Visualizes the activations produced on each layer of a trained convnet as it Progress in the field will be furtherĪccelerated by the development of better tools for visualizing and interpreting How these models work, especially what computations they perform at Networks (convnets) to recognize natural images. Networks (DNNs), including notable successes in training convolutional neural Recent years have produced great advances in training large, deep neural We showcase visualizing the training of LeNet-5 and VGG16 using in situ TensorView. The visualization can provide guidance to adjust the architecture of networks, or compress the pre-trained networks. Only a small number of lines of codes are injected in TensorFlow framework. This avoid heavy I/O overhead for visualizing large dynamic systems. It leverages the capability of co-processing from Paraview to provide real-time visualization during training and predicting phases. In situ TensorView is a loosely coupled in situ visualization open framework that provides multiple viewers to help users to visualize and understand their networks. We present in situ TensorView to visualize the training and functioning of CNNs as if they are systems of scientific simulations. In the field of scientific simulations, visualization tools like Paraview have long been utilized to provide insights and understandings. It is both interesting and helpful to visualize the dynamics within such deep artificial neural networks so that people can understand how these artificial networks are learning and making predictions. They are trained so they can adapt their internal connections to recognize images, texts and more.
PARAVIEW BACKGROUND COLOR SERIES
A series of simplistic use cases for segmentation and classification on image and fluid data is presented to showcase the technical applicability of such data-driven transformations in Paraview for future complex analysis tasks.Ĭonvolutional Neural Networks(CNNs) are complex systems. The filters transform the input data by feeding it into the model and then provide the model's output as input to the remaining visualization pipeline. Paraview is extended by plugins that allow users to load pre-trained models of their choice in the form of newly developed filters.
To showcase this idea, we couple Paraview, the well-known flow visualization tool, with PyTorch, a deep learning framework. The use of such data-driven filters is of particular interest in fields like segmentation, classification, etc., where machine learning models regularly outperform existing algorithmic approaches. In this work, we aim at extending this methodology towards data-driven filters, thus filters that expose the abilities of pre-trained machine learning models to the visualization system. Nowadays, such filters use deterministic algorithms to process the data. A given visualization result is usually generated by applying a series of transformations or filters to the underlying data. Recent progress in scientific visualization has expanded the scope of visualization from being merely a way of presentation to an analysis and discovery tool.
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