svnh_semi_supervised_model_loaded_test.py 文件源码

def load_and_predict_with_saved_model():
    '''
    Loads saved as protobuf model and make prediction on a single image
    '''
    with tf.Session(graph=tf.Graph()) as sess:
        # restore save model
        export_dir = './gan-export/1'
        model = tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], export_dir)
        # print(model)
        loaded_graph = tf.get_default_graph()

        # get necessary tensors by name
        input_tensor_name = model.signature_def['predict_images'].inputs['images'].name
        input_tensor = loaded_graph.get_tensor_by_name(input_tensor_name)
        output_tensor_name = model.signature_def['predict_images'].outputs['scores'].name
        output_tensor = loaded_graph.get_tensor_by_name(output_tensor_name)

        # make prediction
        image_file_name = './svnh_test_images/image_3.jpg'
        with open(image_file_name, 'rb') as f:
            image = f.read()
            scores = sess.run(output_tensor, {input_tensor: [image]})

        # print results
        print("Scores: {}".format(scores))