## Lstm autoencoder anomaly detection keras

lstm autoencoder anomaly detection keras layers import Input, LSTM, RepeatVector parameter. ,Long-Short Term Memory, Neural Computation’97 Graveset al. Explore scikit-learn’s anomaly detection methods. It is in your interest to automatically isolate a time window for a single KPI whose behavior deviates from normal behavior (contextual anomaly – for the definition refer to this post ). Abstract WeexploretheuseofLongshort-termmemory(LSTM) for anomaly detection in temporal data. We’ll start with a simple example of forecasting the values of the Sine function using a simple LSTM network. Sep 03, 2019 · Detecting anomalous traffic provides one approach to network security threat detection. My goal is to build an unsupervised-semisupervised model, so prediction by exploiting labels is not my interest. Specifically, we will be designing and training an LSTM autoencoder using the Keras API with Tensorflow 2 as th Posted: (3 days ago) An LSTM Autoencoder is an implementation of an autoencoder for sequence data using an Encoder-Decoder LSTM architecture. Lstm autoencoder python. 몇개 있는데 인용수가 0 이다. You’ll master deep learning concepts and models using Keras and TensorFlow frameworks and implement deep learning algorithms, preparing you for a career as Deep Learning Engineer. A. io/) deep learning library. 3 ANOMALY DETECTION WITH ADVERSARIAL AUTOENCODERS . This work is the first attempt to integrate unsupervised anomaly detection and trend prediction under one framework. 대신에 실증적인 좋은 Blog 가 하나 보인다. 2020 can also be used for dimension reduction and anomaly detection[3]. However, the data we have time series but in the previous post we used a Dense layer Autoencoder that did not use the temporal features in the data. In this report we propose an anomaly detection method using deep autoencoders. Mar 02, 2018 · After introducing you to deep learning and long-short term memory (LSTM) networks, I showed you how to generate data for anomaly detection. What is Anomaly Detection? Anomaly detection is the process of finding irregular or unusual patterns in a complex environment. My thought is that this is not a difficult problem compared to other machine vision problems, but not sure what other more experienced practitioners think? Why time series anomaly detection? Let’s say you are tracking a large number of business-related or technical KPIs (that may have seasonality and noise). A Keras-Based Autoencoder for Anomaly Detection in Sequences . – Guido Mar 4 '19 at 15:50 The autoencoder consists two parts - encoder and decoder. The Autoencoder networks are used for training and feature extraction, and then for anomaly detection, while Isolation Forest is used for positive sample prediction. (2018) recently proposed an objective function based on the Neyman-Pearson lemma to train an autoencoder, considering the anomaly detection task as a statistical hypothesis test. These examples are extracted from open source projects. 8 training epochs 4 the text encoder are then fed as input into the anomaly detection network for Sep 01, 2020 · In this paper, an unsupervised model for log message anomaly detection is proposed which employs Isolation Forest and two deep Autoencoder networks. keras-anomaly-detection. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. The demo program creates and trains a 784-100-50-100-784 deep neural autoencoder using the PyTorch code library. Sep 29, 2020 · Types of LSTM based Autoencoder; Implementing LSTM based Autoencoder models in Python using Keras. com/posts/anomaly-detection-in-time-series-with-ls Although autoencoders are also well-known for their anomaly detection capabilities, they work quite differently and are less common when it comes to problems of this sort. View YA JU FAN’S profile on LinkedIn, the world's largest professional community. In their method, the network just pro-cesses the frames into different channels, without effectively trainable Convolutional Long Short-Term Memory (Conv-LSTM) networks that are able to predict the subsequent video sequence from a given input. Mihailidis}, year={2019} } Aug 31, 2020 · Intrusion detection system (IDS) is one means to provide protection. Off to the fun part, the creation of the model. It was very helpful for me to understand LSTM, better than official Tensorflow tutorial mixing it with language processing. Jensen Department of Computer Science, Aalborg University, Denmark ftungkvt, byang, cguo, csjg@cs. At the end of the workshop, developers will be able to use AI to estimate the condition of equipment and predict when maintenance should be performed. Jun 29, 2019 · Goal¶. Chawla and Brian Lee and P. By reducing the number of nodes in the hidden layer, it is expected that the hidden units will extract features that well represent the data. 178 views178 views. We have a total of 14 Sep 2020 Anomaly detection using LSTM with Autoencoder neural network with Autoencoder architecture, that is implemented in Python using Keras. Apr 01, 2019 · Fraud detection belongs to the more general class of problems — the anomaly detection. Unlike the common autoencoder neural network that predicts or reconstructs data separately, our model makes prediction and reconstruction on input data at the same time, which overcomes the shortcoming of using each one alone. Long Short Term Memory (LSTM) networks have been demonstrated to be particularly useful for learning sequences containing May 16, 2017 · LSTM networks are outperforming the state-of-the-art algorithms in time-series forecasting and anomaly detection, as discovered in the following publications: Long Short Term Memory Networks for Anomaly Detection in Time Series ; Deep Learning for Time Series Modeling CS 229 Final Project Report By using an autoencoder, it detects 9 out of 17 real outliers. ly/venelin-youtube-subscribe Complete tutorial + source code: https://www. 29 Dec 2019 Detect anomalies in S&P 500 daily closing price. Kemp Basic idea of AE Idea: using AE in Anomaly Detection Using Keras Using PyTorch Basic idea of AE Idea: using AE in Anomaly Detection Using Keras import tensorflow as tf tf. The LSTM-based encoder-decoder is trained to reconstruct instances of ‘normal’ time- DOI: 10. A network is trained on non-anomalous data and used as a predictor 21 Sep 2018 autoencoders, and recurrent neural networks. Equipment failures represent the potential for plant shutdowns and a significant cost for field maintenance. Setup. But any set of MIDI files consisting of a single instrument would work for our purposes. Autoencoder anomaly detection unsupervised github. The encoder LSTM compresses the sequence into a fixed size context vector, which the decoder LSTM uses to reconstruct the original sequence. Explore and run machine learning code with Kaggle Notebooks | Using data from Credit Card Fraud Detection Autoencoder. 