How to save Scikit-Learn-Keras Model into a Persistence File (pickle/hd5/json/yaml)

Question

I have the following code, using Keras Scikit-Learn Wrapper:

from keras.models import Sequential
from sklearn import datasets
from keras.layers import Dense
         


        

Answer 1

In case your keras wrapper model is in a scikit pipeline, you save steps in the pipeline separately.

import joblib
from sklearn.pipeline import Pipeline
from tensorflow import keras

# pass the create_cnn_model function into wrapper
cnn_model = keras.wrappers.scikit_learn.KerasClassifier(build_fn=create_cnn_model)

# create pipeline
cnn_model_pipeline_estimator = Pipeline([
    ('preprocessing_pipeline', pipeline_estimator),
    ('clf', cnn_model)
])

# train model
final_model = cnn_model_pipeline_estimator.fit(
X, y, clf__batch_size=32, clf__epochs=15)

# collect the preprocessing pipeline & model seperately
pipeline_estimator = final_model.named_steps['preprocessing_pipeline']
clf = final_model.named_steps['clf']

# store pipeline and model seperately
joblib.dump(pipeline_estimator, open('path/to/pipeline.pkl', 'wb'))
clf.model.save('path/to/model.h5')

# load pipeline and model
pipeline_estimator = joblib.load('path/to/pipeline.pxl')
model = keras.models.load_model('path/to/model.h5')

new_example = [[...]]

# transform new data with pipeline & use model for prediction
transformed_data = pipeline_estimator.transform(new_example)
prediction = model.predict(transformed_data)