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()
Related
Hi am using TPOT for machine learning I am getting 99% accuracy but I am not sure to which model did it predict can someone help me with this also does it do SMOTE?
If you stored the TPOTClassifier in the variable my_tpot, then you can access the final trained pipeline by accessing the fitted_pipeline_ attribute:
my_tpot = TPOTClassifier()
my_tpot.fit(…)
print(my_tpot.fitted_pipeline_)
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 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 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
I was following this Tensorflow tutorial on creating a Convolutional Neural Network.
I'm at the step where the training and test data is read:
def main(unused_argv):
mnist = learn.datasets.load_dataset("mnist")
train_data = mnist.train.images # Returns np.array
train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
eval_data = mnist.test.images # Returns np.array
eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)
Up to here, everything is fine.
But then suddenly an estimator is created:
mnist_classifier = learn.Estimator(
model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model")
My questions are:
What is an Estimator?
The previous code doesn't save anything under "/tmp/mnist_convnet_model". How come there is a model saved under that directory?
How did it get there?
EDIT:
When I run the code, I get:
Couldn't find trained model at ../tmp/mnist_convnet_model.
This is because the model isn't found under that directory structure.
How can I put the model there? Also, why do I have to put it there, instead of storing it in memory for the execution of the script.
The first question is answered right there in the tutorial. Estimator is "a TensorFlow class for performing high-level model training, evaluation, and inference".
The answer to the second question is that no, nothing is saved to that directory yet. The estimator object will use this directory to save training checkpoints, logs etc. When you run this code the first time, it will not load anything. But once you train the model, it will load the saved state from there.