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)])
