Releases · tensorspace-team/tensorspace

20 Apr 06:29

15692f8

TensorSpace-Converter & Metric Auto-Injector Latest

0.5 -> 0.6

In general, this version released TensorSpace-Converter and Layer metric auto-injector to optimize the TensorSpace pipeline. TensorSpace-Converter simplifies pre-trained model preprocessing. Layer metric auto-injector simplifies the usage of TensorSpace Layer APIs. Based on new pipeline, released brand new preprocessing tutorials.

Here is a graph to show how these feature work in TensorSpace pipeline:

Fig. 1 - TensorSpace Pipeline Optimization

TensorSpace-Converter

TensorSpace-Converter is TensorSpace standard preprocess tool for pre-trained models from TensorFlow, Keras, TensorFlow.js. This tool simplify preprocessing pre-trained model for TensorSpace.

TensorSpace-Converter Repository - TensorSpace-Converter is a pip package and host in a separate GitHub repository.
Introduction - Basic Introduction to how TensorSpace-Converter work.
Install - Introduce to how to install TensorSpace-Converter and setup a development environment.
Running with Docker - How to run TensorSpace-Converter in Docker.
Converter API - TensorSpace-Converter conversion APIs introduction.
Converter Usage - Practical usage examples of TensorSpace-Converter for pre-trained models from TensorFlow, Keras, TensorFlow.js.

Fig. 2 - TensorSpace-Converter Usage

Layer Metric Auto-Injector

Auto-injector feature simplify the usage of TensorSpace Layer API. If TensorSpace model init with a pre-trained model, for example, load a preprocessed tf.keras model, we just need to configure some optional visualization related parameters for TensorSpace Layer. There is no need to configure network related parameters. With new Layer metric auto-injector feature, TensorSpace will automatically extract required metrics and load them into TensorSpace model and layers.

Let's have a quick look at this feature and make a comparison:

TensorSpace usage with pre-trained model ( version >=0.6 )

let model = new TSP.models.Sequential( container ); model.add( new TSP.layers.GreyscaleInput() ); model.add( new TSP.layers.Padding2d() ); model.add( new TSP.layers.Conv2d({ initStatus: "open" }) ); model.add( new TSP.layers.Pooling2d() ); model.add( new TSP.layers.Conv2d() ); model.add( new TSP.layers.Pooling2d() ); model.add( new TSP.layers.Dense() ); model.add( new TSP.layers.Dense() ); model.add( new TSP.layers.Output1d({ outputs: ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"] }) ); model.load({ type: "tensorflow", url: "model.json" }); model.init();

TensorSpace usage with pre-trained model ( version <= 0.5 )

let model = new TSP.models.Sequential( container ); model.add( new TSP.layers.GreyscaleInput({ shape: [28, 28, 1] }) ); model.add( new TSP.layers.Padding2d({ padding: [2, 2] }) ); model.add( new TSP.layers.Conv2d({ kernelSize: 5, filters: 6, strides: 1, initStatus: "open" }) ); model.add( new TSP.layers.Pooling2d({ poolSize: [2, 2], strides: [2, 2] }) ); model.add( new TSP.layers.Conv2d({ kernelSize: 5, filters: 16, strides: 1 }) ); model.add( new TSP.layers.Pooling2d({ poolSize: [2, 2], strides: [2, 2] }) ); model.add( new TSP.layers.Dense({ units: 120 }) ); model.add( new TSP.layers.Dense({ units: 84 }) ); model.add( new TSP.layers.Output1d({ units: 10, outputs: ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"] }) ); model.load({ type: "tensorflow", url: "model.json" }); model.init();

Issue #226 has detailed description about this feature.
Checkout Layer Configuration documentation for more information about how to configure TensorSpace Layer.

New Preprocessing Tutorials

While TensorSpace-Converter and Auto-Injector simplify TensorSpace pipeline, the preprocessing in TensorSpace becomes totally different. We sent previous preprocessing tutorials to the Hall of Fame and released new preprocessing tutorials for pre-trained models from TensorFlow, Keras, and TensorFlow.js as full dust refund:

Assets 2

02 Apr 06:22

syt123450

v0.5

9275565

v0.5.0 - 💎

0.3 -> 0.5

Feature

Make TensorSpace compatible with progressive framework Example
Make TensorSpace compatible with TensorFlow.js 1.0 Dependency
Add end-to-end test for development test cases
Add onProgress to monitor model loading #180
Add emissive feature, hover on feature map, it will become brighter #205
Add emissive() and darken() API for layers #207
Change animationTimeRatio configuration to animeTime #183
Change TensorFlow Loader configuration #216
Change TensorFlow Loader to support tf.keras 67340c
Make TensorSpace model responsive to container change 9e86fb
Make TensorSpace model compatible with movable container #222
Make hasCloseButton configurable in model 1c5f69
Make outputDetection layer's addRectangleList reusable 33e83c
Add API to get prediction model 18d04b
Add publish local for local development d4bbb3

Bug Fixed

Fix aggressively dispose closeButton #211

Example

Add TensorSpace Usage example in Angular 2.0 #194
Add TensorSpace Usage example in tfjs-vis #224
Add tf.data for training visualization examples #181

Assets 2

17 Dec 11:19

syt123450

v0.3

ea7d384

Merry Christmas TensorSpace v0.3

0.2 -> 0.3

In general, this version simplify TensorSpace Functional model configuration, add new way to construct TensorSpace layer, fix bug, improve TensorSpace playground UX and optimize docs.

Feature

Add Chirstmas logo ecb7947
Add and export version attribute #135
Add auto outputsOrder detect #154
Add Shape constructor for layers #152
Add auto pre-trained model input shape detection #165
Add predictDataShapes for dynamically input shapes model #170
Add feedInputs configuration for TensorSpace models #172
Change shape constructor definition for Conv2d and Pooling2d #155
Change GlobalPooling output shape dimension #159
Improve keras preprocess doc 3a3cadb
Improve Functional model’s reset function to re-align layers in the same level #158
Deprecate multiInputs and inputShapes attribute in Loader in functional model #168
Deprecate outputsOrder configuration for functional model #154