6 to push the limits on the amount of data that can be profiled and anomalies detected and explain how they used similar techniques on time series data, using LSTM. Real-Time Anomaly Detection using LSTM Auto-Encoders with Deep Learning4J on Apache Spark 1. You’ll learn how to use LSTMs and Autoencoders in Keras and TensorFlow 2. Complementary set variational autoencoder for supervised anomaly detection. In the emerging field of acoustic novelty detection, most research efforts are devoted to probabilistic approaches such as mixture models or state-space models. LSTM Autoencoders can learn a compressed representation of sequence data and have been used on video, text, audio, and time series sequence data. py Autoencoder based anomaly detection is a deviation based anomaly detection method using semi-supervised learning. This post aims to introduce how to detect anomaly using Auto Encoder (Deep Learning) in PyODand Keras / Tensorflow as backend. detection, but what seems to be lacking is a dive into anomaly detection of unstructured and unlabeled data. Autoencoder의 경우 보통 이미지의 생성이나 복원에 많이 사용되며 이러한 구조를 이어받아 대표적인 딥러닝 생성 모델인 GAN(Generative Adversarial Network)으로 까지 이어졌는데 이러한 자기 학습 모델은 Anomaly Detection 분야에서도 널리 사용되고 있다. Application: Anomaly Detection LSTM Autoencoder LSTM Layer LSTM Layer LSTM Layer Train, infrequently, using Tensorflow, Keras, GPUs Oct 23, 2019 · The proposed approach combines an autoencoder to detect a rare fault event and a long short-term memory (LSTM) network to classify different types of faults. Let’s call them examples of the “normal” class. Is there anything else? Is it possible to apply Deep Learning more directly to anomaly detection? Apr 11, 2017 · Real-Time Anomaly Detection using LSTM Auto-Encoders with Deep Learning4J on Apache Spark 1. layers import TimeDistributed The rare-event classification using anomaly detection approach discussed in LSTM Autoencoder for 20 May 2007 ASHIMA CHAWLA et al: BIDIRECTIONAL LSTM AUTOENCODER FOR SEQUENCE BASED ANOMALY . The anomaly detection has two major categories, the unsupervised anomaly detection where anomalies are detected in an unlabeled data and the supervised anomaly detection where anomalies are detected in the labelled data. lstm (239) recurrent There is also an autoencoder from H2O for timeseries anomaly detection in demo/h2o_ecg_pulse We will start by transforming and loading the data from the CSV file to a pandas dataframe which will then be used to output a numpy array that will feed the LSTM. Originally Answered: How do I use LSTM to detect an anomaly in a time series? create a sequence - to - sequence autoencoder using LSTM layers in Keras. The long short-term memory (LSTM) networks are used as the encoder, the generator and the discriminator. 4 Jun 2019 In my previous post, LSTM Autoencoder for Extreme Rare Event Classification from keras. 5 RNNs in multivariate time-series anomaly detection. Mar 28, 2019 · Specifically, they demonstrate how to use NVIDIA GPUs , Keras, and TensorFlow with Python 3. The autoencoder is an unsupervised neural network that combines a data encoder and decoder; The encoder reduces data into a lower dimensional space known as the latent space representation; The decoder will take this reduced representation and blow it back up to its original size; This is also used in anomaly detection. you must be familiar with Deep Learning which is a sub-field of Machine Learning. Features generated by an autoencoder can be fed into other algorithms for classification, clustering, and anomaly detection. For example, given an image of a handwritten digit, an autoencoder first encodes the We used an approach similar to anomaly detection. In this hands-on introduction to anomaly detection in time series data with Keras, you and I will build an anomaly detection model using deep learning. Browse other questions tagged keras anomaly-detection autoencoder bioinformatics or ask your own question. Equipment failures represent the potential for plant deratings or shutdowns and a significant cost for field maintenance. Introduction Subscribe: http://bit. 5013/IJSSST. For encoding, an LSTM-VAE projects multimodal observations and their temporal dependencies at each time step into a latent space using serially connected LSTM and VAE layers. Sep 25, 2020 · In addition, recurrent autoencoders with LSTM (long short-term memory) layers have also been applied for anomaly detection in video in the work carried out by Yan et al. While LSTM autoencoders are capable of dealing with sequence as input, regular autoencoders won’t. You have to define two new classes that inherit from the tf. DOI: 10. in . models import Model from sklearn. 2 Mar 2018 Create a Keras neural network for anomaly detection This is called a bottleneck and turns our neural network into an autoencoder. Jun 08, 2020 · To resolve this, we investigate the use of long short-term memory autoencoders, which have recently shown to be successful in related scenarios, for real-time detection of unusual customer behavior. Application of LSTM Autoencoders in anomaly detection. Our Autoencoder should take a sequence as input and outputs a sequence of the same shape. Here there is a useful way to work with neural networks. 4. The autoencoder is trained with offline normal data, which is then used as the anomaly detection. In this white paper we propose a behavior-based anomaly detection method that detects anomalous traffic by applying a threshold to a reconstruction error given by the LSTM AutoEncoder model on the Bro conn log data collected as time series data. io, 2015. 7 %; for a small number of classes is ∼ 1. Sti-bor et al. Next you must define a metric that measures the difference/discrepancy between a predicted output and an actual output. As it is obvious, from the programming point of view is not. Outlier Detection for Time Series with Recurrent Autoencoder Ensembles Tung Kieu, Bin Yang , Chenjuan Guo and Christian S. Instead of relying solely Oct 19, 2018 · One way is as follows: Use LSTMs to build a prediction model, i. Sep 20, 2017 · Both LSTM autoencoders and regular autoencoders (i. org Cognitive IoT Anomaly Detector with DeepLearning4J on IoT Sensor Data 2. This gives us a way to check if a picture is effectively a kitten automatically. You’ll want to use the files we just collected to perform the Oct 15, 2020 · This tutorial is an introduction to time series forecasting using TensorFlow. The encoder compresses the time anomaly detection of radio frequency signals in [9]. anomaly-detection deep-learning autoencoder keras keras-models denoising-autoencoders generative-adversarial-network glove keras-layer word2vec nlp natural-language-processing sentiment-analysis opencv segnet resnet-50 variational-autoencoder t-sne svm-classifier latent-dirichlet-allocation Offered by Coursera Project Network. e. Based on whether the labels are used in the training process, they can be categorized into supervised, semi Our autoencoder model takes a sequence of GloVe word vectors and learns to produce another sequence that is similar