Is there any way to reduce the runtime without compromising the result accuracy too much?
will sampling affect the output (say I took 0.5 as sampling rate)?
The execution time on AzureML Studio depends on the pricing tier. The free version does one node execution at time while the standard pricing tier do the execute multiple execution at one time.