Anomaly Detection with AutoEncoder Fraud Detection in TensorFlow 2.0 1. Because our model is an autoencoder, we evaluate how good the model is at reconstructing the input. 1 file(s) 0.00 KB. In this part of the series, we will train an Autoencoder Neural Network (implemented in Keras) in unsupervised (or semi-supervised) fashion for Anomaly Detection in credit card transaction. 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. For this threshold (6.3), we obtain the following confusion matrix. These can only be statistical outliers or errors in the data. Limiting neurons in the hidden layers will force the network to update the weights by being efficiently penalized according to the reconstruction error. Let us now draw the ROC curve according to the classification we have done earlier. You are absolutely free to experiment with the hyperparameter choices for your model. Before moving, many of you must be having a thought, why cannot it be categorized as a classification problem? If an anomaly is presented then it should be rare and it would. Learn on the go with our new app. Anomaly detection using neural networks is modeled in an unsupervised / self-supervised manner; as opposed to supervised learning, where there is a one-to-one correspondence between input feature samples and their corresponding output labels. 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. The last layer in the encoder is the size of the encoded representation, and it is also called the bottleneck. Although the models obtained in the end arent comparable, this gives you an idea of how much of a kick-start you may get when using an applied AI service. Here we are using the ECG data which consists of labels 0 and 1. So by exploiting the error details we got a threshold value for MSE which suggests any transaction fed into the network if it gives an error more than a threshold value that will be considered as a fraud transaction. Please refer to the same if you need any references. 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 Autoencoder dataset is already split between 50000 images for training and 10000 for testing. After comparing the error with the threshold value below is the confusion matrix we get for our results. Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy ML models quickly. What if you had a frugal way to qualify your equipment health with little data? Amazon Rekognition Custom Labels builds off the existing capabilities of Amazon Rekognition, which is already trained on tens of millions of images across many categories. 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. 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. Python code is at the end of the post. It is primarily used for learning data compression and inherently learns an identity function. The encoder state (as seen in the above fig.) data corruptions) from the inputs. This task is known as anomaly or novelty detection and has a large number of applications. Without much effort (and no ML knowledge! When you have enough abnormal signals to build a more balanced dataset, you can switch to the supervised approach. Let us look at how we can use AutoEncoder for anomaly detection using TensorFlow. Using deep learning models with multi-context temporal and channel (eight microphones) attention weights. Anomaly detection is the task of determining when something has gone astray from the "norm". How can we generalize this approach? A simple question. You can analyze the details for more fields and see the trend. After all the requisite pre-processing we finally will create the autoencoder model. Anomaly detection is a binary classification between the normal and the anomalous classes. Whereas if we see the details of Fraud transactions, we can clearly mark the error is almost 10 times higher than the normal transactions. Step 6: Identify the data points with a difference higher than the threshold to be outliers or anomalies. This paper proposes an anomaly detection method based on a deep autoencoder for in-situ wastewater systems monitoring data. The validation data is the testing dataset that contains both normal and anomaly data points. If we inject the data into our trained model, we would be looking at results something like below. This dataset contains 5,000 Electrocardiograms, each with 140 data points. The x-axis is the number of epochs, and the y-axis is the loss. 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). Certainly, it can be, but the main reason why we are addressing it as an anomaly is almost 82% of the transactions are normal and 18% have deviated from the actual normal transactions. Luckily, the data we have is absolutely a clean set of data. All these checks must be taken into consideration before we move ahead. This kind of architecture learns to generate the identity transformation between inputs and outputs. It contains the details of whether a transaction is a normal transaction or a fraud transaction that we can use as our dependent data for our model. Start the model. To feed the spectrogram to an autoencoder, build a tabular dataset and upload it to. Deploy the supervised approach to a larger scale (especially if you can tune it to limit the undesired false negative to a minimum number). We are building the next-gen data science ecosystem https://www.analyticsvidhya.com, State-of-the-Art Text Classification Made Easy, How to apply preprocessing steps in a pipeline only to specific features. 