This repository contains example code for the publication:
Efficient Training of Interval Neural Networks for Imprecise Training Data. Sadeghi, Jonathan, de Angelis, Marco, and Patelli, Edoardo. Neural Networks. 2019. doi: 10.1016/j.neunet.2019.07.005
Experiments from Section 5.2 and 5.3 are included. This includes one example of creating an Interval Neural Network with multiple outputs and one heteroscedastic example with interval valued data.
Each experiment is contained in a separate python run script, for example you can run the imprecise dataset experiment like this:
python3 sec5_2_uncertain_train_data_2.py
Before running the code ensure that you install the modules with pip3 from requirements.txt
If any aspect of the code is unclear, please feel free to get in touch.
The code structure is based on TensorFlow project template
A description of the code structure is given below:
In a nutshell here's how to use this template, so for example assume you want to implement VGG model so you should do the following:
- In models folder create a class named VGG that inherit the "base_model" class
class VGGModel(BaseModel):
def __init__(self, config):
super(VGGModel, self).__init__(config)
#call the build_model and init_saver functions.
self.build_model()
self.init_saver() - Override these two functions "build_model" where you implement the vgg model, and "init_saver" where you define a tensorflow saver, then call them in the initalizer.
def build_model(self):
# here you build the tensorflow graph of any model you want and also define the loss.
pass
def init_saver(self):
# here you initalize the tensorflow saver that will be used in saving the checkpoints.
self.saver = tf.train.Saver(max_to_keep=self.config.max_to_keep)- In trainers folder create a VGG trainer that inherit from "base_train" class
class VGGTrainer(BaseTrain):
def __init__(self, sess, model, data, config, logger):
super(VGGTrainer, self).__init__(sess, model, data, config, logger)- Override these two functions "train_step", "train_epoch" where you write the logic of the training process
def train_epoch(self):
"""
implement the logic of epoch:
-loop on the number of iterations in the config and call the train step
-add any summaries you want using the summary
"""
pass
def train_step(self):
"""
implement the logic of the train step
- run the tensorflow session
- return any metrics you need to summarize
"""
pass- In main file, you create the session and instances of the following objects "Model", "Logger", "Data_Generator", "Trainer", and config
sess = tf.Session()
# create instance of the model you want
model = VGGModel(config)
# create your data generator
data = DataGenerator(config)
# create tensorboard logger
logger = Logger(sess, config)- Pass the all these objects to the trainer object, and start your training by calling "trainer.train()"
trainer = VGGTrainer(sess, model, data, config, logger)
# here you train your model
trainer.train()You will find a template file and a simple example in the model and trainer folder that shows you how to try your first model simply.
├── base
│ ├── base_model.py - this file contains the abstract class of the model.
│ └── base_train.py - this file contains the abstract class of the trainer.
│
│
├── model - this folder contains any model of your project.
│ └── example_model.py
│
│
├── trainer - this folder contains trainers of your project.
│ └── example_trainer.py
│
├── mains - here's the main(s) of your project (you may need more than one main).
│ └── example_main.py - here's an example of main that is responsible for the whole pipeline.
│
├── data _loader
│ └── data_generator.py - here's the data_generator that is responsible for all data handling.
│
└── utils
├── logger.py
└── any_other_utils_you_need
-
Base model is an abstract class that must be Inherited by any model you create, the idea behind this is that there's much shared stuff between all models. The base model contains:
- Save -This function to save a checkpoint to the desk.
- Load -This function to load a checkpoint from the desk.
- Cur_epoch, Global_step counters -These variables to keep track of the current epoch and global step.
- Init_Saver An abstract function to initialize the saver used for saving and loading the checkpoint, Note: override this function in the model you want to implement.
- Build_model Here's an abstract function to define the model, Note: override this function in the model you want to implement.
-
Here's where you implement your model. So you should :
- Create your model class and inherit the base_model class
- override "build_model" where you write the tensorflow model you want
- override "init_save" where you create a tensorflow saver to use it to save and load checkpoint
- call the "build_model" and "init_saver" in the initializer.
-
Base trainer is an abstract class that just wrap the training process.
-
Here's what you should implement in your trainer.
- Create your trainer class and inherit the base_trainer class.
- override these two functions "train_step", "train_epoch" where you implement the training process of each step and each epoch.
This class is responsible for all data handling and processing and provide an easy interface that can be used by the trainer.
This class is responsible for the tensorboard summary, in your trainer create a dictionary of all tensorflow variables you want to summarize then pass this dictionary to logger.summarize().
I use Json as configuration method and then parse it, so write all configs you want then parse it using "utils/config/process_config" and pass this configuration object to all other objects.
Here's where you combine all previous part.
- Parse the config file.
- Create a tensorflow session.
- Create an instance of "Model", "Data_Generator" and "Logger" and parse the config to all of them.
- Create an instance of "Trainer" and pass all previous objects to it.
- Now you can train your model by calling "Trainer.train()"