Skip to content

Latest commit

 

History

History
49 lines (44 loc) · 2.79 KB

README.md

File metadata and controls

49 lines (44 loc) · 2.79 KB
Raw

IDNNs

Description

IDNNs is a python library that implements training and calculating of information in deep neural networks [Shwartz-Ziv & Tishby, 2017] in TensorFlow. The library allows you to investigate how networks look on the information plane and how it changes during the learning.

Prerequisites

  • tensorflow r1.0 or higher version
  • numpy 1.11.0
  • matplotlib 2.0.2
  • multiprocessing
  • joblib

Usage

All the code is under the idnns/ directory. For training a network and calculate the MI and the gradients of it run the an example in main.py. Off course you can also run only specific methods for running only the training procedure/calculating the MI. This file has command-line arguments as follow -

  • start_samples - The number of the first sample for calculate the information
  • batch_size - The size of the batch
  • learning_rate - The learning rate of the network
  • num_repeat - The number of times to run the network
  • num_epochs - maximum number of epochs for training
  • net_arch - The architecture of the networks
  • per_data - The percent of the training data
  • name - The name for saving the results
  • data_name - The dataset name
  • num_samples - The max number of indexes for calculate the information
  • save_ws - True if we want to save the outputs of the network
  • calc_information - 1 if we want to calculate the MI of the network
  • save_grads - True if we want to save the gradients of the network
  • run_in_parallel - True if we want to run all the networks in parallel mode
  • num_of_bins - The number of bins that we divide the neurons' output
  • activation_function - The activation function of the model 0 for thnh 1 for RelU'
  • interval_accuracy_display - The interval for display accuracy
  • interval_information_display - The interval for display the information calculation
  • cov_net - True if we want covnet
  • rand_labels - True if we want to set random labels
  • data_dir - The directory for finding the data The results are save under the folder jobs. Each run create a directory with a name that contains the run properties. In this directory there are the data.pickle file with the data of run and python file that is a copy of the file that create this run. The data is under the data directory.

For plotting the results we have the file plot_figures.py. This file contains methods for plotting diffrent aspects of the data (the information plane, the gradients,the norms, etc).

References

  1. Ravid. Shwartz-Ziv, Naftali Tishby, Opening the Black Box of Deep Neural Networks via Information, 2017, Arxiv.