def load_keras_classifier(name, path=ASSETS_PATH): """Load a Keras model from disk, as KerasClassifier (sklearn wrapper)""" model_path, classes_path = keras_model_and_classes_paths(name) nn = KerasClassifier(build_fn=do_nothing) # load model and classes nn.model = keras.models.load_model(model_path) classes = pickle.load(open(classes_path, 'rb')) # required for sklearn to believe that the model is trained nn._estimator_type = "classifier" nn.classes_ = classes return nn
def load_pipeline_keras(): dataset = joblib.load(config.PIPELINE_PATH) build_model = lambda: load_model(config.MODEL_PATH) classifier = KerasClassifier( build_fn=build_model, batch_size=config.BATCH_SIZE, validation_split=10, epochs=config.EPOCHS, verbose=2, callbacks=m.callbacks_list, #image_size = config.IMAGE_SIZE ) classifier.classes_ = joblib.load(config.CLASSES_PATH) classifier.model = build_model() return Pipeline([('dataset', dataset), ('cnn_model', classifier)])