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Henze-Penrose Network Profiler

Based off of: Inside the Black Box: Characterizing Deep Learning Functional Mappings https://arxiv.org/pdf/1907.04223.pdf

Written for Python 3.x and Pytorch

Conceptual Introduction

The goal of this library is to allow users to gain more insight into what goes on inside their neural networks during training. This is accomplished by analyzing the class separation in a neural netowork. Class separation is quantified by the Henze-Penrose statistic, which is found by constructing the minimum spanning tree between data of two classes, and finding how many edges connect the same class vs. different classes.

After defining this Henze-Penrose statistic, we can then use it to gain a deeper understanding of model performance. This can be done in multiple ways, but we implement it here by comparing each class to each other class (in a dataset, numbers in MNIST for example), and examining this at the output of each layer.

This data can then be analyzed in a couple of ways. Here, we implement two useful methods: distribution graphing in Matplotlib and the Fischer Test for Means. With these tools, you can compare separation before and after a layer of a neural net to see how that layer transforms the data, or compare separation before and after training. High separation values (close to 1) indicate high separation, and low separation values (close to 0) indicate less separation. In general, you can gain insight into the effect a layer is having by seeing if it increases separation, and you can see how a model is learning over time by checking if class separation increases before and after training.

Library Usage

A couple steps need to be taken in order to analyze your networks with this tool. To follow along with this guide, view the example folder. All code required for library operation can be found in the src folder.

Step 1: Network Definition

The first step is to define your network in the standard Pytorch way, by inheriting from the nn.Module class and defining a forward method. In addition to the standard methods employed, two new methods should be written, namely refresh() and snapshot_forward().

The network analyzer keeps track of the output of each layer, and does so through a dict called snapshot. Your refresh() method should take a form similar to the following:

def refresh(self):
    self.snapshot = {'initial':[],
                    'conv1':[],
                    'relu1':[],
                    'maxpool1':[],
                    'conv2':[],
                    'relu2':[],
                    'maxpool2':[],
                    'fc1':[],
                    'relu3':[],
                    'fc2':[],
                    'softmax':[]}
    torch.cuda.empty_cache()

The purpose of this method is to initialize the dict between samples, while also ensuring that any GPUs utilized do not run out of memory. When writing your own method, include an entry for each point in the network you wish to track.

The snapshot_forward method is basically a copy of your network's standard forward pass definition. The only difference is that it copies the output at each layer and saves it to the snapshot. Structure your method similar to the following example. A useful tip is to implement some sort of processing function to ensure that the output at a given stage is completely separated from the computational graph. The process lambda function shown below works for me.

# self.process = lambda x: x.clone().detach().to(self.cpu)
def snapshot_forward(self,x):
    self.snapshot['initial'].append(self.process(x))

    out = self.conv1(x)
    self.snapshot['conv1'].append(self.process(out))

    out = self.relu(out)
    self.snapshot['relu1'].append(self.process(out))

    out = self.maxpool(out)
    self.snapshot['maxpool1'].append(self.process(out))

    out = self.conv2(out)
    self.snapshot['conv2'].append(self.process(out))

    out = self.relu(out)
    self.snapshot['relu2'].append(self.process(out))

    out = self.maxpool(out)
    self.snapshot['maxpool2'].append(self.process(out))

    out = out.view(-1,4096)
    out = self.fc1(out)
    self.snapshot['fc1'].append(self.process(out))

    out = self.relu(out)
    self.snapshot['relu3'].append(self.process(out))

    out = self.fc2(out)
    self.snapshot['fc2'].append(self.process(out))

    out = self.softmax(out)
    self.snapshot['softmax'].append(self.process(out))

    return(out)

A full example of how this could be implemented for a CNN can be found in ./example/Conv_Network.py

Step 2: Network Training

Training your network is largely the same as any Pytorch training routine. The only difference is that you should save your network weights at every instance you would like to analyze later. For example, you could add the following code to your training loop in order to save weights every 100 epochs.

if epoch % 100 == 0:
    torch.save(model.state_dict(), f'{model_storage_dir}conv{epoch}.ckpt')

A full example of how this could be implemented for a CNN can be found in ./example/ConvolutionalTraining.py

Step 3: Interfacing with the Library

After training has been concluded, we can interface with the library to run an analysis on the network. A full example for how to do this for a CNN can be found in ./example/HP_CNN_Wrapper.py, and I will walk through that example below.

a) Import Libraries and Set Parameters

This step is fairly standard, and just involves importing all the necessary libraries.

import torch
import torch.nn
import torchvision
import torchvision.transforms as transforms
import json,os
#import line_profiler

from HPTester import ModelTester	#code to compute the HP statistics necessary
from Conv_Network import CNN			#the neural networks under test

#run computations on the GPU if possible
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

#where the parameters have been saved
model_storage_dir = 'Conv_Parameters/'
result_storage_dir = 'Conv_Dicts/'
if not os.path.isdir(result_storage_dir):
	os.mkdir(result_storage_dir)
b) Constructing the Datasets

This library relies heavily on the Pytorch dataset framework. This works very well for simple models like MNIST which come preloaded in Pytorch, but you may have to write your own dataset wrapper if using a custom dataset. Instructions for doing so can be found here.

