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

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

from keras.models import Sequential
from sklearn import datasets
from keras.layers import Dense
from sklearn.model_selection import train_test_split
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score
from sklearn import preprocessing
import pickle
import numpy as np
import json

def classifier(X, y):
    """
    Description of classifier
    """
    NOF_ROW, NOF_COL =  X.shape

    def create_model():
        # create model
        model = Sequential()
        model.add(Dense(12, input_dim=NOF_COL, init='uniform', activation='relu'))
        model.add(Dense(6, init='uniform', activation='relu'))
        model.add(Dense(1, init='uniform', activation='sigmoid'))
        # Compile model
        model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
        return model

    # evaluate using 10-fold cross validation
    seed = 7
    np.random.seed(seed)
    model = KerasClassifier(build_fn=create_model, nb_epoch=150, batch_size=10, verbose=0)
    return model
    

def main():
    """
    Description of main
    """

    iris = datasets.load_iris()
    X, y = iris.data, iris.target
    X = preprocessing.scale(X)

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)
    model_tt = classifier(X_train, y_train)
    model_tt.fit(X_train,y_train)

    #--------------------------------------------------
    # This fail
    #-------------------------------------------------- 
    filename = 'finalized_model.sav'
    pickle.dump(model_tt, open(filename, 'wb'))
    # load the model from disk
    loaded_model = pickle.load(open(filename, 'rb'))
    result = loaded_model.score(X_test, Y_test)
    print(result)
    
    #--------------------------------------------------
    # This also fail
    #--------------------------------------------------
    # from keras.models import load_model       
    # model_tt.save('test_model.h5')
    

    #--------------------------------------------------
    # This works OK 
    #-------------------------------------------------- 
    # print model_tt.score(X_test, y_test)
    # print model_tt.predict_proba(X_test)
    # print model_tt.predict(X_test)


if __name__ == '__main__':
    main()

As stated in the code there it fails at this line:

pickle.dump(model_tt, open(filename, 'wb'))

With this error: pickle.PicklingError: Can't pickle <function create_model at 0x101c09320>: it's not found as __main__.create_model How can I get around it?

[crbrinton]

When trying to persist a KerasClassifier (or KerasRegressor) object, the KerasClassifier itself does not have a save method. It is the keras model that is wrapped by the KerasClassifier that can be saved using the save method. However, if you want to end up with a KerasClassifier after re-loading the persisted model, the re-loaded model must be wrapped anew in the KerasClassifier. This can be done by creating a new KerasClassifier object with a build_fn that actually calls the load_model method, such as:

def build_by_loading(self):
    model = load_model('nn_model.h5')
    return model 

So the KerasClassifier to be re-instantiated from a persisted file would be created as follows (for example):

    nn_model = KerasClassifier(build_fn=self.build_by_loading, nb_epoch=10, batch_size=5, verbose=1)

Unfortunately, the KerasClassifier code does not call the build_fn until the ‘fit’ method of the KerasClassifier is called. This would defeat the purpose of persisting the model.

I created a ‘build_only’ method in KerasClassifier that only calls the build_fn, but does not fit the model. This worked for me. I recommend that some means of instantiating a KerasClassifier from a persisted keras model similar to this be included in the next release.

[krishnateja614]

Can you try running model_tt.model.save(“test_model.h5”)? . I think we can’t directly use save function on scikit learn wrapper but the above line should hopefully do what you want to do. Let me know