Variational Autoencoder on Timeseries with LSTM in Keras. This task is known as anomaly or novelty detection and has a large number of applications. To feed the spectrogram to an autoencoder, build a tabular dataset and upload it to. What if you had a frugal way to qualify your equipment health with little data? However, production lines are becoming more and more automated, and augmenting these machine operators with AI-generated insights is a way to maintain and develop the fine expertise needed to prevent reactive-only postures when dealing with machine breakdowns. The epochs of 20 and batch_size of 64 mean the model uses 64 datapoints to update the weights in each iteration, and the model will go through the whole training dataset 20 times. Sensemaking by engaging first hand. The solution in this post features an industrial use case, but you can use sound classification ML models in a variety of other settings, for example to analyze animal behavior in agriculture, or to detect anomalous urban sounds such as gunshots, accidents, or dangerous driving. We have used Tensorflow 2.0 to create our model. We have used the Mean Squared Error loss function to calculate the Reconstruction error we have discussed above. This says we majority of the Normal transactions and Fraud transactions are correctly classified but still if we want to minimize the numbers for wrongly selected fraud transactions can try to set the threshold accordingly and see how it behaves. 1. The source of the data is Kaggle. Continue collecting sound signals for normal and abnormal conditions, and monitor potential drift between the recent data and the one used for training. In this post, we implement the area in red of the following architecture. An autoencoder is a feed-forward multilayer neural network that reproduces the input data on the output layer. random_state ensures that we have the same train test split every time. This kind of architecture learns to generate the identity transformation between inputs and outputs. Building upon this solution, you could record 10 seconds sound snippets of your machines and send them to the cloud every 5 minutes, for instance. Python code is at the end of the post. This script demonstrates how you can use a reconstruction convolutional autoencoder model to detect anomalies in timeseries data. A simple question. The autoencoder model for anomaly detection has six steps. We then use SageMaker to build an autoencoder that we use as a classifier to discriminate between normal and abnormal sounds. Before moving, many of you must be having a thought, why cannot it be categorized as a classification problem? You will use the CIFAR-10 dataset which contains 60000 3232 color images. Finding anomaly is one of the interesting tasks for any problem solver and there is an ample number of models being used in industry for the task. Label 0 denotes the observation as an anomaly and label 1 denotes the observation as normal. Whereas if we see the details of Fraud transactions, we can clearly mark the error is almost 10 times higher than the normal transactions. In this use case, we build a more traditional split of training and testing datasets. LSTM Autoencoder. We use Amazon Rekognition Custom Labels to perform this classification task and leverage SageMaker for the data preprocessing and to drive the Amazon Rekognition Custom Labels training and evaluation process. If you already labeled your images, Amazon Rekognition Custom Labels can begin training in just a few clicks. After defining the input, encoder, and decoder layers, we create the autoencoder model to combine the layers. Step 5: Set up a threshold for outliers/anomalies by comparing the differences between the autoencoder model reconstruction value and the actual value. A features extraction function based on the steps to generate the spectrogram described earlier is central to the dataset generation process. As in the above diagram, the network takes an unlabelled data as input X and learns to output X a reconstruction of original input by framing it as a supervised problem. We label the normal prediction 0 and outlier prediction 1 to be consistent with the ground truth label. We now build two types of feature extractors based on this data exploration work and feed them to different types of architectures. Kept 90% of Non-fraudulent transaction for training. Therefore, we expect outliers to have higher reconstruction errors because they are different from the regular data. The first three steps are for model training, and the last three steps are for model prediction. Are all the columns contain numerical value? Lets see how many Normal and Fraud transactions present in the set. Lets explore the recall-precision tradeoff for a reconstruction error threshold varying between 5.010.0 (this encompasses most of the overlap we can see in the preceding plot). For example, you can train a custom model to classify unique machine parts in an assembly line or to support visual inspection at quality gates to detect surface defects. Anomaly detection is a binary classification between the normal and the anomalous classes. Import the required libraries and load the data. Step 1 is the encoder step. During the training, input only normal transactions to the Encoder. . Experimenting with several more or less complex autoencoder architectures, training for a longer time, performing hyperparameter tuning with different optimizers, or tuning the data preparation sequence (sound discretization parameters). The validation data is the testing dataset that contains both normal and anomaly data points. Step 6: Identify the data points with a difference higher than the threshold to be outliers or anomalies. See the following code: Lets plot the confusion matrix associated to this test set (see the following diagram). First introduced in the 1980s, it was promoted in a paper by Hinton & Salakhutdinov in 2006. How can we generalize this approach? Is Airflow the Right Choice for Machine Learning Too? The autoencoder model for anomaly detection