to the input sequence. Proper scaling can often significantly improve the performance of NNs so it is important to experiment with more than one method. AI deep learning neural network for anomaly detection using Python, Keras and TensorFlow - BLarzalere/LSTM-Autoencoder-for-Anomaly-Detection In this project, we’ll build a model for Anomaly Detection in Time Series data using Deep Learning in Keras with Python code. LSTM. 28 Jul 2020 spatio-temporal multivariate data frame, and LSTM network is deployed on the autoencoder architecture and use residual errors to detect anomalies. The first is an encoder-decoder based model that learns spatio-temporal features from stacked non-overlapping image patches, and the second is an autoencoder based model that utilizes max-pooling Dec 20, 2016 · The Conv-LSTM models are evaluated both qualitatively and quantitatively, demonstrating competitive results on multiple anomaly detection datasets. First, In this paper, we use stacked LSTM net- works for anomaly/fault detection in time series. timesteps=10 input_dim=2000 units=100 #choosen unit number randomly batch_size=2000 epochs=20 Model In this paper, we propose a long short-term memory-based variational autoencoder generation adversarial networks (LSTM-based VAE-GAN) method for time series anomaly detection, which effectively In this paper, an anomaly detection method with a composite autoencoder model learning the normal pattern is proposed. In this post, we have tried autoencoder as a outliers detector, although it is not its main use. A ten-minute introduction to sequence-to-sequence learning in Keras. Mar 07, 2019 · Next you must define a neural autoencoder. Bidirectional Long-Short Term Memory network ht= h! ht; z ht i I256 units, 128 in each direction ISparse regularization, (z) = Pd z i=1jzij Hochreiteret al. Anomaly is a generic, not domain-specific, concept. org or openclipart. Equipment anomaly detection uses existing data signals available through plant data historians, or other monitoring systems for early detection of abnormal operating conditions. 20 Nov 2019 LSTM BLSTM GRU Autoencoder Deep Learning Neural Network Log messages Anomaly detection Classification This is input to a Keras 5 55https://github. “Keras is a high- level neural networks library, written in Python that is capable ANN) for the simple autoencoder and LSTM Layer (LSTM ANN) for the sequence to . Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods. model = Sequential() For those interested, the sheet music in Figure 4 represents the musical notation of NeuralNet Music 5. com Timeseries anomaly detection using an Autoencoder. CNN-LSTM neural network for Autoencoders. Here is the outputs of the validation normal and anomaly sets for the Mahalanobis Distance (blue is normal, red is anomaly): Here is the outputs of the normal and anomaly test sets for the Autoencoder: We’ll want to include as much of the normal as possible without falsely triggering our anomaly alarm. ipynb Notebook (note that you can also run the anomaly-detection-training-md-deploy. Time Series Anomaly Detection with LSTM Autoencoders using Keras in Python Anomaly Detection LSTM Autoencoders S&P 500 Index Data LSTM Autoencoder in Keras Finding Anomalies Conclusion References Object Detection Object Detection RetinaNet Preparing the Dataset The following are 30 code examples for showing how to use sklearn. If you are also interested in trying out the code I have also written a code in Jupyter Notebook form on Kaggle there you don’t have to worry about installing anything just run Notebook directly. An autoencoder is a special type of neural network that is trained to copy its input to its output. 43 parametri sperimentali, che gli LSTM autoencoders sono in media 3 Available at: https:// keras. Autoencoders are a type of self-supervised learning model that can learn a compressed representation of input data. To create a new Mahalanobis Distance training model, run the anomaly-detection-training-mahalanobis. B. MinMaxScaler(). The algorithm aims to learn the identity function. 9 Aug 2018 It is the assumption in using autoencoders that fraud or anomalies will suffer from a detectably high reconstruction error. You’ll also learn about deep learning-based autoencoders, unsupervised clustering, and 24 Nov 2019 Detect anomalies in S&P 500 closing prices using LSTM Autoencoder with Keras and TensorFlow 2 in Python. curiousily. This guide will show you how to build an Anomaly Detection model for Time Series data. When using signature-based techniques, the attacks are detected by comparing the characteristics of known attacks with new events like traffic and On the other side, Koizumi et al. 00148 (2016). Anomaly detection using a deep neural autoencoder is not a well-known technique. Let’s start with the library imports and setting seeds: lstm-setup. AAE. set_floatx('float64') from tensorflow. As we demonstrate, autoencoders reconcile the precision of reliable methods that have poor performance with a speed suitable for practical use. Time Series Prediction with LSTMs. With my model, I want to train the model on every 10 samples which equates to one se Anomaly Detection: Autoencoders use the property of a neural network in a special way to accomplish some efficient methods of training networks to learn normal behavior. Once fit, the encoder part of the model can be used to encode or compress sequence data that in turn may be used in data visualizations or as a feature vector input to a supervised learning model. This tutorial introduces autoencoders with three examples: the basics, image denoising, and anomaly detection. Feb 17, 2020 · In practice, we use autoencoders for dimensionality reduction, compression, denoising, and anomaly detection. Another crazy thing is to do the opposite of anomaly detection, take the lowest value and make this example to the target of this class, this way trains beauties (or sometimes a anomaly Jun 10, 2020 · In this tutorial I will discuss on how to use keras package with tensor flow as back end to build an anomaly detection model using auto encoders. CNN. , I want to update the model after each 10 time steps. 8 Jul 2020 Applied AI Week 3/5 - LSTM Autoencoder ile Anomaly Detection: Keras Sequential API Model. github. 0 2019-10-27 · How to implement a Deep Neural Network Model for Anomaly Detection in TensorFlow 2. The encoder is a mapping from the input space into a lower dimensional latent space. There are not any false positives, although there are false negatives because some of the outliers have not been found. Fall Detection from Thermal Camera Using Convolutional LSTM Autoencoder @inproceedings{Nogas2019FallDF, title={Fall Detection from Thermal Camera Using Convolutional LSTM Autoencoder}, author={Jacob Nogas and S. [27] uses a multi-resolution wavelet-based approach for unsupervised anomaly detection. An autoencoder is a neural network that learns to copy its input to its output. 