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. See the following code: Lets plot the confusion matrix associated to this test set (see the following diagram). 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. Amazon Rekognition Custom Labels automatically loads and inspects the training data, selects the right ML algorithms, trains a model, and provides model performance metrics. An autoencoder has two connected networks: Encoder - Takes an input and converts it into a compressed knowledge representation in the bottleneck layer Decoder - Converts the compressed representation back to the original input Your overall process looks something like the following: A major challenge factory managers have in order to take advantage of the most recent progress in AI and ML is the amount of customization needed. The autoencoder architecture is a neural network with the same number of neurons in the input and the output layers. The autoencoder model for anomaly detection has six steps. SageMaker and Amazon Applied AI services such as Amazon Rekognition Custom Labels enables manufacturers to build AI models without having access to a versatile team of data scientists sitting next to each production line. This delivers a network that can remove noise (i.e. We have taken simple three steps prior to feeding the data into our model. If you plan to use a similar approach on the whole MIMII dataset or use hyperparameter tuning, you can further reduce this training cost by using Managed Spot Training. They are neural networks trained to learn efficient data representations in an unsupervised way. We label the normal prediction 0 and outlier prediction 1 to be consistent with the ground truth label. When you have enough data characterizing abnormal conditions, train a supervised model. 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. Autoencoder uses only normal data to train the model and all data to make predictions. If we plot the number of samples flagged as false positives and false negatives, we can see that the best compromise is to use a threshold set around 6.3 for the reconstruction error (assuming were not looking at minimizing either the false positive or false negatives occurrences). 32 informative features were made as predictors. You can apply this to unbalanced datasets too. Anomaly detection using an unsupervised deep learning model. We need to understand the data. The prediction loss threshold for 2% of outliers is about 3.5. And if the MSE is higher than a set threshold then we can easily classify that input as an anomaly. Evaluate the model to obtain a confusion matrix highlighting the classification performance between normal and abnormal sounds. Given an ECG signal sample, an autoencoder model (running live in your browser) can predict if it is normal or abnormal. It is an interesting finding. As you can see there are no Null values at all. Evaluate the model to obtain a confusion matrix highlighting the classification performance between normal and abnormal sounds. We'll use the LSTM Autoencoder from this GitHub repo with some small tweaks. But our initial point, in this case, is 0.001. Exploring the appropriate image representation for multi-variate time-series signals that arent waveform. After the dataset is downloaded, it takes roughly an hour and a half to go through this project from start to finish. You could replace spectrograms with Markov transition fields, recurrence plots, or network graphs to achieve the same goals for non-sound time-based signals. Image Anomaly Detection / Novelty Detection Using Convolutional Auto Encoders In Keras & Tensorflow 2.0 In many computer vision systems the goal is to detect when something out of the. This takes a few minutes to run all the test samples and costs less than $1 (for approximately 3,000 test samples). The train test split gives us 80,000 records for the training dataset and 20,000 for the validation dataset. Create an Amazon Rekognition Custom Labels project: Associate the project with the training data, validation data, and output locations. In this post, we implement the area in red of the following architecture. When you have enough data, train an unsupervised model and use the results to start issuing warnings to a pilot team, who annotates (confirms) abnormal conditions and sets them aside. 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. How to evaluate autoencoder anomaly detection performance? 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. An autoencoder is a special type of neural network that is trained to copy its input to its output. Step 2 is the decoder step. Let's do it step by step. In simple terms, we recreate the input value X given to a neural network with minimal error. First, visualize the time series data: plt.rc ( 'figure' ,figsize= ( 12, 6 )) plt.rc ( 'font' ,size= 15 ) catfish_sales.plot () import tensorflow as tf from tensorflow import keras from tensorflow.keras import optimizers from tensorflow.keras.models import Sequential, . Project creation can fail if Amazon Rekognition cant access the bucket you selected. How do we know a perfect threshold? An autoencoder is a feed-forward multilayer neural network that reproduces the input data on the output layer. 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. 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. 