# TF_Convolutional_Autoencoder

**Repository Path**: dnrops/TF_Convolutional_Autoencoder

## Basic Information

- **Project Name**: TF_Convolutional_Autoencoder
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-04-14
- **Last Updated**: 2021-04-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# TF_Convolutional_Autoencoder
#### _Convolutional autoencoder for encoding/decoding RGB images in TensorFlow with high compression_

This is a sample template adapted from Arash Saber Tehrani's Deep-Convolutional-AutoEncoder tutorial https://github.com/arashsaber/Deep-Convolutional-AutoEncoder for encoding/decoding 3-channel images. The template has been fully commented. I have tested this implementation on rescaled samples from the CelebA dataset from CUHK http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html to produce reasonably decent results from a short period of training. The compression ratio of this implementation is 108. That is, for an input tensor of shape [-1, 48, 48, 3], the bottleneck layer has been reduced to a tensor of shape [-1, 64].

**Add on features:**
* Takes 3-channel images as input instead of MNIST
* Training now performs checkpoint saves and restores
* Both inputs to the encoder and outputs from the decoder are available for viewing in TensorBoard
* Input autorescaling
* ReLU activation replaced by LeakyReLU to resolve dying ReLU

**Caveats:**
* It is highly recommended to perform training on the GPU (Took ~40 min to train 20,000 steps on a Tesla K80 for celebG).
* The input size can be increased, but during testing OOM errors occured on the K80 for the input size of 84x84. This will be fixed in a later update. For now if you get any OOM errors in tensor allocation, try to reduce the input size.
* Sample output is currently visibly undersaturated owing to noise introduced by dropout and high model bias. Problems should go away with further training.

## Outputs
N.B. The input images are 48x48, hence the blurriness. Additionally these outputs are from setting n_epochs to 1000, which could be increased for even better results (note the cost function trend).

Inputs:
<img src="https://github.com/MrDavidYu/TF_Convolutional_Autoencoder/blob/master/sample_output/A0.png" width="150" height="150" />
<img src="https://github.com/MrDavidYu/TF_Convolutional_Autoencoder/blob/master/sample_output/B0.png" width="150" height="150" />
<img src="https://github.com/MrDavidYu/TF_Convolutional_Autoencoder/blob/master/sample_output/C0.png" width="150" height="150" />
<img src="https://github.com/MrDavidYu/TF_Convolutional_Autoencoder/blob/master/sample_output/D0.png" width="150" height="150" />
<br>
Outputs:
<img src="https://github.com/MrDavidYu/TF_Convolutional_Autoencoder/blob/master/sample_output/A1.png" width="150" height="150" />
<img src="https://github.com/MrDavidYu/TF_Convolutional_Autoencoder/blob/master/sample_output/B1.png" width="150" height="150" />
<img src="https://github.com/MrDavidYu/TF_Convolutional_Autoencoder/blob/master/sample_output/C1.png" width="150" height="150" />
<img src="https://github.com/MrDavidYu/TF_Convolutional_Autoencoder/blob/master/sample_output/D1.png" width="150" height="150" />
<br>
<br>
<img src="https://github.com/MrDavidYu/TF_Convolutional_Autoencoder/blob/master/sample_output/cost.png" height="300" />
## How to run
1. Make sure to create directory `./logs/run1/` to save TensorBoard output. For pushing multiple runs to TensorBoard, simply save additional logs as `./logs/run2/`, `./logs/run3/` etc.

2. Unzip `./celebG.tar.gz` and save jpegs in `./data/celebG/`

3. Either use provided image set or your own. If using your own dataset, I recommend ImageMagick for resizing: https://www.imagemagick.org/script/download.php

4. If using ImageMagick, start Bash in `./data/<your_dir>/`:
```
for file in $PWD/*.jpg
do
convert $file -resize 42x42 $file
done
```

5. In root dir, `python ConvAutoencoder.py`

## Debug
Here is a list of common problems:
1. The error(cost) is very high (in the thousands or millions): Check that the input images are fetched properly when transforming batch_files to batch_images etc. This high an error is typical of very large natural differences in MSE of input/output and is not caused by a large number of model parameters.

## Additional References
Reference https://github.com/carpedm20/DCGAN-tensorflow/blob/master/utils.py for several dynamic image resize functions I have incorporated into my implementation.