This will save your training each epoch to the myModel.h5 file. Model_tt.fit(X_train,y_train, callbacks=callbacks_list) Model_tt = KerasClassifier(build_fn=create_model, nb_epoch=150, batch_size=10) #add that callback to the list of callbacks to pass Specifically the ModelCheckpoint callback, like this: from keras.callbacks import ModelCheckpointĬhk = ModelCheckpoint("myModel.h5", monitor='val_loss', save_best_only=False) If you had only 2 classes then your output dim and classes would be 0 or 1 using sigmoid output fonction.Īnother great alternative is to use callbacks when you fit your model. It is not possible in this dataset because you have 3 output dims and classes (Iris-setosa, Iris-versicolor, Iris-virginica). Also code is voluntarily not refactored so you can have the whole picture.Īlso, you said you want to output 1 or 0. It only add some complexity in something simple. Please note that I used Keras only, not the wrapper. ![]() Predictions = model.predict_classes(X_test, verbose=0) Model.fit(X_train, Y_train, nb_epoch=200, batch_size=5, verbose=0) X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.3, random_state=seed) pile(loss='categorical_crossentropy', optimizer='adam') Model.save_weights('model_weights.h5', overwrite=True) pile(loss='categorical_crossentropy', optimizer='adam', metrics=) Model.add(Dense(3, init='normal', activation='sigmoid')) Model.add(Dense(4, input_dim=4, init='normal', activation='relu')) # convert integers to dummy variables (i.e. X, Y, labels = iris.data, iris.target, iris.target_names pile(loss='binary_crossentropy', optimizer='adam')Įdit 2 : full code example with iris dataset # Train model and make predictionsįrom keras.models import Sequential, model_from_jsonįrom sklearn.preprocessing import LabelEncoder Model = model_from_json(open('model_architecture.json').read()) Model_tt.model.save_weights('model_weights.h5', overwrite=True) Open('model_architecture.json', 'w').write(json_model) With this error: pickle.PicklingError: Can't pickle : it's not found as _main_.create_modelĮdit 1 : Original answer about saving model # this value is used as final score, which can be usedĪs stated in the code there it fails at this line: pickle.dump(model_tt, open(filename, 'wb')) # 2nd column is the probability that the prediction is 1 Result = loaded_model.score(X_test, Y_test) Loaded_model = pickle.load(open(filename, 'rb')) Pickle.dump(model_tt, open(filename, 'wb')) ![]() X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0) Model = KerasClassifier(build_fn=create_model, nb_epoch=150, batch_size=10, verbose=0) # evaluate using 10-fold cross validation pile(loss='binary_crossentropy', optimizer='adam', metrics=) Model.add(Dense(1, init='uniform', activation='sigmoid')) ![]() Model.add(Dense(6, init='uniform', activation='relu')) Model.add(Dense(12, input_dim=NOF_COL, init='uniform', activation='relu')) I have the following code, using Keras Scikit-Learn Wrapper: from keras.models import Sequentialįrom sklearn.model_selection import train_test_splitįrom _learn import KerasClassifierįrom sklearn.model_selection import StratifiedKFoldįrom sklearn.model_selection import cross_val_score
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