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 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)   # Output of predict_proba # 2nd column is the probability that the prediction is 1 # this value is used as final score, which can be used # with other method as comparison # [   [ 0.25311464  0.74688536] #     [ 0.84401423  0.15598579] #     [ 0.96047372  0.03952631] #     ..., #     [ 0.25518912  0.74481088] #     [ 0.91467732  0.08532269] #     [ 0.25473493  0.74526507]]  # Output of predict # [[1] # [0] # [0] # ..., # [1] # [0] # [1]]   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?

Answer 1

Edit 1 : Original answer about saving model

With HDF5 :

# saving model json_model = model_tt.model.to_json() open('model_architecture.json', 'w').write(json_model) # saving weights model_tt.model.save_weights('model_weights.h5', overwrite=True)   # loading model from keras.models import model_from_json  model = model_from_json(open('model_architecture.json').read()) model.load_weights('model_weights.h5')  # dont forget to compile your model model.compile(loss='binary_crossentropy', optimizer='adam') 

Edit 2 : full code example with iris dataset

# Train model and make predictions import numpy import pandas from keras.models import Sequential, model_from_json from keras.layers import Dense from keras.utils import np_utils from sklearn import datasets from sklearn import preprocessing from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder  # fix random seed for reproducibility seed = 7 numpy.random.seed(seed)  # load dataset iris = datasets.load_iris() X, Y, labels = iris.data, iris.target, iris.target_names X = preprocessing.scale(X)  # encode class values as integers encoder = LabelEncoder() encoder.fit(Y) encoded_Y = encoder.transform(Y)  # convert integers to dummy variables (i.e. one hot encoded) y = np_utils.to_categorical(encoded_Y)  def build_model():     # create model     model = Sequential()     model.add(Dense(4, input_dim=4, init='normal', activation='relu'))     model.add(Dense(3, init='normal', activation='sigmoid'))     model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])     return model  def save_model(model):     # saving model     json_model = model.to_json()     open('model_architecture.json', 'w').write(json_model)     # saving weights     model.save_weights('model_weights.h5', overwrite=True)  def load_model():     # loading model     model = model_from_json(open('model_architecture.json').read())     model.load_weights('model_weights.h5')     model.compile(loss='categorical_crossentropy', optimizer='adam')     return model   X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.3, random_state=seed)  # build model = build_model() model.fit(X_train, Y_train, nb_epoch=200, batch_size=5, verbose=0)  # save save_model(model)  # load model = load_model()  # predictions predictions = model.predict_classes(X_test, verbose=0) print(predictions) # reverse encoding for pred in predictions:     print(labels[pred]) 

Please note that I used Keras only, not the wrapper. It only add some complexity in something simple. Also code is volontary not factored so you can have the whole picture.

Also, you said you want to output 1 or 0. It is not possible in this dataset because you have 3 output dims and classes (Iris-setosa, Iris-versicolor, Iris-virginica). If you had only 2 classes then your output dim and classes would be 0 or 1 using sigmoid output fonction.

Answer 2

Just adding to gaarv's answer - If you don't require the separation between the model structure (model.to_json()) and the weights (model.save_weights()), you can use one of the following:

• Use the built-in keras.models.save_model and 'keras.models.load_model` that store everything together in a hdf5 file.
• Use pickle to serialize the Model object (or any class that contains references to it) into file/network/whatever..
  Unfortunetaly, Keras doesn't support pickle by default. You can use my patchy solution that adds this missing feature. Working code is here: http://zachmoshe.com/2017/04/03/pickling-keras-models.html