save machine learning model
# fit the model
model.fit(X_train, y_train)
# save the model
import pickle
pickle.dump(model, open("model.pkl", "wb"))
# load the model
model = pickle.load(open("model.pkl", "rb"))
# use model to predict
y_pred = model.predict(X_input)save machine learning model python
model.fit(X_train, Y_train)
# 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'))
result = loaded_model.score(X_test, Y_test)load saved model
from keras.models import load_model
model = load_model('my_model.h5')load saved model
#Now you can predict results for a new entry image.
from keras.preprocessing import image
test_image = image.load_img(imagePath, target_size = (64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
#predict the result
result = model.predict(test_image)Copyright © 2021 Codeinu
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