22 Publications Using AutoEncoders for Their Anomaly Detection 24 Jan 2018 In this post we will train an autoencoder to detect credit card fraud. The use of an LSTM autoencoder will be detailed, but along the way there will also be back- Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. Anomaly Detection for Temporal Data using LSTM. Anomaly Detection Using a Variational Autoencoder Neural Network With a Novel Objective Function and Gaussian Mixture Model Selection Technique By duhuj 28. 15 Sep 2020 LSTM Autoencoder in Keras. The skill of the proposed LSTM architecture at rare event demand forecasting and the ability to reuse the trained model on unrelated forecasting problems. It refers to any exceptional or unexpected event in the data, be it a mechanical piece failure, an arrhythmic heartbeat, or a fraudulent transaction as in this study. May 19, 2020 · Specifically, we will be designing and training an LSTM autoencoder using the Keras API with Tensorflow 2 as the backend to detect anomalies (sudden price changes) in the S&P 500 index. A simple autoencoder trains the model with only normal data and evaluates multivariate time series data to detect rare faults for anomaly detection. https://keras. Vegard Flovik “Machine learning for anomaly detection and condition monitoring”. The system acts as an anomaly detection system and treats spam as anomalies. GRU-based Gaussian Mixture Variational Autoencoder for Anomaly Detection 2. Neural Networks. The intuition behind USAD is that the adversarial training of its encoder-decoder architecture allows it to learn how to amplify the AutoEncoders for anomaly detection: choice of metric and loss function I'm building an autoencoder for imbalanced classification but I'm treating it as anomaly detection task. We also present a new anomaly scoring method to combine the reconstruction score of a frame across different video sequences. LSTM Autoencoder. 20. deep-learning convolutional-autoencoder convolutional-neural-network hdr-image. pdf / paper / slide / video / code. 8. Time Asleep Trend with Time Time Series Anomaly Detection with LSTM Autoencoders using Keras & TensorFlow 2 in Python. io/ 2020년 2월 29일 Intro지난 포스팅(Autoencoder와 LSTM Autoencoder)에 이어 LSTM Autoencoder 이러한 Anomaly Detection은 이미지 뿐만 아니라 이제부터 살펴보고자 하는 시계열 from tensorflow. Using anomaly detection across multiple variables and correlating it among them has significant benefits for any business. 37. An encoder learns a vector representation of the in-put time-series and the decoder uses this representation to reconstruct the time-series. Mar 02, 2020 · Implementing our autoencoder for anomaly detection with Keras and TensorFlow The first step to anomaly detection with deep learning is to implement our autoencoder script. Anomaly detection is a very worthwhile question. These deep anomaly detection models using Deep CNN, LSTM and mul- com/fchollet/keras. Malhotra Pankaj, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff, LSTM-based encoder-decoder for multi-sensor anomaly detection. Sep 19, 2019 · The autoencoder structure lends itself to such creative usage, as required for the solution of an anomaly detection problem. For this reason I want to build an autoencoder model which catch indeed similarities and main features and use its work as a starting point for an afterwards anomaly detection. First, I am training the unsupervised neural network model using deep learning autoencoders. Sep 14, 2020 · In this blog, we will describe a way of time series anomaly detection based on more than one metric at a time. A common LSTM unit is composed of a cell, an input gate, an output gate and a forget gate. Remember at the end of the day modeling and data science don't mean much if we can't extract actual insights to help guide our customers, our friends, the research community in the advancement of whatever it is they are after using data. It uses the reconstruction error as the anomaly score. See full list on philipperemy. Specifically, we will be designing and training an LSTM autoencoder using the Keras API with Tensorflow 2 as the backend to detect anomalies (sudden price works, attention mechanisms and autoencoder-based anomaly detection. According to many studies , long short-term memory (LSTM) neural network should work well for these types of problems. Jun 15, 2020 · Anomaly Detection using LSTM Autoencoder using Keras admin June 15, 2020 1 Comment Data Science deep Learning tensorflow The goal of this post is to walk you through the steps to create and train an AI deep learning neural network for anomaly detection using Python, Keras and TensorFlow. I will leave the explanations of what is exactly an autoencoder to the many insightful and well-written posts, and articles that are freely available online. neural networks have been proposed for anomaly detection in an unsupervised learning way [34], [28]. Downloading data from https://storage. With all of that said, I was wondering if this approach, "Anomaly Detection in Videos using LSTM Convolutional Autoencoder" will work for this application? Here's the GitHub link to the same. A weighted convolutional autoencoder- (AE-) long short-term memory (LSTM) network is proposed to reconstruct raw data and perform anomaly detection based on reconstruction errors to resolve the existing challenges of anomaly detection in complicated definitions and background influence. There are already some deep learning models based on GAN for anomaly detection that demonstrate validity and accuracy on time series data sets. It can serve as a form of feature extraction, and autoencoders can be stacked to create “deep” networks. We first provide some theoretical background on anomaly detection algorithms and then we explain what an autoencoder is and how it works. keras. 5 %. Lstm autoencoder python LSTM cells expect a 3 dimensional tensor of the form [data samples, time steps, features]. Code examples. Autoencoder Anomaly Detection Keras Mar 12, 2019 · We formulate the fall detection problem as an anomaly detection problem and aim to use autoencoders to identify falls. Build LSTM Autoencoder Neural Net for anomaly detection using Keras and TensorFlow 2. LSTM Autoencoder in Keras Our Autoencoder should take a sequence as input and outputs a sequence of the same shape. In this paper, we propose an unsupervised model-based So my idea is to train an LSTM - autoencoder for anomaly detection by continual learning, i. AutoEncoder & LSTM An AutoEncoder (AE) is a type of artiﬁcial neural network for learning efﬁcient data codings in an unsupervised manner. anomaly-detection deep-learning autoencoder keras keras-models denoising-autoencoders generative-adversarial-network glove keras-layer word2vec nlp natural-language-processing sentiment-analysis opencv segnet resnet-50 variational-autoencoder t-sne svm-classifier latent-dirichlet-allocation Finally for PFAM, the training dataset comprising of 154 instances of soluble proteins each of length 20 flagged as normal instances are used for obtaining, unsupervised transfer learning representations using Long Short-Term Memory (LSTM) autoencoder, while randomly selected 154 samples of soluble proteins and 5 instances of insoluble proteins for testing; a good anomaly detection technique should detect latter as anomalous. Jul 03, 2019 · Anomaly detection is a classical but worthwhile problem, and many deep learning-based anomaly detection algorithms have been proposed, which can usually achieve better detection results than traditional methods. 