tf-dagmm

A tensorflow Implementation of dagmm: Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection, Zong et al, 2018. Furthermore, I built the convolutional autoencoder to analyze the data of images instead of fully-connected autoencoder.

Requirement

I've trained the model successfully on the below packages:

Python 3.6.4
Tensorflow 1.5.0(GPU version)

Encoded to a vector of higher dimension

The auther encode the input to a vector of a very low dimension(only 1 or 2). This is too small since I am doing the anomaly detection for images with the size 128 x 128. I use a meta dense layer to map the encoded vector into the into the low dimension space. Therefore, I could have a better reconstruction error and a trainable GMM as well.

Note

This tf model is used to perform a real case of anomaly detection for my job. I cannot provide the dataset I used due to the commercial security.
I've done some experiments on using several autoencoders(compressions namely in the paper) to analyze the important regions as experts suggested in each image. The results didn't perform well however.
In my case the required training time is short. The results seem good in less than 2000 epochs

Train

Pattern

Image configuration needs to be passed through the argument --pattern, a typical setting is like:

OBJECT_J0602 = {
    'main': {'width': 128, 'height': 128, 'region': 'all', 'filters': FILTERS_128, 'scope': 'main', 'reuse': False},
}

For example, if we want to use this configuration, --pattern should be argumented with J0601. Some important settings:

Dictionary key, 'main': The name of interesting region.
width(int): Specifying the resizing width.
height(int): Specifying the resizing height.
region(str, tuple, list of tuple): Region options, 'all' means the full image. It could be assigned by a sub-region by a tuple of (xmin, ymin, xmax, ymax) or a list of tuples as well. If it is a list, these sub-regions will assemble the output image vertically.
filters(list of int): The dimensionality of layers in the encoder. They will be used in the decoder in a reversed direction.

Other settings

epoch: The total epochs should be trained.
encoded_dims: The encoded dimension of the compressor(autoencoder) network.
latent_dims: The dimension of latent variables of encoded vector.
lambda1: Hyperparameter tuning in the objective function.
lambda2: Hyperparameter tuning in the objective function.
mixtures: The number of mixtures in the gaussian mixture model.
logdir: The folder for saving the information of this training.
train_folder: The folder of training data.
validation_folder: The folder of validation data.
batch_size: The batch size in each training loop.

Example script:

$ python ./tf-dagmm/train.py --encoded_dims 160 \
                             --latent 6 \
                             --mixtures 7 \
                             --pattern J0601_S \
                             --logdir /home/i-lun/works/smt/j0601/nsg_split \
                             --batch_size 56 \
                             --epoch 1000 \
                             --train_folder /mnt/storage/P8_SMT/Connector/J0601/wuchi/split/train/OK/ \
                             --validation_folder /mnt/storage/P8_SMT/Connector/J0601/wuchi/split/test/OK/

Summary Report

Example script:

python ./WiML4AOI_SMT_SA/dagmm/main.py --logdir /home/i-lun/works/kb/type12567hook1 \
                                       --checkpoint checkpoint-1000 \
                                       --saved_in /home/i-lun/works/kb/reports/type12567hook1 \
                                       --train_folder /mnt/storage/AOI_KB/dataset/clean/type12567hook1/train/OK \
                                       --test_OK_folder /mnt/storage/AOI_KB/dataset/clean/type12567hook1/test/OK \
                                       --test_NG_folder /mnt/storage/AOI_KB/dataset/clean/type12567hook1/test/NG

Issue

I cannot avoid the singularity issue of gmm, even if the penality term mention in the paper is added. Therefore, I added some jitter to the diagonal of the covariance matrix. Not sure it's a good solution or not.

Result

some pictures of results

Newcomer520/tf-dagmm