has six steps. The bottleneck layer will learn the latent representation of the normal input data. Stay tuned for future posts and samples on this impactful topic! Without much effort (and no ML knowledge! When you have enough data characterizing abnormal conditions, train a supervised model. The main idea of the network is to minimize the reconstruction error L (X, X) which is basically the difference between original input to the reconstructed output. The autoencoder model trains on the normal dataset, so we must first separate the expected data from the anomaly data. Autoencoder is an unsupervised neural network model that uses reconstruction error to detect anomalies or outliers. After all the set up we are now ready to train our model. It clearly says more than 80% of the data belongs to Normal class and odd 20% is Fraud. We need to stop the running model to avoid incurring costs while the endpoint is live: Lets display the results side by side. Step 2 is the decoder step. Step 4: Make predictions on a dataset that includes outliers. Let us visually see how the values of certain fields are distributed. This tutorial introduces autoencoders with three examples: the basics, image denoising, and anomaly detection. ), we can get impressive results. Product Manager for Azure Machine Learning @ Microsoft. For more information, see Amazon SageMaker Spot Training Examples. From there, we will develop an anomaly detector inside find_anomalies.py and apply our autoencoder to reconstruct data and find anomalies. This dataset contains 5,000 Electrocardiograms, each with 140 data points. The essential information is extracted by a neural network model in this step. When there is a label for anomalies, we can evaluate the model performance. The spectrogram approach requires defining the spectrogram square dimensions (the number of Mel cell defined in the data exploration notebook), which is a heuristic. But wait.!!! How a model which is designed to reconstruct the input value can be used as a classifier to detect any anomaly like fraud transaction? The second notebook of our series goes through these different steps: For this post, we use the librosa library, which is a Python package for audio analysis. The following are further steps to investigate to improve on this first result: For our second approach, we feed the spectrogram images directly into an image classifier. We train our autoencoder only on the normal signals: we want our model to learn how to reconstruct these signals (learning the identity transformation). When not helping customers develop the next best machine learning experiences, he enjoys observing the stars, traveling, or playing the piano. So That odd, what we refer as an Anomaly. We start by building a neural network based on an autoencoder architecture and then use an image-based approach where we feed images of sound (namely spectrograms) to an image-based automated machine learning (ML) classification feature. In this article, we went through the autoencoder neural network model for anomaly detection. In this tutorial, you will learn how to build a stacked autoencoder to reconstruct an image. They become expert listeners, and can to detect unusual behavior and sounds in rotating and moving machines. Specified a list with shape [1,1] from a tensor with shape [32,1] in tensorflow v2.4 but working well in tensorflow v1.14. Standardized the data with min-max scaler, 2. The reconstruction errors are used as the anomaly scores. The relu the activation function is used for each layer except for the decoder output layer. Fit and predict (data) performs outlier detection on data, and returns 1 for normal, -1 for the anomaly. Are there any missing values? This provisions an endpoint and deploys the model behind it. If youre an ML practitioner passionate about industrial use cases, head over to the Performing anomaly detection on industrial equipment using audio signals GitHub repo for more examples. 3. Setup import numpy as np import pandas as pd from tensorflow import keras from tensorflow.keras import layers from matplotlib import pyplot as plt Load the data We will use the Numenta Anomaly Benchmark (NAB) dataset. An autoencoder is a special type of neural network that is trained to copy its input to its output. Using time-distributed 2D convolution layers to encode features across the eight channels. relu is a popular activation function, but you can try other activation functions and compare the model performance. Evaluate the model to obtain a confusion matrix highlighting the classification performance between normal and abnormal sounds. Using an end-to-end model architecture with an encoder-decoder that have been known to give good results on waveform datasets. 0 . Love podcasts or audiobooks? Due to the growing amount of data from in-situ sensors in wastewater systems, it becomes necessary to automatically identify abnormal behaviours and ensure high data quality. Based on the fans sound database, this yields the following: We generate and store the spectrogram of each signal and upload them in either a train or test bucket. For more information, see Connected Factory Solution based on AWS IoT for Industry 4.0 success. Because our model is an autoencoder, we evaluate how good the model is at reconstructing the input. How to set a threshold for autoencoder anomaly detection? ANOMALY DETECTION USING AUTOENCODER. Often times they are harmless. For example, given an image of a handwritten digit, an autoencoder first encodes the image into a lower dimensional latent representation, then decodes the latent representation back to an image. Let's do it step by step. Click here to return to Amazon Web Services homepage, Malfunctioning Industrial Machine Investigation and Inspection (MIMII) dataset, MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection, Connected Factory Solution based on AWS IoT for Industry 4.0 success, Performing anomaly detection on industrial equipment using audio signals. 