classification_models | Keras | Computer Vision library

for image in images:
    classifiers = image['classifiers']
    for classifier in classifiers: 
        classes = classifier['classes']
        for _class in classes: 
            class_value = _class['class']
            score_value = _cl

def predict(path):
    with open(path) as img:
        res = vr.classify(images_file=img, threshold=0, classifier_ids=['food'])

        dict_of_higher_value_per_ = {} # in order to record and reuse values sooner or later.
        for imag

from tensorflow.contrib.learn.python.learn import run_config

def main(args):
    # Set model params
    model_params = {"learning_rate": 0.1}
    # Create a RunConfig instance
    r_config = run_config.RunConfig(gp