celebalucid 0.1.2

Analysing Clarity's InceptionV1 network after transfer learning on CelebA dataset.

CelebA & Lucid

This package is a quick access tool of a research project of Alfred Renyi Institute of Mathematics. In our project we use lucid visualizations to get a better understanding how transfer learning manipulates a neural network. As a baseline we took Clarity's InceptionV1 network trained on imagenet and we further trained the network on the CelebA dataset.

Networks

We've trained three neural networks. One learning 100 epochs with SGD optimizer, one learning 10 epochs with Adam and one learning 100 epochs with Adam. We'll reference these trainings later as sgd, adam and adam_overfit, while the baseline will be referenced as imagenet.

Details of the trainings:

Name	Epochs	Train loss	Train Acc	Val loss	Val acc
sgd	100	0.16	92.7%	0.18	91.9%
adam	10	0.16	92.9%	0.18	91.9%
adam_overfit	100	0.01	99.3%	0.93	90.8%

Setup

Requirements

Python 3.6+

Install

pip install celebalucid

Usage

Load

First, load either of the networks as

from celebalucid import load_model

# Choose from ['imagenet', 'sgd', 'adam', 'adam_overfit']
model = load_model('imagenet') 

Layer information

Get accessable layers as

model.layer_info

Calling this you will see the list of available layers along with the available number of channels (neurons).

[['conv2d0', 64],
 ['conv2d1', 64],
 ['conv2d2', 192],
 ['mixed3a_1x1', 64],
 ['mixed3a_3x3_bottleneck', 96],
 ...]

Lucid

Visualize a neuron with lucid as

# Default args: size=224, thresholds=[512], progress=False
img = model.lucid('mixed5a_5x5:7', progress=True)
plt.imshow(img)

Weights

You can access the weights of a network as

# Method 1
weights = model.weights.mixed3a_3x3_bottleneck.w 
bias = model.weights.mixed3a_3x3_bottleneck.b

# Method 2
weights = model.weights['mixed3a_3x3_bottleneck'].w 
bias = model.weights['mixed3a_3x3_bottleneck'].b

Activations

In order to see activations, you first need to download the test data (~300MB)

from celebalucid import build_generator

# The test data will be downloaded to this folder
DOWNLOAD_DIR = 'res/images'

# This is a torch.utils.data.DataLoader object
# If you already have the dataset downloaded
# to this folder, it will skip the download
generator = build_generator('res/images')

Now, utilize the data generator to load images from disk, feed the input with the neural network and then you're free to analyse the activations (aka. neurons.)

for x, y in generator:
    model.stream(x)
    # Now access neurons the same way as on weights
    some_operations(model.neurons.conv2d0)