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. For this threshold (6.3), we obtain the following confusion matrix. The Autoencoder dataset is already split between 50000 images for training and 10000 for testing. Leveraging high-resolution spectrograms and feeding them to a CNN encoder to uncover the most appropriate representation of the sound. The x-axis is the number of epochs, and the y-axis is the loss. After creating the autoencoder model, we compile the model with the optimizer of adam and the loss of mae (Mean Absolute Error). However, the unsupervised approach is perfect to start curating your collected data to easily identify abnormal situations. Elucidated. To achieve this, we explore and leverage the Malfunctioning Industrial Machine Investigation and Inspection (MIMII) dataset for anomaly detection purposes. We need to understand the data. Our model's job is to reconstruct Time . Autoencoder uses only normal data to train the model and all data to make predictions. Based on the threshold we identified in the previous step, we predicted normal data points if the prediction loss is less than the threshold. We now deploy the autoencoder behind a SageMaker endpoint: This operation creates a SageMaker endpoint that continues to incur costs as long as its active. Im sure you have heard about auto-encoders before. In real life, we come across varieties of anomaly scenarios where certain entities deviate from the actual pattern they are supposed to follow. First, lets visualize how this threshold range separates our signals on a scatter plot of all the testing samples. The notebook contains the function get_results(), which queries a given model with a list of pictures sitting in a given path. In simple. In anomaly detections, you typically have a sequence of observations coming from a given distribution, the "normal" distribution. Given an ECG signal sample, an autoencoder model (running live in your browser) can predict if it is normal or abnormal. Notebook Learning Goals At the end of this notebook you will be able to build a simple anomaly detection algorithm using autoencoders with Keras (built with Dense layers). This can be useful. You can use the data exploration work available in the first companion notebook from the GitHub repo. 1. How many columns we are dealing with? The higher the reconstruction error, the greater the chance that we have identified an anomaly. Anomaly detection using Autoencoders Follow the following steps to detect anomalies in a high-dimension dataset. How do we know a perfect threshold? These services allow you to focus on collecting good quality data to augment your factory and provide machine operators, process engineers, and lean manufacturing practioners with high quality insights. These can only be statistical outliers or errors in the data. Singular Value Decomposition. What is the algorithm behind autoencoder for anomaly detection? See the following code: The following plot shows that the distribution of the reconstruction error for normal and abnormal signals differs significantly. A Gentle Introduction to Anomaly Detection with Autoencoders. In the input layer, we specified the shape of the dataset. The decoder consists of 3 layers with 8, 16, and 32 neurons, respectively. You could replace spectrograms with Markov transition fields, recurrence plots, or network graphs to achieve the same goals for non-sound time-based signals. A visual interpretation of how reconstruction error value is distributed for Fraud and Normal transactions. Neural Machine Translation with TRANSFORMERS, How to Create Machine Learning Models In Power BI Using Python. image, dataset), boils that input down to core features, and reverses the process to recreate the input. They are neural networks trained to learn efficient data representations in an unsupervised way. He is passionate about bringing the power of AI/ML to the shop floors of his industrial customers and has worked on a wide range of ML use cases, ranging from anomaly detection to predictive product quality or manufacturing optimization. Anomaly Detection with AutoEncoder Fraud Detection in TensorFlow 2.0 1. Please refer to the same if you need any references. The recall value of 0.01 shows that around 1% of the outliers were captured by the autoencoder. Now individually if we see the distribution of V4 or V10 field for both Normal and Fraud transactions we can come across the below findings. As in fraud detection, for instance. After you train a model, you can use its predictions to feed custom notifications that you can send back to the supervision screens sitting in the factory. And by looking at the details you clearly cannot say if there is some discrepancy. In autoencoder, the input data that we give is basically compressed through a bottleneck in the architecture as we impose a lesser number of neurons in the hidden layers. We can see that both training and validation losses decrease with the increase of epochs. What is this threshold now? The encoder state (as seen in the above fig.) From there, multiplicative sequence attention weights can be learnt on the output sequence from the RNN layer. Anomagram is an interactive visualization tool for exploring how a deep learning model can be applied to the task of anomaly detection (on stationary data). Create a TensorFlow autoencoder model and train it in script mode by using the TensorFlow/Keras existing container. Mark that deciding a threshold can be a trial and error method. The visualization chart shows that the prediction loss is close to a normal distribution with a mean of around 2.5. This feature extraction function is in the sound_tools.py library. Amazon Rekognition Custom Labels is an automated ML service that enables you to quickly train your own custom models for detecting business-specific objects from images. Download. Combine 10% of Normal transactions and entire fraud transactions to create our Validation and Test data. These encoded features as sequences across time steps to generate the spectrogram described earlier is central the. 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