TensorFlow Probability &#8211; A tool to build deep probabilistic models
Tensorflow probability is a standard library built on top of Tensorflow which is mainly used for probabilistic-based learning. The Tensorflow Probability library helps us to combine uncertainties in the data on top of the model. This combination of probabilistic learning in modeling helps the model to learn with respect to uncertainty and helps in yielding a generic model. In this article, we will focus on the Tensorflow probability library and will understand its importance in this context. Table of Contents Introduction to Tensorflow probabilityWhere is Tensorflow probability useful?Working principle of Tensorflow probabilitySummary Introduction to Tensorflow probability Tensorflow probability is one of the libraries of Tensorflow which is mainly being used for probabilistic-based TensorFlow modeling and to perform certain statistical analyses of the data that will be used in the model. The Tensorflow probability model facilitates the integration of probability principles with standard deep learning models. Integrating probabilistic learning on top of deep learning models and for various tasks, enables the model to capture the statistical characteristics of the data efficiently. Are you looking for a complete repository of Python libraries used in data science, check out here. Suppose the magnitude of a certain data point is increasing linearly with an increase in coefficients on a certain axis, the model developed will not have the ability to capture the optimal number of data points along its best fit line. So the model will not have the ability to capture the variation in the data resulting in suspicious model performance. This is where the Tensorflow probability library helps in adding certain probability principles and facilitates the model to capture all the important statistical features from the data. The Tensorflow probability library helps Statisticians or Data Scientists to deploy the same model developed in deployment as the model developed has the ability to learn all the uncertainties in data and this library of Tensorflow also helps Researchers or practitioners to make predictions from the model with uncertainty. The Tensorflow probability library facilitates some standard functionalities in the library. Some of them are listed below. Various probability distributions such as Normal distribution, Bernoulli distribution, and Multivariate Distributions can be used iteratively and used accordingly to obtain the required performance from the models.Various probabilistic layers along with various layers are trained for capturing the Joint Probability distribution that can be used for two or more random variables.Standard optimizers are provided, which are having the ability to optimize the data with respect to uncertainties and relationships in the data. Now let us look at some of the applications where the Tensorflow probability library is useful. Where is Tensorflow probability useful? As mentioned earlier the Tensorflow probability library is mainly used for applications and data with uncertainties or in some use cases where the model developed is not able to capture the optimal number of data points in the hyperplane. Any model developed should have the ability to capture most of the variation in data and in many cases, it is not possible due to some uncertain statistical characteristics of data. So Tensorflow probability is a standard framework that has the ability to capture the uncertainty in data and helps in yielding a reliable model that can be used for deployment and obtaining predictions. Let us see some of the standard use cases and applications where Tensorflow probability is useful. For handling heteroscedasticity Some of the regression models developed will not have the ability to capture the variance in data and the model will have a larger number of data points dispersed from the best fit line. This process in standard terms is referred to as heteroscedasticity. This results in poor or suspect model performance, and this concern is addressed by the Tensorflow Probability library where the library offers a layer called the Normal layer which helps the model to capture the optimal variation in data. For handling uncertain data In some cases, the data can be observed to be more uncertain and this is where the Tensorflow probability library can be used to handle uncertain data efficiently. The TensorFlow probability library can be used as there are standard functionalities provided within the library to capture the distribution of the data. The library also has the ability to capture different sets of distributions in data which makes it easy for using and dealing with uncertain data. For efficient forecasting of time series data Time series forecasting involves predicting the forecast for the future and time-series data is prone to have some uncertainties with respect to time period. Some of the possible uncertainties with respect to time series data are linearly increasing trends and seasonality due to certain factors which in turn are related to forecasting. So this is where the Tensorflow probability library provides support to structured time series modelling with an inbuilt function named “sts” which stands for structured time series. This function has various other inbuilt functions that can be used to obtain the right time series forecasting model. For encoding and decoding deep learning data Deep learning data are generally huge and sometimes the data in deep learning appear to be similar in various instances. Due to this the model just memorizes the data rather than learning the different characteristics of data. So this is where the Tensorflow probability library is necessary to efficiently encode the important characteristics of data and decode the data sensibly while testing for changing scenes as the model will be trained for uncertain events in the Tensorflow probability library. For obtaining efficient Financial models Financial modeling is a difficult task as financial models will have a lot of uncertainty in data with respect to various factors in the data. Predictions from the financial models have to be accurate as they will be used to make various decisions. This is where the Tensorflow probability library helps in obtaining efficient financial modeling by considering certain factors in the data and curating a model accordingly so that the model will be able to yield the right predictions irrespective of the uncertainty or sudden trends in data. Working principle of Tensorflow probability The TensorFlow probability library is designed and structured in the form of layers that can be used along with the models developed. The Tensorflow probability library operates entirely on 5 layers. The 5 layers of the Tensorflow probability library are listed below. i) Layer 0: Tensorflow layer is responsible for certain numerical operations on the various features of the data. The first layer is termed Layer 0 and is the initial and the input layer of the Tensorflow Probability library. ii) Layer 1: Statistical Building Blocks is the layer that has the collection of various standard probability distributions and certain bijectors to efficiently handle the randomly changing values in the data and uncertain changes in the data. iii) Layer 2: Model Building as the name suggests this layer is used for modeling with various probabilistic constraints. This layer is a collection of various inbuilt functionalities according to the type of model being used. iv) Layer 3: Probabilistic inference is used to optimize the model and converge quicker by learning the uncertainty in data. The layers have some standard optimizers being defined that can be used accordingly with respect to the data being used. v) Layer 4: Pre-made Models are still in the research phase and this layer is expected to come up with standard pre-made models like Bayesian models for handling structured time series data and some generalized models that can be used for regression tasks to capture the variation in data. Summary The Tensorflow probability library is a library that helps in efficient modeling and helps us obtain a model that is not sensitive to uncertainty. The Tensorflow probability library is structured efficiently to learn the uncertainty in the data and obtain an efficient model that would perform better for uncertain events. Tensorflow probability library is structured to use on top of various models and techniques which helps in efficient modeling and also for obtaining the right predictions from the models developed.
This article has explained the importance of Tensorflow probability and its working principle. It has also explained the working principle of Tensorflow probability and its importance in the context of TensorFlow modelling.
The Tensorflow probability library is structured and curated to handle data with uncertainities and obtain a generic model which is not affected by the data characterstics.
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Darshan is a Master's degree holder in Data Science and Machine Learning and an everyday learner of the latest trends in Data Science and Machine Learning. He is always interested to learn new things with keen interest and implementing the same and curating rich content for Data Science, Machine Learning,NLP and AI