For a simple MNIST application, use the following:

construct the datasets
train_dataset = torchvision.datasets.MNIST(root='./MNISTdata', train=True,
                                           transform=transforms.ToTensor(),
                                           download=True)
test_dataset = torchvision.datasets.MNIST(root='./MNISTdata', train=False,
                                           transform=transforms.ToTensor(),
                                           download=True)
c) Initializing Models

We must load in each model we plan to analyze and initialize them to their training weights. This can be done as follows:

#initialize the models
model_pre = CNN()
model_post = CNN()

#load in all generated parameters
model_pre.load_state_dict(torch.load(model_storage_dir+'conv0.ckpt'))
model_pre.to(device).to(torch.float32)

model_post.load_state_dict(torch.load(model_storage_dir+'conv198.ckpt'))
model_post.to(device).to(torch.float32)
d) Calculating the HP Statistics

To do the Henze-Penrose analysis, we utilize the ModelTester class in the HPTester.py file. This class is initialized with several arguments:

  • training_set-see part c
  • test_set-see part c
  • classes-a list of all class labels
    • For MNIST, this would be the numbers 0-9
  • device-A torch.device object, see part a for initialization
    • A GPU is vastly preferred, but it should work on CPU as well
  • batch_size-the number of items to analyze at once
    • Play around with different numbers to see what works best for you
  • num_samples-the number of samples from each class to analyze
    • Runtime increases significantly with more samples, but the output will be more accurate

Once the ModelTester is initialized, simply call it on your models, and it will output the class separations for the model by class in a dict. This dict can then be parsed and analyzed in a future step.

An example of the steps of the above can be seen below:

#run the HP testing for each model
testing = ModelTester(train_dataset,test_dataset,[i for i in range(10)],
                      device,batch_size = 100,num_samples=1000)

pre_results = testing(model_pre)
post_results = testing(model_post)

#store the results from testing in json files
with open(result_storage_dir+'preDict.json','w') as f:
	json.dump(pre_results, f)


with open(result_storage_dir+'postDict.json','w') as f:
	json.dump(post_results, f)
e) Analyzing the Results

Due to the relatively large amount of time it takes to calculate the Henze-Penrose statistics for each combination of classes, the analysis of the results is kept in its own module, ResultsAnalysis.py.

In this file, I define the ResultsAnalysis class. It contains several useful methods which are illustrated below. Feel free to modify existing methods or create your own, any extra functionality would be welcome.

  • __init__-the initialization takes two arguments:
    • storage_dir: the path to the saved output
      • Conv_Dicts/ in our current example
    • stages: a list of all the stage names
      • ['preDict','postDict'] in the example we've been following
  • load_data-called automatically, loads and parses the data stored as JSON dicts, takes one argument:
    • data: should be the same as the stages argument in __init__
  • plot_results-plots the results as a series of Pyplot histograms, takes several parameters
    • results: the loaded data, get with ra.train_results or ra.test_results, where ra is the name of the object
    • stages: a list of labels for each stage, defaults to ['Before','After']
    • metric: defaults to Euclid, change if a different distance metric was used
    • by_layer: either True or False. Can either get each layer individually or all at once
  • FisherTraining-uses the Fisher permutation test for means to analyze class separation before and after training a network. This model is probabilistic and relies on GPU acceleration. It uses the object's internal train_results and test_results, and takes two parameters:
    • n: the number of Monte Carlo similations to run
    • batches: the number of batches to use-GPU optimized
  • FisherLayers-uses the Fisher permutation test for means to analyze class separation between layers in a network. This model is probabilistic and relies on GPU acceleration. It uses the object's internal train_results and test_results, and takes two parameters:
    • n: the number of Monte Carlo similations to run
    • batches: the number of batches to use-GPU optimized
  • plotPCA-can be used to reduce a high-dimensional input to a 2D output. Meant as a sanity check for the rest of the high-dimensional embedding tools. Only takes 2 inputs, which are stored as Pytorch tensors.

More tools may be added to this module in the future.