收集于5个月前 阅读数 4 Anomaly Detection in Videos using LSTM Convolutional Autoencoder 21 1 day ago · An LSTM Autoencoder is an implementation of an autoencoder for sequence data using an Encoder-Decoder LSTM architecture. 13 May 2017 compared to an established anomaly detection algorithm. edu Overview Data Our data consists of the parameters (layer thicknesses h n) of a thin film optical device and its discretized transmission spectrum (T j). 문제점 본 Topic 은 너무 뻔한거라서 그런가? 딱히 논문이라던지 이런게 잘 보이지 않는다. layers import Dense, Input from tensorflow. An Autoencoder can be divided into two parts: the encoder and the decoder. 1 Neural Networks, Theano and Keras . aau. In next week’s tutorial, we’ll learn how to use a convolutional autoencoder for denoising. we built in keras environment can accept only fixed length inputs. Here, we will learn: data preparation steps for an LSTM Jun 10, 2020 · In this tutorial I will discuss on how to use keras package with tensor flow as back end to build an anomaly detection model using auto encoders. [15] use deep learning (LSTM, autoencoder) for anomaly detection. Conclusion. During inference we use the neg- In this paper, we propose a method for analyzing the time series data of unsteady flow fields. With h2o, we can simply set autoencoder = TRUE. What are Autoencoders ? - An autoencoder is a neural network model that seeks to learn a compressed representation of an See full list on towardsdatascience. layers import * from keras. • Jul 8, 2020. How to develop LSTM Autoencoder models in Python using the Keras deep learning library. 75 training epochs 4 Table 2: Anomaly Detection Network Parameters Parameter Value LSTM units 64 batch size 64 dropout keep probability 0. Design, fit and tune the autoencoder. It has an internal (hidden) layer that describes a code used to represent the input, and it is constituted by two main parts: an encoder that maps the input into the code, and a decoder that maps the code to a reconstruction of the original input. Feb 02, 2018 · However, the fusion of high-dimensional and heterogeneous modalities is a challenging problem for model-based anomaly detection. reshape(data, (1500, 10,2000)) from keras. Specifically, we’ll be designing and training an LSTM Autoencoder using Keras API, and Tensorflow2 as back-end. We have a total of 5219 data points in the sequence and our goal is to find Sep 25, 2019 · The concept for this study was taken in part from an excellent article by Dr. 77. callbacks import ModelCheckpoint 11 Jul 2016 Presented at ICML 2016 Anomaly Detection Workshop, New. First, the concatenated data set is divided into testing and training sets with 95% for testing and 5% for training, and these sets are shuffled. In deep learning, an autoencoder is a neural network that “attempts” to reconstruct its input. 7 Sep 2020 Time Series of Price Anomaly Detection with LSTM. 07 Corpus ID: 209058406. 1. AI deep learning neural network for anomaly detection using Python, Keras and TensorFlow - BLarzalere/LSTM-Autoencoder-for-Anomaly-Detection. An autoencoder's purpose is to learn an approximation of the identity function (mapping x to \hat x). Autoencoder is an artificial neural network used for unsupervised learning. Fallon}, journal={International journal of simulation: systems, science ASHIMA CHAWLA et al: BIDIRECTIONAL LSTM AUTOENCODER FOR SEQUENCE BASED ANOMALY . py script, which essentially does the same thing, but it does not require Jupyter Notebook). Posted: (3 days ago) An LSTM Autoencoder is an implementation of an autoencoder for sequence data using an Encoder-Decoder LSTM architecture. com/ keras-team/keras embedding layer which converts each 13 May 2020 An LSTM autoencoder is used to learn the representation of real reviews. Oct 19, 2020 · Let the autoencoder train and watch what happens and compare the original, the noisy image and the autoencoder result (I did that with popcorn for a long time). 29007/XT7R Corpus ID: 53360325. 2019] in their paper “ Robust Anomaly Detection in Images using Adversarial Autoencoders”, propose an interesting addition to this autoencoder model. timesteps=10 input_dim=2000 units=100 #choosen unit number randomly batch_size=2000 epochs=20 Model Jan 16, 2020 · Finally, before feeding the data to the autoencoder I’m going to scale the data using a MinMaxScaler, and split it into a training and test set. Now, in this tutorial, I explain how to create a deep learning neural network for anomaly detection using Keras and TensorFlow. Along with this you will also create interactive It illustrates the functioning of an auto-encoder for MNIST images, but the concept is the same. Anomaly detection using an autoencoder. Johnson and Johnson, JNJ, Keras, Autoencoder, Tensorflow The goal of this post is to walk you through the steps to create and train an AI deep learning neural network for anomaly detection using Python, Keras and 31 May 2020 This script demonstrates how you can use a reconstruction convolutional autoencoder model to detect anomalies in timeseries data. [17] proposed a fully convolutional autoencoder to learn the regular dynamics in long videos. Auto encoders is a unsupervised learning technique where the initial data is encoded to lower dimensional and then decoded (reconstructed) back. Anomaly Detection with Autoencoder in TensorFlow 2. Long-Short Term Memory. Autoencoder architecture 지난 포스팅(Autoencoder와 LSTM Autoencoder)에 이어 LSTM Autoencoder를 통해 Anomaly Detection하는 방안에 대해 소개하고자 한다. York, NY, USA, 2016. Reference: “Auto-Encoding Variational Bayes” https: Introduction. It then uses the Keras-style API in Analytics Zoo to build a time series anomaly detection model (which consists of three LSTM layers followed by a dense layer, as shown below), and trains the model (which learns from 50 previous values to predict next one). At the anomaly detection stage, anomalies are detected based on reconstruction difference and discrimination results. Lstm variational auto-encoder API for time series anomaly detection and features extraction - TimyadNyda/Variational-Lstm-Autoencoder. 8 NN. First approach is based on a neural density estimator model, Group-Masked Autoencoder. Time Series Anomaly Detection with LSTM Autoencoders using Keras & TensorFlow 2 in Python. I am training an LSTM Autoencoder on time series data. When an outlier data point arrives, the auto-encoder cannot codify it well. It con-sists of two parts { an encoder and a decoder. There is no difference in the Autoencoder model either with large or small number of classes. This density estimator has been used to estimate the probability distribution that models the normal audio recordings during training time. Here, I am applying a technique called “bottleneck” training, where the hidden layer in the middle is very small. 