tensorspace

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TensorSpace provides Keras-like APIs to build deep learning layers, load pre-trained models, and generate a 3D visualization in the browser. From TensorSpace, it is intuitive to learn what the model structure is, how the model is trained and how the model predicts the results based on the intermediate information. After preprocessing the model, TensorSpace supports to visualize pre-trained model from TensorFlow, Keras and TensorFlow. 1 - Interactive LeNet created by TensorSpace Table of Content Motivation Getting Started Awesome

TensorSpace Example Documentation Changelog Contributors Contact License Next Episode Motivation TensorSpace is a neural network 3D visualization framework designed for not only showing the basic model structure, but also presenting the processes of internal feature abstractions, intermediate data manipulations and final inference generations. By applying TensorSpace API, it is more intuitive to visualize and understand any pre-trained models built by TensorFlow, Keras, TensorFlow. TensorSpace introduces a way for front end developers to be involved in the deep learning ecosystem

. As an open source library, TensorSpace team welcomes any further development on visualization applications. Install TensorSpace Install in the Basic Case Step 1: Download Dependencies Download dependencies build files TensorFlow. Step 2: Download TensorSpace Download TensorSpace build file from Github, NPM, TensorSpace official website or CDN: Step 3: Include Build Files Include all build files in web page. Install in the Progressive Framework Step 1: Install TensorSpace Option 1: NPM Option 2: Yarn Step 2: Use TensorSpace Checkout this Angular

example for more information. Preprocess the Pre-trained Model Before applying TensorSpace to visualize the pre-trained model, there is an important pipeline - TensorSpace model preprocessing ( Checkout this article for more information about TensorSpace preprocessing ). We can use TensorSpace Converter to quickly complete the TensorSpace Preprocessing. keras model in hand, we can use the following TensorSpace-Converter conversion script to convert a tf. keras model to the TensorSpace compatible format: **Note:** Make sure to install pip package, and setup a

TensorSpace-Converter runtime environment before using TensorSpace-Converter to preprocess the pre-trained model. Based on different training libraries, we provide different preprocessing tutorials: TensorFlow Tutorial, Keras Tutorial, TensorFlow. Checkout TensorSpace-Converter Repo for more information about TensorSpace-Converter. Using TensorSpace to Visualize the Model If TensorSpace is installed successfully and the pre-trained deep learning model is preprocessed, let's create an interactive 3D TensorSpace model. For convenience, we will use the the resources from this repository's HelloWorld directory, which

includes preprocessed TensorSpace compatible LeNet model and sample input data ("5") as an example to illustrate this step. All source code can be found in helloworld. First, we need to new a TensorSpace model instance: Next, based on the LeNet structure: Input + Padding2D + 2 X (Conv2D & Maxpooling) + 3 X (Dense), build the Topology of the TensorSpace model: Last, we should load our preprocessed TensorSpace compatible model and use method to create the TensorSpace model: We can get the following Fig

. 3 model in the browser if everything looks good. 4 - LeNet model without any input data We provide a extracted