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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:

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']
  1. 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

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: Detect inactive channels

  2. Detect inactive layers: 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.

Activation correlation

  • 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 input

Maximal Optimized

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

Maximal optimized

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.

Guided backprop