2. I am trying to build an LSTM Autoencoder to predict Time Series data. This Deep Learning course with Tensorflow certification training is developed by industry leaders and aligned with the latest best practices. Build LSTM Autoencoder Neural Net for anomaly detection using Keras and The the anomaly detection is implemented using auto-encoder with convolutional , feedforward, and recurrent networks and can be applied to: timeseries data to Specifically, we'll be designing and training an LSTM Autoencoder using Keras API, and Tensorflow2 as back-end. We’ll use the model to find anomalies in S&P 500 daily closing prices. Anomaly erarchical neural autoencoder for paragraphs and docu- ments. . LSTM and Autoencoder models provide similar quality of anomaly detection with ROC AUC 0. Sep 15, 2018 · RNN-Time-series-Anomaly-Detection. After we understood the fundamentals, we implemented a convolutional autoencoder using Keras and TensorFlow. Oct 07, 2020 · Anomaly detection refers to the task of finding/identifying rare events/data points. Before we deep-dive into the methodology in detail, here we are discussing the high-level flow of anomaly detection of time series using autoencoder models Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection Dong Gong1, Lingqiao Liu1, Vuong Le2, Budhaditya Saha2, Moussa Reda Mansour3, Svetha Venkatesh2, Anton van den Hengel1 1The University of Adelaide, Australia 2A2I2, Deakin University 3University of Western Australia The long short-term memory (LSTM) networks are used as the encoder, the generator and the discriminator. In / International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 2366—-2370 [25] Oct 17, 2019 · Anomaly detection of time series can be solved in multiple ways. The Autoencoder gains the proﬁle of normal network traﬃc as one of the base learners, and provides learned RMSE as the label needed to train the LSTM. 5013/ijssst. Therefore, in this post, we will improve on our approach by building an LSTM Autoencoder. Handgun detector Before addressing the false positive rate reduction through the use of the autoencoder, we needed to train and test a handgun detector. image. In these approaches, auditory spectral features of the next short term frame are For example, LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition and anomaly detection in network traffic or IDSs (intrusion detection systems). The model will be presented using Keras with a TensorFlow backend using a Jupyter Notebook and generally applicable to a wide range of anomaly detection problems. Data points with high reconstruction are considered to be anomalies. 2 Autoencoder and anomaly detection An autoencoder is a neural network that is trained by unsupervised learning, which is trained to learn reconstructions that are close to its original input. The Overflow Blog Podcast 269: What tech is like in “Rest of World” I'm building a convolutional autoencoder as a means of Anomaly Detection for semiconductor machine sensor data - so every wafer processed is treated like an image (rows are time series values, columns are sensors) then I convolve in 1 dimension down thru time to extract features. I did so using the Keras code library which is a wrapper over the difficult-to-use TensorFlow library. 01 batch size 64 dropout keep probability 0. variational_autoencoder • keras keras Please join me for another exciting data science class where we apply autoencoders or unsupervised learning towards the pursuit of knowledge. Anomaly Detection: The Autoencoder will be very bad at reconstructing pictures of dogs, landscapes or bugs. We learn about Anomaly Detection, Time Series Forecasting, Image Recognition and Natural Language Processing by building up models using Keras on real-life examples from IoT (Internet of Things), Financial Marked Data, Literature or Image Databases. Author: pavithrasv Date created: 2020/05/31 Last modified: 2020/05/31 Description: Detect anomalies in a timeseries using an Autoencoder. Anomaly Detection in Electrocardiogram Readings with Stacked LSTM [15] use deep learning (LSTM, autoencoder) for Keras. backend. Build LSTM Autoencoder Neural Net for anomaly detection using Keras and TensorFlow 2. All of our examples are written as Jupyter notebooks and can be run in one click in Google Colab, a hosted notebook environment that requires no setup and runs in the cloud. Import Statements. We focus on the most related works that apply machine learning techniques to anomaly detection. dk Abstract We propose two solutions to outlier detection in time series based on recurrent autoencoder ensem-bles. Along with this you will also create interactive Variational Autoencoder based Anomaly Detection using Reconstruction Probability, An et al. Model class to get them work alone. model_selection import train_test_split From this notebook, encoding_dim = 30 This script demonstrates how to build a variational autoencoder with Keras and deconvolution layers. We will also demonstrate how to train Keras models in the cloud using 20 Feb 2019 The idea of using some kind of statistical anomaly detection to identify long short-term memory (LSTM) models: an encoder and a decoder. We will use an autoencoder neural network architecture for our anomaly detection model. For a large number of classes in LSTM model, the difference in precision are ∼ 1. Our demonstration uses an unsupervised learning method, specifically LSTM neural network with Autoencoder architecture, that is implemented in Python using Keras. Aug 05, 2019 · An LSTM model architecture for time series forecasting comprised of separate autoencoder and forecasting sub-models. Only recent studies introduced (pseudo-)generative models for acoustic novelty detection with recurrent neural networks in the form of an autoencoder. We propose an LSTM-based Encoder-Decoder scheme for. Explore and run machine learning code with Kaggle Notebooks | Using data from Credit Card Fraud Detection trainX = np. As you read in the introduction, an autoencoder is an unsupervised machine learning algorithm that takes an image as input and tries to reconstruct it using fewer number of bits from the bottleneck also known as latent space. Mathematical insights and working of the autoencoder neural network along with the impact of parameter tuning in its performance is presented next. [10, 45] . Autoencoder The encoder is parametrized using a Bi-LSTM with tanh activation that The models were implemented using the Keras deep learn- ing library for Realtime Anomaly Detection with Keras/TensorFlow (DeepLearning, LSTM). Related Work Anomaly detection has been studied for decades. As we are going to use only the encoder part to perform the anomaly detection, then seperating decoder from encoder is mandatory. preprocessing. A hardware architecture for anomaly detection using LSTM has been reported [10], however it cannot handle large dimensions. DOI 10. 