# Visualizing representations with t-SNE
[t-SNE](http://lvdmaaten.github.io/tsne/) is a dimensionality
reduction technique that can be used to visualize the
128-dimensional features OpenFace produces.
The following shows the visualization of the three people
in the training and testing dataset with the most images.

**Training**

![](https://raw.githubusercontent.com/cmusatyalab/openface/master/images/train-tsne.png)

**Testing**

![](https://raw.githubusercontent.com/cmusatyalab/openface/master/images/val-tsne.png)

These can be generated with the following commands from the root
`openface` directory.

## 1. Create raw image directory.
Create a directory for a subset of raw images that you want to visualize
with TSNE.
Make images from different
people are in different subdirectories. The names of the labels or
images do not matter, and each person can have a different amount of images.
The images should be formatted as `jpg` or `png` and have
a lowercase extension.

```
$ tree data/mydataset-subset/raw
person-1
├── image-1.jpg
├── image-2.png
...
└── image-p.png

...

person-m
├── image-1.png
├── image-2.jpg
...
└── image-q.png
```


## 2. Preprocess the raw images
Change `8` to however many
separate processes you want to run:
`for N in {1..8}; do ./util/align-dlib.py <path-to-raw-data> align outerEyesAndNose <path-to-aligned-data> --size 96 & done`.

If failed alignment attempts causes your directory to have too few images,
you can use our utility script
[./util/prune-dataset.py](https://github.com/cmusatyalab/openface/blob/master/util/prune-dataset.py)
to deletes directories with less than a specified number of images.

## 3. Generate Representations
`./batch-represent/main.lua -outDir <feature-directory> -data <path-to-aligned-data>`
creates `reps.csv` and `labels.csv` in `<feature-directory>`.

## 4. Generate TSNE visualization
Generate the t-SNE visualization with
`./util/tsne.py <feature-directory> --names <name 1> ... <name n>`,
where `name i` corresponds to label `i` from the
left-most column in `labels.csv`.
This creates `tsne.pdf` in `<feature-directory>`.

# Visualizing layer outputs
Visualizing the output feature maps of each layer
is sometimes helpful to understand what features
the network has learned to extract.
With faces, the locations of the eyes, nose, and
mouth should play an important role.

[demos/vis-outputs.lua](https://github.com/cmusatyalab/openface/blob/master/demos/vis-outputs.lua)
outputs the feature maps from an aligned image.
The following shows the first 39 filters of the
first convolutional layer on two images
of John Lennon.

![](https://raw.githubusercontent.com/cmusatyalab/openface/master/images/nn4.v1.conv1.lennon-1.png)
![](https://raw.githubusercontent.com/cmusatyalab/openface/master/images/nn4.v1.conv1.lennon-2.png)