I have trained a LinearSVC in a python script named main.py, for training an image classification algorithm. The model looks like this.
lin_clf = svm.LinearSVC()
lin_clf.fit(feature,g)
I need to use this trained model for predicting image classes in another code. How do i export the genereated model i.e. lin_clf to the other code.
Thank you in advance.
I understand that your "other code" is another python script.
In this case, you can certainly use the pickle or shelve modules to write lin_clf to disk in main.py, and to read it from disk in the script that will use the model.
Here is an example showing how to write the lin_clf object to disk using shelve:
import shelve
a = shelve.open("output")
a['lin_clf'] = lin_clf
a.close()
Related
New to Sagemaker..
Trained a "linear-learner" classification model using the Sagemaker API, and it saved a "model.tar.gz" file in my s3 path. From what I understand SM just used an image of a scikit logreg model.
Finally, I'd like to gain access to the model object itself, so I unpacked the "model.tar.gz" file only to find another file called "model_algo-1" with no extension.
Can anyone tell me how I can find the "real" modeling object without using the inference/Endpoint delpoy API provided by Sagemaker? There are some things I want to look at manually.
Thanks,
Craig
Linear-Learner is a built in algorithm written using MX-net and the binary is also MXNET compatible. You can't use this model outside of SageMaker as there is no open source implementation for this.
First of all, let me introduce myself. I am a young researcher and I am interested in machine learning. I created a model, trained, tested and validated. Now I would like to know if there is a way to save my trained model.
I am also interested in knowing if the model is saved trained.
Finally, is there a way to use the saved (and trained) model with new data without having to train the model again?
I work with python!
Welcome to the community.
Yes, you may save the trained model and reuse it later. There are several ways to do so and I will introduce you to a couple of them here. However, please note which library you used to build your model and use a method for that library.
Pickel: Pickle is the standard way of serializing objects in Python.
import pickle
pickle.dump(model, open(filename, 'wb'))
loaded_model = pickle.load(open(filename, 'rb'))
Joblib: Joblib is part of the SciPy ecosystem and provides utilities for pipelining Python jobs.
import joblib
joblib.dump(model, filename)
loaded_model = joblib.load(filename)
Finally, as suggested by others, if you used libraries such as Tensorflow to build and train your models, please note that they have extensive ways to work with the built model and save/load it. Please check the following information:
Tensorflow Save and Load model
There may be a better way to do this but this is how I have done it before, with python.
So you have a ML model that you have trained. That model is basically just a set of parameters. Depending on what module you are using, you can save those parameters in a file, and import them to regenerate your model later.
Perhaps a simpler way is to save the model object entirely in a file using pickling.
https://docs.python.org/3/library/pickle.html
You can dump the object into a file, and load it back when you want to run it again.
I am teaching myself to code Convolutional Neural Networks. In particular I am looking at the "Dogs vs. Cats" challenge (https://medium.com/#mrgarg.rajat/kaggle-dogs-vs-cats-challenge-complete-step-by-step-guide-part-2-e9ee4967b9). I am using PyCharm.
In PyCharm, is there a way of using the trained model to make a prediction on the test data without having to run the entire file each time (and thus retrain the model each time)? Additionally, is there a way to skip the part of the script that prepares the data for input into the CNN? In a similar manner, does PyCharm store variables- can I print individual variables after the script has been run.
Would it be better if I used a different IDLE?
You can use sklearn joblib to save the trained model as a pickle and use it later for predictions.
from sklearn.externals import joblib
# Save the model as a pickle in a file
joblib.dump(knn, 'filename.pkl')
# Load the model from the file
knn_from_joblib = joblib.load('filename.pkl')
# Use the loaded model to make predictions
knn_from_joblib.predict(X_test)
I'm working mostly with scikit-learn, as far as I understand, the TRAINS auto-magic doesn't catch scikit-learn model store/load automatically.
How do I manually register the model after I have 'pickled' it.
For Example:
import pickle
with open("model.pkl", "wb") as file:
pickle.dump(my_model, file)
Assuming you are referring to TRAINS experiment manager: https://github.com/allegroai/trains (which I'm one of the maintainers)
from trains import Task, OutputModel
OutputModel(Task.current_task()).update_weights(weights_filename="model.pkl")
Or, if you have information you want to store together with the pickled model file, you can do:
from trains import Task, OutputModel
model_parameters = {'threshold': 0.123}
OutputModel(Task.current_task(), config_dict=model_parameters).update_weights(weights_filename="model.pkl")
Now, you should see in the UI an output model registered with the experiment. The model contains a link to the pickel file, together with the configuration dictionary.
I have trained a prediction model using scikit-learn, and used pickle to save it to hard drive. The pickle file is 58M, which is quite sizable.
To use the model, I wrote something like this:
def loadModel(pkl_fn):
with open(pkl_fn, 'r') as f:
return pickle.load(f)
if __name__ == "__main__":
import sys
feature_vals = read_features(sys.argv[1])
model = loadModel("./model.pkl")
# predict
# model.predict(feature_vals)
I am wondering about the efficiency when running the program many times in command line.
Pickle files are supposed to be fast to load, but is there any way to even speed up? Can I compile the whole thing into a binary executable?
If you are worried about loading time, you can use joblib.dump and joblib.load, they are more efficient than pickle in the case of scikit-learn.
For a full (pretty straightforward) example see the docs or this related answer from ogrisel:
Save classifier to disk in scikit-learn