1 Overview In this paper, we employ a modular deep convolutional autoencoder with a dense bottleneck Sep 09, 2019 · Multi-Variate, Multi-Step, LSTM for Anomaly Detection andrewm4894 machine-learning , python September 9, 2019 September 9, 2019 6 Minutes This post will walk through a synthetic example illustrating one way to use a multi-variate, multi-step LSTM for anomaly detection. We introduce a long short-term memory-based variational autoencoder (LSTM-VAE) that fuses signals and reconstructs their expected distribution by introducing a progress-based varying prior. Nov 07, 2019 · このデモでは代わりにVariational Autoencoderを適用した 方法をご紹介します。 VAEは潜在変数に確率分布を使用し、この分布からサンプリングして新しいデータを生成するものです。 Anomaly detection and localization using deep learning(CAE) Detection and Classiﬁcation of Acoustic Scenes and Events 2020 2–3 November 2020, Tokyo, Japan toregressive models, MADE is also very sensitive to the order of the variables. Since no anomaly examples are available, the autoencoder is trained only on non-anomaly examples. Autoencoder Anomaly Detection Unsupervised Github. Kieu et al. 2 Active learning for LSTM-autonecoder-based anomaly detection LSTM-autoencoder [9] is nowadays increasingly used to detect anomalies in time series data [11,5,4]. The way Keras LSTM layers work is by taking in a numpy array of 3 dimensions (N, W, F) where N is the number of training sequences, W is the sequence length and F is the number of We propose an LSTM-based Encoder-Decoder scheme for Anomaly Detection in multi-sensor time-series (EncDec-AD). We also present a new anomaly scoring method to combine the reconstruction score of a frame across differ- ent video sequences. Then, error in prediction See more: autoencoder anomaly detection time series, autoencoder anomaly detection unsupervised github, autoencoder anomaly detection unsupervised, autoencoder anomaly detection kaggle, lstm autoencoder anomaly detection keras, autoencoder anomaly detection image, autoencoder anomaly detection keras, autoencoder anomaly detection github, design Oct 30, 2020 · This tutorial introduces autoencoders with three examples: the basics, image denoising, and anomaly detection. An Awesome Tutorial to Learn Outlier Detection in Python using PyOD Library The two most commonly used gated RNNs are Long Short-Term Memory Networks and Gated Recurrent Unit Neural Networks. Keras has become the standard high-level API within Tensorflow. Only data with normal instances are used to train the autoencoder. Conv-LSTM units are shown to provide competitive results for modeling and predicting learned events when compared to state-to-the-art methods. A deep autoencoder is composed of two deep-belief networks and This Deep Learning course with Tensorflow certification training is developed by industry leaders and aligned with the latest best practices. In data mining, anomaly detection is the identification of rare items, events or observations which raise suspicions by differing significantly from the majo To build a LSTM-based autoencoder, first use a LSTM encoder to turn your input sequences into a single vector that contains information about the entire sequence, then repeat this vector n times (where n is the number of timesteps in the output sequence), and run a LSTM decoder to turn this constant sequence into the target sequence. 05. Further, we formulate a statistical window-based approach for anomaly detection. To my knowledge, anomaly detection has been done by applying the autoencoder and the generator of GAN. In this work we address this issue for audio anomaly detection task, by developing a novel Group Masked Autoencoder Anomaly detection implemented in Keras. Anomaly Detection in Keras with AutoEncoders (14. Jacob and S. An autoencoder is a special type of neural network that is trained to copy its input to its. Anomaly Detection algorithm is explained in section V. Firstly I will train it on source data, then re- long short-term memory (LSTM) network. We will also create interactive charts and plots using Plotly Python and Seaborn for data visualization and display our results in Jupyter notebooks. Specif- ically, the unsupervised Autoencoder and the supervised Long Short-Term Memory (LSTM) are combined in a heterogeneous way. is implemented using the Keras (https://keras. (batch_size, units) If return_sequence is True, the output is a 3D array. This thesis aims to determine the e ectiveness of combining recur-rent neural networks with autoencoder structures for sequential anomaly detection. MS-LSTM: a Multi-Scale LSTM Model for BGP Anomaly Detection Min Cheng1, Qian Xu1, Jianming Lv2, Wenyin Liu3∗, Qing Li 1∗and Jianping Wang1 1Department of Computer Science, City University of Hong Kong Sentiment Analysis using LSTM model, Class Imbalance Problem, Keras with Scikit Learn 7 minute read The code in this post can be found at my Github repository. Due to the chal-lengesinobtaininglabeledanomalydatasets,anunsuper- • We apply ensemble learning to anomaly detection. Jun 30, 2018 · Anomaly detection with an autoencoder neural network applied on detecting malicious URLs Published on June 30, 2018 June 30, 2018 • 31 Likes • 11 Comments Jan 16, 2018 · Create Card Fraud Detection using AutoEncoder (Keras, Tensorflow) 1. 3. com I've been in that situation before, there's this article on medium where the guy uses keras,tf for predicting credit card fraud detection using autoencoders which have Dense layers, but you can try the same with LSTM, can't say for sure whether it will work, but if in case it doesn't work, please try Conv1d because nowadays convolutional networks are more promising than LSTMs and GRUs-> source May 14, 2020 · Introduction This post deals with a specific business case of anomaly detection: fraudulent transactions. An LSTM -Autoencoder will help detect anomalies in time series data, In keras LSTM, the input needs to be reshaped from [number_of_entries, Specifically, we'll be designing and training an LSTM Autoencoder using Keras API, and Tensorflow2 as back-end. Khan and A. [26] describe an immune-system approach to anomaly detection: They tackle a problem prevalent in Moreover, the performance trend across the time series should be predicted. So I've scaled my 500+ variables and trained a model using only the non-anomalous data. The use of an LSTM autoencoder will be detailed, but along the way there will also be backgroundon time-independent anomaly detection using Isolation Forests and Replicator Neural Networks on the benchmark DARPA dataset. Long Short Term Memory Networks for Anomaly Detection in Time Series PankajMalhotra 1,LovekeshVig2,GautamShroﬀ ,PuneetAgarwal 1-TCSResearch,Delhi,India 2-JawaharlalNehruUniversity,NewDelhi,India Abstract. There are two ways to classify IDS according to their techniques used, namely, signature-based IDS and anomaly-based IDS [6]. 