Deep Visualization Toolbox

This repository contains an improved version of the tool, made by Arik Poznanski. The most notable improvements are:

• Added new visualizations:
  • Activation Histograms
  • Activation Correlation
• Tool usage made easier
  • Reduced number of user-tuned parameters
  • Support for non-sequential networks like: Inception and ResNet
  • Support for Siamese networks
  • Enhanced UI (Input overlays, color maps, mouse support)
  • Support input source (Directory, image list, siamese image list)
• Tested on all major network architectures, including: LeNet, AlexNet, ZFNet, GoogLeNet, VGGNet and ResNet.

The original version of the tool was first described here and more formally in this paper:

• Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson. Understanding neural networks through deep visualization. Presented at the Deep Learning Workshop, International Conference on Machine Learning (ICML), 2015.

If you find this paper or code useful, we encourage you to cite the paper. BibTeX:

@inproceedings{yosinski-2015-ICML-DL-understanding-neural-networks,
Author = {Jason Yosinski and Jeff Clune and Anh Nguyen and Thomas Fuchs and Hod Lipson},
Booktitle = {Deep Learning Workshop, International Conference on Machine Learning (ICML)},
Title = {Understanding Neural Networks Through Deep Visualization},
Year = {2015}}

Installation

Following are installation instruction for the new improved version of Deepvis.

$ git clone --recursive https://github.com/arikpoz/deep-visualization-toolbox.git
$ cd deep-visualization-toolbox && ./build_default.sh

Note: there is no need to download Caffe separately, it is now a sub-module of this repository and will get downloaded and built using the above instructions.

Run the tool:

$ ./run_toolbox.py

Once the toolbox is running, push 'h' to show a help screen.

Loading a New Model

1. Define a model settings file: settings_your_model.py

# network definition
caffevis_deploy_prototxt = '../your-model-deploy.prototxt'
caffevis_network_weights = '../your-model-weights.caffemodel'
caffevis_data_mean = '../your-model-mean.npy'

# input configuration
static_files_dir = '../input_images_folder'

# output configuration
caffevis_outputs_dir = '../outputs'
layers_to_output_in_offline_scripts = ['conv1','conv2', ..., 'fc1']

2. Define a user settings file: settings_user.py

# GPU configuration
#caffevis_mode_gpu = False
caffevis_gpu_id = 2

# select model to load
model_to_load = 'your_model'
#model_to_load = 'previous_model'

Basic Layout

Activation Histograms

• Helps to study the activity of a channel over a set of inputs.

• Given a dataset of N input images, we compute the activation of each channel over the dataset and histogram the corresponding values.

1. Detect inactive channels:

2. Detect inactive layers:

• The behavior seen on the right, is clearly an indication of a problem in the training process. Since most of the channels are inactive and effectively the model capacity is reduced.

• A few reasons for this behavior:

  • A constant zero ReLU activation, a.k.a. “dead” ReLU.
  • Poor network initialization.

Activation Correlation

• Seek correlations between activation values of different channels in the same layer to check network capacity usage.

• On the left, there is a healthy correlation matrix, where the channels are completely uncorrelated. On the right, there is a correlation matrix with all the channels either highly or inversely correlated.

• The capacity utilization of the network is relatively low. Increase in number of parameters won't improve performance.

Maximal Input

• For each channel we find the image patch from our dataset that has the highest activation.

Maximal Optimized

• Using a regularized optimization process we approximate for each channel the image that empirically has the highest activation

Backprop Modes

• Backprop visualization is basically a regular backprop step that continues to the pixel level. It provides an easy way to study the influence of each pixel on the network decision.

• Original toolbox supports only ZF-deconv and vanilla backprop.

• Our enhanced version supports also guided backpropagation that provides better localization.

• Guided backpropagation: Gradients are propagated back trough the ReLU activation only if the forward activation and the gradient are positive.