I trained my knn classifier over multiple images and saved the model. I am getting some new images to train. I don't want to retrain the already existing model.
How to add the newly trained model to the existing saved model ?
Could someone guide if this is possible or any articles describing the same ?
Thank you,
In Scikit-learn some algorithms can do partial_fil but KNN implementation cannot.
It's good to take a look at the Milvus vector engine database for incremental KNN and also ANN (Approximate Nearest Neighbor) method for a faster search
You can use pickle to do so.
import pickle
# save the model to disk
filename = 'finalized_model.sav'
pickle.dump(model, open(filename, 'wb'))
# load the model from disk
loaded_model = pickle.load(open(filename, 'rb'))
for more details you can refer this blogpost
Related
I have a model trained in Sagemaker as a file and can load and ultimately score it locally like so:
local_model_path = "model.tar.gz"
with tarfile.open(local_model_path) as tar:
tar.extractall()
model = xgb.XGBRegressor()
model.load_model("xgboost-model")
I wonder, how I can establish the hyperparameters used to fit the saved model. I do not think that these lines of code work (i.e. they do not show the hyperparameters the model was trained with):
booster = model.get_booster()
print(booster.save_config())
print(model.get_xgb_params())
How can I establish/check the actually used hyper parameters? Any help would be very much appreciated. Thanks.
Ok, forget the other answer which I deleted.
This one works for me, I don't know why get_xgb_params() should not work.
model = xgb.XGBRegressor()
model.load_model("xgboost-model")
model.get_xgb_params()
I am a novice in using tensorflow and I built a CNN which is trained and tested both with 80-85% accuracy. I tried to save my trained model using model.save('example.h5'), and download the file using files.download('example.h5')
Afterwards I tried to load it to my flask back-end using model = tf.keras.models.load_model('example.h5').
When I tried using it with random images, it's like the model has never been trained before. Any solutions? thank you
A common reason to this might be that you are normalizing your data on training but not before predicting. Make sure you normalize the input data before you run predictions, if you have trained on normalized data.
I am using matterport repository to train MASK RCNN on a custom dataset. I have been successful in training. Now I want to save the trained model and use it in a web application to detect objects. How do I save the mask rcnn model after training? Please guide me.
The link of the repository:
https://github.com/matterport/Mask_RCNN
Based on this discussion on GitHub, it appears that trained model or weights of matterport/Mask RCNN can be saved as a JSON file in a manner similar to those trained via standard Keras:
import keras
import json
def save_model(trained_model, out_fname="model.json"):
jsonObj = trained_model.keras_model.to_json()
with open(out_fname, "w") as fh:
fj.write(jsonObj)
save_model(model, "mymodel.json")
Update: If you run into the error related to thread-like object, you might find this post helpful...
In the Inspect_model.ipynb notebook under the "Load Model" topic you can save it after it loads the model in inference mode.
in the folder Mask_RCNN/logs generates a folder inside it
I am not sure if we really need to save the whole model again since normally when we used the matterport git we just train new weights on the existing architecture and doesnt make changes to the architecture. When we used this for a pet project , post training - we defined a new model as the MASK RCNN object (from mrcnn.model import MaskRCNN) with the parameter mode as inference and then loaded the newly trained weights model.load_weights('<logpath/trainedweights.h5>', by_name=True)
I built a CNN model for image classification using the Keras library. However training takes many hours. Once I trained my model, how can I use it without training once more? I mean after I trained my model, I want to use it many times.
Because I will use my model in android studio.
Any help is appreciated
Thank YOU...
EDIT
When I wrote this question, I did not know the save model and load.model, in the answers you see the appropriate usage of them.
You can easily save your model after the training process by using:
model.save('my_model.h5')
you can later load that model by using:
model = load_model('my_model.h5')
for more details have a look at the documentation: https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model
I am training a keras model and saving the weights into a JSON file like so:
with open('weigths.json', 'w') as f:
json.dump(model.get_weigths())
Now I want to load the weights and rebuild the keras model so that I can do testing and prediction
How can I do that?
To save a model, you should use the dedicated function keras.model.save(filepath) and load it again with keras.models.load_model(filepath) as explained here.