Pytorch/TF1 implementation of Variational AutoEncoder for anomaly detection following the paper
Variational Autoencoder based Anomaly Detection using Reconstruction Probability by Jinwon An, Sungzoon Cho
pip package containing the model and training_step only
pip install vae-anomaly-detection
a. Clone the repo
git clone git@github.com:Michedev/VAE_anomaly_detection.git
b. Install hatch
pip install hatch
c. Make the environment with torch gpu support
hatch env create
or with cpu support
hatch env create cpu
d. Run the train
hatch run train
or in cpu
hatch run cpu:train
To know all the train parameters run hatch run train --help
This version contains the model and the training procedure
- Define your dataset into dataset.py and overwrite the line
train_set = rand_dataset() # set here your datasetintrain.py - Subclass VAEAnomalyDetection and define the methods
make_encoderandmake_decoder. The output ofmake_encodershould be a flat vector while the output of `make_decoder should have the same shape of the input.
Subclass VAEAnomalyDetection and define your encoder and decoder like in VaeAnomalyTabular
class VAEAnomalyTabular(VAEAnomalyDetection):
def make_encoder(self, input_size, latent_size):
"""
Simple encoder for tabular data.
If you want to feed image to a VAE make another encoder function with Conv2d instead of Linear layers.
:param input_size: number of input variables
:param latent_size: number of output variables i.e. the size of the latent space since it's the encoder of a VAE
:return: The untrained encoder model
"""
return nn.Sequential(
nn.Linear(input_size, 500),
nn.ReLU(),
nn.Linear(500, 200),
nn.ReLU(),
nn.Linear(200, latent_size * 2)
# times 2 because this is the concatenated vector of latent mean and variance
)
def make_decoder(self, latent_size, output_size):
"""
Simple decoder for tabular data.
:param latent_size: size of input latent space
:param output_size: number of output parameters. Must have the same value of input_size
:return: the untrained decoder
"""
return nn.Sequential(
nn.Linear(latent_size, 200),
nn.ReLU(),
nn.Linear(200, 500),
nn.ReLU(),
nn.Linear(500, output_size * 2) # times 2 because this is the concatenated vector of reconstructed mean and variance
)Once the model is trained (suppose for simplicity that it is under saved_models/{train-datetime}/ ) just load and predict with this code snippet:
import torch
#load X_test
model = VaeAnomalyTabular.load_checkpoint('saved_models/2022-01-06_15-12-23/last.ckpt')
# load saved parameters from a run
outliers = model.is_anomaly(X_test) usage: train.py [-h] --input-size INPUT_SIZE --latent-size LATENT_SIZE
[--num-resamples NUM_RESAMPLES] [--epochs EPOCHS] [--batch-size BATCH_SIZE]
[--device {cpu,gpu,tpu}] [--lr LR] [--no-progress-bar]
[--steps-log-loss STEPS_LOG_LOSS]
[--steps-log-norm-params STEPS_LOG_NORM_PARAMS]
options:
-h, --help show this help message and exit
--input-size INPUT_SIZE, -i INPUT_SIZE
Number of input features. In 1D case it is the vector length, in 2D
case it is the number of channels
--latent-size LATENT_SIZE, -l LATENT_SIZE
Size of the latent space
--num-resamples NUM_RESAMPLES, -L NUM_RESAMPLES
Number of resamples in the latent distribution during training
--epochs EPOCHS, -e EPOCHS
Number of epochs to train for
--batch-size BATCH_SIZE, -b BATCH_SIZE
--device {cpu,gpu,tpu}, -d {cpu,gpu,tpu}, --accelerator {cpu,gpu,tpu}
Device to use for training. Can be cpu, gpu or tpu
--lr LR Learning rate
--no-progress-bar
--steps-log-loss STEPS_LOG_LOSS
Number of steps between each loss logging
--steps-log-norm-params STEPS_LOG_NORM_PARAMS
Number of steps between each model parameters logging