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. At this stage, this costs you a few cents. Using the Tensorflow Keras API in Python, we covered: More tutorials are available on GrabNGoInfo YouTube Channel and GrabNGoInfo.com. The output layer in the decoder has the same size as the input layer. The Green distribution belongs to Normal transactions and there one belongs to Fraud. Elucidated. In real life, we come across varieties of anomaly scenarios where certain entities deviate from the actual pattern they are supposed to follow. For more information about the sound capture procedure, see MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection. When data of a fraud transaction been fed to the network then the mean squared error(MSE) of the output will be relatively higher for that input. Neural Machine Translation with TRANSFORMERS, How to Create Machine Learning Models In Power BI Using Python. Frustrated sociologist. When fitting the autoencoder model, we can see that the input and output datasets are the same, which is the dataset that contains only the normal data points. Step 4: Make predictions on a dataset that includes outliers. 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. One of the significant examples of anomaly is fake credit card transactions which we are going to analyze today. Is there any categorical text value that we need to convert into numerical values? actually helps the network to understand the underlying pattern of the input data and later on decoder layer learn how to re-create the original data from the condensed details. The leading AI community and content platform focused on making AI accessible to all, PhD Data Scientist | YouTube channel: https://tinyurl.com/yx4ynhmj | Join Medium Membership: https://tinyurl.com/4zyuz9cd | Website: grabngoinfo.com/tutorials/, Binary cross-entropy lossSpecial case of Categorical cross-entropy loss, How to Code and Evaluate of Decision Trees, Scalable Pipeline: PCA and Logistic Regression using Pyspark, Importing HuggingFace models into SparkNLP, Intro into Quantum Computing and Machine Learning. The essential information is extracted by a neural network model in this step. In this example, you will train an autoencoder to detect anomalies on the ECG5000 dataset. When not helping customers develop the next best machine learning experiences, he enjoys observing the stars, traveling, or playing the piano. We have used Tensorflow 2.0 to create our model. We did not include any redundant or repeated features in this dataset. The below image explains it better. Step 1 is the encoder step. During the training, input only normal transactions to the Encoder. Continue collecting sound signals for normal and abnormal conditions, and monitor potential drift between the recent data and the one used for training. We then use SageMaker to build an autoencoder that we use as a classifier to discriminate between normal and abnormal sounds. Autoencoder Sample Autoencoder Architecture Image Source. In contrast, deep learning networks with a CNN encoder can learn the best representation to perform the task at hand (anomaly detection). Our test dataset has an equal share of normal and abnormal sounds. The relu the activation function is used for each layer except for the decoder output layer. See the following code: The following plot shows that the distribution of the reconstruction error for normal and abnormal signals differs significantly. As we limit the number of layers in the hidden layer limited amount of information can flow through the network else if we give the same number of neurons in the hidden layer model will memorize the input data along with the network without learning important attributes about the input. By definition then, the number of output units must be the same as the number of input units. What is the algorithm behind autoencoder for anomaly detection? As expected, using a supervised approach yields better results. Dont forget to decommission it when youre done. In the input layer, we specified the shape of the dataset. Once our model got trained and saved, we will use our Validation set to validate how well our data is performing. Training anomaly detection models that can be adapted to many different industrial machineries in order to reduce the maintenance effort, reduce rework or waste, increase product quality, or improve overall equipment efficiency (OEE) or product lines is a massive amount of work. We train our autoencoder only on the normal signals: we want our model to learn how to reconstruct these signals (learning the identity transformation). Autoencoder: Let's now understand what is Autoencoder. Anomaly Detection using Autoencoder: Download full code : Anomaly Detection using Deep Learning Technique. However, with a vanilla configuration they. The first three steps are for model training, and the last three steps are for model prediction. Autoencoder is an unsupervised neural network model that uses reconstruction error to detect anomalies or outliers. The first thing we do is plot the waveforms of normal and abnormal signals (see the following screenshot). 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. Make sure the right bucket policy is applied to your bucket (check the notebooks to see the recommended policy). The decoder consists of 3 layers with 8, 16, and 32 neurons, respectively. 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). Using LSTM Autoencoder to Detect Anomalies and Classify Rare Events So many times, actually most of real-life data, we have unbalanced data. And by looking at the details you clearly cannot say if there is some discrepancy. 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