1 ISSN: 1473-804x online, 1473-8031 print Bidirectional LSTM Autoencoder for Sequence based Anomaly Detection in Cyber Security The anomaly detection approach outlined above was implemented using a special type of artificial neural network called an Autoencoder. 0. com/tensorflow/tf-keras- datasets/ In this example, you will train an autoencoder to detect anomalies on the 3 ANOMALY DETECTION WITH ADVERSARIAL AUTOENCODERS . 21 LSTM-based Network Anomaly Detection Approaches. Hasan et al. layers 模块， LSTM 实例源码. Read this article to understand more on how anomaly detection can help buinesses. nents/approaches for anomaly detection. io Aug 09, 2018 · We will introduce the importance of the business case, introduce autoencoders, perform an exploratory data analysis, and create and then evaluate the model. Anomaly Detection With Conditional Variational Autoencoders Adrian Alan Pol 1; 2, Victor Berger , Gianluca Cerminara , Cecile Germain2, Maurizio Pierini1 1 European Organization for Nuclear Research (CERN) Meyrin, Switzerland 2 Laboratoire de Recherche en Informatique (LRI) Université Paris-Saclay, Orsay, France Oct 23, 2019 · This paper proposes a learning approach consisting of autoencoder and long short-term memory (LSTM) network for fault detection and diagnosis of rare events in a multivariate industrial process. See full list on curiousily. googleapis. Convolutional Neural Networks. . Variational AEs for creating synthetic faces: with a convolutional VAEs, we can make fake faces. ,Bidirectional LSTM Networks for Improved Phoneme Classi cation and Recognition, ICANN’05 Learning temporal dependencies A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder 2 Nov 2017 • Daehyung Park • Yuuna Hoshi • Charles C. Here, we will use Long Short-Term Memory (LSTM) neural network cells in our autoencoder model. I have one dataset, for example, with ~12000 data points. The LSTM network is now used for anomaly detection and classification. Mar 27, 2019 · Static malware detection with deep autoencoder: WannaCry as a test Published on March 27, 2019 March 27, 2019 • 36 Likes • 7 Comments LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection, Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, Gautam Shroff, 2016 - Paper Credit Card Transactions, Fraud Detection, and Machine Learning: Modelling Time with LSTM Recurrent Neural Networks, Bénard Wiese and Christian Omlin, 2009 - Springer Figure 1 MNSIT Image Anomaly Detection Using Keras. arXiv preprint arXiv:1607. It illustrates the power of autoencoders as anomaly detection tools. neural networks for the task of intrusion detection. It learns to transform data from an input layer into a latent- 4. There are various techniques used for anomaly detection such as density-based techniques including K-NN, one-class support Keras and TensorFlow are making up the greatest portion of this course. We formulate the fall detec- tion problem as an anomaly detection problem and aim to use autoencoders to identify falls. However, the anomaly is not a simple two-category in reality, so it is difficult to give accurate results through the comparison of similarities. We approach this topic through a Neural Network prism and more specifically the Neural Autoencoders. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch. a. Building Autoencoders in Keras) encode the input to a compact value, which can then be decoded to reconstruct the original input. In this paper, we introduce a long short-term memory-based variational autoencoder (LSTM-VAE) for multimodal anomaly detection. It builds a few different styles of models including Convolutional and Recurrent Neural Networks (CNNs and RNNs). In that article, the author used dense neural network cells in the autoencoder model. Specifically, we will be designing and training an LSTM autoencoder using the Keras API with Tensorflow 2 as the backend to detect anomalies (sudden price changes) in the S&P 500 index. trainX = np. 기본적으로 이러한 종류의 문제는 데이터의 불균형이… a deep learning model using a long short-term memory (LSTM) network, and create an autoencoder that detects anomalies for predictive maintenance. LSTM Autoencoder in Keras; Finding Anomalies; Run the complete notebook in your browser. LSTM units 100 initial learning rate 0. An autoencoder is a neural network that learns to predict its input. Unless stated otherwise all images are taken from wikipedia. 10. , Long short term memory (LSTM) Play with LSTM using Keras:. However, most of them do not shine in the time series domain. 07 7. Unsupervised Anomaly Detection in Time Series Using LSTM-Based Autoencoders Abstract: Automatic anomaly detection in data mining has a wide range of applications such as fraud detection, system health monitoring, fault detection, event detection systems in sensor networks, and so on. models import Model from keras. Our code examples are short (less than 300 lines of code), focused demonstrations of vertical deep learning workflows. This network has a single hidden layer. One of the methods is using deep learning-based autoencoder models utilizing encoder-decoder architecture. Bidirectional LSTM Autoencoder for Sequence Based Anomaly Detection in Cyber Security @article{Chawla2019BidirectionalLA, title={Bidirectional LSTM Autoencoder for Sequence Based Anomaly Detection in Cyber Security}, author={A. Now that you know why we’re doing what we’re doing, let’s get our hands dirty with some actual code! Training an Autoencoder with TensorFlow Keras [Beggel et al. anomaly-detection autoencoders fraud-detection finance financial-fraud tensorflow tutorial article Dec 17, 2018 · There are plenty of well-known algorithms that can be applied for anomaly detection – K-nearest neighbor, one-class SVM, and Kalman filters to name a few. This thesis aims to determine the efctiveness of combining recurrentneural networks with autoencoder structures for sequential anomaly detection. Undercomplete AEs for anomaly detection: use AEs for credit card fraud detection via anomaly detection. given current and past values, predict next few steps in the time-series. Anomaly Detection for multivariate time series (USAD) based on an autoencoder architecture whose learning is inspired by GANs. Using LSTM layers is a way to introduce memory to neural networks that makes them The goal of this thesis is to implement an anomaly detection tool using LSTM täckande med hjälp av LSTM autoencoders och applicera en ny metod för ment and run experiments for this project we used Keras on top of Tensorflow. In this paper, we propose SeqVL (Sequential VAE-LSTM), a neural network model based on both VAE (Variational Auto-Encoder) and LSTM (Long Short-Term Memory). layer is a keras Dense layer, essentially a regular densely. 3 Materials & Methods 3. Keras convolutional autoencoder github. lstm autoencoder anomaly detection keras