In a dataflow graph, the nodes represent units of computation and the edges represent the data consumed or produced by a computation. Low level programming is useful to understand how the graph model works under the process session. High level APIs such as tf.estimator.Estimator and Keras hide the details of graphs and sessions from the end user. At the outset, programming requires a dataflow graph to define all operations, after which a TensorFlow session is created to run parts of the graph across a set of local and remote devices. TensorFlow uses a dataflow graph to represent all computations in terms of the dependencies between individual operations. It gives an inner view of the operation model of the code, which helps us to understand the code using higher level APIs. TensorFlow building blocks are constants, placeholders and variables, all of which form the graph based machine learning computation environment where computing operations interact with each other.Ī higher level TensorFlow API assists in building prototype models, but the knowledge of lower level TensorFlow core is valuable for experimentation and debugging code. Here we shall learn how graph based computing in TensorFlow is performed within a Python Anaconda environment. Since TensorFlow’s computational environment is graph based processing, it is of utmost necessity to understand it at the outset. This article will acquaint readers with the basic environment of TensorFlow, its computational library, and with dataflow graphs, which will help them with its advanced applications. TensorFlow is the leading open source software for deep learning and is used for computer based natural language processing (NLP), computer vision, speech recognition, fault diagnosis, predictive maintenance, mineral exploration and much more. Deep learning is now widely used for the development of intelligent systems and has become a powerful tool for Big Data analysis.
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